diff --git a/manifest.json b/manifest.json index 4fd7140ce35141e1d8c99ca228510265f09d6785..d7b7b79f2efb17f29dc6b6053f7d899f7476a19a 100644 --- a/manifest.json +++ b/manifest.json @@ -1,376 +1,1371 @@ { - "name": "nemotron-asr-streaming-multilingual-0.6b-coreml", - "version": "1.0.0", + "name": "Nemotron 3.5 ASR Streaming Multilingual 0.6B — CoreML", "base_model": "nvidia/nemotron-asr-streaming-multilingual-0.6b", - "base_model_class": "nemo.collections.asr.models.rnnt_bpe_models_prompt.EncDecRNNTBPEModelWithPrompt", - "framework": "coreml", - "deployment_target": { - "ios": "17.0", - "macos": "14.0" - }, - "quantization": { - "encoder": "int8", - "preprocessor": "fp16", - "decoder": "fp16", - "joint": "fp16" - }, - "model": { - "sample_rate": 16000, - "mel_features": 128, - "vocab_size": 13087, - "blank_idx": 13087, - "encoder_dim": 1024, - "decoder_hidden": 640, - "decoder_layers": 2, - "num_prompts": 128, - "default_prompt_id": 101 - }, - "loader": { - "library": "fluidaudio", - "class": "StreamingNemotronMultilingualAsrManager", - "preferred_format": "mlmodelc", - "fallback_format": "mlpackage" - }, - "variants": [ - { - "name": "80ms", - "chunk_ms": 80, - "chunk_mel_frames": 8, - "pre_encode_cache": 9, - "total_mel_frames": 17, - "att_context_size": [ - 56, - 0 + "architecture": "Conformer encoder + RNN-T decoder", + "runtime": "CoreML / Apple Neural Engine", + "benchmark_machine": "Apple M5 Pro / macOS 26.5", + "recipe": "LAYERPOS[42,13] mixed-precision encoder + per-language vocab pruning + B1 fusion + triple-stage pipelining + smart-spec K=4", + "no_retraining": true, + "no_calibration": true, + "tiers_ms": [ + 560, + 1120, + 2240, + 4480 + ], + "recommended_tier_ms": 2240, + "models": [ + "de", + "en", + "es", + "fr", + "it", + "ja", + "multilingual", + "pt", + "zh" + ], + "ship_count": 36, + "notes": [ + "2240ms (2s) is the RTFx-per-latency sweet spot for every model (+20-44% over 1120ms, quality-neutral).", + "Multilingual peaks at 2240ms (74.6 RTFx); its 4480ms tier craters (19.4) because the 13088-vocab joint exceeds ANE working-set.", + "560ms is the lowest-latency tier but off the trained 14-frame attention tiling: lower RTFx (~57) and a small quality cost. en measured (57.2 RTFx); other 560ms ships shipped unbenched.", + "Portuguese 2240ms WER is +3.1pp vs 1120ms (B1-fallback path, chunk-sensitive).", + "de/zh/ja keep-sets were derived from FLEURS-test transcripts: in-domain/optimistic numbers + OOV risk on out-of-domain text. Rebuild keep-set from a broader corpus for production.", + "Each /ms/ dir is a self-contained FluidAudio model bundle (point --model-dir at it)." + ], + "ships": [ + { + "path": "en/560ms", + "language": "English", + "language_code": "en-US", + "chunk_ms": 560, + "latency_s": 0.56, + "chunk_mel_frames": 56, + "total_mel_frames": 65, + "att_context": [ + 42, + 13 ], - "cache_channel_shape": [ - 1, - 24, - 56, - 1024 - ], - "cache_time_shape": [ - 1, - 24, - 1024, - 8 - ], - "components": { - "preprocessor": { - "mlpackage": { - "path": "80ms/preprocessor.mlpackage", - "size_bytes": 608879 - }, - "mlmodelc": { - "path": "80ms/preprocessor.mlmodelc", - "size_bytes": 617594 - } - }, - "encoder": { - "mlpackage": { - "path": "80ms/encoder.mlpackage", - "size_bytes": 593323585 - }, - "mlmodelc": { - "path": "80ms/encoder.mlmodelc", - "size_bytes": 593506646 - } - }, - "decoder": { - "mlpackage": { - "path": "80ms/decoder.mlpackage", - "size_bytes": 29881569 - }, - "mlmodelc": { - "path": 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-} +} \ No newline at end of file diff --git a/multilingual/1120ms/decoder.mlmodelc/analytics/coremldata.bin b/multilingual/1120ms/decoder.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..646ff8b67c27f7e1035df022ec9e0691346d2780 --- /dev/null +++ b/multilingual/1120ms/decoder.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9c32520b84ded2c698000854a77228adf394db522b5a3c25f7737415aae7ed0d +size 243 diff --git a/multilingual/1120ms/decoder.mlmodelc/coremldata.bin b/multilingual/1120ms/decoder.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..b50e1931d2e3334018b12d034c8ff8a57896a265 --- /dev/null +++ b/multilingual/1120ms/decoder.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fa812bb65dd2a3bef6acf584b2abd5d0f26f4d09afaccf6c5dfd41e630d0fd1b +size 433 diff --git a/multilingual/1120ms/decoder.mlmodelc/model.mil b/multilingual/1120ms/decoder.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..697235107988a50dadcf7b2334d72723c3d73048 --- /dev/null +++ b/multilingual/1120ms/decoder.mlmodelc/model.mil @@ -0,0 +1,64 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.5.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] +{ + func main(tensor c_in, tensor h_in, tensor token, tensor token_length) { + int32 y_axis_0 = const()[name = string("y_axis_0"), val = int32(0)]; + int32 y_batch_dims_0 = const()[name = string("y_batch_dims_0"), val = int32(0)]; + bool y_validate_indices_0 = const()[name = string("y_validate_indices_0"), val = bool(false)]; + tensor module_prediction_embed_weight_to_fp16 = const()[name = string("module_prediction_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + string token_to_int16_dtype_0 = const()[name = string("token_to_int16_dtype_0"), val = string("int16")]; + tensor token_to_int16 = cast(dtype = token_to_int16_dtype_0, x = token)[name = string("cast_8")]; + tensor y_cast_fp16_cast_uint16 = gather(axis = y_axis_0, batch_dims = y_batch_dims_0, indices = token_to_int16, validate_indices = y_validate_indices_0, x = module_prediction_embed_weight_to_fp16)[name = string("y_cast_fp16_cast_uint16")]; + tensor input_3_perm_0 = const()[name = string("input_3_perm_0"), val = tensor([1, 0, 2])]; + int32 split_0_num_splits_0 = const()[name = string("split_0_num_splits_0"), val = int32(2)]; + int32 split_0_axis_0 = const()[name = string("split_0_axis_0"), val = int32(0)]; + string h_in_to_fp16_dtype_0 = const()[name = string("h_in_to_fp16_dtype_0"), val = string("fp16")]; + tensor h_in_to_fp16 = cast(dtype = h_in_to_fp16_dtype_0, x = h_in)[name = string("cast_7")]; + tensor split_0_cast_fp16_0, tensor split_0_cast_fp16_1 = split(axis = split_0_axis_0, num_splits = split_0_num_splits_0, x = h_in_to_fp16)[name = string("split_0_cast_fp16")]; + int32 split_1_num_splits_0 = const()[name = string("split_1_num_splits_0"), val = int32(2)]; + int32 split_1_axis_0 = const()[name = string("split_1_axis_0"), val = int32(0)]; + string c_in_to_fp16_dtype_0 = const()[name = string("c_in_to_fp16_dtype_0"), val = string("fp16")]; + tensor c_in_to_fp16 = cast(dtype = c_in_to_fp16_dtype_0, x = c_in)[name = string("cast_6")]; + tensor split_1_cast_fp16_0, tensor split_1_cast_fp16_1 = split(axis = split_1_axis_0, num_splits = split_1_num_splits_0, x = c_in_to_fp16)[name = string("split_1_cast_fp16")]; + tensor input_lstm_layer_0_lstm_h0_squeeze_axes_0 = const()[name = string("input_lstm_layer_0_lstm_h0_squeeze_axes_0"), val = tensor([0])]; + tensor input_lstm_layer_0_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_lstm_layer_0_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_0)[name = string("input_lstm_layer_0_lstm_h0_squeeze_cast_fp16")]; + tensor input_lstm_layer_0_lstm_c0_squeeze_axes_0 = const()[name = string("input_lstm_layer_0_lstm_c0_squeeze_axes_0"), val = tensor([0])]; + tensor input_lstm_layer_0_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_lstm_layer_0_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_0)[name = string("input_lstm_layer_0_lstm_c0_squeeze_cast_fp16")]; + string input_lstm_layer_0_direction_0 = const()[name = string("input_lstm_layer_0_direction_0"), val = string("forward")]; + bool input_lstm_layer_0_output_sequence_0 = const()[name = string("input_lstm_layer_0_output_sequence_0"), val = bool(true)]; + string input_lstm_layer_0_recurrent_activation_0 = const()[name = string("input_lstm_layer_0_recurrent_activation_0"), val = string("sigmoid")]; + string input_lstm_layer_0_cell_activation_0 = const()[name = string("input_lstm_layer_0_cell_activation_0"), val = string("tanh")]; + string input_lstm_layer_0_activation_0 = const()[name = string("input_lstm_layer_0_activation_0"), val = string("tanh")]; + tensor concat_1_to_fp16 = const()[name = string("concat_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16752768)))]; + tensor concat_2_to_fp16 = const()[name = string("concat_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20029632)))]; + tensor concat_0_to_fp16 = const()[name = string("concat_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23306496)))]; + tensor input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = y_cast_fp16_cast_uint16)[name = string("transpose_2")]; + tensor input_lstm_layer_0_cast_fp16_0, tensor input_lstm_layer_0_cast_fp16_1, tensor input_lstm_layer_0_cast_fp16_2 = lstm(activation = input_lstm_layer_0_activation_0, bias = concat_0_to_fp16, cell_activation = input_lstm_layer_0_cell_activation_0, direction = input_lstm_layer_0_direction_0, initial_c = input_lstm_layer_0_lstm_c0_squeeze_cast_fp16, initial_h = input_lstm_layer_0_lstm_h0_squeeze_cast_fp16, output_sequence = input_lstm_layer_0_output_sequence_0, recurrent_activation = input_lstm_layer_0_recurrent_activation_0, weight_hh = concat_2_to_fp16, weight_ih = concat_1_to_fp16, x = input_3_cast_fp16)[name = string("input_lstm_layer_0_cast_fp16")]; + tensor input_lstm_h0_squeeze_axes_0 = const()[name = string("input_lstm_h0_squeeze_axes_0"), val = tensor([0])]; + tensor input_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_1)[name = string("input_lstm_h0_squeeze_cast_fp16")]; + tensor input_lstm_c0_squeeze_axes_0 = const()[name = string("input_lstm_c0_squeeze_axes_0"), val = tensor([0])]; + tensor input_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_1)[name = string("input_lstm_c0_squeeze_cast_fp16")]; + string input_direction_0 = const()[name = string("input_direction_0"), val = string("forward")]; + bool input_output_sequence_0 = const()[name = string("input_output_sequence_0"), val = bool(true)]; + string input_recurrent_activation_0 = const()[name = string("input_recurrent_activation_0"), val = string("sigmoid")]; + string input_cell_activation_0 = const()[name = string("input_cell_activation_0"), val = string("tanh")]; + string input_activation_0 = const()[name = string("input_activation_0"), val = string("tanh")]; + tensor concat_4_to_fp16 = const()[name = string("concat_4_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23311680)))]; + tensor concat_5_to_fp16 = const()[name = string("concat_5_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26588544)))]; + tensor concat_3_to_fp16 = const()[name = string("concat_3_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29865408)))]; + tensor input_cast_fp16_0, tensor input_cast_fp16_1, tensor input_cast_fp16_2 = lstm(activation = input_activation_0, bias = concat_3_to_fp16, cell_activation = input_cell_activation_0, direction = input_direction_0, initial_c = input_lstm_c0_squeeze_cast_fp16, initial_h = input_lstm_h0_squeeze_cast_fp16, output_sequence = input_output_sequence_0, recurrent_activation = input_recurrent_activation_0, weight_hh = concat_5_to_fp16, weight_ih = concat_4_to_fp16, x = input_lstm_layer_0_cast_fp16_0)[name = string("input_cast_fp16")]; + int32 obj_3_axis_0 = const()[name = string("obj_3_axis_0"), val = int32(0)]; + tensor obj_3_cast_fp16 = stack(axis = obj_3_axis_0, values = (input_lstm_layer_0_cast_fp16_1, input_cast_fp16_1))[name = string("obj_3_cast_fp16")]; + string obj_3_cast_fp16_to_fp32_dtype_0 = const()[name = string("obj_3_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + int32 obj_axis_0 = const()[name = string("obj_axis_0"), val = int32(0)]; + tensor obj_cast_fp16 = stack(axis = obj_axis_0, values = (input_lstm_layer_0_cast_fp16_2, input_cast_fp16_2))[name = string("obj_cast_fp16")]; + string obj_cast_fp16_to_fp32_dtype_0 = const()[name = string("obj_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 2, 0])]; + string transpose_0_cast_fp16_to_fp32_dtype_0 = const()[name = string("transpose_0_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor transpose_0_cast_fp16 = transpose(perm = transpose_0_perm_0, x = input_cast_fp16_0)[name = string("transpose_1")]; + tensor decoder_out = cast(dtype = transpose_0_cast_fp16_to_fp32_dtype_0, x = transpose_0_cast_fp16)[name = string("cast_3")]; + tensor c_out = cast(dtype = obj_cast_fp16_to_fp32_dtype_0, x = obj_cast_fp16)[name = string("cast_4")]; + tensor h_out = cast(dtype = obj_3_cast_fp16_to_fp32_dtype_0, x = obj_3_cast_fp16)[name = string("cast_5")]; + tensor token_length_tmp = identity(x = token_length)[name = string("token_length_tmp")]; + } -> (decoder_out, h_out, c_out); +} \ No newline at end of file diff --git a/multilingual/1120ms/decoder.mlmodelc/weights/weight.bin b/multilingual/1120ms/decoder.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..8b094b9de3ad890a887d85c198a8ee88800323fa --- /dev/null +++ b/multilingual/1120ms/decoder.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2e49d029739602d265719f0040fcf5328c007a1ed62d0c6ea621e0b2aaeb9a64 +size 29870592 diff --git a/multilingual/1120ms/decoder.mlpackage/Data/com.apple.CoreML/model.mlmodel b/multilingual/1120ms/decoder.mlpackage/Data/com.apple.CoreML/model.mlmodel new file mode 100644 index 0000000000000000000000000000000000000000..3b86cf52f95837fc5e90f3e8cf6373bde60c5ad1 --- /dev/null +++ b/multilingual/1120ms/decoder.mlpackage/Data/com.apple.CoreML/model.mlmodel @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c26ee345b7763ed9f217561572b1386719956de5b58e9174f5586926b4ab85c5 +size 10360 diff --git a/multilingual/1120ms/decoder.mlpackage/Data/com.apple.CoreML/weights/weight.bin b/multilingual/1120ms/decoder.mlpackage/Data/com.apple.CoreML/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..8b094b9de3ad890a887d85c198a8ee88800323fa --- /dev/null +++ b/multilingual/1120ms/decoder.mlpackage/Data/com.apple.CoreML/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2e49d029739602d265719f0040fcf5328c007a1ed62d0c6ea621e0b2aaeb9a64 +size 29870592 diff --git a/multilingual/1120ms/decoder.mlpackage/Manifest.json b/multilingual/1120ms/decoder.mlpackage/Manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..1ac968d8fcfbdfdead1150534e3677d2f045db51 --- /dev/null +++ b/multilingual/1120ms/decoder.mlpackage/Manifest.json @@ -0,0 +1,18 @@ +{ + "fileFormatVersion": "1.0.0", + "itemInfoEntries": { + "542DC13B-08DF-47C7-AAAA-C2F9DE67BB37": { + "author": "com.apple.CoreML", + "description": "CoreML Model Weights", + "name": "weights", + "path": "com.apple.CoreML/weights" + }, + "8B23B00A-4F60-49E4-B460-719FB6B05887": { + "author": "com.apple.CoreML", + "description": "CoreML Model Specification", + "name": "model.mlmodel", + "path": "com.apple.CoreML/model.mlmodel" + } + }, + "rootModelIdentifier": "8B23B00A-4F60-49E4-B460-719FB6B05887" +} diff --git a/multilingual/1120ms/decoder_joint.mlmodelc/analytics/coremldata.bin b/multilingual/1120ms/decoder_joint.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..11215e473da8d2cc24d47de983947493613791d7 --- /dev/null +++ b/multilingual/1120ms/decoder_joint.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3b314763ced4d2bd27484b8ec2a9c60939b724f8ab60b32d29ad0c03f6192599 +size 243 diff --git a/multilingual/1120ms/decoder_joint.mlmodelc/coremldata.bin b/multilingual/1120ms/decoder_joint.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..a036c0b966fa4c57af9b0d7699bfb5e37c53f4d4 --- /dev/null +++ b/multilingual/1120ms/decoder_joint.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:729baa5678fde0b9fa3e46044cb8eafcb96249bff4c306740e1c40ce326b7101 +size 454 diff --git a/multilingual/1120ms/decoder_joint.mlmodelc/model.mil b/multilingual/1120ms/decoder_joint.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..e0c611c93d86fefc0f5758164fe03174da291441 --- /dev/null +++ b/multilingual/1120ms/decoder_joint.mlmodelc/model.mil @@ -0,0 +1,83 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.5.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] +{ + func main(tensor c_in, tensor encoder, tensor h_in, tensor token, tensor token_length) { + int32 y_axis_0 = const()[name = string("y_axis_0"), val = int32(0)]; + int32 y_batch_dims_0 = const()[name = string("y_batch_dims_0"), val = int32(0)]; + bool y_validate_indices_0 = const()[name = string("y_validate_indices_0"), val = bool(false)]; + tensor decoder_module_prediction_embed_weight_to_fp16 = const()[name = string("decoder_module_prediction_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + string token_to_int16_dtype_0 = const()[name = string("token_to_int16_dtype_0"), val = string("int16")]; + tensor token_to_int16 = cast(dtype = token_to_int16_dtype_0, x = token)[name = string("cast_9")]; + tensor y_cast_fp16_cast_uint16 = gather(axis = y_axis_0, batch_dims = y_batch_dims_0, indices = token_to_int16, validate_indices = y_validate_indices_0, x = decoder_module_prediction_embed_weight_to_fp16)[name = string("y_cast_fp16_cast_uint16")]; + tensor input_3_perm_0 = const()[name = string("input_3_perm_0"), val = tensor([1, 0, 2])]; + int32 split_0_num_splits_0 = const()[name = string("split_0_num_splits_0"), val = int32(2)]; + int32 split_0_axis_0 = const()[name = string("split_0_axis_0"), val = int32(0)]; + string h_in_to_fp16_dtype_0 = const()[name = string("h_in_to_fp16_dtype_0"), val = string("fp16")]; + tensor h_in_to_fp16 = cast(dtype = h_in_to_fp16_dtype_0, x = h_in)[name = string("cast_8")]; + tensor split_0_cast_fp16_0, tensor split_0_cast_fp16_1 = split(axis = split_0_axis_0, num_splits = split_0_num_splits_0, x = h_in_to_fp16)[name = string("split_0_cast_fp16")]; + int32 split_1_num_splits_0 = const()[name = string("split_1_num_splits_0"), val = int32(2)]; + int32 split_1_axis_0 = const()[name = string("split_1_axis_0"), val = int32(0)]; + string c_in_to_fp16_dtype_0 = const()[name = string("c_in_to_fp16_dtype_0"), val = string("fp16")]; + tensor c_in_to_fp16 = cast(dtype = c_in_to_fp16_dtype_0, x = c_in)[name = string("cast_7")]; + tensor split_1_cast_fp16_0, tensor split_1_cast_fp16_1 = split(axis = split_1_axis_0, num_splits = split_1_num_splits_0, x = c_in_to_fp16)[name = string("split_1_cast_fp16")]; + tensor input_5_lstm_layer_0_lstm_h0_squeeze_axes_0 = const()[name = string("input_5_lstm_layer_0_lstm_h0_squeeze_axes_0"), val = tensor([0])]; + tensor input_5_lstm_layer_0_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_5_lstm_layer_0_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_0)[name = string("input_5_lstm_layer_0_lstm_h0_squeeze_cast_fp16")]; + tensor input_5_lstm_layer_0_lstm_c0_squeeze_axes_0 = const()[name = string("input_5_lstm_layer_0_lstm_c0_squeeze_axes_0"), val = tensor([0])]; + tensor input_5_lstm_layer_0_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_5_lstm_layer_0_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_0)[name = string("input_5_lstm_layer_0_lstm_c0_squeeze_cast_fp16")]; + string input_5_lstm_layer_0_direction_0 = const()[name = string("input_5_lstm_layer_0_direction_0"), val = string("forward")]; + bool input_5_lstm_layer_0_output_sequence_0 = const()[name = string("input_5_lstm_layer_0_output_sequence_0"), val = bool(true)]; + string input_5_lstm_layer_0_recurrent_activation_0 = const()[name = string("input_5_lstm_layer_0_recurrent_activation_0"), val = string("sigmoid")]; + string input_5_lstm_layer_0_cell_activation_0 = const()[name = string("input_5_lstm_layer_0_cell_activation_0"), val = string("tanh")]; + string input_5_lstm_layer_0_activation_0 = const()[name = string("input_5_lstm_layer_0_activation_0"), val = string("tanh")]; + tensor concat_1_to_fp16 = const()[name = string("concat_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16752768)))]; + tensor concat_2_to_fp16 = const()[name = string("concat_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20029632)))]; + tensor concat_0_to_fp16 = const()[name = string("concat_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23306496)))]; + tensor input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = y_cast_fp16_cast_uint16)[name = string("transpose_4")]; + tensor input_5_lstm_layer_0_cast_fp16_0, tensor input_5_lstm_layer_0_cast_fp16_1, tensor input_5_lstm_layer_0_cast_fp16_2 = lstm(activation = input_5_lstm_layer_0_activation_0, bias = concat_0_to_fp16, cell_activation = input_5_lstm_layer_0_cell_activation_0, direction = input_5_lstm_layer_0_direction_0, initial_c = input_5_lstm_layer_0_lstm_c0_squeeze_cast_fp16, initial_h = input_5_lstm_layer_0_lstm_h0_squeeze_cast_fp16, output_sequence = input_5_lstm_layer_0_output_sequence_0, recurrent_activation = input_5_lstm_layer_0_recurrent_activation_0, weight_hh = concat_2_to_fp16, weight_ih = concat_1_to_fp16, x = input_3_cast_fp16)[name = string("input_5_lstm_layer_0_cast_fp16")]; + tensor input_5_lstm_h0_squeeze_axes_0 = const()[name = string("input_5_lstm_h0_squeeze_axes_0"), val = tensor([0])]; + tensor input_5_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_5_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_1)[name = string("input_5_lstm_h0_squeeze_cast_fp16")]; + tensor input_5_lstm_c0_squeeze_axes_0 = const()[name = string("input_5_lstm_c0_squeeze_axes_0"), val = tensor([0])]; + tensor input_5_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_5_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_1)[name = string("input_5_lstm_c0_squeeze_cast_fp16")]; + string input_5_direction_0 = const()[name = string("input_5_direction_0"), val = string("forward")]; + bool input_5_output_sequence_0 = const()[name = string("input_5_output_sequence_0"), val = bool(true)]; + string input_5_recurrent_activation_0 = const()[name = string("input_5_recurrent_activation_0"), val = string("sigmoid")]; + string input_5_cell_activation_0 = const()[name = string("input_5_cell_activation_0"), val = string("tanh")]; + string input_5_activation_0 = const()[name = string("input_5_activation_0"), val = string("tanh")]; + tensor concat_4_to_fp16 = const()[name = string("concat_4_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23311680)))]; + tensor concat_5_to_fp16 = const()[name = string("concat_5_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26588544)))]; + tensor concat_3_to_fp16 = const()[name = string("concat_3_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29865408)))]; + tensor input_5_cast_fp16_0, tensor input_5_cast_fp16_1, tensor input_5_cast_fp16_2 = lstm(activation = input_5_activation_0, bias = concat_3_to_fp16, cell_activation = input_5_cell_activation_0, direction = input_5_direction_0, initial_c = input_5_lstm_c0_squeeze_cast_fp16, initial_h = input_5_lstm_h0_squeeze_cast_fp16, output_sequence = input_5_output_sequence_0, recurrent_activation = input_5_recurrent_activation_0, weight_hh = concat_5_to_fp16, weight_ih = concat_4_to_fp16, x = input_5_lstm_layer_0_cast_fp16_0)[name = string("input_5_cast_fp16")]; + int32 obj_3_axis_0 = const()[name = string("obj_3_axis_0"), val = int32(0)]; + tensor obj_3_cast_fp16 = stack(axis = obj_3_axis_0, values = (input_5_lstm_layer_0_cast_fp16_1, input_5_cast_fp16_1))[name = string("obj_3_cast_fp16")]; + string obj_3_cast_fp16_to_fp32_dtype_0 = const()[name = string("obj_3_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + int32 obj_axis_0 = const()[name = string("obj_axis_0"), val = int32(0)]; + tensor obj_cast_fp16 = stack(axis = obj_axis_0, values = (input_5_lstm_layer_0_cast_fp16_2, input_5_cast_fp16_2))[name = string("obj_cast_fp16")]; + string obj_cast_fp16_to_fp32_dtype_0 = const()[name = string("obj_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor transpose_1_perm_0 = const()[name = string("transpose_1_perm_0"), val = tensor([1, 0, 2])]; + tensor input_7_perm_0 = const()[name = string("input_7_perm_0"), val = tensor([0, 2, 1])]; + string encoder_to_fp16_dtype_0 = const()[name = string("encoder_to_fp16_dtype_0"), val = string("fp16")]; + tensor joint_module_enc_weight_to_fp16 = const()[name = string("joint_module_enc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29870592)))]; + tensor joint_module_enc_bias_to_fp16 = const()[name = string("joint_module_enc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31181376)))]; + tensor encoder_to_fp16 = cast(dtype = encoder_to_fp16_dtype_0, x = encoder)[name = string("cast_4")]; + tensor input_7_cast_fp16 = transpose(perm = input_7_perm_0, x = encoder_to_fp16)[name = string("transpose_2")]; + tensor linear_0_cast_fp16 = linear(bias = joint_module_enc_bias_to_fp16, weight = joint_module_enc_weight_to_fp16, x = input_7_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor joint_module_pred_weight_to_fp16 = const()[name = string("joint_module_pred_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31182720)))]; + tensor joint_module_pred_bias_to_fp16 = const()[name = string("joint_module_pred_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32001984)))]; + tensor transpose_1_cast_fp16 = transpose(perm = transpose_1_perm_0, x = input_5_cast_fp16_0)[name = string("transpose_3")]; + tensor linear_1_cast_fp16 = linear(bias = joint_module_pred_bias_to_fp16, weight = joint_module_pred_weight_to_fp16, x = transpose_1_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_79_axes_0 = const()[name = string("op_79_axes_0"), val = tensor([2])]; + tensor var_79_cast_fp16 = expand_dims(axes = var_79_axes_0, x = linear_0_cast_fp16)[name = string("op_79_cast_fp16")]; + tensor var_80_axes_0 = const()[name = string("op_80_axes_0"), val = tensor([1])]; + tensor var_80_cast_fp16 = expand_dims(axes = var_80_axes_0, x = linear_1_cast_fp16)[name = string("op_80_cast_fp16")]; + tensor input_11_cast_fp16 = add(x = var_79_cast_fp16, y = var_80_cast_fp16)[name = string("input_11_cast_fp16")]; + tensor input_13_cast_fp16 = relu(x = input_11_cast_fp16)[name = string("input_13_cast_fp16")]; + tensor joint_module_joint_net_2_weight_to_fp16 = const()[name = string("joint_module_joint_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32003328)))]; + tensor joint_module_joint_net_2_bias_to_fp16 = const()[name = string("joint_module_joint_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48756032)))]; + tensor linear_2_cast_fp16 = linear(bias = joint_module_joint_net_2_bias_to_fp16, weight = joint_module_joint_net_2_weight_to_fp16, x = input_13_cast_fp16)[name = string("linear_2_cast_fp16")]; + string linear_2_cast_fp16_to_fp32_dtype_0 = const()[name = string("linear_2_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor logits = cast(dtype = linear_2_cast_fp16_to_fp32_dtype_0, x = linear_2_cast_fp16)[name = string("cast_3")]; + tensor c_out = cast(dtype = obj_cast_fp16_to_fp32_dtype_0, x = obj_cast_fp16)[name = string("cast_5")]; + tensor h_out = cast(dtype = obj_3_cast_fp16_to_fp32_dtype_0, x = obj_3_cast_fp16)[name = string("cast_6")]; + tensor token_length_tmp = identity(x = token_length)[name = string("token_length_tmp")]; + } -> (logits, h_out, c_out); +} \ No newline at end of file diff --git a/multilingual/1120ms/decoder_joint.mlmodelc/weights/weight.bin b/multilingual/1120ms/decoder_joint.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..c9ff4a76fa3fff489b780debbcd78bdc261f1489 --- /dev/null +++ b/multilingual/1120ms/decoder_joint.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f283edec035d616e9e1372419dd9bfd8a2de2c92a70b23f6a0f5ed93366ebb03 +size 48782272 diff --git a/multilingual/1120ms/decoder_joint.mlpackage/Data/com.apple.CoreML/model.mlmodel b/multilingual/1120ms/decoder_joint.mlpackage/Data/com.apple.CoreML/model.mlmodel new file mode 100644 index 0000000000000000000000000000000000000000..abae2ff390063679ad4ac25d546acf5853a04d16 --- /dev/null +++ b/multilingual/1120ms/decoder_joint.mlpackage/Data/com.apple.CoreML/model.mlmodel @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:507c3a291a78a11f62b898c64e611016f518d1af658b2aa55c054e0a1029f7ea +size 13746 diff --git a/multilingual/1120ms/decoder_joint.mlpackage/Data/com.apple.CoreML/weights/weight.bin b/multilingual/1120ms/decoder_joint.mlpackage/Data/com.apple.CoreML/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..c9ff4a76fa3fff489b780debbcd78bdc261f1489 --- /dev/null +++ b/multilingual/1120ms/decoder_joint.mlpackage/Data/com.apple.CoreML/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f283edec035d616e9e1372419dd9bfd8a2de2c92a70b23f6a0f5ed93366ebb03 +size 48782272 diff --git a/multilingual/1120ms/decoder_joint.mlpackage/Manifest.json b/multilingual/1120ms/decoder_joint.mlpackage/Manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..baad94a8850f47a141e9357ae3ea3cee9981fcc9 --- /dev/null +++ b/multilingual/1120ms/decoder_joint.mlpackage/Manifest.json @@ -0,0 +1,18 @@ +{ + "fileFormatVersion": "1.0.0", + "itemInfoEntries": { + "627E3113-852A-47BD-981E-FAB26C6AB6D0": { + "author": "com.apple.CoreML", + "description": "CoreML Model Weights", + "name": "weights", + "path": "com.apple.CoreML/weights" + }, + "7EEB6F52-3184-47E3-98CD-28268604F7F1": { + "author": "com.apple.CoreML", + "description": "CoreML Model Specification", + "name": "model.mlmodel", + "path": "com.apple.CoreML/model.mlmodel" + } + }, + "rootModelIdentifier": "7EEB6F52-3184-47E3-98CD-28268604F7F1" +} diff --git a/multilingual/1120ms/decoder_joint_noencproj.mlmodelc/analytics/coremldata.bin b/multilingual/1120ms/decoder_joint_noencproj.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..ea284470a3fb674426cdea416f38e1e49cfb8994 --- /dev/null +++ b/multilingual/1120ms/decoder_joint_noencproj.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c4e3c25c06d72ba514e93ae2ea0dd313f057622a3fb70f65de3bee4b80b3946b +size 243 diff --git a/multilingual/1120ms/decoder_joint_noencproj.mlmodelc/coremldata.bin b/multilingual/1120ms/decoder_joint_noencproj.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..5876c180574a6a160cf59c709341a44cf45df1bf --- /dev/null +++ b/multilingual/1120ms/decoder_joint_noencproj.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c9ea28113904cfeca8e5684fb8b54358eb48338f26c894739e3bd076848dcadd +size 519 diff --git a/multilingual/1120ms/decoder_joint_noencproj.mlmodelc/model.mil b/multilingual/1120ms/decoder_joint_noencproj.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..0bb38a83354a8f578eda404248b0895118b55ad5 --- /dev/null +++ b/multilingual/1120ms/decoder_joint_noencproj.mlmodelc/model.mil @@ -0,0 +1,91 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.10.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] +{ + func main(tensor c_in, tensor encoder_proj, tensor h_in, tensor token, tensor token_length) { + int32 y_batch_dims_0 = const()[name = string("y_batch_dims_0"), val = int32(0)]; + bool y_validate_indices_0 = const()[name = string("y_validate_indices_0"), val = bool(false)]; + tensor decoder_module_prediction_embed_weight_to_fp16 = const()[name = string("decoder_module_prediction_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + string token_to_int16_dtype_0 = const()[name = string("token_to_int16_dtype_0"), val = string("int16")]; + string cast_1_dtype_0 = const()[name = string("cast_1_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor token_to_int16 = cast(dtype = token_to_int16_dtype_0, x = token)[name = string("cast_10")]; + tensor cast_1 = cast(dtype = cast_1_dtype_0, x = token_to_int16)[name = string("cast_9")]; + tensor greater_equal_0 = greater_equal(x = cast_1, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(13088)]; + tensor add_2 = add(x = cast_1, y = slice_by_index_0)[name = string("add_2")]; + tensor select_0 = select(a = cast_1, b = add_2, cond = greater_equal_0)[name = string("select_0")]; + int32 y_cast_fp16_cast_uint16_axis_0 = const()[name = string("y_cast_fp16_cast_uint16_axis_0"), val = int32(0)]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_8")]; + tensor y_cast_fp16_cast_uint16_cast_uint16 = gather(axis = y_cast_fp16_cast_uint16_axis_0, batch_dims = y_batch_dims_0, indices = select_0_to_int16, validate_indices = y_validate_indices_0, x = decoder_module_prediction_embed_weight_to_fp16)[name = string("y_cast_fp16_cast_uint16_cast_uint16")]; + tensor input_3_perm_0 = const()[name = string("input_3_perm_0"), val = tensor([1, 0, 2])]; + int32 split_0_num_splits_0 = const()[name = string("split_0_num_splits_0"), val = int32(2)]; + int32 split_0_axis_0 = const()[name = string("split_0_axis_0"), val = int32(0)]; + string h_in_to_fp16_dtype_0 = const()[name = string("h_in_to_fp16_dtype_0"), val = string("fp16")]; + tensor h_in_to_fp16 = cast(dtype = h_in_to_fp16_dtype_0, x = h_in)[name = string("cast_7")]; + tensor split_0_cast_fp16_0, tensor split_0_cast_fp16_1 = split(axis = split_0_axis_0, num_splits = split_0_num_splits_0, x = h_in_to_fp16)[name = string("split_0_cast_fp16")]; + int32 split_1_num_splits_0 = const()[name = string("split_1_num_splits_0"), val = int32(2)]; + int32 split_1_axis_0 = const()[name = string("split_1_axis_0"), val = int32(0)]; + string c_in_to_fp16_dtype_0 = const()[name = string("c_in_to_fp16_dtype_0"), val = string("fp16")]; + tensor c_in_to_fp16 = cast(dtype = c_in_to_fp16_dtype_0, x = c_in)[name = string("cast_6")]; + tensor split_1_cast_fp16_0, tensor split_1_cast_fp16_1 = split(axis = split_1_axis_0, num_splits = split_1_num_splits_0, x = c_in_to_fp16)[name = string("split_1_cast_fp16")]; + tensor input_5_lstm_layer_0_lstm_h0_squeeze_axes_0 = const()[name = string("input_5_lstm_layer_0_lstm_h0_squeeze_axes_0"), val = tensor([0])]; + tensor input_5_lstm_layer_0_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_5_lstm_layer_0_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_0)[name = string("input_5_lstm_layer_0_lstm_h0_squeeze_cast_fp16")]; + tensor input_5_lstm_layer_0_lstm_c0_squeeze_axes_0 = const()[name = string("input_5_lstm_layer_0_lstm_c0_squeeze_axes_0"), val = tensor([0])]; + tensor input_5_lstm_layer_0_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_5_lstm_layer_0_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_0)[name = string("input_5_lstm_layer_0_lstm_c0_squeeze_cast_fp16")]; + string input_5_lstm_layer_0_direction_0 = const()[name = string("input_5_lstm_layer_0_direction_0"), val = string("forward")]; + bool input_5_lstm_layer_0_output_sequence_0 = const()[name = string("input_5_lstm_layer_0_output_sequence_0"), val = bool(true)]; + string input_5_lstm_layer_0_recurrent_activation_0 = const()[name = string("input_5_lstm_layer_0_recurrent_activation_0"), val = string("sigmoid")]; + string input_5_lstm_layer_0_cell_activation_0 = const()[name = string("input_5_lstm_layer_0_cell_activation_0"), val = string("tanh")]; + string input_5_lstm_layer_0_activation_0 = const()[name = string("input_5_lstm_layer_0_activation_0"), val = string("tanh")]; + tensor concat_1_to_fp16 = const()[name = string("concat_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16752768)))]; + tensor concat_2_to_fp16 = const()[name = string("concat_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20029632)))]; + tensor concat_0_to_fp16 = const()[name = string("concat_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23306496)))]; + tensor input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = y_cast_fp16_cast_uint16_cast_uint16)[name = string("transpose_3")]; + tensor input_5_lstm_layer_0_cast_fp16_0, tensor input_5_lstm_layer_0_cast_fp16_1, tensor input_5_lstm_layer_0_cast_fp16_2 = lstm(activation = input_5_lstm_layer_0_activation_0, bias = concat_0_to_fp16, cell_activation = input_5_lstm_layer_0_cell_activation_0, direction = input_5_lstm_layer_0_direction_0, initial_c = input_5_lstm_layer_0_lstm_c0_squeeze_cast_fp16, initial_h = input_5_lstm_layer_0_lstm_h0_squeeze_cast_fp16, output_sequence = input_5_lstm_layer_0_output_sequence_0, recurrent_activation = input_5_lstm_layer_0_recurrent_activation_0, weight_hh = concat_2_to_fp16, weight_ih = concat_1_to_fp16, x = input_3_cast_fp16)[name = string("input_5_lstm_layer_0_cast_fp16")]; + tensor input_5_lstm_h0_squeeze_axes_0 = const()[name = string("input_5_lstm_h0_squeeze_axes_0"), val = tensor([0])]; + tensor input_5_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_5_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_1)[name = string("input_5_lstm_h0_squeeze_cast_fp16")]; + tensor input_5_lstm_c0_squeeze_axes_0 = const()[name = string("input_5_lstm_c0_squeeze_axes_0"), val = tensor([0])]; + tensor input_5_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_5_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_1)[name = string("input_5_lstm_c0_squeeze_cast_fp16")]; + string input_5_direction_0 = const()[name = string("input_5_direction_0"), val = string("forward")]; + bool input_5_output_sequence_0 = const()[name = string("input_5_output_sequence_0"), val = bool(true)]; + string input_5_recurrent_activation_0 = const()[name = string("input_5_recurrent_activation_0"), val = string("sigmoid")]; + string input_5_cell_activation_0 = const()[name = string("input_5_cell_activation_0"), val = string("tanh")]; + string input_5_activation_0 = const()[name = string("input_5_activation_0"), val = string("tanh")]; + tensor concat_4_to_fp16 = const()[name = string("concat_4_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23311680)))]; + tensor concat_5_to_fp16 = const()[name = string("concat_5_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26588544)))]; + tensor concat_3_to_fp16 = const()[name = string("concat_3_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29865408)))]; + tensor input_5_cast_fp16_0, tensor input_5_cast_fp16_1, tensor input_5_cast_fp16_2 = lstm(activation = input_5_activation_0, bias = concat_3_to_fp16, cell_activation = input_5_cell_activation_0, direction = input_5_direction_0, initial_c = input_5_lstm_c0_squeeze_cast_fp16, initial_h = input_5_lstm_h0_squeeze_cast_fp16, output_sequence = input_5_output_sequence_0, recurrent_activation = input_5_recurrent_activation_0, weight_hh = concat_5_to_fp16, weight_ih = concat_4_to_fp16, x = input_5_lstm_layer_0_cast_fp16_0)[name = string("input_5_cast_fp16")]; + int32 obj_3_axis_0 = const()[name = string("obj_3_axis_0"), val = int32(0)]; + tensor obj_3_cast_fp16 = stack(axis = obj_3_axis_0, values = (input_5_lstm_layer_0_cast_fp16_1, input_5_cast_fp16_1))[name = string("obj_3_cast_fp16")]; + string obj_3_cast_fp16_to_fp32_dtype_0 = const()[name = string("obj_3_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + int32 obj_axis_0 = const()[name = string("obj_axis_0"), val = int32(0)]; + tensor obj_cast_fp16 = stack(axis = obj_axis_0, values = (input_5_lstm_layer_0_cast_fp16_2, input_5_cast_fp16_2))[name = string("obj_cast_fp16")]; + string obj_cast_fp16_to_fp32_dtype_0 = const()[name = string("obj_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor transpose_1_perm_0 = const()[name = string("transpose_1_perm_0"), val = tensor([1, 0, 2])]; + tensor joint_module_pred_weight_to_fp16 = const()[name = string("joint_module_pred_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29870592)))]; + tensor joint_module_pred_bias_to_fp16 = const()[name = string("joint_module_pred_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30689856)))]; + tensor transpose_1_cast_fp16 = transpose(perm = transpose_1_perm_0, x = input_5_cast_fp16_0)[name = string("transpose_2")]; + tensor linear_0_cast_fp16 = linear(bias = joint_module_pred_bias_to_fp16, weight = joint_module_pred_weight_to_fp16, x = transpose_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor f_axes_0 = const()[name = string("f_axes_0"), val = tensor([2])]; + string encoder_proj_to_fp16_dtype_0 = const()[name = string("encoder_proj_to_fp16_dtype_0"), val = string("fp16")]; + tensor encoder_proj_to_fp16 = cast(dtype = encoder_proj_to_fp16_dtype_0, x = encoder_proj)[name = string("cast_3")]; + tensor f_cast_fp16 = expand_dims(axes = f_axes_0, x = encoder_proj_to_fp16)[name = string("f_cast_fp16")]; + tensor g_axes_0 = const()[name = string("g_axes_0"), val = tensor([1])]; + tensor g_cast_fp16 = expand_dims(axes = g_axes_0, x = linear_0_cast_fp16)[name = string("g_cast_fp16")]; + tensor input_9_cast_fp16 = add(x = f_cast_fp16, y = g_cast_fp16)[name = string("input_9_cast_fp16")]; + tensor input_11_cast_fp16 = relu(x = input_9_cast_fp16)[name = string("input_11_cast_fp16")]; + tensor joint_module_joint_net_2_weight_to_fp16 = const()[name = string("joint_module_joint_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30691200)))]; + tensor joint_module_joint_net_2_bias_to_fp16 = const()[name = string("joint_module_joint_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47443904)))]; + tensor linear_1_cast_fp16 = linear(bias = joint_module_joint_net_2_bias_to_fp16, weight = joint_module_joint_net_2_weight_to_fp16, x = input_11_cast_fp16)[name = string("linear_1_cast_fp16")]; + int32 var_83 = const()[name = string("op_83"), val = int32(-1)]; + tensor var_85_softmax_cast_fp16 = softmax(axis = var_83, x = linear_1_cast_fp16)[name = string("op_85_softmax_cast_fp16")]; + fp32 var_85_epsilon_0 = const()[name = string("op_85_epsilon_0"), val = fp32(0x1p-149)]; + tensor var_85_cast_fp16 = log(epsilon = var_85_epsilon_0, x = var_85_softmax_cast_fp16)[name = string("op_85_cast_fp16")]; + string var_85_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_85_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor logits = cast(dtype = var_85_cast_fp16_to_fp32_dtype_0, x = var_85_cast_fp16)[name = string("cast_2")]; + tensor c_out = cast(dtype = obj_cast_fp16_to_fp32_dtype_0, x = obj_cast_fp16)[name = string("cast_4")]; + tensor h_out = cast(dtype = obj_3_cast_fp16_to_fp32_dtype_0, x = obj_3_cast_fp16)[name = string("cast_5")]; + tensor token_length_tmp = identity(x = token_length)[name = string("token_length_tmp")]; + } -> (logits, h_out, c_out); +} \ No newline at end of file diff --git a/multilingual/1120ms/decoder_joint_noencproj.mlmodelc/weights/weight.bin b/multilingual/1120ms/decoder_joint_noencproj.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..a04dee90b1d0140bff13a5c28dd8d3b4054a8679 --- /dev/null +++ b/multilingual/1120ms/decoder_joint_noencproj.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c7ddfe0cb3be2e2896258d91a95483b898e8a274c49fee256e8effd86dc64dda +size 47470144 diff --git a/multilingual/1120ms/decoder_joint_noencproj.mlpackage/Data/com.apple.CoreML/model.mlmodel b/multilingual/1120ms/decoder_joint_noencproj.mlpackage/Data/com.apple.CoreML/model.mlmodel new file mode 100644 index 0000000000000000000000000000000000000000..79ca0efcf3d22f4ffa11f41012bd5933da668872 --- /dev/null +++ b/multilingual/1120ms/decoder_joint_noencproj.mlpackage/Data/com.apple.CoreML/model.mlmodel @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0cd7f3fdcb96eec2c46b11da7cb758c2c7ec851677cc1d089bf0388138761c22 +size 14631 diff --git a/multilingual/1120ms/decoder_joint_noencproj.mlpackage/Data/com.apple.CoreML/weights/weight.bin b/multilingual/1120ms/decoder_joint_noencproj.mlpackage/Data/com.apple.CoreML/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..a04dee90b1d0140bff13a5c28dd8d3b4054a8679 --- /dev/null +++ b/multilingual/1120ms/decoder_joint_noencproj.mlpackage/Data/com.apple.CoreML/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c7ddfe0cb3be2e2896258d91a95483b898e8a274c49fee256e8effd86dc64dda +size 47470144 diff --git a/multilingual/1120ms/decoder_joint_noencproj.mlpackage/Manifest.json b/multilingual/1120ms/decoder_joint_noencproj.mlpackage/Manifest.json new file 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b/multilingual/1120ms/encoder.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:afb78b1b16fbeee7e0c4953a6401b77ed7b9fa6e27ba9c55ac386ca27e1033cf +size 243 diff --git a/multilingual/1120ms/encoder.mlmodelc/coremldata.bin b/multilingual/1120ms/encoder.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..9b8258af58fe28da3cb3654487c6a7dcb4a8c5be --- /dev/null +++ b/multilingual/1120ms/encoder.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3e316a70a3a1d1d51edcee4b8c6f386d40684c8892cf84c5d2c478c10458ce6d +size 572 diff --git a/multilingual/1120ms/encoder.mlmodelc/model.mil b/multilingual/1120ms/encoder.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..0cdcac695ef892aa5cbc30a357b38f4a51a3f454 --- /dev/null +++ b/multilingual/1120ms/encoder.mlmodelc/model.mil @@ -0,0 +1,4413 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}})] +{ + func main(tensor cache_channel, tensor cache_len, tensor cache_time, tensor mel, tensor mel_length, tensor prompt_id) { + tensor value_3_perm_0 = const()[name = string("value_3_perm_0"), val = tensor([1, 0, 2, 3])]; + string cache_channel_to_fp16_dtype_0 = const()[name = string("cache_channel_to_fp16_dtype_0"), val = string("fp16")]; + tensor value_5_perm_0 = const()[name = string("value_5_perm_0"), val = tensor([1, 0, 2, 3])]; + string cache_time_to_fp16_dtype_0 = const()[name = string("cache_time_to_fp16_dtype_0"), val = string("fp16")]; + int32 var_58 = const()[name = string("op_58"), val = int32(-1)]; + int32 var_67 = const()[name = string("op_67"), val = int32(1)]; + tensor x_1_perm_0 = const()[name = string("x_1_perm_0"), val = tensor([0, 2, 1])]; + string mel_to_fp16_dtype_0 = const()[name = string("mel_to_fp16_dtype_0"), val = string("fp16")]; + tensor tensor_1_axes_0 = const()[name = string("tensor_1_axes_0"), val = tensor([1])]; + tensor mel_to_fp16 = cast(dtype = mel_to_fp16_dtype_0, x = mel)[name = string("cast_18")]; + tensor x_1_cast_fp16 = transpose(perm = x_1_perm_0, x = mel_to_fp16)[name = string("transpose_367")]; + tensor tensor_1_cast_fp16 = expand_dims(axes = tensor_1_axes_0, x = x_1_cast_fp16)[name = string("tensor_1_cast_fp16")]; + tensor expand_dims_0 = const()[name = string("expand_dims_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor var_136_axes_0 = const()[name = string("op_136_axes_0"), val = tensor([1])]; + tensor var_136 = expand_dims(axes = var_136_axes_0, x = mel_length)[name = string("op_136")]; + tensor time_mask_1 = less(x = expand_dims_0, y = var_136)[name = string("time_mask_1")]; + tensor var_138_axes_0 = const()[name = string("op_138_axes_0"), val = tensor([-1])]; + tensor var_138 = expand_dims(axes = var_138_axes_0, x = time_mask_1)[name = string("op_138")]; + tensor var_140_reps_0 = const()[name = string("op_140_reps_0"), val = tensor([1, 1, 128])]; + tensor var_140 = tile(reps = var_140_reps_0, x = var_138)[name = string("op_140")]; + tensor var_146_axes_0 = const()[name = string("op_146_axes_0"), val = tensor([1])]; + string cast_4_to_fp16_dtype_0 = const()[name = string("cast_4_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_140_to_fp16 = cast(dtype = cast_4_to_fp16_dtype_0, x = var_140)[name = string("cast_17")]; + tensor var_146_cast_fp16 = expand_dims(axes = var_146_axes_0, x = var_140_to_fp16)[name = string("op_146_cast_fp16")]; + tensor input_1_cast_fp16 = mul(x = tensor_1_cast_fp16, y = var_146_cast_fp16)[name = string("input_1_cast_fp16")]; + tensor input_3_pad_0 = const()[name = string("input_3_pad_0"), val = tensor([0, 0, 0, 0, 2, 1, 2, 1])]; + string input_3_mode_0 = const()[name = string("input_3_mode_0"), val = string("constant")]; + fp16 const_9_to_fp16 = const()[name = string("const_9_to_fp16"), val = fp16(0x0p+0)]; + tensor input_3_cast_fp16 = pad(constant_val = const_9_to_fp16, mode = input_3_mode_0, pad = input_3_pad_0, x = input_1_cast_fp16)[name = string("input_3_cast_fp16")]; + string tensor_3_pad_type_0 = const()[name = string("tensor_3_pad_type_0"), val = string("valid")]; + tensor tensor_3_strides_0 = const()[name = string("tensor_3_strides_0"), val = tensor([2, 2])]; + tensor tensor_3_pad_0 = const()[name = string("tensor_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor tensor_3_dilations_0 = const()[name = string("tensor_3_dilations_0"), val = tensor([1, 1])]; + int32 tensor_3_groups_0 = const()[name = string("tensor_3_groups_0"), val = int32(1)]; + tensor encoder_pre_encode_conv_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(640))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3008))))[name = string("encoder_pre_encode_conv_0_weight_to_fp16_quantized")]; + tensor encoder_pre_encode_conv_0_bias_to_fp16 = const()[name = string("encoder_pre_encode_conv_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3584)))]; + tensor tensor_3_cast_fp16 = conv(bias = encoder_pre_encode_conv_0_bias_to_fp16, dilations = tensor_3_dilations_0, groups = tensor_3_groups_0, pad = tensor_3_pad_0, pad_type = tensor_3_pad_type_0, strides = tensor_3_strides_0, weight = encoder_pre_encode_conv_0_weight_to_fp16_quantized, x = input_3_cast_fp16)[name = string("tensor_3_cast_fp16")]; + string cast_2_to_fp16_dtype_0 = const()[name = string("cast_2_to_fp16_dtype_0"), val = string("fp16")]; + fp16 var_159_promoted_to_fp16 = const()[name = string("op_159_promoted_to_fp16"), val = fp16(0x1p+1)]; + tensor mel_length_to_fp16 = cast(dtype = cast_2_to_fp16_dtype_0, x = mel_length)[name = string("cast_16")]; + tensor var_160_cast_fp16 = add(x = mel_length_to_fp16, y = var_159_promoted_to_fp16)[name = string("op_160_cast_fp16")]; + fp16 var_161_promoted_to_fp16 = const()[name = string("op_161_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_162_cast_fp16 = add(x = var_160_cast_fp16, y = var_161_promoted_to_fp16)[name = string("op_162_cast_fp16")]; + fp16 var_163_promoted_to_fp16 = const()[name = string("op_163_promoted_to_fp16"), val = fp16(0x1.8p+1)]; + tensor var_164_cast_fp16 = sub(x = var_162_cast_fp16, y = var_163_promoted_to_fp16)[name = string("op_164_cast_fp16")]; + fp16 var_55_promoted_to_fp16 = const()[name = string("op_55_promoted_to_fp16"), val = fp16(0x1p+1)]; + tensor floor_div_0_cast_fp16 = floor_div(x = var_164_cast_fp16, y = var_55_promoted_to_fp16)[name = string("floor_div_0_cast_fp16")]; + fp16 var_166_promoted_to_fp16 = const()[name = string("op_166_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor current_lengths_3_cast_fp16 = add(x = floor_div_0_cast_fp16, y = var_166_promoted_to_fp16)[name = string("current_lengths_3_cast_fp16")]; + string cast_5_dtype_0 = const()[name = string("cast_5_dtype_0"), val = string("int32")]; + tensor expand_dims_1 = const()[name = string("expand_dims_1"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4160)))]; + tensor var_175_axes_0 = const()[name = string("op_175_axes_0"), val = tensor([1])]; + tensor current_lengths_3_cast_fp16_to_int32 = cast(dtype = cast_5_dtype_0, x = current_lengths_3_cast_fp16)[name = string("cast_15")]; + tensor var_175 = expand_dims(axes = var_175_axes_0, x = current_lengths_3_cast_fp16_to_int32)[name = string("op_175")]; + tensor time_mask_3 = less(x = expand_dims_1, y = var_175)[name = string("time_mask_3")]; + tensor var_177_axes_0 = const()[name = string("op_177_axes_0"), val = tensor([-1])]; + tensor var_177 = expand_dims(axes = var_177_axes_0, x = time_mask_3)[name = string("op_177")]; + tensor var_179_reps_0 = const()[name = string("op_179_reps_0"), val = tensor([1, 1, 65])]; + tensor var_179 = tile(reps = var_179_reps_0, x = var_177)[name = string("op_179")]; + tensor var_185_axes_0 = const()[name = string("op_185_axes_0"), val = tensor([1])]; + string cast_6_to_fp16_dtype_0 = const()[name = string("cast_6_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_179_to_fp16 = cast(dtype = cast_6_to_fp16_dtype_0, x = var_179)[name = string("cast_14")]; + tensor var_185_cast_fp16 = expand_dims(axes = var_185_axes_0, x = var_179_to_fp16)[name = string("op_185_cast_fp16")]; + tensor expanded_mask_3_reps_0 = const()[name = string("expanded_mask_3_reps_0"), val = tensor([1, 256, 1, 1])]; + tensor expanded_mask_3_cast_fp16 = tile(reps = expanded_mask_3_reps_0, x = var_185_cast_fp16)[name = string("expanded_mask_3_cast_fp16")]; + tensor input_5_cast_fp16 = mul(x = tensor_3_cast_fp16, y = expanded_mask_3_cast_fp16)[name = string("input_5_cast_fp16")]; + tensor tensor_5_cast_fp16 = relu(x = input_5_cast_fp16)[name = string("tensor_5_cast_fp16")]; + tensor input_7_cast_fp16 = mul(x = tensor_5_cast_fp16, y = expanded_mask_3_cast_fp16)[name = string("input_7_cast_fp16")]; + tensor input_9_pad_0 = const()[name = string("input_9_pad_0"), val = tensor([0, 0, 0, 0, 2, 1, 2, 1])]; + string input_9_mode_0 = const()[name = string("input_9_mode_0"), val = string("constant")]; + fp16 const_23_to_fp16 = const()[name = string("const_23_to_fp16"), val = fp16(0x0p+0)]; + tensor input_9_cast_fp16 = pad(constant_val = const_23_to_fp16, mode = input_9_mode_0, pad = input_9_pad_0, x = input_7_cast_fp16)[name = string("input_9_cast_fp16")]; + string tensor_7_pad_type_0 = const()[name = string("tensor_7_pad_type_0"), val = string("valid")]; + tensor tensor_7_strides_0 = const()[name = string("tensor_7_strides_0"), val = tensor([2, 2])]; + int32 tensor_7_groups_0 = const()[name = string("tensor_7_groups_0"), val = int32(256)]; + tensor tensor_7_pad_0 = const()[name = string("tensor_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor tensor_7_dilations_0 = const()[name = string("tensor_7_dilations_0"), val = tensor([1, 1])]; + tensor encoder_pre_encode_conv_2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4480))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6848))))[name = string("encoder_pre_encode_conv_2_weight_to_fp16_quantized")]; + tensor encoder_pre_encode_conv_2_bias_to_fp16 = const()[name = string("encoder_pre_encode_conv_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7424)))]; + tensor tensor_7_cast_fp16 = conv(bias = encoder_pre_encode_conv_2_bias_to_fp16, dilations = tensor_7_dilations_0, groups = tensor_7_groups_0, pad = tensor_7_pad_0, pad_type = tensor_7_pad_type_0, strides = tensor_7_strides_0, weight = encoder_pre_encode_conv_2_weight_to_fp16_quantized, x = input_9_cast_fp16)[name = string("tensor_7_cast_fp16")]; + fp16 var_207_promoted_to_fp16 = const()[name = string("op_207_promoted_to_fp16"), val = fp16(0x1p+1)]; + tensor var_208_cast_fp16 = add(x = current_lengths_3_cast_fp16, y = var_207_promoted_to_fp16)[name = string("op_208_cast_fp16")]; + fp16 var_209_promoted_to_fp16 = const()[name = string("op_209_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_210_cast_fp16 = add(x = var_208_cast_fp16, y = var_209_promoted_to_fp16)[name = string("op_210_cast_fp16")]; + fp16 var_211_promoted_to_fp16 = const()[name = string("op_211_promoted_to_fp16"), val = fp16(0x1.8p+1)]; + tensor var_212_cast_fp16 = sub(x = var_210_cast_fp16, y = var_211_promoted_to_fp16)[name = string("op_212_cast_fp16")]; + fp16 var_55_promoted_1_to_fp16 = const()[name = string("op_55_promoted_1_to_fp16"), val = fp16(0x1p+1)]; + tensor floor_div_1_cast_fp16 = floor_div(x = var_212_cast_fp16, y = var_55_promoted_1_to_fp16)[name = string("floor_div_1_cast_fp16")]; + fp16 var_214_promoted_to_fp16 = const()[name = string("op_214_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor current_lengths_5_cast_fp16 = add(x = floor_div_1_cast_fp16, y = var_214_promoted_to_fp16)[name = string("current_lengths_5_cast_fp16")]; + string cast_7_dtype_0 = const()[name = string("cast_7_dtype_0"), val = string("int32")]; + tensor expand_dims_2 = const()[name = string("expand_dims_2"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8000)))]; + tensor var_223_axes_0 = const()[name = string("op_223_axes_0"), val = tensor([1])]; + tensor current_lengths_5_cast_fp16_to_int32 = cast(dtype = cast_7_dtype_0, x = current_lengths_5_cast_fp16)[name = string("cast_13")]; + tensor var_223 = expand_dims(axes = var_223_axes_0, x = current_lengths_5_cast_fp16_to_int32)[name = string("op_223")]; + tensor time_mask_5 = less(x = expand_dims_2, y = var_223)[name = string("time_mask_5")]; + tensor var_225_axes_0 = const()[name = string("op_225_axes_0"), val = tensor([-1])]; + tensor var_225 = expand_dims(axes = var_225_axes_0, x = time_mask_5)[name = string("op_225")]; + tensor var_227_reps_0 = const()[name = string("op_227_reps_0"), val = tensor([1, 1, 33])]; + tensor var_227 = tile(reps = var_227_reps_0, x = var_225)[name = string("op_227")]; + tensor var_233_axes_0 = const()[name = string("op_233_axes_0"), val = tensor([1])]; + string cast_8_to_fp16_dtype_0 = const()[name = string("cast_8_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_227_to_fp16 = cast(dtype = cast_8_to_fp16_dtype_0, x = var_227)[name = string("cast_12")]; + tensor var_233_cast_fp16 = expand_dims(axes = var_233_axes_0, x = var_227_to_fp16)[name = string("op_233_cast_fp16")]; + tensor expanded_mask_7_reps_0 = const()[name = string("expanded_mask_7_reps_0"), val = tensor([1, 256, 1, 1])]; + tensor expanded_mask_7_cast_fp16 = tile(reps = expanded_mask_7_reps_0, x = var_233_cast_fp16)[name = string("expanded_mask_7_cast_fp16")]; + tensor input_11_cast_fp16 = mul(x = tensor_7_cast_fp16, y = expanded_mask_7_cast_fp16)[name = string("input_11_cast_fp16")]; + string tensor_9_pad_type_0 = const()[name = string("tensor_9_pad_type_0"), val = string("valid")]; + tensor tensor_9_strides_0 = const()[name = string("tensor_9_strides_0"), val = tensor([1, 1])]; + tensor tensor_9_pad_0 = const()[name = string("tensor_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor tensor_9_dilations_0 = const()[name = string("tensor_9_dilations_0"), val = tensor([1, 1])]; + int32 tensor_9_groups_0 = const()[name = string("tensor_9_groups_0"), val = int32(1)]; + tensor encoder_pre_encode_conv_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73792))))[name = string("encoder_pre_encode_conv_3_weight_to_fp16_quantized")]; + tensor encoder_pre_encode_conv_3_bias_to_fp16 = const()[name = string("encoder_pre_encode_conv_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(74368)))]; + tensor tensor_9_cast_fp16 = conv(bias = encoder_pre_encode_conv_3_bias_to_fp16, dilations = tensor_9_dilations_0, groups = tensor_9_groups_0, pad = tensor_9_pad_0, pad_type = tensor_9_pad_type_0, strides = tensor_9_strides_0, weight = encoder_pre_encode_conv_3_weight_to_fp16_quantized, x = input_11_cast_fp16)[name = string("tensor_9_cast_fp16")]; + tensor input_13_cast_fp16 = mul(x = tensor_9_cast_fp16, y = expanded_mask_7_cast_fp16)[name = string("input_13_cast_fp16")]; + tensor tensor_11_cast_fp16 = relu(x = input_13_cast_fp16)[name = string("tensor_11_cast_fp16")]; + tensor input_15_cast_fp16 = mul(x = tensor_11_cast_fp16, y = expanded_mask_7_cast_fp16)[name = string("input_15_cast_fp16")]; + tensor input_17_pad_0 = const()[name = string("input_17_pad_0"), val = tensor([0, 0, 0, 0, 2, 1, 2, 1])]; + string input_17_mode_0 = const()[name = string("input_17_mode_0"), val = string("constant")]; + fp16 const_41_to_fp16 = const()[name = string("const_41_to_fp16"), val = fp16(0x0p+0)]; + tensor input_17_cast_fp16 = pad(constant_val = const_41_to_fp16, mode = input_17_mode_0, pad = input_17_pad_0, x = input_15_cast_fp16)[name = string("input_17_cast_fp16")]; + string tensor_13_pad_type_0 = const()[name = string("tensor_13_pad_type_0"), val = string("valid")]; + tensor tensor_13_strides_0 = const()[name = string("tensor_13_strides_0"), val = tensor([2, 2])]; + int32 tensor_13_groups_0 = const()[name = string("tensor_13_groups_0"), val = int32(256)]; + tensor tensor_13_pad_0 = const()[name = string("tensor_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor tensor_13_dilations_0 = const()[name = string("tensor_13_dilations_0"), val = tensor([1, 1])]; + tensor encoder_pre_encode_conv_5_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(74944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77312))))[name = string("encoder_pre_encode_conv_5_weight_to_fp16_quantized")]; + tensor encoder_pre_encode_conv_5_bias_to_fp16 = const()[name = string("encoder_pre_encode_conv_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77888)))]; + tensor tensor_13_cast_fp16 = conv(bias = encoder_pre_encode_conv_5_bias_to_fp16, dilations = tensor_13_dilations_0, groups = tensor_13_groups_0, pad = tensor_13_pad_0, pad_type = tensor_13_pad_type_0, strides = tensor_13_strides_0, weight = encoder_pre_encode_conv_5_weight_to_fp16_quantized, x = input_17_cast_fp16)[name = string("tensor_13_cast_fp16")]; + fp16 var_270_promoted_to_fp16 = const()[name = string("op_270_promoted_to_fp16"), val = fp16(0x1p+1)]; + tensor var_271_cast_fp16 = add(x = current_lengths_5_cast_fp16, y = var_270_promoted_to_fp16)[name = string("op_271_cast_fp16")]; + fp16 var_272_promoted_to_fp16 = const()[name = string("op_272_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_273_cast_fp16 = add(x = var_271_cast_fp16, y = var_272_promoted_to_fp16)[name = string("op_273_cast_fp16")]; + fp16 var_274_promoted_to_fp16 = const()[name = string("op_274_promoted_to_fp16"), val = fp16(0x1.8p+1)]; + tensor var_275_cast_fp16 = sub(x = var_273_cast_fp16, y = var_274_promoted_to_fp16)[name = string("op_275_cast_fp16")]; + fp16 var_55_promoted_2_to_fp16 = const()[name = string("op_55_promoted_2_to_fp16"), val = fp16(0x1p+1)]; + tensor floor_div_2_cast_fp16 = floor_div(x = var_275_cast_fp16, y = var_55_promoted_2_to_fp16)[name = string("floor_div_2_cast_fp16")]; + fp16 var_277_promoted_to_fp16 = const()[name = string("op_277_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor current_lengths_cast_fp16 = add(x = floor_div_2_cast_fp16, y = var_277_promoted_to_fp16)[name = string("current_lengths_cast_fp16")]; + string cast_9_dtype_0 = const()[name = string("cast_9_dtype_0"), val = string("int32")]; + tensor expand_dims_3 = const()[name = string("expand_dims_3"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78464)))]; + tensor var_286_axes_0 = const()[name = string("op_286_axes_0"), val = tensor([1])]; + tensor current_lengths_cast_fp16_to_int32 = cast(dtype = cast_9_dtype_0, x = current_lengths_cast_fp16)[name = string("cast_11")]; + tensor var_286 = expand_dims(axes = var_286_axes_0, x = current_lengths_cast_fp16_to_int32)[name = string("op_286")]; + tensor time_mask = less(x = expand_dims_3, y = var_286)[name = string("time_mask")]; + tensor var_288_axes_0 = const()[name = string("op_288_axes_0"), val = tensor([-1])]; + tensor var_288 = expand_dims(axes = var_288_axes_0, x = time_mask)[name = string("op_288")]; + tensor var_290_reps_0 = const()[name = string("op_290_reps_0"), val = tensor([1, 1, 17])]; + tensor var_290 = tile(reps = var_290_reps_0, x = var_288)[name = string("op_290")]; + tensor var_296_axes_0 = const()[name = string("op_296_axes_0"), val = tensor([1])]; + string cast_10_to_fp16_dtype_0 = const()[name = string("cast_10_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_290_to_fp16 = cast(dtype = cast_10_to_fp16_dtype_0, x = var_290)[name = string("cast_10")]; + tensor var_296_cast_fp16 = expand_dims(axes = var_296_axes_0, x = var_290_to_fp16)[name = string("op_296_cast_fp16")]; + tensor expanded_mask_13_reps_0 = const()[name = string("expanded_mask_13_reps_0"), val = tensor([1, 256, 1, 1])]; + tensor expanded_mask_13_cast_fp16 = tile(reps = expanded_mask_13_reps_0, x = var_296_cast_fp16)[name = string("expanded_mask_13_cast_fp16")]; + tensor input_19_cast_fp16 = mul(x = tensor_13_cast_fp16, y = expanded_mask_13_cast_fp16)[name = string("input_19_cast_fp16")]; + string tensor_15_pad_type_0 = const()[name = string("tensor_15_pad_type_0"), val = string("valid")]; + tensor tensor_15_strides_0 = const()[name = string("tensor_15_strides_0"), val = tensor([1, 1])]; + tensor tensor_15_pad_0 = const()[name = string("tensor_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor tensor_15_dilations_0 = const()[name = string("tensor_15_dilations_0"), val = tensor([1, 1])]; + int32 tensor_15_groups_0 = const()[name = string("tensor_15_groups_0"), val = int32(1)]; + tensor encoder_pre_encode_conv_6_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78592))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144192))))[name = string("encoder_pre_encode_conv_6_weight_to_fp16_quantized")]; + tensor encoder_pre_encode_conv_6_bias_to_fp16 = const()[name = string("encoder_pre_encode_conv_6_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144768)))]; + tensor tensor_15_cast_fp16 = conv(bias = encoder_pre_encode_conv_6_bias_to_fp16, dilations = tensor_15_dilations_0, groups = tensor_15_groups_0, pad = tensor_15_pad_0, pad_type = tensor_15_pad_type_0, strides = tensor_15_strides_0, weight = encoder_pre_encode_conv_6_weight_to_fp16_quantized, x = input_19_cast_fp16)[name = string("tensor_15_cast_fp16")]; + tensor input_21_cast_fp16 = mul(x = tensor_15_cast_fp16, y = expanded_mask_13_cast_fp16)[name = string("input_21_cast_fp16")]; + tensor tensor_cast_fp16 = relu(x = input_21_cast_fp16)[name = string("tensor_cast_fp16")]; + tensor x_3_cast_fp16 = mul(x = tensor_cast_fp16, y = expanded_mask_13_cast_fp16)[name = string("x_3_cast_fp16")]; + tensor var_330_perm_0 = const()[name = string("op_330_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_331 = const()[name = string("op_331"), val = tensor([1, 16, -1])]; + tensor var_330_cast_fp16 = transpose(perm = var_330_perm_0, x = x_3_cast_fp16)[name = string("transpose_366")]; + tensor input_23_cast_fp16 = reshape(shape = var_331, x = var_330_cast_fp16)[name = string("input_23_cast_fp16")]; + tensor encoder_pre_encode_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145344))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4601856))))[name = string("encoder_pre_encode_out_weight_to_fp16_quantized")]; + tensor encoder_pre_encode_out_bias_to_fp16 = const()[name = string("encoder_pre_encode_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4603968)))]; + tensor linear_0_cast_fp16 = linear(bias = encoder_pre_encode_out_bias_to_fp16, weight = encoder_pre_encode_out_weight_to_fp16_quantized, x = input_23_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_341_begin_0 = const()[name = string("op_341_begin_0"), val = tensor([0, 2, 0])]; + tensor var_341_end_0 = const()[name = string("op_341_end_0"), val = tensor([1, 16, 1024])]; + tensor var_341_end_mask_0 = const()[name = string("op_341_end_mask_0"), val = tensor([true, true, true])]; + tensor var_341_cast_fp16 = slice_by_index(begin = var_341_begin_0, end = var_341_end_0, end_mask = var_341_end_mask_0, x = linear_0_cast_fp16)[name = string("op_341_cast_fp16")]; + int32 var_343 = const()[name = string("op_343"), val = int32(2)]; + tensor var_344 = sub(x = current_lengths_cast_fp16_to_int32, y = var_343)[name = string("op_344")]; + string var_344_promoted_to_fp16_dtype_0 = const()[name = string("op_344_promoted_to_fp16_dtype_0"), val = string("fp16")]; + fp16 var_61_promoted_to_fp16 = const()[name = string("op_61_promoted_to_fp16"), val = fp16(0x0p+0)]; + fp16 const_61_to_fp16 = const()[name = string("const_61_to_fp16"), val = fp16(inf)]; + tensor var_344_to_fp16 = cast(dtype = var_344_promoted_to_fp16_dtype_0, x = var_344)[name = string("cast_9")]; + tensor clip_0_cast_fp16 = clip(alpha = var_61_promoted_to_fp16, beta = const_61_to_fp16, x = var_344_to_fp16)[name = string("clip_0_cast_fp16")]; + tensor max_audio_length_1 = const()[name = string("max_audio_length_1"), val = tensor([14])]; + fp16 var_360_promoted_to_fp16 = const()[name = string("op_360_promoted_to_fp16"), val = fp16(0x1.5p+5)]; + tensor padding_length_cast_fp16 = add(x = clip_0_cast_fp16, y = var_360_promoted_to_fp16)[name = string("padding_length_cast_fp16")]; + int32 const_63 = const()[name = string("const_63"), val = int32(-1)]; + tensor var_362 = mul(x = cache_len, y = const_63)[name = string("op_362")]; + int32 var_363 = const()[name = string("op_363"), val = int32(42)]; + tensor offset = add(x = var_362, y = var_363)[name = string("offset")]; + tensor var_403_axes_0 = const()[name = string("op_403_axes_0"), val = tensor([-1])]; + tensor var_403_cast_fp16 = expand_dims(axes = var_403_axes_0, x = padding_length_cast_fp16)[name = string("op_403_cast_fp16")]; + tensor var_402_promoted_to_fp16 = const()[name = string("op_402_promoted_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4606080)))]; + tensor pad_mask_1_cast_fp16 = less(x = var_402_promoted_to_fp16, y = var_403_cast_fp16)[name = string("pad_mask_1_cast_fp16")]; + tensor expand_dims_5 = const()[name = string("expand_dims_5"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4606272)))]; + tensor var_409_axes_0 = const()[name = string("op_409_axes_0"), val = tensor([-1])]; + tensor var_409 = expand_dims(axes = var_409_axes_0, x = offset)[name = string("op_409")]; + tensor pad_mask_off = greater_equal(x = expand_dims_5, y = var_409)[name = string("pad_mask_off")]; + tensor pad_mask_3 = logical_and(x = pad_mask_off, y = pad_mask_1_cast_fp16)[name = string("pad_mask_3")]; + tensor var_412_axes_0 = const()[name = string("op_412_axes_0"), val = tensor([1])]; + tensor var_412 = expand_dims(axes = var_412_axes_0, x = pad_mask_3)[name = string("op_412")]; + tensor var_413 = const()[name = string("op_413"), val = tensor([1, 56, 1])]; + tensor pad_mask_for_att_mask_1 = tile(reps = var_413, x = var_412)[name = string("pad_mask_for_att_mask_1")]; + tensor var_415_perm_0 = const()[name = string("op_415_perm_0"), val = tensor([0, 2, 1])]; + tensor var_415 = transpose(perm = var_415_perm_0, x = pad_mask_for_att_mask_1)[name = string("transpose_365")]; + tensor pad_mask_for_att_mask = logical_and(x = pad_mask_for_att_mask_1, y = var_415)[name = string("pad_mask_for_att_mask")]; + tensor const_71 = const()[name = string("const_71"), val = tensor([[[true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, 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false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, 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false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, 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true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true]]])]; + tensor att_mask_9 = logical_and(x = pad_mask_for_att_mask, y = const_71)[name = string("att_mask_9")]; + tensor att_mask = logical_not(x = att_mask_9)[name = string("att_mask")]; + tensor pad_mask_5 = logical_not(x = pad_mask_3)[name = string("pad_mask_5")]; + tensor pad_mask_begin_0 = const()[name = string("pad_mask_begin_0"), val = tensor([0, 42])]; + tensor pad_mask_end_0 = const()[name = string("pad_mask_end_0"), val = tensor([1, 56])]; + tensor pad_mask_end_mask_0 = const()[name = string("pad_mask_end_mask_0"), val = tensor([true, true])]; + tensor pad_mask = slice_by_index(begin = pad_mask_begin_0, end = pad_mask_end_0, end_mask = pad_mask_end_mask_0, x = pad_mask_5)[name = string("pad_mask")]; + tensor mask_9_begin_0 = const()[name = string("mask_9_begin_0"), val = tensor([0, 42, 0])]; + tensor mask_9_end_0 = const()[name = string("mask_9_end_0"), val = tensor([1, 56, 56])]; + tensor mask_9_end_mask_0 = const()[name = string("mask_9_end_mask_0"), val = tensor([true, true, true])]; + tensor mask_9 = slice_by_index(begin = mask_9_begin_0, end = mask_9_end_0, end_mask = mask_9_end_mask_0, x = att_mask)[name = string("mask_9")]; + tensor cache_1_begin_0 = const()[name = string("cache_1_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor cache_1_end_0 = const()[name = string("cache_1_end_0"), val = tensor([1, 1, 42, 1024])]; + tensor cache_1_end_mask_0 = const()[name = string("cache_1_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_1_squeeze_mask_0 = const()[name = string("cache_1_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_channel_to_fp16 = cast(dtype = cache_channel_to_fp16_dtype_0, x = cache_channel)[name = string("cast_8")]; + tensor value_3_cast_fp16 = transpose(perm = value_3_perm_0, x = cache_channel_to_fp16)[name = string("transpose_364")]; + tensor cache_1_cast_fp16 = slice_by_index(begin = cache_1_begin_0, end = cache_1_end_0, end_mask = cache_1_end_mask_0, squeeze_mask = cache_1_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_1_cast_fp16")]; + tensor cache_3_begin_0 = const()[name = string("cache_3_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor cache_3_end_0 = const()[name = string("cache_3_end_0"), val = tensor([1, 1, 1024, 8])]; + tensor cache_3_end_mask_0 = const()[name = string("cache_3_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_3_squeeze_mask_0 = const()[name = string("cache_3_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_time_to_fp16 = cast(dtype = cache_time_to_fp16_dtype_0, x = cache_time)[name = string("cast_7")]; + tensor value_5_cast_fp16 = transpose(perm = value_5_perm_0, x = cache_time_to_fp16)[name = string("transpose_363")]; + tensor cache_3_cast_fp16 = slice_by_index(begin = cache_3_begin_0, end = cache_3_end_0, end_mask = cache_3_end_mask_0, squeeze_mask = cache_3_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_3_cast_fp16")]; + tensor input_27_axes_0 = const()[name = string("input_27_axes_0"), val = tensor([-1])]; + tensor encoder_layers_0_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_0_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4606592)))]; + tensor encoder_layers_0_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_0_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4608704)))]; + fp16 var_41_to_fp16 = const()[name = string("op_41_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_27_cast_fp16 = layer_norm(axes = input_27_axes_0, beta = encoder_layers_0_norm_feed_forward1_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_0_norm_feed_forward1_weight_to_fp16, x = var_341_cast_fp16)[name = string("input_27_cast_fp16")]; + tensor encoder_layers_0_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4610816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8805184))))[name = string("encoder_layers_0_feed_forward1_linear1_weight_to_fp16_quantized")]; + tensor encoder_layers_0_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_0_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8813440)))]; + tensor linear_1_cast_fp16 = linear(bias = encoder_layers_0_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_0_feed_forward1_linear1_weight_to_fp16_quantized, x = input_27_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor input_31_cast_fp16 = silu(x = linear_1_cast_fp16)[name = string("input_31_cast_fp16")]; + tensor encoder_layers_0_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8821696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13016064))))[name = string("encoder_layers_0_feed_forward1_linear2_weight_to_fp16_quantized")]; + tensor encoder_layers_0_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_0_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13018176)))]; + tensor linear_2_cast_fp16 = linear(bias = encoder_layers_0_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_0_feed_forward1_linear2_weight_to_fp16_quantized, x = input_31_cast_fp16)[name = string("linear_2_cast_fp16")]; + fp16 var_454_to_fp16 = const()[name = string("op_454_to_fp16"), val = fp16(0x1p-1)]; + tensor var_455_cast_fp16 = mul(x = linear_2_cast_fp16, y = var_454_to_fp16)[name = string("op_455_cast_fp16")]; + tensor input_37_cast_fp16 = add(x = var_341_cast_fp16, y = var_455_cast_fp16)[name = string("input_37_cast_fp16")]; + tensor key_1_axes_0 = const()[name = string("key_1_axes_0"), val = tensor([-1])]; + tensor encoder_layers_0_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_0_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13020288)))]; + tensor encoder_layers_0_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_0_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13022400)))]; + tensor key_1_cast_fp16 = layer_norm(axes = key_1_axes_0, beta = encoder_layers_0_norm_self_att_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_0_norm_self_att_weight_to_fp16, x = input_37_cast_fp16)[name = string("key_1_cast_fp16")]; + bool input_39_interleave_0 = const()[name = string("input_39_interleave_0"), val = bool(false)]; + tensor input_39_cast_fp16 = concat(axis = var_67, interleave = input_39_interleave_0, values = (cache_1_cast_fp16, key_1_cast_fp16))[name = string("input_39_cast_fp16")]; + tensor var_477_begin_0 = const()[name = string("op_477_begin_0"), val = tensor([0, 14, 0])]; + tensor var_477_end_0 = const()[name = string("op_477_end_0"), val = tensor([1, 42, 1024])]; + tensor var_477_end_mask_0 = const()[name = string("op_477_end_mask_0"), val = tensor([true, true, true])]; + tensor var_477_cast_fp16 = slice_by_index(begin = var_477_begin_0, end = var_477_end_0, end_mask = var_477_end_mask_0, x = cache_1_cast_fp16)[name = string("op_477_cast_fp16")]; + bool var_483_interleave_0 = const()[name = string("op_483_interleave_0"), val = bool(false)]; + tensor var_483_cast_fp16 = concat(axis = var_67, interleave = var_483_interleave_0, values = (var_477_cast_fp16, key_1_cast_fp16))[name = string("op_483_cast_fp16")]; + tensor encoder_layers_0_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13024512))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14073152))))[name = string("encoder_layers_0_self_attn_linear_q_weight_to_fp16_quantized")]; + tensor encoder_layers_0_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_0_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14075264)))]; + tensor linear_3_cast_fp16 = linear(bias = encoder_layers_0_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_0_self_attn_linear_q_weight_to_fp16_quantized, x = key_1_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_488 = const()[name = string("op_488"), val = tensor([1, -1, 8, 128])]; + tensor q_1_cast_fp16 = reshape(shape = var_488, x = linear_3_cast_fp16)[name = string("q_1_cast_fp16")]; + tensor encoder_layers_0_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14077376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15126016))))[name = string("encoder_layers_0_self_attn_linear_k_weight_to_fp16_quantized")]; + tensor encoder_layers_0_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_0_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15128128)))]; + tensor linear_4_cast_fp16 = linear(bias = encoder_layers_0_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_0_self_attn_linear_k_weight_to_fp16_quantized, x = input_39_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_493 = const()[name = string("op_493"), val = tensor([1, -1, 8, 128])]; + tensor k_1_cast_fp16 = reshape(shape = var_493, x = linear_4_cast_fp16)[name = string("k_1_cast_fp16")]; + tensor encoder_layers_0_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15130240))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16178880))))[name = string("encoder_layers_0_self_attn_linear_v_weight_to_fp16_quantized")]; + tensor encoder_layers_0_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_0_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16180992)))]; + tensor linear_5_cast_fp16 = linear(bias = encoder_layers_0_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_0_self_attn_linear_v_weight_to_fp16_quantized, x = input_39_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_498 = const()[name = string("op_498"), val = tensor([1, -1, 8, 128])]; + tensor v_1_cast_fp16 = reshape(shape = var_498, x = linear_5_cast_fp16)[name = string("v_1_cast_fp16")]; + tensor value_9_perm_0 = const()[name = string("value_9_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_0_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_0_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16183104)))]; + tensor var_511_cast_fp16 = add(x = q_1_cast_fp16, y = encoder_layers_0_self_attn_pos_bias_u_to_fp16)[name = string("op_511_cast_fp16")]; + tensor encoder_layers_0_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_0_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16185216)))]; + tensor var_513_cast_fp16 = add(x = q_1_cast_fp16, y = encoder_layers_0_self_attn_pos_bias_v_to_fp16)[name = string("op_513_cast_fp16")]; + tensor q_with_bias_v_1_perm_0 = const()[name = string("q_with_bias_v_1_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_7_transpose_x_0 = const()[name = string("x_7_transpose_x_0"), val = bool(false)]; + bool x_7_transpose_y_0 = const()[name = string("x_7_transpose_y_0"), val = bool(false)]; + tensor op_515_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16187328))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16301056))))[name = string("op_515_to_fp16_quantized")]; + tensor q_with_bias_v_1_cast_fp16 = transpose(perm = q_with_bias_v_1_perm_0, x = var_513_cast_fp16)[name = string("transpose_362")]; + tensor x_7_cast_fp16 = matmul(transpose_x = x_7_transpose_x_0, transpose_y = x_7_transpose_y_0, x = q_with_bias_v_1_cast_fp16, y = op_515_to_fp16_quantized)[name = string("x_7_cast_fp16")]; + tensor x_9_pad_0 = const()[name = string("x_9_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_9_mode_0 = const()[name = string("x_9_mode_0"), val = string("constant")]; + fp16 const_79_to_fp16 = const()[name = string("const_79_to_fp16"), val = fp16(0x0p+0)]; + tensor x_9_cast_fp16 = pad(constant_val = const_79_to_fp16, mode = x_9_mode_0, pad = x_9_pad_0, x = x_7_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor var_523 = const()[name = string("op_523"), val = tensor([1, 8, -1, 14])]; + tensor x_11_cast_fp16 = reshape(shape = var_523, x = x_9_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor var_527_begin_0 = const()[name = string("op_527_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_527_end_0 = const()[name = string("op_527_end_0"), val = tensor([1, 8, 112, 14])]; + tensor var_527_end_mask_0 = const()[name = string("op_527_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_527_cast_fp16 = slice_by_index(begin = var_527_begin_0, end = var_527_end_0, end_mask = var_527_end_mask_0, x = x_11_cast_fp16)[name = string("op_527_cast_fp16")]; + tensor var_528 = const()[name = string("op_528"), val = tensor([1, 8, 14, 111])]; + tensor matrix_bd_1_cast_fp16 = reshape(shape = var_528, x = var_527_cast_fp16)[name = string("matrix_bd_1_cast_fp16")]; + bool matrix_ac_1_transpose_x_0 = const()[name = string("matrix_ac_1_transpose_x_0"), val = bool(false)]; + bool matrix_ac_1_transpose_y_0 = const()[name = string("matrix_ac_1_transpose_y_0"), val = bool(false)]; + tensor transpose_96_perm_0 = const()[name = string("transpose_96_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_97_perm_0 = const()[name = string("transpose_97_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_97 = transpose(perm = transpose_97_perm_0, x = k_1_cast_fp16)[name = string("transpose_360")]; + tensor transpose_96 = transpose(perm = transpose_96_perm_0, x = var_511_cast_fp16)[name = string("transpose_361")]; + tensor matrix_ac_1_cast_fp16 = matmul(transpose_x = matrix_ac_1_transpose_x_0, transpose_y = matrix_ac_1_transpose_y_0, x = transpose_96, y = transpose_97)[name = string("matrix_ac_1_cast_fp16")]; + tensor matrix_bd_3_begin_0 = const()[name = string("matrix_bd_3_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_3_end_0 = const()[name = string("matrix_bd_3_end_0"), val = tensor([1, 8, 14, 56])]; + tensor matrix_bd_3_end_mask_0 = const()[name = string("matrix_bd_3_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_3_cast_fp16 = slice_by_index(begin = matrix_bd_3_begin_0, end = matrix_bd_3_end_0, end_mask = matrix_bd_3_end_mask_0, x = matrix_bd_1_cast_fp16)[name = string("matrix_bd_3_cast_fp16")]; + tensor var_537_cast_fp16 = add(x = matrix_ac_1_cast_fp16, y = matrix_bd_3_cast_fp16)[name = string("op_537_cast_fp16")]; + fp16 _inversed_scores_1_y_0_to_fp16 = const()[name = string("_inversed_scores_1_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_1_cast_fp16 = mul(x = var_537_cast_fp16, y = _inversed_scores_1_y_0_to_fp16)[name = string("_inversed_scores_1_cast_fp16")]; + tensor mask_11_axes_0 = const()[name = string("mask_11_axes_0"), val = tensor([1])]; + tensor mask_11 = expand_dims(axes = mask_11_axes_0, x = mask_9)[name = string("mask_11")]; + fp16 var_44_to_fp16 = const()[name = string("op_44_to_fp16"), val = fp16(-0x1.388p+13)]; + tensor scores_3_cast_fp16 = select(a = var_44_to_fp16, b = _inversed_scores_1_cast_fp16, cond = mask_11)[name = string("scores_3_cast_fp16")]; + tensor var_543_cast_fp16 = softmax(axis = var_58, x = scores_3_cast_fp16)[name = string("op_543_cast_fp16")]; + fp16 var_43_to_fp16 = const()[name = string("op_43_to_fp16"), val = fp16(0x0p+0)]; + tensor input_41_cast_fp16 = select(a = var_43_to_fp16, b = var_543_cast_fp16, cond = mask_11)[name = string("input_41_cast_fp16")]; + bool x_13_transpose_x_0 = const()[name = string("x_13_transpose_x_0"), val = bool(false)]; + bool x_13_transpose_y_0 = const()[name = string("x_13_transpose_y_0"), val = bool(false)]; + tensor value_9_cast_fp16 = transpose(perm = value_9_perm_0, x = v_1_cast_fp16)[name = string("transpose_359")]; + tensor x_13_cast_fp16 = matmul(transpose_x = x_13_transpose_x_0, transpose_y = x_13_transpose_y_0, x = input_41_cast_fp16, y = value_9_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor var_547_perm_0 = const()[name = string("op_547_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_548 = const()[name = string("op_548"), val = tensor([1, -1, 1024])]; + tensor var_547_cast_fp16 = transpose(perm = var_547_perm_0, x = x_13_cast_fp16)[name = string("transpose_358")]; + tensor input_43_cast_fp16 = reshape(shape = var_548, x = var_547_cast_fp16)[name = string("input_43_cast_fp16")]; + tensor encoder_layers_0_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16301376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17350016))))[name = string("encoder_layers_0_self_attn_linear_out_weight_to_fp16_quantized")]; + tensor encoder_layers_0_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_0_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17352128)))]; + tensor linear_7_cast_fp16 = linear(bias = encoder_layers_0_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_0_self_attn_linear_out_weight_to_fp16_quantized, x = input_43_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor input_47_cast_fp16 = add(x = input_37_cast_fp16, y = linear_7_cast_fp16)[name = string("input_47_cast_fp16")]; + tensor x_17_axes_0 = const()[name = string("x_17_axes_0"), val = tensor([-1])]; + tensor encoder_layers_0_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_0_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17354240)))]; + tensor encoder_layers_0_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_0_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17356352)))]; + tensor x_17_cast_fp16 = layer_norm(axes = x_17_axes_0, beta = encoder_layers_0_norm_conv_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_0_norm_conv_weight_to_fp16, x = input_47_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor input_49_perm_0 = const()[name = string("input_49_perm_0"), val = tensor([0, 2, 1])]; + string input_51_pad_type_0 = const()[name = string("input_51_pad_type_0"), val = string("valid")]; + tensor input_51_strides_0 = const()[name = string("input_51_strides_0"), val = tensor([1])]; + tensor input_51_pad_0 = const()[name = string("input_51_pad_0"), val = tensor([0, 0])]; + tensor input_51_dilations_0 = const()[name = string("input_51_dilations_0"), val = tensor([1])]; + int32 input_51_groups_0 = const()[name = string("input_51_groups_0"), val = int32(1)]; + tensor encoder_layers_0_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17358464))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19455680))))[name = string("encoder_layers_0_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_49_cast_fp16 = transpose(perm = input_49_perm_0, x = x_17_cast_fp16)[name = string("transpose_357")]; + tensor input_51_cast_fp16 = conv(dilations = input_51_dilations_0, groups = input_51_groups_0, pad = input_51_pad_0, pad_type = input_51_pad_type_0, strides = input_51_strides_0, weight = encoder_layers_0_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_49_cast_fp16)[name = string("input_51_cast_fp16")]; + int32 x_19_split_num_splits_0 = const()[name = string("x_19_split_num_splits_0"), val = int32(2)]; + int32 x_19_split_axis_0 = const()[name = string("x_19_split_axis_0"), val = int32(1)]; + tensor x_19_split_cast_fp16_0, tensor x_19_split_cast_fp16_1 = split(axis = x_19_split_axis_0, num_splits = x_19_split_num_splits_0, x = input_51_cast_fp16)[name = string("x_19_split_cast_fp16")]; + tensor x_19_split_1_sigmoid_cast_fp16 = sigmoid(x = x_19_split_cast_fp16_1)[name = string("x_19_split_1_sigmoid_cast_fp16")]; + tensor x_19_cast_fp16 = mul(x = x_19_split_cast_fp16_0, y = x_19_split_1_sigmoid_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor var_574_axes_0 = const()[name = string("op_574_axes_0"), val = tensor([1])]; + tensor var_574 = expand_dims(axes = var_574_axes_0, x = pad_mask)[name = string("op_574")]; + tensor input_53_cast_fp16 = select(a = var_43_to_fp16, b = x_19_cast_fp16, cond = var_574)[name = string("input_53_cast_fp16")]; + bool new_x_3_interleave_0 = const()[name = string("new_x_3_interleave_0"), val = bool(false)]; + tensor new_x_3_cast_fp16 = concat(axis = var_58, interleave = new_x_3_interleave_0, values = (cache_3_cast_fp16, input_53_cast_fp16))[name = string("new_x_3_cast_fp16")]; + tensor var_587_begin_0 = const()[name = string("op_587_begin_0"), val = tensor([0, 0, 14])]; + tensor var_587_end_0 = const()[name = string("op_587_end_0"), val = tensor([1, 1024, 22])]; + tensor var_587_end_mask_0 = const()[name = string("op_587_end_mask_0"), val = tensor([true, true, true])]; + tensor var_587_cast_fp16 = slice_by_index(begin = var_587_begin_0, end = var_587_end_0, end_mask = var_587_end_mask_0, x = new_x_3_cast_fp16)[name = string("op_587_cast_fp16")]; + string x_21_pad_type_0 = const()[name = string("x_21_pad_type_0"), val = string("valid")]; + int32 x_21_groups_0 = const()[name = string("x_21_groups_0"), val = int32(1024)]; + tensor x_21_strides_0 = const()[name = string("x_21_strides_0"), val = tensor([1])]; + tensor x_21_pad_0 = const()[name = string("x_21_pad_0"), val = tensor([0, 0])]; + tensor x_21_dilations_0 = const()[name = string("x_21_dilations_0"), val = tensor([1])]; + tensor encoder_layers_0_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19459840))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19469120))))[name = string("encoder_layers_0_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_21_cast_fp16 = conv(dilations = x_21_dilations_0, groups = x_21_groups_0, pad = x_21_pad_0, pad_type = x_21_pad_type_0, strides = x_21_strides_0, weight = encoder_layers_0_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_3_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor input_55_perm_0 = const()[name = string("input_55_perm_0"), val = tensor([0, 2, 1])]; + tensor x_23_axes_0 = const()[name = string("x_23_axes_0"), val = tensor([-1])]; + tensor encoder_layers_0_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_0_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19471232)))]; + tensor encoder_layers_0_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_0_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19473344)))]; + tensor input_55_cast_fp16 = transpose(perm = input_55_perm_0, x = x_21_cast_fp16)[name = string("transpose_356")]; + tensor x_23_cast_fp16 = layer_norm(axes = x_23_axes_0, beta = encoder_layers_0_conv_batch_norm_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_0_conv_batch_norm_weight_to_fp16, x = input_55_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor input_57_perm_0 = const()[name = string("input_57_perm_0"), val = tensor([0, 2, 1])]; + tensor input_57_cast_fp16 = transpose(perm = input_57_perm_0, x = x_23_cast_fp16)[name = string("transpose_355")]; + tensor input_59_cast_fp16 = silu(x = input_57_cast_fp16)[name = string("input_59_cast_fp16")]; + string x_25_pad_type_0 = const()[name = string("x_25_pad_type_0"), val = string("valid")]; + tensor x_25_strides_0 = const()[name = string("x_25_strides_0"), val = tensor([1])]; + tensor x_25_pad_0 = const()[name = string("x_25_pad_0"), val = tensor([0, 0])]; + tensor x_25_dilations_0 = const()[name = string("x_25_dilations_0"), val = tensor([1])]; + int32 x_25_groups_0 = const()[name = string("x_25_groups_0"), val = int32(1)]; + tensor encoder_layers_0_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19475456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20524096))))[name = string("encoder_layers_0_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_25_cast_fp16 = conv(dilations = x_25_dilations_0, groups = x_25_groups_0, pad = x_25_pad_0, pad_type = x_25_pad_type_0, strides = x_25_strides_0, weight = encoder_layers_0_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_59_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor input_61_perm_0 = const()[name = string("input_61_perm_0"), val = tensor([0, 2, 1])]; + tensor input_61_cast_fp16 = transpose(perm = input_61_perm_0, x = x_25_cast_fp16)[name = string("transpose_354")]; + tensor input_63_cast_fp16 = add(x = input_47_cast_fp16, y = input_61_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor input_65_axes_0 = const()[name = string("input_65_axes_0"), val = tensor([-1])]; + tensor encoder_layers_0_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_0_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20526208)))]; + tensor encoder_layers_0_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_0_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20528320)))]; + tensor input_65_cast_fp16 = layer_norm(axes = input_65_axes_0, beta = encoder_layers_0_norm_feed_forward2_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_0_norm_feed_forward2_weight_to_fp16, x = input_63_cast_fp16)[name = string("input_65_cast_fp16")]; + tensor encoder_layers_0_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20530432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24724800))))[name = string("encoder_layers_0_feed_forward2_linear1_weight_to_fp16_quantized")]; + tensor encoder_layers_0_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_0_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24733056)))]; + tensor linear_8_cast_fp16 = linear(bias = encoder_layers_0_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_0_feed_forward2_linear1_weight_to_fp16_quantized, x = input_65_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor input_69_cast_fp16 = silu(x = linear_8_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor encoder_layers_0_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24741312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28935680))))[name = string("encoder_layers_0_feed_forward2_linear2_weight_to_fp16_quantized")]; + tensor encoder_layers_0_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_0_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28937792)))]; + tensor linear_9_cast_fp16 = linear(bias = encoder_layers_0_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_0_feed_forward2_linear2_weight_to_fp16_quantized, x = input_69_cast_fp16)[name = string("linear_9_cast_fp16")]; + fp16 var_630_to_fp16 = const()[name = string("op_630_to_fp16"), val = fp16(0x1p-1)]; + tensor var_631_cast_fp16 = mul(x = linear_9_cast_fp16, y = var_630_to_fp16)[name = string("op_631_cast_fp16")]; + tensor input_75_cast_fp16 = add(x = input_63_cast_fp16, y = var_631_cast_fp16)[name = string("input_75_cast_fp16")]; + tensor input_77_axes_0 = const()[name = string("input_77_axes_0"), val = tensor([-1])]; + tensor encoder_layers_0_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_0_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28939904)))]; + tensor encoder_layers_0_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_0_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28942016)))]; + tensor input_77_cast_fp16 = layer_norm(axes = input_77_axes_0, beta = encoder_layers_0_norm_out_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_0_norm_out_weight_to_fp16, x = input_75_cast_fp16)[name = string("input_77_cast_fp16")]; + tensor cache_5_begin_0 = const()[name = string("cache_5_begin_0"), val = tensor([1, 0, 0, 0])]; + tensor cache_5_end_0 = const()[name = string("cache_5_end_0"), val = tensor([2, 1, 42, 1024])]; + tensor cache_5_end_mask_0 = const()[name = string("cache_5_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_5_squeeze_mask_0 = const()[name = string("cache_5_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_5_cast_fp16 = slice_by_index(begin = cache_5_begin_0, end = cache_5_end_0, end_mask = cache_5_end_mask_0, squeeze_mask = cache_5_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_5_cast_fp16")]; + tensor cache_7_begin_0 = const()[name = string("cache_7_begin_0"), val = tensor([1, 0, 0, 0])]; + tensor cache_7_end_0 = const()[name = string("cache_7_end_0"), val = tensor([2, 1, 1024, 8])]; + tensor cache_7_end_mask_0 = const()[name = string("cache_7_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_7_squeeze_mask_0 = const()[name = string("cache_7_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_7_cast_fp16 = slice_by_index(begin = cache_7_begin_0, end = cache_7_end_0, end_mask = cache_7_end_mask_0, squeeze_mask = cache_7_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_7_cast_fp16")]; + tensor input_79_axes_0 = const()[name = string("input_79_axes_0"), val = tensor([-1])]; + tensor encoder_layers_1_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_1_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28944128)))]; + tensor encoder_layers_1_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_1_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28946240)))]; + tensor input_79_cast_fp16 = layer_norm(axes = input_79_axes_0, beta = encoder_layers_1_norm_feed_forward1_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_1_norm_feed_forward1_weight_to_fp16, x = input_77_cast_fp16)[name = string("input_79_cast_fp16")]; + tensor encoder_layers_1_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28948352))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33142720))))[name = string("encoder_layers_1_feed_forward1_linear1_weight_to_fp16_quantized")]; + tensor encoder_layers_1_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_1_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33150976)))]; + tensor linear_10_cast_fp16 = linear(bias = encoder_layers_1_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_1_feed_forward1_linear1_weight_to_fp16_quantized, x = input_79_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor input_83_cast_fp16 = silu(x = linear_10_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor encoder_layers_1_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33159232))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37353600))))[name = string("encoder_layers_1_feed_forward1_linear2_weight_to_fp16_quantized")]; + tensor encoder_layers_1_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_1_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37355712)))]; + tensor linear_11_cast_fp16 = linear(bias = encoder_layers_1_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_1_feed_forward1_linear2_weight_to_fp16_quantized, x = input_83_cast_fp16)[name = string("linear_11_cast_fp16")]; + fp16 var_667_to_fp16 = const()[name = string("op_667_to_fp16"), val = fp16(0x1p-1)]; + tensor var_668_cast_fp16 = mul(x = linear_11_cast_fp16, y = var_667_to_fp16)[name = string("op_668_cast_fp16")]; + tensor input_89_cast_fp16 = add(x = input_77_cast_fp16, y = var_668_cast_fp16)[name = string("input_89_cast_fp16")]; + tensor key_3_axes_0 = const()[name = string("key_3_axes_0"), val = tensor([-1])]; + tensor encoder_layers_1_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_1_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37357824)))]; + tensor encoder_layers_1_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_1_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37359936)))]; + tensor key_3_cast_fp16 = layer_norm(axes = key_3_axes_0, beta = encoder_layers_1_norm_self_att_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_1_norm_self_att_weight_to_fp16, x = input_89_cast_fp16)[name = string("key_3_cast_fp16")]; + bool input_91_interleave_0 = const()[name = string("input_91_interleave_0"), val = bool(false)]; + tensor input_91_cast_fp16 = concat(axis = var_67, interleave = input_91_interleave_0, values = (cache_5_cast_fp16, key_3_cast_fp16))[name = string("input_91_cast_fp16")]; + tensor var_690_begin_0 = const()[name = string("op_690_begin_0"), val = tensor([0, 14, 0])]; + tensor var_690_end_0 = const()[name = string("op_690_end_0"), val = tensor([1, 42, 1024])]; + tensor var_690_end_mask_0 = const()[name = string("op_690_end_mask_0"), val = tensor([true, true, true])]; + tensor var_690_cast_fp16 = slice_by_index(begin = var_690_begin_0, end = var_690_end_0, end_mask = var_690_end_mask_0, x = cache_5_cast_fp16)[name = string("op_690_cast_fp16")]; + bool var_696_interleave_0 = const()[name = string("op_696_interleave_0"), val = bool(false)]; + tensor var_696_cast_fp16 = concat(axis = var_67, interleave = var_696_interleave_0, values = (var_690_cast_fp16, key_3_cast_fp16))[name = string("op_696_cast_fp16")]; + tensor encoder_layers_1_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37362048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38410688))))[name = string("encoder_layers_1_self_attn_linear_q_weight_to_fp16_quantized")]; + tensor encoder_layers_1_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_1_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38412800)))]; + tensor linear_12_cast_fp16 = linear(bias = encoder_layers_1_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_1_self_attn_linear_q_weight_to_fp16_quantized, x = key_3_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_701 = const()[name = string("op_701"), val = tensor([1, -1, 8, 128])]; + tensor q_7_cast_fp16 = reshape(shape = var_701, x = linear_12_cast_fp16)[name = string("q_7_cast_fp16")]; + tensor encoder_layers_1_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38414912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39463552))))[name = string("encoder_layers_1_self_attn_linear_k_weight_to_fp16_quantized")]; + tensor encoder_layers_1_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_1_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39465664)))]; + tensor linear_13_cast_fp16 = linear(bias = encoder_layers_1_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_1_self_attn_linear_k_weight_to_fp16_quantized, x = input_91_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_706 = const()[name = string("op_706"), val = tensor([1, -1, 8, 128])]; + tensor k_5_cast_fp16 = reshape(shape = var_706, x = linear_13_cast_fp16)[name = string("k_5_cast_fp16")]; + tensor encoder_layers_1_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39467776))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40516416))))[name = string("encoder_layers_1_self_attn_linear_v_weight_to_fp16_quantized")]; + tensor encoder_layers_1_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_1_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40518528)))]; + tensor linear_14_cast_fp16 = linear(bias = encoder_layers_1_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_1_self_attn_linear_v_weight_to_fp16_quantized, x = input_91_cast_fp16)[name = string("linear_14_cast_fp16")]; + tensor var_711 = const()[name = string("op_711"), val = tensor([1, -1, 8, 128])]; + tensor v_3_cast_fp16 = reshape(shape = var_711, x = linear_14_cast_fp16)[name = string("v_3_cast_fp16")]; + tensor value_11_perm_0 = const()[name = string("value_11_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_1_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_1_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40520640)))]; + tensor var_724_cast_fp16 = add(x = q_7_cast_fp16, y = encoder_layers_1_self_attn_pos_bias_u_to_fp16)[name = string("op_724_cast_fp16")]; + tensor encoder_layers_1_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_1_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40522752)))]; + tensor var_726_cast_fp16 = add(x = q_7_cast_fp16, y = encoder_layers_1_self_attn_pos_bias_v_to_fp16)[name = string("op_726_cast_fp16")]; + tensor q_with_bias_v_3_perm_0 = const()[name = string("q_with_bias_v_3_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_33_transpose_x_0 = const()[name = string("x_33_transpose_x_0"), val = bool(false)]; + bool x_33_transpose_y_0 = const()[name = string("x_33_transpose_y_0"), val = bool(false)]; + tensor op_728_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40524864))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40638592))))[name = string("op_728_to_fp16_quantized")]; + tensor q_with_bias_v_3_cast_fp16 = transpose(perm = q_with_bias_v_3_perm_0, x = var_726_cast_fp16)[name = string("transpose_353")]; + tensor x_33_cast_fp16 = matmul(transpose_x = x_33_transpose_x_0, transpose_y = x_33_transpose_y_0, x = q_with_bias_v_3_cast_fp16, y = op_728_to_fp16_quantized)[name = string("x_33_cast_fp16")]; + tensor x_35_pad_0 = const()[name = string("x_35_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_35_mode_0 = const()[name = string("x_35_mode_0"), val = string("constant")]; + fp16 const_92_to_fp16 = const()[name = string("const_92_to_fp16"), val = fp16(0x0p+0)]; + tensor x_35_cast_fp16 = pad(constant_val = const_92_to_fp16, mode = x_35_mode_0, pad = x_35_pad_0, x = x_33_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor var_736 = const()[name = string("op_736"), val = tensor([1, 8, -1, 14])]; + tensor x_37_cast_fp16 = reshape(shape = var_736, x = x_35_cast_fp16)[name = string("x_37_cast_fp16")]; + tensor var_740_begin_0 = const()[name = string("op_740_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_740_end_0 = const()[name = string("op_740_end_0"), val = tensor([1, 8, 112, 14])]; + tensor var_740_end_mask_0 = const()[name = string("op_740_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_740_cast_fp16 = slice_by_index(begin = var_740_begin_0, end = var_740_end_0, end_mask = var_740_end_mask_0, x = x_37_cast_fp16)[name = string("op_740_cast_fp16")]; + tensor var_741 = const()[name = string("op_741"), val = tensor([1, 8, 14, 111])]; + tensor matrix_bd_5_cast_fp16 = reshape(shape = var_741, x = var_740_cast_fp16)[name = string("matrix_bd_5_cast_fp16")]; + bool matrix_ac_3_transpose_x_0 = const()[name = string("matrix_ac_3_transpose_x_0"), val = bool(false)]; + bool matrix_ac_3_transpose_y_0 = const()[name = string("matrix_ac_3_transpose_y_0"), val = bool(false)]; + tensor transpose_98_perm_0 = const()[name = string("transpose_98_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_99_perm_0 = const()[name = string("transpose_99_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_99 = transpose(perm = transpose_99_perm_0, x = k_5_cast_fp16)[name = string("transpose_351")]; + tensor transpose_98 = transpose(perm = transpose_98_perm_0, x = var_724_cast_fp16)[name = string("transpose_352")]; + tensor matrix_ac_3_cast_fp16 = matmul(transpose_x = matrix_ac_3_transpose_x_0, transpose_y = matrix_ac_3_transpose_y_0, x = transpose_98, y = transpose_99)[name = string("matrix_ac_3_cast_fp16")]; + tensor matrix_bd_7_begin_0 = const()[name = string("matrix_bd_7_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_7_end_0 = const()[name = string("matrix_bd_7_end_0"), val = tensor([1, 8, 14, 56])]; + tensor matrix_bd_7_end_mask_0 = const()[name = string("matrix_bd_7_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_7_cast_fp16 = slice_by_index(begin = matrix_bd_7_begin_0, end = matrix_bd_7_end_0, end_mask = matrix_bd_7_end_mask_0, x = matrix_bd_5_cast_fp16)[name = string("matrix_bd_7_cast_fp16")]; + tensor var_750_cast_fp16 = add(x = matrix_ac_3_cast_fp16, y = matrix_bd_7_cast_fp16)[name = string("op_750_cast_fp16")]; + fp16 _inversed_scores_5_y_0_to_fp16 = const()[name = string("_inversed_scores_5_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_5_cast_fp16 = mul(x = var_750_cast_fp16, y = _inversed_scores_5_y_0_to_fp16)[name = string("_inversed_scores_5_cast_fp16")]; + tensor scores_7_cast_fp16 = select(a = var_44_to_fp16, b = _inversed_scores_5_cast_fp16, cond = mask_11)[name = string("scores_7_cast_fp16")]; + tensor var_756_cast_fp16 = softmax(axis = var_58, x = scores_7_cast_fp16)[name = string("op_756_cast_fp16")]; + tensor input_93_cast_fp16 = select(a = var_43_to_fp16, b = var_756_cast_fp16, cond = mask_11)[name = string("input_93_cast_fp16")]; + bool x_39_transpose_x_0 = const()[name = string("x_39_transpose_x_0"), val = bool(false)]; + bool x_39_transpose_y_0 = const()[name = string("x_39_transpose_y_0"), val = bool(false)]; + tensor value_11_cast_fp16 = transpose(perm = value_11_perm_0, x = v_3_cast_fp16)[name = string("transpose_350")]; + tensor x_39_cast_fp16 = matmul(transpose_x = x_39_transpose_x_0, transpose_y = x_39_transpose_y_0, x = input_93_cast_fp16, y = value_11_cast_fp16)[name = string("x_39_cast_fp16")]; + tensor var_760_perm_0 = const()[name = string("op_760_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_761 = const()[name = string("op_761"), val = tensor([1, -1, 1024])]; + tensor var_760_cast_fp16 = transpose(perm = var_760_perm_0, x = x_39_cast_fp16)[name = string("transpose_349")]; + tensor input_95_cast_fp16 = reshape(shape = var_761, x = var_760_cast_fp16)[name = string("input_95_cast_fp16")]; + tensor encoder_layers_1_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40638912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41687552))))[name = string("encoder_layers_1_self_attn_linear_out_weight_to_fp16_quantized")]; + tensor encoder_layers_1_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_1_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41689664)))]; + tensor linear_16_cast_fp16 = linear(bias = encoder_layers_1_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_1_self_attn_linear_out_weight_to_fp16_quantized, x = input_95_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor input_99_cast_fp16 = add(x = input_89_cast_fp16, y = linear_16_cast_fp16)[name = string("input_99_cast_fp16")]; + tensor x_43_axes_0 = const()[name = string("x_43_axes_0"), val = tensor([-1])]; + tensor encoder_layers_1_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_1_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41691776)))]; + tensor encoder_layers_1_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_1_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41693888)))]; + tensor x_43_cast_fp16 = layer_norm(axes = x_43_axes_0, beta = encoder_layers_1_norm_conv_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_1_norm_conv_weight_to_fp16, x = input_99_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor input_101_perm_0 = const()[name = string("input_101_perm_0"), val = tensor([0, 2, 1])]; + string input_103_pad_type_0 = const()[name = string("input_103_pad_type_0"), val = string("valid")]; + tensor input_103_strides_0 = const()[name = string("input_103_strides_0"), val = tensor([1])]; + tensor input_103_pad_0 = const()[name = string("input_103_pad_0"), val = tensor([0, 0])]; + tensor input_103_dilations_0 = const()[name = string("input_103_dilations_0"), val = tensor([1])]; + int32 input_103_groups_0 = const()[name = string("input_103_groups_0"), val = int32(1)]; + tensor encoder_layers_1_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41696000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43793216))))[name = string("encoder_layers_1_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_101_cast_fp16 = transpose(perm = input_101_perm_0, x = x_43_cast_fp16)[name = string("transpose_348")]; + tensor input_103_cast_fp16 = conv(dilations = input_103_dilations_0, groups = input_103_groups_0, pad = input_103_pad_0, pad_type = input_103_pad_type_0, strides = input_103_strides_0, weight = encoder_layers_1_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_101_cast_fp16)[name = string("input_103_cast_fp16")]; + int32 x_45_split_num_splits_0 = const()[name = string("x_45_split_num_splits_0"), val = int32(2)]; + int32 x_45_split_axis_0 = const()[name = string("x_45_split_axis_0"), val = int32(1)]; + tensor x_45_split_cast_fp16_0, tensor x_45_split_cast_fp16_1 = split(axis = x_45_split_axis_0, num_splits = x_45_split_num_splits_0, x = input_103_cast_fp16)[name = string("x_45_split_cast_fp16")]; + tensor x_45_split_1_sigmoid_cast_fp16 = sigmoid(x = x_45_split_cast_fp16_1)[name = string("x_45_split_1_sigmoid_cast_fp16")]; + tensor x_45_cast_fp16 = mul(x = x_45_split_cast_fp16_0, y = x_45_split_1_sigmoid_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor input_105_cast_fp16 = select(a = var_43_to_fp16, b = x_45_cast_fp16, cond = var_574)[name = string("input_105_cast_fp16")]; + bool new_x_7_interleave_0 = const()[name = string("new_x_7_interleave_0"), val = bool(false)]; + tensor new_x_7_cast_fp16 = concat(axis = var_58, interleave = new_x_7_interleave_0, values = (cache_7_cast_fp16, input_105_cast_fp16))[name = string("new_x_7_cast_fp16")]; + tensor var_800_begin_0 = const()[name = string("op_800_begin_0"), val = tensor([0, 0, 14])]; + tensor var_800_end_0 = const()[name = string("op_800_end_0"), val = tensor([1, 1024, 22])]; + tensor var_800_end_mask_0 = const()[name = string("op_800_end_mask_0"), val = tensor([true, true, true])]; + tensor var_800_cast_fp16 = slice_by_index(begin = var_800_begin_0, end = var_800_end_0, end_mask = var_800_end_mask_0, x = new_x_7_cast_fp16)[name = string("op_800_cast_fp16")]; + string x_47_pad_type_0 = const()[name = string("x_47_pad_type_0"), val = string("valid")]; + int32 x_47_groups_0 = const()[name = string("x_47_groups_0"), val = int32(1024)]; + tensor x_47_strides_0 = const()[name = string("x_47_strides_0"), val = tensor([1])]; + tensor x_47_pad_0 = const()[name = string("x_47_pad_0"), val = tensor([0, 0])]; + tensor x_47_dilations_0 = const()[name = string("x_47_dilations_0"), val = tensor([1])]; + tensor encoder_layers_1_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43797376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43806656))))[name = string("encoder_layers_1_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_47_cast_fp16 = conv(dilations = x_47_dilations_0, groups = x_47_groups_0, pad = x_47_pad_0, pad_type = x_47_pad_type_0, strides = x_47_strides_0, weight = encoder_layers_1_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_7_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor input_107_perm_0 = const()[name = string("input_107_perm_0"), val = tensor([0, 2, 1])]; + tensor x_49_axes_0 = const()[name = string("x_49_axes_0"), val = tensor([-1])]; + tensor encoder_layers_1_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_1_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43808768)))]; + tensor encoder_layers_1_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_1_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43810880)))]; + tensor input_107_cast_fp16 = transpose(perm = input_107_perm_0, x = x_47_cast_fp16)[name = string("transpose_347")]; + tensor x_49_cast_fp16 = layer_norm(axes = x_49_axes_0, beta = encoder_layers_1_conv_batch_norm_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_1_conv_batch_norm_weight_to_fp16, x = input_107_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor input_109_perm_0 = const()[name = string("input_109_perm_0"), val = tensor([0, 2, 1])]; + tensor input_109_cast_fp16 = transpose(perm = input_109_perm_0, x = x_49_cast_fp16)[name = string("transpose_346")]; + tensor input_111_cast_fp16 = silu(x = input_109_cast_fp16)[name = string("input_111_cast_fp16")]; + string x_51_pad_type_0 = const()[name = string("x_51_pad_type_0"), val = string("valid")]; + tensor x_51_strides_0 = const()[name = string("x_51_strides_0"), val = tensor([1])]; + tensor x_51_pad_0 = const()[name = string("x_51_pad_0"), val = tensor([0, 0])]; + tensor x_51_dilations_0 = const()[name = string("x_51_dilations_0"), val = tensor([1])]; + int32 x_51_groups_0 = const()[name = string("x_51_groups_0"), val = int32(1)]; + tensor encoder_layers_1_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43812992))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44861632))))[name = string("encoder_layers_1_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_51_cast_fp16 = conv(dilations = x_51_dilations_0, groups = x_51_groups_0, pad = x_51_pad_0, pad_type = x_51_pad_type_0, strides = x_51_strides_0, weight = encoder_layers_1_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_111_cast_fp16)[name = string("x_51_cast_fp16")]; + tensor input_113_perm_0 = const()[name = string("input_113_perm_0"), val = tensor([0, 2, 1])]; + tensor input_113_cast_fp16 = transpose(perm = input_113_perm_0, x = x_51_cast_fp16)[name = string("transpose_345")]; + tensor input_115_cast_fp16 = add(x = input_99_cast_fp16, y = input_113_cast_fp16)[name = string("input_115_cast_fp16")]; + tensor input_117_axes_0 = const()[name = string("input_117_axes_0"), val = tensor([-1])]; + tensor encoder_layers_1_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_1_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44863744)))]; + tensor encoder_layers_1_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_1_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44865856)))]; + tensor input_117_cast_fp16 = layer_norm(axes = input_117_axes_0, beta = encoder_layers_1_norm_feed_forward2_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_1_norm_feed_forward2_weight_to_fp16, x = input_115_cast_fp16)[name = string("input_117_cast_fp16")]; + tensor encoder_layers_1_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44867968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49062336))))[name = string("encoder_layers_1_feed_forward2_linear1_weight_to_fp16_quantized")]; + tensor encoder_layers_1_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_1_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49070592)))]; + tensor linear_17_cast_fp16 = linear(bias = encoder_layers_1_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_1_feed_forward2_linear1_weight_to_fp16_quantized, x = input_117_cast_fp16)[name = string("linear_17_cast_fp16")]; + tensor input_121_cast_fp16 = silu(x = linear_17_cast_fp16)[name = string("input_121_cast_fp16")]; + tensor encoder_layers_1_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49078848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53273216))))[name = string("encoder_layers_1_feed_forward2_linear2_weight_to_fp16_quantized")]; + tensor encoder_layers_1_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_1_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53275328)))]; + tensor linear_18_cast_fp16 = linear(bias = encoder_layers_1_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_1_feed_forward2_linear2_weight_to_fp16_quantized, x = input_121_cast_fp16)[name = string("linear_18_cast_fp16")]; + fp16 var_843_to_fp16 = const()[name = string("op_843_to_fp16"), val = fp16(0x1p-1)]; + tensor var_844_cast_fp16 = mul(x = linear_18_cast_fp16, y = var_843_to_fp16)[name = string("op_844_cast_fp16")]; + tensor input_127_cast_fp16 = add(x = input_115_cast_fp16, y = var_844_cast_fp16)[name = string("input_127_cast_fp16")]; + tensor input_129_axes_0 = const()[name = string("input_129_axes_0"), val = tensor([-1])]; + tensor encoder_layers_1_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_1_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53277440)))]; + tensor encoder_layers_1_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_1_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53279552)))]; + tensor input_129_cast_fp16 = layer_norm(axes = input_129_axes_0, beta = encoder_layers_1_norm_out_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_1_norm_out_weight_to_fp16, x = input_127_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor cache_9_begin_0 = const()[name = string("cache_9_begin_0"), val = tensor([2, 0, 0, 0])]; + tensor cache_9_end_0 = const()[name = string("cache_9_end_0"), val = tensor([3, 1, 42, 1024])]; + tensor cache_9_end_mask_0 = const()[name = string("cache_9_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_9_squeeze_mask_0 = const()[name = string("cache_9_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_9_cast_fp16 = slice_by_index(begin = cache_9_begin_0, end = cache_9_end_0, end_mask = cache_9_end_mask_0, squeeze_mask = cache_9_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_9_cast_fp16")]; + tensor cache_11_begin_0 = const()[name = string("cache_11_begin_0"), val = tensor([2, 0, 0, 0])]; + tensor cache_11_end_0 = const()[name = string("cache_11_end_0"), val = tensor([3, 1, 1024, 8])]; + tensor cache_11_end_mask_0 = const()[name = string("cache_11_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_11_squeeze_mask_0 = const()[name = string("cache_11_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_11_cast_fp16 = slice_by_index(begin = cache_11_begin_0, end = cache_11_end_0, end_mask = cache_11_end_mask_0, squeeze_mask = cache_11_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_11_cast_fp16")]; + tensor input_131_axes_0 = const()[name = string("input_131_axes_0"), val = tensor([-1])]; + tensor encoder_layers_2_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_2_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53281664)))]; + tensor encoder_layers_2_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_2_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53283776)))]; + tensor input_131_cast_fp16 = layer_norm(axes = input_131_axes_0, beta = encoder_layers_2_norm_feed_forward1_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_2_norm_feed_forward1_weight_to_fp16, x = input_129_cast_fp16)[name = string("input_131_cast_fp16")]; + tensor encoder_layers_2_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53285888))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57480256))))[name = string("encoder_layers_2_feed_forward1_linear1_weight_to_fp16_quantized")]; + tensor encoder_layers_2_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_2_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57488512)))]; + tensor linear_19_cast_fp16 = linear(bias = encoder_layers_2_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_2_feed_forward1_linear1_weight_to_fp16_quantized, x = input_131_cast_fp16)[name = string("linear_19_cast_fp16")]; + tensor input_135_cast_fp16 = silu(x = linear_19_cast_fp16)[name = string("input_135_cast_fp16")]; + tensor encoder_layers_2_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57496768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61691136))))[name = string("encoder_layers_2_feed_forward1_linear2_weight_to_fp16_quantized")]; + tensor encoder_layers_2_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_2_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61693248)))]; + tensor linear_20_cast_fp16 = linear(bias = encoder_layers_2_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_2_feed_forward1_linear2_weight_to_fp16_quantized, x = input_135_cast_fp16)[name = string("linear_20_cast_fp16")]; + fp16 var_880_to_fp16 = const()[name = string("op_880_to_fp16"), val = fp16(0x1p-1)]; + tensor var_881_cast_fp16 = mul(x = linear_20_cast_fp16, y = var_880_to_fp16)[name = string("op_881_cast_fp16")]; + tensor input_141_cast_fp16 = add(x = input_129_cast_fp16, y = var_881_cast_fp16)[name = string("input_141_cast_fp16")]; + tensor key_5_axes_0 = const()[name = string("key_5_axes_0"), val = tensor([-1])]; + tensor encoder_layers_2_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_2_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61695360)))]; + tensor encoder_layers_2_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_2_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61697472)))]; + tensor key_5_cast_fp16 = layer_norm(axes = key_5_axes_0, beta = encoder_layers_2_norm_self_att_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_2_norm_self_att_weight_to_fp16, x = input_141_cast_fp16)[name = string("key_5_cast_fp16")]; + bool input_143_interleave_0 = const()[name = string("input_143_interleave_0"), val = bool(false)]; + tensor input_143_cast_fp16 = concat(axis = var_67, interleave = input_143_interleave_0, values = (cache_9_cast_fp16, key_5_cast_fp16))[name = string("input_143_cast_fp16")]; + tensor var_903_begin_0 = const()[name = string("op_903_begin_0"), val = tensor([0, 14, 0])]; + tensor var_903_end_0 = const()[name = string("op_903_end_0"), val = tensor([1, 42, 1024])]; + tensor var_903_end_mask_0 = const()[name = string("op_903_end_mask_0"), val = tensor([true, true, true])]; + tensor var_903_cast_fp16 = slice_by_index(begin = var_903_begin_0, end = var_903_end_0, end_mask = var_903_end_mask_0, x = cache_9_cast_fp16)[name = string("op_903_cast_fp16")]; + bool var_909_interleave_0 = const()[name = string("op_909_interleave_0"), val = bool(false)]; + tensor var_909_cast_fp16 = concat(axis = var_67, interleave = var_909_interleave_0, values = (var_903_cast_fp16, key_5_cast_fp16))[name = string("op_909_cast_fp16")]; + tensor encoder_layers_2_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61699584))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62748224))))[name = string("encoder_layers_2_self_attn_linear_q_weight_to_fp16_quantized")]; + tensor encoder_layers_2_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_2_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62750336)))]; + tensor linear_21_cast_fp16 = linear(bias = encoder_layers_2_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_2_self_attn_linear_q_weight_to_fp16_quantized, x = key_5_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_914 = const()[name = string("op_914"), val = tensor([1, -1, 8, 128])]; + tensor q_13_cast_fp16 = reshape(shape = var_914, x = linear_21_cast_fp16)[name = string("q_13_cast_fp16")]; + tensor encoder_layers_2_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62752448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63801088))))[name = string("encoder_layers_2_self_attn_linear_k_weight_to_fp16_quantized")]; + tensor encoder_layers_2_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_2_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63803200)))]; + tensor linear_22_cast_fp16 = linear(bias = encoder_layers_2_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_2_self_attn_linear_k_weight_to_fp16_quantized, x = input_143_cast_fp16)[name = string("linear_22_cast_fp16")]; + tensor var_919 = const()[name = string("op_919"), val = tensor([1, -1, 8, 128])]; + tensor k_9_cast_fp16 = reshape(shape = var_919, x = linear_22_cast_fp16)[name = string("k_9_cast_fp16")]; + tensor encoder_layers_2_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63805312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64853952))))[name = string("encoder_layers_2_self_attn_linear_v_weight_to_fp16_quantized")]; + tensor encoder_layers_2_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_2_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64856064)))]; + tensor linear_23_cast_fp16 = linear(bias = encoder_layers_2_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_2_self_attn_linear_v_weight_to_fp16_quantized, x = input_143_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor var_924 = const()[name = string("op_924"), val = tensor([1, -1, 8, 128])]; + tensor v_5_cast_fp16 = reshape(shape = var_924, x = linear_23_cast_fp16)[name = string("v_5_cast_fp16")]; + tensor value_13_perm_0 = const()[name = string("value_13_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_2_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_2_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64858176)))]; + tensor var_937_cast_fp16 = add(x = q_13_cast_fp16, y = encoder_layers_2_self_attn_pos_bias_u_to_fp16)[name = string("op_937_cast_fp16")]; + tensor encoder_layers_2_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_2_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64860288)))]; + tensor var_939_cast_fp16 = add(x = q_13_cast_fp16, y = encoder_layers_2_self_attn_pos_bias_v_to_fp16)[name = string("op_939_cast_fp16")]; + tensor q_with_bias_v_5_perm_0 = const()[name = string("q_with_bias_v_5_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_59_transpose_x_0 = const()[name = string("x_59_transpose_x_0"), val = bool(false)]; + bool x_59_transpose_y_0 = const()[name = string("x_59_transpose_y_0"), val = bool(false)]; + tensor op_941_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64862400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64976128))))[name = string("op_941_to_fp16_quantized")]; + tensor q_with_bias_v_5_cast_fp16 = transpose(perm = q_with_bias_v_5_perm_0, x = var_939_cast_fp16)[name = string("transpose_344")]; + tensor x_59_cast_fp16 = matmul(transpose_x = x_59_transpose_x_0, transpose_y = x_59_transpose_y_0, x = q_with_bias_v_5_cast_fp16, y = op_941_to_fp16_quantized)[name = string("x_59_cast_fp16")]; + tensor x_61_pad_0 = const()[name = string("x_61_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_61_mode_0 = const()[name = string("x_61_mode_0"), val = string("constant")]; + fp16 const_105_to_fp16 = const()[name = string("const_105_to_fp16"), val = fp16(0x0p+0)]; + tensor x_61_cast_fp16 = pad(constant_val = const_105_to_fp16, mode = x_61_mode_0, pad = x_61_pad_0, x = x_59_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor var_949 = const()[name = string("op_949"), val = tensor([1, 8, -1, 14])]; + tensor x_63_cast_fp16 = reshape(shape = var_949, x = x_61_cast_fp16)[name = string("x_63_cast_fp16")]; + tensor var_953_begin_0 = const()[name = string("op_953_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_953_end_0 = const()[name = string("op_953_end_0"), val = tensor([1, 8, 112, 14])]; + tensor var_953_end_mask_0 = const()[name = string("op_953_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_953_cast_fp16 = slice_by_index(begin = var_953_begin_0, end = var_953_end_0, end_mask = var_953_end_mask_0, x = x_63_cast_fp16)[name = string("op_953_cast_fp16")]; + tensor var_954 = const()[name = string("op_954"), val = tensor([1, 8, 14, 111])]; + tensor matrix_bd_9_cast_fp16 = reshape(shape = var_954, x = var_953_cast_fp16)[name = string("matrix_bd_9_cast_fp16")]; + bool matrix_ac_5_transpose_x_0 = const()[name = string("matrix_ac_5_transpose_x_0"), val = bool(false)]; + bool matrix_ac_5_transpose_y_0 = const()[name = string("matrix_ac_5_transpose_y_0"), val = bool(false)]; + tensor transpose_100_perm_0 = const()[name = string("transpose_100_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_101_perm_0 = const()[name = string("transpose_101_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_101 = transpose(perm = transpose_101_perm_0, x = k_9_cast_fp16)[name = string("transpose_342")]; + tensor transpose_100 = transpose(perm = transpose_100_perm_0, x = var_937_cast_fp16)[name = string("transpose_343")]; + tensor matrix_ac_5_cast_fp16 = matmul(transpose_x = matrix_ac_5_transpose_x_0, transpose_y = matrix_ac_5_transpose_y_0, x = transpose_100, y = transpose_101)[name = string("matrix_ac_5_cast_fp16")]; + tensor matrix_bd_11_begin_0 = const()[name = string("matrix_bd_11_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_11_end_0 = const()[name = string("matrix_bd_11_end_0"), val = tensor([1, 8, 14, 56])]; + tensor matrix_bd_11_end_mask_0 = const()[name = string("matrix_bd_11_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_11_cast_fp16 = slice_by_index(begin = matrix_bd_11_begin_0, end = matrix_bd_11_end_0, end_mask = matrix_bd_11_end_mask_0, x = matrix_bd_9_cast_fp16)[name = string("matrix_bd_11_cast_fp16")]; + tensor var_963_cast_fp16 = add(x = matrix_ac_5_cast_fp16, y = matrix_bd_11_cast_fp16)[name = string("op_963_cast_fp16")]; + fp16 _inversed_scores_9_y_0_to_fp16 = const()[name = string("_inversed_scores_9_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_9_cast_fp16 = mul(x = var_963_cast_fp16, y = _inversed_scores_9_y_0_to_fp16)[name = string("_inversed_scores_9_cast_fp16")]; + tensor scores_11_cast_fp16 = select(a = var_44_to_fp16, b = _inversed_scores_9_cast_fp16, cond = mask_11)[name = string("scores_11_cast_fp16")]; + tensor var_969_cast_fp16 = softmax(axis = var_58, x = scores_11_cast_fp16)[name = string("op_969_cast_fp16")]; + tensor input_145_cast_fp16 = select(a = var_43_to_fp16, b = var_969_cast_fp16, cond = mask_11)[name = string("input_145_cast_fp16")]; + bool x_65_transpose_x_0 = const()[name = string("x_65_transpose_x_0"), val = bool(false)]; + bool x_65_transpose_y_0 = const()[name = string("x_65_transpose_y_0"), val = bool(false)]; + tensor value_13_cast_fp16 = transpose(perm = value_13_perm_0, x = v_5_cast_fp16)[name = string("transpose_341")]; + tensor x_65_cast_fp16 = matmul(transpose_x = x_65_transpose_x_0, transpose_y = x_65_transpose_y_0, x = input_145_cast_fp16, y = value_13_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor var_973_perm_0 = const()[name = string("op_973_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_974 = const()[name = string("op_974"), val = tensor([1, -1, 1024])]; + tensor var_973_cast_fp16 = transpose(perm = var_973_perm_0, x = x_65_cast_fp16)[name = string("transpose_340")]; + tensor input_147_cast_fp16 = reshape(shape = var_974, x = var_973_cast_fp16)[name = string("input_147_cast_fp16")]; + tensor encoder_layers_2_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64976448))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65762944))))[name = string("encoder_layers_2_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_2_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_2_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65763136)))]; + tensor linear_25_cast_fp16 = linear(bias = encoder_layers_2_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_2_self_attn_linear_out_weight_to_fp16_palettized, x = input_147_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor input_151_cast_fp16 = add(x = input_141_cast_fp16, y = linear_25_cast_fp16)[name = string("input_151_cast_fp16")]; + tensor x_69_axes_0 = const()[name = string("x_69_axes_0"), val = tensor([-1])]; + tensor encoder_layers_2_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_2_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65765248)))]; + tensor encoder_layers_2_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_2_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65767360)))]; + tensor x_69_cast_fp16 = layer_norm(axes = x_69_axes_0, beta = encoder_layers_2_norm_conv_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_2_norm_conv_weight_to_fp16, x = input_151_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor input_153_perm_0 = const()[name = string("input_153_perm_0"), val = tensor([0, 2, 1])]; + string input_155_pad_type_0 = const()[name = string("input_155_pad_type_0"), val = string("valid")]; + tensor input_155_strides_0 = const()[name = string("input_155_strides_0"), val = tensor([1])]; + tensor input_155_pad_0 = const()[name = string("input_155_pad_0"), val = tensor([0, 0])]; + tensor input_155_dilations_0 = const()[name = string("input_155_dilations_0"), val = tensor([1])]; + int32 input_155_groups_0 = const()[name = string("input_155_groups_0"), val = int32(1)]; + tensor encoder_layers_2_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65769472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67866688))))[name = string("encoder_layers_2_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_153_cast_fp16 = transpose(perm = input_153_perm_0, x = x_69_cast_fp16)[name = string("transpose_339")]; + tensor input_155_cast_fp16 = conv(dilations = input_155_dilations_0, groups = input_155_groups_0, pad = input_155_pad_0, pad_type = input_155_pad_type_0, strides = input_155_strides_0, weight = encoder_layers_2_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_153_cast_fp16)[name = string("input_155_cast_fp16")]; + int32 x_71_split_num_splits_0 = const()[name = string("x_71_split_num_splits_0"), val = int32(2)]; + int32 x_71_split_axis_0 = const()[name = string("x_71_split_axis_0"), val = int32(1)]; + tensor x_71_split_cast_fp16_0, tensor x_71_split_cast_fp16_1 = split(axis = x_71_split_axis_0, num_splits = x_71_split_num_splits_0, x = input_155_cast_fp16)[name = string("x_71_split_cast_fp16")]; + tensor x_71_split_1_sigmoid_cast_fp16 = sigmoid(x = x_71_split_cast_fp16_1)[name = string("x_71_split_1_sigmoid_cast_fp16")]; + tensor x_71_cast_fp16 = mul(x = x_71_split_cast_fp16_0, y = x_71_split_1_sigmoid_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor input_157_cast_fp16 = select(a = var_43_to_fp16, b = x_71_cast_fp16, cond = var_574)[name = string("input_157_cast_fp16")]; + bool new_x_11_interleave_0 = const()[name = string("new_x_11_interleave_0"), val = bool(false)]; + tensor new_x_11_cast_fp16 = concat(axis = var_58, interleave = new_x_11_interleave_0, values = (cache_11_cast_fp16, input_157_cast_fp16))[name = string("new_x_11_cast_fp16")]; + tensor var_1013_begin_0 = const()[name = string("op_1013_begin_0"), val = tensor([0, 0, 14])]; + tensor var_1013_end_0 = const()[name = string("op_1013_end_0"), val = tensor([1, 1024, 22])]; + tensor var_1013_end_mask_0 = const()[name = string("op_1013_end_mask_0"), val = tensor([true, true, true])]; + tensor var_1013_cast_fp16 = slice_by_index(begin = var_1013_begin_0, end = var_1013_end_0, end_mask = var_1013_end_mask_0, x = new_x_11_cast_fp16)[name = string("op_1013_cast_fp16")]; + string x_73_pad_type_0 = const()[name = string("x_73_pad_type_0"), val = string("valid")]; + int32 x_73_groups_0 = const()[name = string("x_73_groups_0"), val = int32(1024)]; + tensor x_73_strides_0 = const()[name = string("x_73_strides_0"), val = tensor([1])]; + tensor x_73_pad_0 = const()[name = string("x_73_pad_0"), val = tensor([0, 0])]; + tensor x_73_dilations_0 = const()[name = string("x_73_dilations_0"), val = tensor([1])]; + tensor encoder_layers_2_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67870848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67880128))))[name = string("encoder_layers_2_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_73_cast_fp16 = conv(dilations = x_73_dilations_0, groups = x_73_groups_0, pad = x_73_pad_0, pad_type = x_73_pad_type_0, strides = x_73_strides_0, weight = encoder_layers_2_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_11_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor input_159_perm_0 = const()[name = string("input_159_perm_0"), val = tensor([0, 2, 1])]; + tensor x_75_axes_0 = const()[name = string("x_75_axes_0"), val = tensor([-1])]; + tensor encoder_layers_2_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_2_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67882240)))]; + tensor encoder_layers_2_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_2_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67884352)))]; + tensor input_159_cast_fp16 = transpose(perm = input_159_perm_0, x = x_73_cast_fp16)[name = string("transpose_338")]; + tensor x_75_cast_fp16 = layer_norm(axes = x_75_axes_0, beta = encoder_layers_2_conv_batch_norm_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_2_conv_batch_norm_weight_to_fp16, x = input_159_cast_fp16)[name = string("x_75_cast_fp16")]; + tensor input_161_perm_0 = const()[name = string("input_161_perm_0"), val = tensor([0, 2, 1])]; + tensor input_161_cast_fp16 = transpose(perm = input_161_perm_0, x = x_75_cast_fp16)[name = string("transpose_337")]; + tensor input_163_cast_fp16 = silu(x = input_161_cast_fp16)[name = string("input_163_cast_fp16")]; + string x_77_pad_type_0 = const()[name = string("x_77_pad_type_0"), val = string("valid")]; + tensor x_77_strides_0 = const()[name = string("x_77_strides_0"), val = tensor([1])]; + tensor x_77_pad_0 = const()[name = string("x_77_pad_0"), val = tensor([0, 0])]; + tensor x_77_dilations_0 = const()[name = string("x_77_dilations_0"), val = tensor([1])]; + int32 x_77_groups_0 = const()[name = string("x_77_groups_0"), val = int32(1)]; + tensor encoder_layers_2_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67886464))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68935104))))[name = string("encoder_layers_2_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_77_cast_fp16 = conv(dilations = x_77_dilations_0, groups = x_77_groups_0, pad = x_77_pad_0, pad_type = x_77_pad_type_0, strides = x_77_strides_0, weight = encoder_layers_2_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_163_cast_fp16)[name = string("x_77_cast_fp16")]; + tensor input_165_perm_0 = const()[name = string("input_165_perm_0"), val = tensor([0, 2, 1])]; + tensor input_165_cast_fp16 = transpose(perm = input_165_perm_0, x = x_77_cast_fp16)[name = string("transpose_336")]; + tensor input_167_cast_fp16 = add(x = input_151_cast_fp16, y = input_165_cast_fp16)[name = string("input_167_cast_fp16")]; + tensor input_169_axes_0 = const()[name = string("input_169_axes_0"), val = tensor([-1])]; + tensor encoder_layers_2_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_2_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68937216)))]; + tensor encoder_layers_2_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_2_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68939328)))]; + tensor input_169_cast_fp16 = layer_norm(axes = input_169_axes_0, beta = encoder_layers_2_norm_feed_forward2_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_2_norm_feed_forward2_weight_to_fp16, x = input_167_cast_fp16)[name = string("input_169_cast_fp16")]; + tensor encoder_layers_2_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68941440))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72087232))))[name = string("encoder_layers_2_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_2_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_2_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72087424)))]; + tensor linear_26_cast_fp16 = linear(bias = encoder_layers_2_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_2_feed_forward2_linear1_weight_to_fp16_palettized, x = input_169_cast_fp16)[name = string("linear_26_cast_fp16")]; + tensor input_173_cast_fp16 = silu(x = linear_26_cast_fp16)[name = string("input_173_cast_fp16")]; + tensor encoder_layers_2_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72095680))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75241472))))[name = string("encoder_layers_2_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_2_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_2_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75241664)))]; + tensor linear_27_cast_fp16 = linear(bias = encoder_layers_2_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_2_feed_forward2_linear2_weight_to_fp16_palettized, x = input_173_cast_fp16)[name = string("linear_27_cast_fp16")]; + fp16 var_1056_to_fp16 = const()[name = string("op_1056_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1057_cast_fp16 = mul(x = linear_27_cast_fp16, y = var_1056_to_fp16)[name = string("op_1057_cast_fp16")]; + tensor input_179_cast_fp16 = add(x = input_167_cast_fp16, y = var_1057_cast_fp16)[name = string("input_179_cast_fp16")]; + tensor input_181_axes_0 = const()[name = string("input_181_axes_0"), val = tensor([-1])]; + tensor encoder_layers_2_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_2_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75243776)))]; + tensor encoder_layers_2_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_2_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75245888)))]; + tensor input_181_cast_fp16 = layer_norm(axes = input_181_axes_0, beta = encoder_layers_2_norm_out_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_2_norm_out_weight_to_fp16, x = input_179_cast_fp16)[name = string("input_181_cast_fp16")]; + tensor cache_13_begin_0 = const()[name = string("cache_13_begin_0"), val = tensor([3, 0, 0, 0])]; + tensor cache_13_end_0 = const()[name = string("cache_13_end_0"), val = tensor([4, 1, 42, 1024])]; + tensor cache_13_end_mask_0 = const()[name = string("cache_13_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_13_squeeze_mask_0 = const()[name = string("cache_13_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_13_cast_fp16 = slice_by_index(begin = cache_13_begin_0, end = cache_13_end_0, end_mask = cache_13_end_mask_0, squeeze_mask = cache_13_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_13_cast_fp16")]; + tensor cache_15_begin_0 = const()[name = string("cache_15_begin_0"), val = tensor([3, 0, 0, 0])]; + tensor cache_15_end_0 = const()[name = string("cache_15_end_0"), val = tensor([4, 1, 1024, 8])]; + tensor cache_15_end_mask_0 = const()[name = string("cache_15_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_15_squeeze_mask_0 = const()[name = string("cache_15_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_15_cast_fp16 = slice_by_index(begin = cache_15_begin_0, end = cache_15_end_0, end_mask = cache_15_end_mask_0, squeeze_mask = cache_15_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_15_cast_fp16")]; + tensor input_183_axes_0 = const()[name = string("input_183_axes_0"), val = tensor([-1])]; + tensor encoder_layers_3_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_3_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75248000)))]; + tensor encoder_layers_3_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_3_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75250112)))]; + tensor input_183_cast_fp16 = layer_norm(axes = input_183_axes_0, beta = encoder_layers_3_norm_feed_forward1_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_3_norm_feed_forward1_weight_to_fp16, x = input_181_cast_fp16)[name = string("input_183_cast_fp16")]; + tensor encoder_layers_3_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75252224))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78398016))))[name = string("encoder_layers_3_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_3_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_3_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78398208)))]; + tensor linear_28_cast_fp16 = linear(bias = encoder_layers_3_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_3_feed_forward1_linear1_weight_to_fp16_palettized, x = input_183_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor input_187_cast_fp16 = silu(x = linear_28_cast_fp16)[name = string("input_187_cast_fp16")]; + tensor encoder_layers_3_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78406464))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81552256))))[name = string("encoder_layers_3_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_3_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_3_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81552448)))]; + tensor linear_29_cast_fp16 = linear(bias = encoder_layers_3_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_3_feed_forward1_linear2_weight_to_fp16_palettized, x = input_187_cast_fp16)[name = string("linear_29_cast_fp16")]; + fp16 var_1093_to_fp16 = const()[name = string("op_1093_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1094_cast_fp16 = mul(x = linear_29_cast_fp16, y = var_1093_to_fp16)[name = string("op_1094_cast_fp16")]; + tensor input_193_cast_fp16 = add(x = input_181_cast_fp16, y = var_1094_cast_fp16)[name = string("input_193_cast_fp16")]; + tensor key_7_axes_0 = const()[name = string("key_7_axes_0"), val = tensor([-1])]; + tensor encoder_layers_3_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_3_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81554560)))]; + tensor encoder_layers_3_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_3_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81556672)))]; + tensor key_7_cast_fp16 = layer_norm(axes = key_7_axes_0, beta = encoder_layers_3_norm_self_att_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_3_norm_self_att_weight_to_fp16, x = input_193_cast_fp16)[name = string("key_7_cast_fp16")]; + bool input_195_interleave_0 = const()[name = string("input_195_interleave_0"), val = bool(false)]; + tensor input_195_cast_fp16 = concat(axis = var_67, interleave = input_195_interleave_0, values = (cache_13_cast_fp16, key_7_cast_fp16))[name = string("input_195_cast_fp16")]; + tensor var_1116_begin_0 = const()[name = string("op_1116_begin_0"), val = tensor([0, 14, 0])]; + tensor var_1116_end_0 = const()[name = string("op_1116_end_0"), val = tensor([1, 42, 1024])]; + tensor var_1116_end_mask_0 = const()[name = string("op_1116_end_mask_0"), val = tensor([true, true, true])]; + tensor var_1116_cast_fp16 = slice_by_index(begin = var_1116_begin_0, end = var_1116_end_0, end_mask = var_1116_end_mask_0, x = cache_13_cast_fp16)[name = string("op_1116_cast_fp16")]; + bool var_1122_interleave_0 = const()[name = string("op_1122_interleave_0"), val = bool(false)]; + tensor var_1122_cast_fp16 = concat(axis = var_67, interleave = var_1122_interleave_0, values = (var_1116_cast_fp16, key_7_cast_fp16))[name = string("op_1122_cast_fp16")]; + tensor encoder_layers_3_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81558784))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82345280))))[name = string("encoder_layers_3_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_3_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_3_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82345472)))]; + tensor linear_30_cast_fp16 = linear(bias = encoder_layers_3_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_3_self_attn_linear_q_weight_to_fp16_palettized, x = key_7_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor var_1127 = const()[name = string("op_1127"), val = tensor([1, -1, 8, 128])]; + tensor q_19_cast_fp16 = reshape(shape = var_1127, x = linear_30_cast_fp16)[name = string("q_19_cast_fp16")]; + tensor encoder_layers_3_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82347584))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83134080))))[name = string("encoder_layers_3_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_3_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_3_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83134272)))]; + tensor linear_31_cast_fp16 = linear(bias = encoder_layers_3_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_3_self_attn_linear_k_weight_to_fp16_palettized, x = input_195_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor var_1132 = const()[name = string("op_1132"), val = tensor([1, -1, 8, 128])]; + tensor k_13_cast_fp16 = reshape(shape = var_1132, x = linear_31_cast_fp16)[name = string("k_13_cast_fp16")]; + tensor encoder_layers_3_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83136384))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83922880))))[name = string("encoder_layers_3_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_3_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_3_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83923072)))]; + tensor linear_32_cast_fp16 = linear(bias = encoder_layers_3_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_3_self_attn_linear_v_weight_to_fp16_palettized, x = input_195_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor var_1137 = const()[name = string("op_1137"), val = tensor([1, -1, 8, 128])]; + tensor v_7_cast_fp16 = reshape(shape = var_1137, x = linear_32_cast_fp16)[name = string("v_7_cast_fp16")]; + tensor value_15_perm_0 = const()[name = string("value_15_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_3_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_3_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83925184)))]; + tensor var_1150_cast_fp16 = add(x = q_19_cast_fp16, y = encoder_layers_3_self_attn_pos_bias_u_to_fp16)[name = string("op_1150_cast_fp16")]; + tensor encoder_layers_3_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_3_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83927296)))]; + tensor var_1152_cast_fp16 = add(x = q_19_cast_fp16, y = encoder_layers_3_self_attn_pos_bias_v_to_fp16)[name = string("op_1152_cast_fp16")]; + tensor q_with_bias_v_7_perm_0 = const()[name = string("q_with_bias_v_7_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_85_transpose_x_0 = const()[name = string("x_85_transpose_x_0"), val = bool(false)]; + bool x_85_transpose_y_0 = const()[name = string("x_85_transpose_y_0"), val = bool(false)]; + tensor op_1154_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83929408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84043136))))[name = string("op_1154_to_fp16_quantized")]; + tensor q_with_bias_v_7_cast_fp16 = transpose(perm = q_with_bias_v_7_perm_0, x = var_1152_cast_fp16)[name = string("transpose_335")]; + tensor x_85_cast_fp16 = matmul(transpose_x = x_85_transpose_x_0, transpose_y = x_85_transpose_y_0, x = q_with_bias_v_7_cast_fp16, y = op_1154_to_fp16_quantized)[name = string("x_85_cast_fp16")]; + tensor x_87_pad_0 = const()[name = string("x_87_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_87_mode_0 = const()[name = string("x_87_mode_0"), val = string("constant")]; + fp16 const_118_to_fp16 = const()[name = string("const_118_to_fp16"), val = fp16(0x0p+0)]; + tensor x_87_cast_fp16 = pad(constant_val = const_118_to_fp16, mode = x_87_mode_0, pad = x_87_pad_0, x = x_85_cast_fp16)[name = string("x_87_cast_fp16")]; + tensor var_1162 = const()[name = string("op_1162"), val = tensor([1, 8, -1, 14])]; + tensor x_89_cast_fp16 = reshape(shape = var_1162, x = x_87_cast_fp16)[name = string("x_89_cast_fp16")]; + tensor var_1166_begin_0 = const()[name = string("op_1166_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_1166_end_0 = const()[name = string("op_1166_end_0"), val = tensor([1, 8, 112, 14])]; + tensor var_1166_end_mask_0 = const()[name = string("op_1166_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1166_cast_fp16 = slice_by_index(begin = var_1166_begin_0, end = var_1166_end_0, end_mask = var_1166_end_mask_0, x = x_89_cast_fp16)[name = string("op_1166_cast_fp16")]; + tensor var_1167 = const()[name = string("op_1167"), val = tensor([1, 8, 14, 111])]; + tensor matrix_bd_13_cast_fp16 = reshape(shape = var_1167, x = var_1166_cast_fp16)[name = string("matrix_bd_13_cast_fp16")]; + bool matrix_ac_7_transpose_x_0 = const()[name = string("matrix_ac_7_transpose_x_0"), val = bool(false)]; + bool matrix_ac_7_transpose_y_0 = const()[name = string("matrix_ac_7_transpose_y_0"), val = bool(false)]; + tensor transpose_102_perm_0 = const()[name = string("transpose_102_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_103_perm_0 = const()[name = string("transpose_103_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_103 = transpose(perm = transpose_103_perm_0, x = k_13_cast_fp16)[name = string("transpose_333")]; + tensor transpose_102 = transpose(perm = transpose_102_perm_0, x = var_1150_cast_fp16)[name = string("transpose_334")]; + tensor matrix_ac_7_cast_fp16 = matmul(transpose_x = matrix_ac_7_transpose_x_0, transpose_y = matrix_ac_7_transpose_y_0, x = transpose_102, y = transpose_103)[name = string("matrix_ac_7_cast_fp16")]; + tensor matrix_bd_15_begin_0 = const()[name = string("matrix_bd_15_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_15_end_0 = const()[name = string("matrix_bd_15_end_0"), val = tensor([1, 8, 14, 56])]; + tensor matrix_bd_15_end_mask_0 = const()[name = string("matrix_bd_15_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_15_cast_fp16 = slice_by_index(begin = matrix_bd_15_begin_0, end = matrix_bd_15_end_0, end_mask = matrix_bd_15_end_mask_0, x = matrix_bd_13_cast_fp16)[name = string("matrix_bd_15_cast_fp16")]; + tensor var_1176_cast_fp16 = add(x = matrix_ac_7_cast_fp16, y = matrix_bd_15_cast_fp16)[name = string("op_1176_cast_fp16")]; + fp16 _inversed_scores_13_y_0_to_fp16 = const()[name = string("_inversed_scores_13_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_13_cast_fp16 = mul(x = var_1176_cast_fp16, y = _inversed_scores_13_y_0_to_fp16)[name = string("_inversed_scores_13_cast_fp16")]; + tensor scores_15_cast_fp16 = select(a = var_44_to_fp16, b = _inversed_scores_13_cast_fp16, cond = mask_11)[name = string("scores_15_cast_fp16")]; + tensor var_1182_cast_fp16 = softmax(axis = var_58, x = scores_15_cast_fp16)[name = string("op_1182_cast_fp16")]; + tensor input_197_cast_fp16 = select(a = var_43_to_fp16, b = var_1182_cast_fp16, cond = mask_11)[name = string("input_197_cast_fp16")]; + bool x_91_transpose_x_0 = const()[name = string("x_91_transpose_x_0"), val = bool(false)]; + bool x_91_transpose_y_0 = const()[name = string("x_91_transpose_y_0"), val = bool(false)]; + tensor value_15_cast_fp16 = transpose(perm = value_15_perm_0, x = v_7_cast_fp16)[name = string("transpose_332")]; + tensor x_91_cast_fp16 = matmul(transpose_x = x_91_transpose_x_0, transpose_y = x_91_transpose_y_0, x = input_197_cast_fp16, y = value_15_cast_fp16)[name = string("x_91_cast_fp16")]; + tensor var_1186_perm_0 = const()[name = string("op_1186_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1187 = const()[name = string("op_1187"), val = tensor([1, -1, 1024])]; + tensor var_1186_cast_fp16 = transpose(perm = var_1186_perm_0, x = x_91_cast_fp16)[name = string("transpose_331")]; + tensor input_199_cast_fp16 = reshape(shape = var_1187, x = var_1186_cast_fp16)[name = string("input_199_cast_fp16")]; + tensor encoder_layers_3_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84043456))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84829952))))[name = string("encoder_layers_3_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_3_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_3_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84830144)))]; + tensor linear_34_cast_fp16 = linear(bias = encoder_layers_3_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_3_self_attn_linear_out_weight_to_fp16_palettized, x = input_199_cast_fp16)[name = string("linear_34_cast_fp16")]; + tensor input_203_cast_fp16 = add(x = input_193_cast_fp16, y = linear_34_cast_fp16)[name = string("input_203_cast_fp16")]; + tensor x_95_axes_0 = const()[name = string("x_95_axes_0"), val = tensor([-1])]; + tensor encoder_layers_3_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_3_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84832256)))]; + tensor encoder_layers_3_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_3_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84834368)))]; + tensor x_95_cast_fp16 = layer_norm(axes = x_95_axes_0, beta = encoder_layers_3_norm_conv_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_3_norm_conv_weight_to_fp16, x = input_203_cast_fp16)[name = string("x_95_cast_fp16")]; + tensor input_205_perm_0 = const()[name = string("input_205_perm_0"), val = tensor([0, 2, 1])]; + string input_207_pad_type_0 = const()[name = string("input_207_pad_type_0"), val = string("valid")]; + tensor input_207_strides_0 = const()[name = string("input_207_strides_0"), val = tensor([1])]; + tensor input_207_pad_0 = const()[name = string("input_207_pad_0"), val = tensor([0, 0])]; + tensor input_207_dilations_0 = const()[name = string("input_207_dilations_0"), val = tensor([1])]; + int32 input_207_groups_0 = const()[name = string("input_207_groups_0"), val = int32(1)]; + tensor encoder_layers_3_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84836480))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86933696))))[name = string("encoder_layers_3_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_205_cast_fp16 = transpose(perm = input_205_perm_0, x = x_95_cast_fp16)[name = string("transpose_330")]; + tensor input_207_cast_fp16 = conv(dilations = input_207_dilations_0, groups = input_207_groups_0, pad = input_207_pad_0, pad_type = input_207_pad_type_0, strides = input_207_strides_0, weight = encoder_layers_3_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_205_cast_fp16)[name = string("input_207_cast_fp16")]; + int32 x_97_split_num_splits_0 = const()[name = string("x_97_split_num_splits_0"), val = int32(2)]; + int32 x_97_split_axis_0 = const()[name = string("x_97_split_axis_0"), val = int32(1)]; + tensor x_97_split_cast_fp16_0, tensor x_97_split_cast_fp16_1 = split(axis = x_97_split_axis_0, num_splits = x_97_split_num_splits_0, x = input_207_cast_fp16)[name = string("x_97_split_cast_fp16")]; + tensor x_97_split_1_sigmoid_cast_fp16 = sigmoid(x = x_97_split_cast_fp16_1)[name = string("x_97_split_1_sigmoid_cast_fp16")]; + tensor x_97_cast_fp16 = mul(x = x_97_split_cast_fp16_0, y = x_97_split_1_sigmoid_cast_fp16)[name = string("x_97_cast_fp16")]; + tensor input_209_cast_fp16 = select(a = var_43_to_fp16, b = x_97_cast_fp16, cond = var_574)[name = string("input_209_cast_fp16")]; + bool new_x_15_interleave_0 = const()[name = string("new_x_15_interleave_0"), val = bool(false)]; + tensor new_x_15_cast_fp16 = concat(axis = var_58, interleave = new_x_15_interleave_0, values = (cache_15_cast_fp16, input_209_cast_fp16))[name = string("new_x_15_cast_fp16")]; + tensor var_1226_begin_0 = const()[name = string("op_1226_begin_0"), val = tensor([0, 0, 14])]; + tensor var_1226_end_0 = const()[name = string("op_1226_end_0"), val = tensor([1, 1024, 22])]; + tensor var_1226_end_mask_0 = const()[name = string("op_1226_end_mask_0"), val = tensor([true, true, true])]; + tensor var_1226_cast_fp16 = slice_by_index(begin = var_1226_begin_0, end = var_1226_end_0, end_mask = var_1226_end_mask_0, x = new_x_15_cast_fp16)[name = string("op_1226_cast_fp16")]; + string x_99_pad_type_0 = const()[name = string("x_99_pad_type_0"), val = string("valid")]; + int32 x_99_groups_0 = const()[name = string("x_99_groups_0"), val = int32(1024)]; + tensor x_99_strides_0 = const()[name = string("x_99_strides_0"), val = tensor([1])]; + tensor x_99_pad_0 = const()[name = string("x_99_pad_0"), val = tensor([0, 0])]; + tensor x_99_dilations_0 = const()[name = string("x_99_dilations_0"), val = tensor([1])]; + tensor encoder_layers_3_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86937856))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86947136))))[name = string("encoder_layers_3_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_99_cast_fp16 = conv(dilations = x_99_dilations_0, groups = x_99_groups_0, pad = x_99_pad_0, pad_type = x_99_pad_type_0, strides = x_99_strides_0, weight = encoder_layers_3_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_15_cast_fp16)[name = string("x_99_cast_fp16")]; + tensor input_211_perm_0 = const()[name = string("input_211_perm_0"), val = tensor([0, 2, 1])]; + tensor x_101_axes_0 = const()[name = string("x_101_axes_0"), val = tensor([-1])]; + tensor encoder_layers_3_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_3_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86949248)))]; + tensor encoder_layers_3_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_3_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86951360)))]; + tensor input_211_cast_fp16 = transpose(perm = input_211_perm_0, x = x_99_cast_fp16)[name = string("transpose_329")]; + tensor x_101_cast_fp16 = layer_norm(axes = x_101_axes_0, beta = encoder_layers_3_conv_batch_norm_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_3_conv_batch_norm_weight_to_fp16, x = input_211_cast_fp16)[name = string("x_101_cast_fp16")]; + tensor input_213_perm_0 = const()[name = string("input_213_perm_0"), val = tensor([0, 2, 1])]; + tensor input_213_cast_fp16 = transpose(perm = input_213_perm_0, x = x_101_cast_fp16)[name = string("transpose_328")]; + tensor input_215_cast_fp16 = silu(x = input_213_cast_fp16)[name = string("input_215_cast_fp16")]; + string x_103_pad_type_0 = const()[name = string("x_103_pad_type_0"), val = string("valid")]; + tensor x_103_strides_0 = const()[name = string("x_103_strides_0"), val = tensor([1])]; + tensor x_103_pad_0 = const()[name = string("x_103_pad_0"), val = tensor([0, 0])]; + tensor x_103_dilations_0 = const()[name = string("x_103_dilations_0"), val = tensor([1])]; + int32 x_103_groups_0 = const()[name = string("x_103_groups_0"), val = int32(1)]; + tensor encoder_layers_3_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86953472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88002112))))[name = string("encoder_layers_3_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_103_cast_fp16 = conv(dilations = x_103_dilations_0, groups = x_103_groups_0, pad = x_103_pad_0, pad_type = x_103_pad_type_0, strides = x_103_strides_0, weight = encoder_layers_3_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_215_cast_fp16)[name = string("x_103_cast_fp16")]; + tensor input_217_perm_0 = const()[name = string("input_217_perm_0"), val = tensor([0, 2, 1])]; + tensor input_217_cast_fp16 = transpose(perm = input_217_perm_0, x = x_103_cast_fp16)[name = string("transpose_327")]; + tensor input_219_cast_fp16 = add(x = input_203_cast_fp16, y = input_217_cast_fp16)[name = string("input_219_cast_fp16")]; + tensor input_221_axes_0 = const()[name = string("input_221_axes_0"), val = tensor([-1])]; + tensor encoder_layers_3_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_3_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88004224)))]; + tensor encoder_layers_3_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_3_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88006336)))]; + tensor input_221_cast_fp16 = layer_norm(axes = input_221_axes_0, beta = encoder_layers_3_norm_feed_forward2_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_3_norm_feed_forward2_weight_to_fp16, x = input_219_cast_fp16)[name = string("input_221_cast_fp16")]; + tensor encoder_layers_3_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88008448))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91154240))))[name = string("encoder_layers_3_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_3_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_3_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91154432)))]; + tensor linear_35_cast_fp16 = linear(bias = encoder_layers_3_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_3_feed_forward2_linear1_weight_to_fp16_palettized, x = input_221_cast_fp16)[name = string("linear_35_cast_fp16")]; + tensor input_225_cast_fp16 = silu(x = linear_35_cast_fp16)[name = string("input_225_cast_fp16")]; + tensor encoder_layers_3_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91162688))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94308480))))[name = string("encoder_layers_3_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_3_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_3_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94308672)))]; + tensor linear_36_cast_fp16 = linear(bias = encoder_layers_3_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_3_feed_forward2_linear2_weight_to_fp16_palettized, x = input_225_cast_fp16)[name = string("linear_36_cast_fp16")]; + fp16 var_1269_to_fp16 = const()[name = string("op_1269_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1270_cast_fp16 = mul(x = linear_36_cast_fp16, y = var_1269_to_fp16)[name = string("op_1270_cast_fp16")]; + tensor input_231_cast_fp16 = add(x = input_219_cast_fp16, y = var_1270_cast_fp16)[name = string("input_231_cast_fp16")]; + tensor input_233_axes_0 = const()[name = string("input_233_axes_0"), val = tensor([-1])]; + tensor encoder_layers_3_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_3_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94310784)))]; + tensor encoder_layers_3_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_3_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94312896)))]; + tensor input_233_cast_fp16 = layer_norm(axes = input_233_axes_0, beta = encoder_layers_3_norm_out_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_3_norm_out_weight_to_fp16, x = input_231_cast_fp16)[name = string("input_233_cast_fp16")]; + tensor cache_17_begin_0 = const()[name = string("cache_17_begin_0"), val = tensor([4, 0, 0, 0])]; + tensor cache_17_end_0 = const()[name = string("cache_17_end_0"), val = tensor([5, 1, 42, 1024])]; + tensor cache_17_end_mask_0 = const()[name = string("cache_17_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_17_squeeze_mask_0 = const()[name = string("cache_17_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_17_cast_fp16 = slice_by_index(begin = cache_17_begin_0, end = cache_17_end_0, end_mask = cache_17_end_mask_0, squeeze_mask = cache_17_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_17_cast_fp16")]; + tensor cache_19_begin_0 = const()[name = string("cache_19_begin_0"), val = tensor([4, 0, 0, 0])]; + tensor cache_19_end_0 = const()[name = string("cache_19_end_0"), val = tensor([5, 1, 1024, 8])]; + tensor cache_19_end_mask_0 = const()[name = string("cache_19_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_19_squeeze_mask_0 = const()[name = string("cache_19_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_19_cast_fp16 = slice_by_index(begin = cache_19_begin_0, end = cache_19_end_0, end_mask = cache_19_end_mask_0, squeeze_mask = cache_19_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_19_cast_fp16")]; + tensor input_235_axes_0 = const()[name = string("input_235_axes_0"), val = tensor([-1])]; + tensor encoder_layers_4_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_4_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94315008)))]; + tensor encoder_layers_4_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_4_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94317120)))]; + tensor input_235_cast_fp16 = layer_norm(axes = input_235_axes_0, beta = encoder_layers_4_norm_feed_forward1_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_4_norm_feed_forward1_weight_to_fp16, x = input_233_cast_fp16)[name = string("input_235_cast_fp16")]; + tensor encoder_layers_4_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94319232))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97465024))))[name = string("encoder_layers_4_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_4_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_4_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97465216)))]; + tensor linear_37_cast_fp16 = linear(bias = encoder_layers_4_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_4_feed_forward1_linear1_weight_to_fp16_palettized, x = input_235_cast_fp16)[name = string("linear_37_cast_fp16")]; + tensor input_239_cast_fp16 = silu(x = linear_37_cast_fp16)[name = string("input_239_cast_fp16")]; + tensor encoder_layers_4_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97473472))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100619264))))[name = string("encoder_layers_4_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_4_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_4_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100619456)))]; + tensor linear_38_cast_fp16 = linear(bias = encoder_layers_4_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_4_feed_forward1_linear2_weight_to_fp16_palettized, x = input_239_cast_fp16)[name = string("linear_38_cast_fp16")]; + fp16 var_1306_to_fp16 = const()[name = string("op_1306_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1307_cast_fp16 = mul(x = linear_38_cast_fp16, y = var_1306_to_fp16)[name = string("op_1307_cast_fp16")]; + tensor input_245_cast_fp16 = add(x = input_233_cast_fp16, y = var_1307_cast_fp16)[name = string("input_245_cast_fp16")]; + tensor key_9_axes_0 = const()[name = string("key_9_axes_0"), val = tensor([-1])]; + tensor encoder_layers_4_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_4_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100621568)))]; + tensor encoder_layers_4_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_4_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100623680)))]; + tensor key_9_cast_fp16 = layer_norm(axes = key_9_axes_0, beta = encoder_layers_4_norm_self_att_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_4_norm_self_att_weight_to_fp16, x = input_245_cast_fp16)[name = string("key_9_cast_fp16")]; + bool input_247_interleave_0 = const()[name = string("input_247_interleave_0"), val = bool(false)]; + tensor input_247_cast_fp16 = concat(axis = var_67, interleave = input_247_interleave_0, values = (cache_17_cast_fp16, key_9_cast_fp16))[name = string("input_247_cast_fp16")]; + tensor var_1329_begin_0 = const()[name = string("op_1329_begin_0"), val = tensor([0, 14, 0])]; + tensor var_1329_end_0 = const()[name = string("op_1329_end_0"), val = tensor([1, 42, 1024])]; + tensor var_1329_end_mask_0 = const()[name = string("op_1329_end_mask_0"), val = tensor([true, true, true])]; + tensor var_1329_cast_fp16 = slice_by_index(begin = var_1329_begin_0, end = var_1329_end_0, end_mask = var_1329_end_mask_0, x = cache_17_cast_fp16)[name = string("op_1329_cast_fp16")]; + bool var_1335_interleave_0 = const()[name = string("op_1335_interleave_0"), val = bool(false)]; + tensor var_1335_cast_fp16 = concat(axis = var_67, interleave = var_1335_interleave_0, values = (var_1329_cast_fp16, key_9_cast_fp16))[name = string("op_1335_cast_fp16")]; + tensor encoder_layers_4_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100625792))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101412288))))[name = string("encoder_layers_4_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_4_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_4_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101412480)))]; + tensor linear_39_cast_fp16 = linear(bias = encoder_layers_4_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_4_self_attn_linear_q_weight_to_fp16_palettized, x = key_9_cast_fp16)[name = string("linear_39_cast_fp16")]; + tensor var_1340 = const()[name = string("op_1340"), val = tensor([1, -1, 8, 128])]; + tensor q_25_cast_fp16 = reshape(shape = var_1340, x = linear_39_cast_fp16)[name = string("q_25_cast_fp16")]; + tensor encoder_layers_4_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101414592))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102201088))))[name = string("encoder_layers_4_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_4_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_4_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102201280)))]; + tensor linear_40_cast_fp16 = linear(bias = encoder_layers_4_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_4_self_attn_linear_k_weight_to_fp16_palettized, x = input_247_cast_fp16)[name = string("linear_40_cast_fp16")]; + tensor var_1345 = const()[name = string("op_1345"), val = tensor([1, -1, 8, 128])]; + tensor k_17_cast_fp16 = reshape(shape = var_1345, x = linear_40_cast_fp16)[name = string("k_17_cast_fp16")]; + tensor encoder_layers_4_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102203392))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102989888))))[name = string("encoder_layers_4_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_4_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_4_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102990080)))]; + tensor linear_41_cast_fp16 = linear(bias = encoder_layers_4_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_4_self_attn_linear_v_weight_to_fp16_palettized, x = input_247_cast_fp16)[name = string("linear_41_cast_fp16")]; + tensor var_1350 = const()[name = string("op_1350"), val = tensor([1, -1, 8, 128])]; + tensor v_9_cast_fp16 = reshape(shape = var_1350, x = linear_41_cast_fp16)[name = string("v_9_cast_fp16")]; + tensor value_17_perm_0 = const()[name = string("value_17_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_4_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_4_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102992192)))]; + tensor var_1363_cast_fp16 = add(x = q_25_cast_fp16, y = encoder_layers_4_self_attn_pos_bias_u_to_fp16)[name = string("op_1363_cast_fp16")]; + tensor encoder_layers_4_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_4_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102994304)))]; + tensor var_1365_cast_fp16 = add(x = q_25_cast_fp16, y = encoder_layers_4_self_attn_pos_bias_v_to_fp16)[name = string("op_1365_cast_fp16")]; + tensor q_with_bias_v_9_perm_0 = const()[name = string("q_with_bias_v_9_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_111_transpose_x_0 = const()[name = string("x_111_transpose_x_0"), val = bool(false)]; + bool x_111_transpose_y_0 = const()[name = string("x_111_transpose_y_0"), val = bool(false)]; + tensor op_1367_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102996416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103110144))))[name = string("op_1367_to_fp16_quantized")]; + tensor q_with_bias_v_9_cast_fp16 = transpose(perm = q_with_bias_v_9_perm_0, x = var_1365_cast_fp16)[name = string("transpose_326")]; + tensor x_111_cast_fp16 = matmul(transpose_x = x_111_transpose_x_0, transpose_y = x_111_transpose_y_0, x = q_with_bias_v_9_cast_fp16, y = op_1367_to_fp16_quantized)[name = string("x_111_cast_fp16")]; + tensor x_113_pad_0 = const()[name = string("x_113_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_113_mode_0 = const()[name = string("x_113_mode_0"), val = string("constant")]; + fp16 const_131_to_fp16 = const()[name = string("const_131_to_fp16"), val = fp16(0x0p+0)]; + tensor x_113_cast_fp16 = pad(constant_val = const_131_to_fp16, mode = x_113_mode_0, pad = x_113_pad_0, x = x_111_cast_fp16)[name = string("x_113_cast_fp16")]; + tensor var_1375 = const()[name = string("op_1375"), val = tensor([1, 8, -1, 14])]; + tensor x_115_cast_fp16 = reshape(shape = var_1375, x = x_113_cast_fp16)[name = string("x_115_cast_fp16")]; + tensor var_1379_begin_0 = const()[name = string("op_1379_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_1379_end_0 = const()[name = string("op_1379_end_0"), val = tensor([1, 8, 112, 14])]; + tensor var_1379_end_mask_0 = const()[name = string("op_1379_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1379_cast_fp16 = slice_by_index(begin = var_1379_begin_0, end = var_1379_end_0, end_mask = var_1379_end_mask_0, x = x_115_cast_fp16)[name = string("op_1379_cast_fp16")]; + tensor var_1380 = const()[name = string("op_1380"), val = tensor([1, 8, 14, 111])]; + tensor matrix_bd_17_cast_fp16 = reshape(shape = var_1380, x = var_1379_cast_fp16)[name = string("matrix_bd_17_cast_fp16")]; + bool matrix_ac_9_transpose_x_0 = const()[name = string("matrix_ac_9_transpose_x_0"), val = bool(false)]; + bool matrix_ac_9_transpose_y_0 = const()[name = string("matrix_ac_9_transpose_y_0"), val = bool(false)]; + tensor transpose_104_perm_0 = const()[name = string("transpose_104_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_105_perm_0 = const()[name = string("transpose_105_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_105 = transpose(perm = transpose_105_perm_0, x = k_17_cast_fp16)[name = string("transpose_324")]; + tensor transpose_104 = transpose(perm = transpose_104_perm_0, x = var_1363_cast_fp16)[name = string("transpose_325")]; + tensor matrix_ac_9_cast_fp16 = matmul(transpose_x = matrix_ac_9_transpose_x_0, transpose_y = matrix_ac_9_transpose_y_0, x = transpose_104, y = transpose_105)[name = string("matrix_ac_9_cast_fp16")]; + tensor matrix_bd_19_begin_0 = const()[name = string("matrix_bd_19_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_19_end_0 = const()[name = string("matrix_bd_19_end_0"), val = tensor([1, 8, 14, 56])]; + tensor matrix_bd_19_end_mask_0 = const()[name = string("matrix_bd_19_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_19_cast_fp16 = slice_by_index(begin = matrix_bd_19_begin_0, end = matrix_bd_19_end_0, end_mask = matrix_bd_19_end_mask_0, x = matrix_bd_17_cast_fp16)[name = string("matrix_bd_19_cast_fp16")]; + tensor var_1389_cast_fp16 = add(x = matrix_ac_9_cast_fp16, y = matrix_bd_19_cast_fp16)[name = string("op_1389_cast_fp16")]; + fp16 _inversed_scores_17_y_0_to_fp16 = const()[name = string("_inversed_scores_17_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_17_cast_fp16 = mul(x = var_1389_cast_fp16, y = _inversed_scores_17_y_0_to_fp16)[name = string("_inversed_scores_17_cast_fp16")]; + tensor scores_19_cast_fp16 = select(a = var_44_to_fp16, b = _inversed_scores_17_cast_fp16, cond = mask_11)[name = string("scores_19_cast_fp16")]; + tensor var_1395_cast_fp16 = softmax(axis = var_58, x = scores_19_cast_fp16)[name = string("op_1395_cast_fp16")]; + tensor input_249_cast_fp16 = select(a = var_43_to_fp16, b = var_1395_cast_fp16, cond = mask_11)[name = string("input_249_cast_fp16")]; + bool x_117_transpose_x_0 = const()[name = string("x_117_transpose_x_0"), val = bool(false)]; + bool x_117_transpose_y_0 = const()[name = string("x_117_transpose_y_0"), val = bool(false)]; + tensor value_17_cast_fp16 = transpose(perm = value_17_perm_0, x = v_9_cast_fp16)[name = string("transpose_323")]; + tensor x_117_cast_fp16 = matmul(transpose_x = x_117_transpose_x_0, transpose_y = x_117_transpose_y_0, x = input_249_cast_fp16, y = value_17_cast_fp16)[name = string("x_117_cast_fp16")]; + tensor var_1399_perm_0 = const()[name = string("op_1399_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1400 = const()[name = string("op_1400"), val = tensor([1, -1, 1024])]; + tensor var_1399_cast_fp16 = transpose(perm = var_1399_perm_0, x = x_117_cast_fp16)[name = string("transpose_322")]; + tensor input_251_cast_fp16 = reshape(shape = var_1400, x = var_1399_cast_fp16)[name = string("input_251_cast_fp16")]; + tensor encoder_layers_4_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103110464))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103896960))))[name = string("encoder_layers_4_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_4_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_4_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103897152)))]; + tensor linear_43_cast_fp16 = linear(bias = encoder_layers_4_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_4_self_attn_linear_out_weight_to_fp16_palettized, x = input_251_cast_fp16)[name = string("linear_43_cast_fp16")]; + tensor input_255_cast_fp16 = add(x = input_245_cast_fp16, y = linear_43_cast_fp16)[name = string("input_255_cast_fp16")]; + tensor x_121_axes_0 = const()[name = string("x_121_axes_0"), val = tensor([-1])]; + tensor encoder_layers_4_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_4_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103899264)))]; + tensor encoder_layers_4_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_4_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103901376)))]; + tensor x_121_cast_fp16 = layer_norm(axes = x_121_axes_0, beta = encoder_layers_4_norm_conv_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_4_norm_conv_weight_to_fp16, x = input_255_cast_fp16)[name = string("x_121_cast_fp16")]; + tensor input_257_perm_0 = const()[name = string("input_257_perm_0"), val = tensor([0, 2, 1])]; + string input_259_pad_type_0 = const()[name = string("input_259_pad_type_0"), val = string("valid")]; + tensor input_259_strides_0 = const()[name = string("input_259_strides_0"), val = tensor([1])]; + tensor input_259_pad_0 = const()[name = string("input_259_pad_0"), val = tensor([0, 0])]; + tensor input_259_dilations_0 = const()[name = string("input_259_dilations_0"), val = tensor([1])]; + int32 input_259_groups_0 = const()[name = string("input_259_groups_0"), val = int32(1)]; + tensor encoder_layers_4_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103903488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106000704))))[name = string("encoder_layers_4_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_257_cast_fp16 = transpose(perm = input_257_perm_0, x = x_121_cast_fp16)[name = string("transpose_321")]; + tensor input_259_cast_fp16 = conv(dilations = input_259_dilations_0, groups = input_259_groups_0, pad = input_259_pad_0, pad_type = input_259_pad_type_0, strides = input_259_strides_0, weight = encoder_layers_4_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_257_cast_fp16)[name = string("input_259_cast_fp16")]; + int32 x_123_split_num_splits_0 = const()[name = string("x_123_split_num_splits_0"), val = int32(2)]; + int32 x_123_split_axis_0 = const()[name = string("x_123_split_axis_0"), val = int32(1)]; + tensor x_123_split_cast_fp16_0, tensor x_123_split_cast_fp16_1 = split(axis = x_123_split_axis_0, num_splits = x_123_split_num_splits_0, x = input_259_cast_fp16)[name = string("x_123_split_cast_fp16")]; + tensor x_123_split_1_sigmoid_cast_fp16 = sigmoid(x = x_123_split_cast_fp16_1)[name = string("x_123_split_1_sigmoid_cast_fp16")]; + tensor x_123_cast_fp16 = mul(x = x_123_split_cast_fp16_0, y = x_123_split_1_sigmoid_cast_fp16)[name = string("x_123_cast_fp16")]; + tensor input_261_cast_fp16 = select(a = var_43_to_fp16, b = x_123_cast_fp16, cond = var_574)[name = string("input_261_cast_fp16")]; + bool new_x_19_interleave_0 = const()[name = string("new_x_19_interleave_0"), val = bool(false)]; + tensor new_x_19_cast_fp16 = concat(axis = var_58, interleave = new_x_19_interleave_0, values = (cache_19_cast_fp16, input_261_cast_fp16))[name = string("new_x_19_cast_fp16")]; + tensor var_1439_begin_0 = const()[name = string("op_1439_begin_0"), val = tensor([0, 0, 14])]; + tensor var_1439_end_0 = const()[name = string("op_1439_end_0"), val = tensor([1, 1024, 22])]; + tensor var_1439_end_mask_0 = const()[name = string("op_1439_end_mask_0"), val = tensor([true, true, true])]; + tensor var_1439_cast_fp16 = slice_by_index(begin = var_1439_begin_0, end = var_1439_end_0, end_mask = var_1439_end_mask_0, x = new_x_19_cast_fp16)[name = string("op_1439_cast_fp16")]; + string x_125_pad_type_0 = const()[name = string("x_125_pad_type_0"), val = string("valid")]; + int32 x_125_groups_0 = const()[name = string("x_125_groups_0"), val = int32(1024)]; + tensor x_125_strides_0 = const()[name = string("x_125_strides_0"), val = tensor([1])]; + tensor x_125_pad_0 = const()[name = string("x_125_pad_0"), val = tensor([0, 0])]; + tensor x_125_dilations_0 = const()[name = string("x_125_dilations_0"), val = tensor([1])]; + tensor encoder_layers_4_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106004864))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106014144))))[name = string("encoder_layers_4_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_125_cast_fp16 = conv(dilations = x_125_dilations_0, groups = x_125_groups_0, pad = x_125_pad_0, pad_type = x_125_pad_type_0, strides = x_125_strides_0, weight = encoder_layers_4_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_19_cast_fp16)[name = string("x_125_cast_fp16")]; + tensor input_263_perm_0 = const()[name = string("input_263_perm_0"), val = tensor([0, 2, 1])]; + tensor x_127_axes_0 = const()[name = string("x_127_axes_0"), val = tensor([-1])]; + tensor encoder_layers_4_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_4_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106016256)))]; + tensor encoder_layers_4_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_4_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106018368)))]; + tensor input_263_cast_fp16 = transpose(perm = input_263_perm_0, x = x_125_cast_fp16)[name = string("transpose_320")]; + tensor x_127_cast_fp16 = layer_norm(axes = x_127_axes_0, beta = encoder_layers_4_conv_batch_norm_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_4_conv_batch_norm_weight_to_fp16, x = input_263_cast_fp16)[name = string("x_127_cast_fp16")]; + tensor input_265_perm_0 = const()[name = string("input_265_perm_0"), val = tensor([0, 2, 1])]; + tensor input_265_cast_fp16 = transpose(perm = input_265_perm_0, x = x_127_cast_fp16)[name = string("transpose_319")]; + tensor input_267_cast_fp16 = silu(x = input_265_cast_fp16)[name = string("input_267_cast_fp16")]; + string x_129_pad_type_0 = const()[name = string("x_129_pad_type_0"), val = string("valid")]; + tensor x_129_strides_0 = const()[name = string("x_129_strides_0"), val = tensor([1])]; + tensor x_129_pad_0 = const()[name = string("x_129_pad_0"), val = tensor([0, 0])]; + tensor x_129_dilations_0 = const()[name = string("x_129_dilations_0"), val = tensor([1])]; + int32 x_129_groups_0 = const()[name = string("x_129_groups_0"), val = int32(1)]; + tensor encoder_layers_4_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106020480))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(107069120))))[name = string("encoder_layers_4_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_129_cast_fp16 = conv(dilations = x_129_dilations_0, groups = x_129_groups_0, pad = x_129_pad_0, pad_type = x_129_pad_type_0, strides = x_129_strides_0, weight = encoder_layers_4_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_267_cast_fp16)[name = string("x_129_cast_fp16")]; + tensor input_269_perm_0 = const()[name = string("input_269_perm_0"), val = tensor([0, 2, 1])]; + tensor input_269_cast_fp16 = transpose(perm = input_269_perm_0, x = x_129_cast_fp16)[name = string("transpose_318")]; + tensor input_271_cast_fp16 = add(x = input_255_cast_fp16, y = input_269_cast_fp16)[name = string("input_271_cast_fp16")]; + tensor input_273_axes_0 = const()[name = string("input_273_axes_0"), val = tensor([-1])]; + tensor encoder_layers_4_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_4_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(107071232)))]; + tensor encoder_layers_4_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_4_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(107073344)))]; + tensor input_273_cast_fp16 = layer_norm(axes = input_273_axes_0, beta = encoder_layers_4_norm_feed_forward2_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_4_norm_feed_forward2_weight_to_fp16, x = input_271_cast_fp16)[name = string("input_273_cast_fp16")]; + tensor encoder_layers_4_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(107075456))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110221248))))[name = string("encoder_layers_4_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_4_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_4_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110221440)))]; + tensor linear_44_cast_fp16 = linear(bias = encoder_layers_4_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_4_feed_forward2_linear1_weight_to_fp16_palettized, x = input_273_cast_fp16)[name = string("linear_44_cast_fp16")]; + tensor input_277_cast_fp16 = silu(x = linear_44_cast_fp16)[name = string("input_277_cast_fp16")]; + tensor encoder_layers_4_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110229696))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113375488))))[name = string("encoder_layers_4_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_4_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_4_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113375680)))]; + tensor linear_45_cast_fp16 = linear(bias = encoder_layers_4_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_4_feed_forward2_linear2_weight_to_fp16_palettized, x = input_277_cast_fp16)[name = string("linear_45_cast_fp16")]; + fp16 var_1482_to_fp16 = const()[name = string("op_1482_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1483_cast_fp16 = mul(x = linear_45_cast_fp16, y = var_1482_to_fp16)[name = string("op_1483_cast_fp16")]; + tensor input_283_cast_fp16 = add(x = input_271_cast_fp16, y = var_1483_cast_fp16)[name = string("input_283_cast_fp16")]; + tensor input_285_axes_0 = const()[name = string("input_285_axes_0"), val = tensor([-1])]; + tensor encoder_layers_4_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_4_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113377792)))]; + tensor encoder_layers_4_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_4_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113379904)))]; + tensor input_285_cast_fp16 = layer_norm(axes = input_285_axes_0, beta = encoder_layers_4_norm_out_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_4_norm_out_weight_to_fp16, x = input_283_cast_fp16)[name = string("input_285_cast_fp16")]; + tensor cache_21_begin_0 = const()[name = string("cache_21_begin_0"), val = tensor([5, 0, 0, 0])]; + tensor cache_21_end_0 = const()[name = string("cache_21_end_0"), val = tensor([6, 1, 42, 1024])]; + tensor cache_21_end_mask_0 = const()[name = string("cache_21_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_21_squeeze_mask_0 = const()[name = string("cache_21_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_21_cast_fp16 = slice_by_index(begin = cache_21_begin_0, end = cache_21_end_0, end_mask = cache_21_end_mask_0, squeeze_mask = cache_21_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_21_cast_fp16")]; + tensor cache_23_begin_0 = const()[name = string("cache_23_begin_0"), val = tensor([5, 0, 0, 0])]; + tensor cache_23_end_0 = const()[name = string("cache_23_end_0"), val = tensor([6, 1, 1024, 8])]; + tensor cache_23_end_mask_0 = const()[name = string("cache_23_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_23_squeeze_mask_0 = const()[name = string("cache_23_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_23_cast_fp16 = slice_by_index(begin = cache_23_begin_0, end = cache_23_end_0, end_mask = cache_23_end_mask_0, squeeze_mask = cache_23_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_23_cast_fp16")]; + tensor input_287_axes_0 = const()[name = string("input_287_axes_0"), val = tensor([-1])]; + tensor encoder_layers_5_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_5_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113382016)))]; + tensor encoder_layers_5_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_5_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113384128)))]; + tensor input_287_cast_fp16 = layer_norm(axes = input_287_axes_0, beta = encoder_layers_5_norm_feed_forward1_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_5_norm_feed_forward1_weight_to_fp16, x = input_285_cast_fp16)[name = string("input_287_cast_fp16")]; + tensor encoder_layers_5_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113386240))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116532032))))[name = string("encoder_layers_5_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_5_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_5_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116532224)))]; + tensor linear_46_cast_fp16 = linear(bias = encoder_layers_5_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_5_feed_forward1_linear1_weight_to_fp16_palettized, x = input_287_cast_fp16)[name = string("linear_46_cast_fp16")]; + tensor input_291_cast_fp16 = silu(x = linear_46_cast_fp16)[name = string("input_291_cast_fp16")]; + tensor encoder_layers_5_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116540480))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119686272))))[name = string("encoder_layers_5_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_5_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_5_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119686464)))]; + tensor linear_47_cast_fp16 = linear(bias = encoder_layers_5_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_5_feed_forward1_linear2_weight_to_fp16_palettized, x = input_291_cast_fp16)[name = string("linear_47_cast_fp16")]; + fp16 var_1519_to_fp16 = const()[name = string("op_1519_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1520_cast_fp16 = mul(x = linear_47_cast_fp16, y = var_1519_to_fp16)[name = string("op_1520_cast_fp16")]; + tensor input_297_cast_fp16 = add(x = input_285_cast_fp16, y = var_1520_cast_fp16)[name = string("input_297_cast_fp16")]; + tensor key_11_axes_0 = const()[name = string("key_11_axes_0"), val = tensor([-1])]; + tensor encoder_layers_5_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_5_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119688576)))]; + tensor encoder_layers_5_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_5_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119690688)))]; + tensor key_11_cast_fp16 = layer_norm(axes = key_11_axes_0, beta = encoder_layers_5_norm_self_att_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_5_norm_self_att_weight_to_fp16, x = input_297_cast_fp16)[name = string("key_11_cast_fp16")]; + bool input_299_interleave_0 = const()[name = string("input_299_interleave_0"), val = bool(false)]; + tensor input_299_cast_fp16 = concat(axis = var_67, interleave = input_299_interleave_0, values = (cache_21_cast_fp16, key_11_cast_fp16))[name = string("input_299_cast_fp16")]; + tensor var_1542_begin_0 = const()[name = string("op_1542_begin_0"), val = tensor([0, 14, 0])]; + tensor var_1542_end_0 = const()[name = string("op_1542_end_0"), val = tensor([1, 42, 1024])]; + tensor var_1542_end_mask_0 = const()[name = string("op_1542_end_mask_0"), val = tensor([true, true, true])]; + tensor var_1542_cast_fp16 = slice_by_index(begin = var_1542_begin_0, end = var_1542_end_0, end_mask = var_1542_end_mask_0, x = cache_21_cast_fp16)[name = string("op_1542_cast_fp16")]; + bool var_1548_interleave_0 = const()[name = string("op_1548_interleave_0"), val = bool(false)]; + tensor var_1548_cast_fp16 = concat(axis = var_67, interleave = var_1548_interleave_0, values = (var_1542_cast_fp16, key_11_cast_fp16))[name = string("op_1548_cast_fp16")]; + tensor encoder_layers_5_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119692800))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120479296))))[name = string("encoder_layers_5_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_5_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_5_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120479488)))]; + tensor linear_48_cast_fp16 = linear(bias = encoder_layers_5_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_5_self_attn_linear_q_weight_to_fp16_palettized, x = key_11_cast_fp16)[name = string("linear_48_cast_fp16")]; + tensor var_1553 = const()[name = string("op_1553"), val = tensor([1, -1, 8, 128])]; + tensor q_31_cast_fp16 = reshape(shape = var_1553, x = linear_48_cast_fp16)[name = string("q_31_cast_fp16")]; + tensor encoder_layers_5_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120481600))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121268096))))[name = string("encoder_layers_5_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_5_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_5_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121268288)))]; + tensor linear_49_cast_fp16 = linear(bias = encoder_layers_5_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_5_self_attn_linear_k_weight_to_fp16_palettized, x = input_299_cast_fp16)[name = string("linear_49_cast_fp16")]; + tensor var_1558 = const()[name = string("op_1558"), val = tensor([1, -1, 8, 128])]; + tensor k_21_cast_fp16 = reshape(shape = var_1558, x = linear_49_cast_fp16)[name = string("k_21_cast_fp16")]; + tensor encoder_layers_5_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121270400))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122056896))))[name = string("encoder_layers_5_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_5_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_5_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122057088)))]; + tensor linear_50_cast_fp16 = linear(bias = encoder_layers_5_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_5_self_attn_linear_v_weight_to_fp16_palettized, x = input_299_cast_fp16)[name = string("linear_50_cast_fp16")]; + tensor var_1563 = const()[name = string("op_1563"), val = tensor([1, -1, 8, 128])]; + tensor v_11_cast_fp16 = reshape(shape = var_1563, x = linear_50_cast_fp16)[name = string("v_11_cast_fp16")]; + tensor value_19_perm_0 = const()[name = string("value_19_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_5_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_5_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122059200)))]; + tensor var_1576_cast_fp16 = add(x = q_31_cast_fp16, y = encoder_layers_5_self_attn_pos_bias_u_to_fp16)[name = string("op_1576_cast_fp16")]; + tensor encoder_layers_5_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_5_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122061312)))]; + tensor var_1578_cast_fp16 = add(x = q_31_cast_fp16, y = encoder_layers_5_self_attn_pos_bias_v_to_fp16)[name = string("op_1578_cast_fp16")]; + tensor q_with_bias_v_11_perm_0 = const()[name = string("q_with_bias_v_11_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_137_transpose_x_0 = const()[name = string("x_137_transpose_x_0"), val = bool(false)]; + bool x_137_transpose_y_0 = const()[name = string("x_137_transpose_y_0"), val = bool(false)]; + tensor op_1580_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122063424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122177152))))[name = string("op_1580_to_fp16_quantized")]; + tensor q_with_bias_v_11_cast_fp16 = transpose(perm = q_with_bias_v_11_perm_0, x = var_1578_cast_fp16)[name = string("transpose_317")]; + tensor x_137_cast_fp16 = matmul(transpose_x = x_137_transpose_x_0, transpose_y = x_137_transpose_y_0, x = q_with_bias_v_11_cast_fp16, y = op_1580_to_fp16_quantized)[name = string("x_137_cast_fp16")]; + tensor x_139_pad_0 = const()[name = string("x_139_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_139_mode_0 = const()[name = string("x_139_mode_0"), val = string("constant")]; + fp16 const_144_to_fp16 = const()[name = string("const_144_to_fp16"), val = fp16(0x0p+0)]; + tensor x_139_cast_fp16 = pad(constant_val = const_144_to_fp16, mode = x_139_mode_0, pad = x_139_pad_0, x = x_137_cast_fp16)[name = string("x_139_cast_fp16")]; + tensor var_1588 = const()[name = string("op_1588"), val = tensor([1, 8, -1, 14])]; + tensor x_141_cast_fp16 = reshape(shape = var_1588, x = x_139_cast_fp16)[name = string("x_141_cast_fp16")]; + tensor var_1592_begin_0 = const()[name = string("op_1592_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_1592_end_0 = const()[name = string("op_1592_end_0"), val = tensor([1, 8, 112, 14])]; + tensor var_1592_end_mask_0 = const()[name = string("op_1592_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1592_cast_fp16 = slice_by_index(begin = var_1592_begin_0, end = var_1592_end_0, end_mask = var_1592_end_mask_0, x = x_141_cast_fp16)[name = string("op_1592_cast_fp16")]; + tensor var_1593 = const()[name = string("op_1593"), val = tensor([1, 8, 14, 111])]; + tensor matrix_bd_21_cast_fp16 = reshape(shape = var_1593, x = var_1592_cast_fp16)[name = string("matrix_bd_21_cast_fp16")]; + bool matrix_ac_11_transpose_x_0 = const()[name = string("matrix_ac_11_transpose_x_0"), val = bool(false)]; + bool matrix_ac_11_transpose_y_0 = const()[name = string("matrix_ac_11_transpose_y_0"), val = bool(false)]; + tensor transpose_106_perm_0 = const()[name = string("transpose_106_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_107_perm_0 = const()[name = string("transpose_107_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_107 = transpose(perm = transpose_107_perm_0, x = k_21_cast_fp16)[name = string("transpose_315")]; + tensor transpose_106 = transpose(perm = transpose_106_perm_0, x = var_1576_cast_fp16)[name = string("transpose_316")]; + tensor matrix_ac_11_cast_fp16 = matmul(transpose_x = matrix_ac_11_transpose_x_0, transpose_y = matrix_ac_11_transpose_y_0, x = transpose_106, y = transpose_107)[name = string("matrix_ac_11_cast_fp16")]; + tensor matrix_bd_23_begin_0 = const()[name = string("matrix_bd_23_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_23_end_0 = const()[name = string("matrix_bd_23_end_0"), val = tensor([1, 8, 14, 56])]; + tensor matrix_bd_23_end_mask_0 = const()[name = string("matrix_bd_23_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_23_cast_fp16 = slice_by_index(begin = matrix_bd_23_begin_0, end = matrix_bd_23_end_0, end_mask = matrix_bd_23_end_mask_0, x = matrix_bd_21_cast_fp16)[name = string("matrix_bd_23_cast_fp16")]; + tensor var_1602_cast_fp16 = add(x = matrix_ac_11_cast_fp16, y = matrix_bd_23_cast_fp16)[name = string("op_1602_cast_fp16")]; + fp16 _inversed_scores_21_y_0_to_fp16 = const()[name = string("_inversed_scores_21_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_21_cast_fp16 = mul(x = var_1602_cast_fp16, y = _inversed_scores_21_y_0_to_fp16)[name = string("_inversed_scores_21_cast_fp16")]; + tensor scores_23_cast_fp16 = select(a = var_44_to_fp16, b = _inversed_scores_21_cast_fp16, cond = mask_11)[name = string("scores_23_cast_fp16")]; + tensor var_1608_cast_fp16 = softmax(axis = var_58, x = scores_23_cast_fp16)[name = string("op_1608_cast_fp16")]; + tensor input_301_cast_fp16 = select(a = var_43_to_fp16, b = var_1608_cast_fp16, cond = mask_11)[name = string("input_301_cast_fp16")]; + bool x_143_transpose_x_0 = const()[name = string("x_143_transpose_x_0"), val = bool(false)]; + bool x_143_transpose_y_0 = const()[name = string("x_143_transpose_y_0"), val = bool(false)]; + tensor value_19_cast_fp16 = transpose(perm = value_19_perm_0, x = v_11_cast_fp16)[name = string("transpose_314")]; + tensor x_143_cast_fp16 = matmul(transpose_x = x_143_transpose_x_0, transpose_y = x_143_transpose_y_0, x = input_301_cast_fp16, y = value_19_cast_fp16)[name = string("x_143_cast_fp16")]; + tensor var_1612_perm_0 = const()[name = string("op_1612_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1613 = const()[name = string("op_1613"), val = tensor([1, -1, 1024])]; + tensor var_1612_cast_fp16 = transpose(perm = var_1612_perm_0, x = x_143_cast_fp16)[name = string("transpose_313")]; + tensor input_303_cast_fp16 = reshape(shape = var_1613, x = var_1612_cast_fp16)[name = string("input_303_cast_fp16")]; + tensor encoder_layers_5_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122177472))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122963968))))[name = string("encoder_layers_5_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_5_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_5_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122964160)))]; + tensor linear_52_cast_fp16 = linear(bias = encoder_layers_5_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_5_self_attn_linear_out_weight_to_fp16_palettized, x = input_303_cast_fp16)[name = string("linear_52_cast_fp16")]; + tensor input_307_cast_fp16 = add(x = input_297_cast_fp16, y = linear_52_cast_fp16)[name = string("input_307_cast_fp16")]; + tensor x_147_axes_0 = const()[name = string("x_147_axes_0"), val = tensor([-1])]; + tensor encoder_layers_5_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_5_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122966272)))]; + tensor encoder_layers_5_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_5_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122968384)))]; + tensor x_147_cast_fp16 = layer_norm(axes = x_147_axes_0, beta = encoder_layers_5_norm_conv_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_5_norm_conv_weight_to_fp16, x = input_307_cast_fp16)[name = string("x_147_cast_fp16")]; + tensor input_309_perm_0 = const()[name = string("input_309_perm_0"), val = tensor([0, 2, 1])]; + string input_311_pad_type_0 = const()[name = string("input_311_pad_type_0"), val = string("valid")]; + tensor input_311_strides_0 = const()[name = string("input_311_strides_0"), val = tensor([1])]; + tensor input_311_pad_0 = const()[name = string("input_311_pad_0"), val = tensor([0, 0])]; + tensor input_311_dilations_0 = const()[name = string("input_311_dilations_0"), val = tensor([1])]; + int32 input_311_groups_0 = const()[name = string("input_311_groups_0"), val = int32(1)]; + tensor encoder_layers_5_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122970496))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125067712))))[name = string("encoder_layers_5_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_309_cast_fp16 = transpose(perm = input_309_perm_0, x = x_147_cast_fp16)[name = string("transpose_312")]; + tensor input_311_cast_fp16 = conv(dilations = input_311_dilations_0, groups = input_311_groups_0, pad = input_311_pad_0, pad_type = input_311_pad_type_0, strides = input_311_strides_0, weight = encoder_layers_5_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_309_cast_fp16)[name = string("input_311_cast_fp16")]; + int32 x_149_split_num_splits_0 = const()[name = string("x_149_split_num_splits_0"), val = int32(2)]; + int32 x_149_split_axis_0 = const()[name = string("x_149_split_axis_0"), val = int32(1)]; + tensor x_149_split_cast_fp16_0, tensor x_149_split_cast_fp16_1 = split(axis = x_149_split_axis_0, num_splits = x_149_split_num_splits_0, x = input_311_cast_fp16)[name = string("x_149_split_cast_fp16")]; + tensor x_149_split_1_sigmoid_cast_fp16 = sigmoid(x = x_149_split_cast_fp16_1)[name = string("x_149_split_1_sigmoid_cast_fp16")]; + tensor x_149_cast_fp16 = mul(x = x_149_split_cast_fp16_0, y = x_149_split_1_sigmoid_cast_fp16)[name = string("x_149_cast_fp16")]; + tensor input_313_cast_fp16 = select(a = var_43_to_fp16, b = x_149_cast_fp16, cond = var_574)[name = string("input_313_cast_fp16")]; + bool new_x_23_interleave_0 = const()[name = string("new_x_23_interleave_0"), val = bool(false)]; + tensor new_x_23_cast_fp16 = concat(axis = var_58, interleave = new_x_23_interleave_0, values = (cache_23_cast_fp16, input_313_cast_fp16))[name = string("new_x_23_cast_fp16")]; + tensor var_1652_begin_0 = const()[name = string("op_1652_begin_0"), val = tensor([0, 0, 14])]; + tensor var_1652_end_0 = const()[name = string("op_1652_end_0"), val = tensor([1, 1024, 22])]; + tensor var_1652_end_mask_0 = const()[name = string("op_1652_end_mask_0"), val = tensor([true, true, true])]; + tensor var_1652_cast_fp16 = slice_by_index(begin = var_1652_begin_0, end = var_1652_end_0, end_mask = var_1652_end_mask_0, x = new_x_23_cast_fp16)[name = string("op_1652_cast_fp16")]; + string x_151_pad_type_0 = const()[name = string("x_151_pad_type_0"), val = string("valid")]; + int32 x_151_groups_0 = const()[name = string("x_151_groups_0"), val = int32(1024)]; + tensor x_151_strides_0 = const()[name = string("x_151_strides_0"), val = tensor([1])]; + tensor x_151_pad_0 = const()[name = string("x_151_pad_0"), val = tensor([0, 0])]; + tensor x_151_dilations_0 = const()[name = string("x_151_dilations_0"), val = tensor([1])]; + tensor encoder_layers_5_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125071872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125081152))))[name = string("encoder_layers_5_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_151_cast_fp16 = conv(dilations = x_151_dilations_0, groups = x_151_groups_0, pad = x_151_pad_0, pad_type = x_151_pad_type_0, strides = x_151_strides_0, weight = encoder_layers_5_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_23_cast_fp16)[name = string("x_151_cast_fp16")]; + tensor input_315_perm_0 = const()[name = string("input_315_perm_0"), val = tensor([0, 2, 1])]; + tensor x_153_axes_0 = const()[name = string("x_153_axes_0"), val = tensor([-1])]; + tensor encoder_layers_5_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_5_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125083264)))]; + tensor encoder_layers_5_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_5_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125085376)))]; + tensor input_315_cast_fp16 = transpose(perm = input_315_perm_0, x = x_151_cast_fp16)[name = string("transpose_311")]; + tensor x_153_cast_fp16 = layer_norm(axes = x_153_axes_0, beta = encoder_layers_5_conv_batch_norm_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_5_conv_batch_norm_weight_to_fp16, x = input_315_cast_fp16)[name = string("x_153_cast_fp16")]; + tensor input_317_perm_0 = const()[name = string("input_317_perm_0"), val = tensor([0, 2, 1])]; + tensor input_317_cast_fp16 = transpose(perm = input_317_perm_0, x = x_153_cast_fp16)[name = string("transpose_310")]; + tensor input_319_cast_fp16 = silu(x = input_317_cast_fp16)[name = string("input_319_cast_fp16")]; + string x_155_pad_type_0 = const()[name = string("x_155_pad_type_0"), val = string("valid")]; + tensor x_155_strides_0 = const()[name = string("x_155_strides_0"), val = tensor([1])]; + tensor x_155_pad_0 = const()[name = string("x_155_pad_0"), val = tensor([0, 0])]; + tensor x_155_dilations_0 = const()[name = string("x_155_dilations_0"), val = tensor([1])]; + int32 x_155_groups_0 = const()[name = string("x_155_groups_0"), val = int32(1)]; + tensor encoder_layers_5_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125087488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(126136128))))[name = string("encoder_layers_5_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_155_cast_fp16 = conv(dilations = x_155_dilations_0, groups = x_155_groups_0, pad = x_155_pad_0, pad_type = x_155_pad_type_0, strides = x_155_strides_0, weight = encoder_layers_5_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_319_cast_fp16)[name = string("x_155_cast_fp16")]; + tensor input_321_perm_0 = const()[name = string("input_321_perm_0"), val = tensor([0, 2, 1])]; + tensor input_321_cast_fp16 = transpose(perm = input_321_perm_0, x = x_155_cast_fp16)[name = string("transpose_309")]; + tensor input_323_cast_fp16 = add(x = input_307_cast_fp16, y = input_321_cast_fp16)[name = string("input_323_cast_fp16")]; + tensor input_325_axes_0 = const()[name = string("input_325_axes_0"), val = tensor([-1])]; + tensor encoder_layers_5_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_5_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(126138240)))]; + tensor encoder_layers_5_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_5_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(126140352)))]; + tensor input_325_cast_fp16 = layer_norm(axes = input_325_axes_0, beta = encoder_layers_5_norm_feed_forward2_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_5_norm_feed_forward2_weight_to_fp16, x = input_323_cast_fp16)[name = string("input_325_cast_fp16")]; + tensor encoder_layers_5_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(126142464))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129288256))))[name = string("encoder_layers_5_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_5_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_5_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129288448)))]; + tensor linear_53_cast_fp16 = linear(bias = encoder_layers_5_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_5_feed_forward2_linear1_weight_to_fp16_palettized, x = input_325_cast_fp16)[name = string("linear_53_cast_fp16")]; + tensor input_329_cast_fp16 = silu(x = linear_53_cast_fp16)[name = string("input_329_cast_fp16")]; + tensor encoder_layers_5_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129296704))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132442496))))[name = string("encoder_layers_5_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_5_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_5_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132442688)))]; + tensor linear_54_cast_fp16 = linear(bias = encoder_layers_5_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_5_feed_forward2_linear2_weight_to_fp16_palettized, x = input_329_cast_fp16)[name = string("linear_54_cast_fp16")]; + fp16 var_1695_to_fp16 = const()[name = string("op_1695_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1696_cast_fp16 = mul(x = linear_54_cast_fp16, y = var_1695_to_fp16)[name = string("op_1696_cast_fp16")]; + tensor input_335_cast_fp16 = add(x = input_323_cast_fp16, y = var_1696_cast_fp16)[name = string("input_335_cast_fp16")]; + tensor input_337_axes_0 = const()[name = string("input_337_axes_0"), val = tensor([-1])]; + tensor encoder_layers_5_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_5_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132444800)))]; + tensor encoder_layers_5_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_5_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132446912)))]; + tensor input_337_cast_fp16 = layer_norm(axes = input_337_axes_0, beta = encoder_layers_5_norm_out_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_5_norm_out_weight_to_fp16, x = input_335_cast_fp16)[name = string("input_337_cast_fp16")]; + tensor cache_25_begin_0 = const()[name = string("cache_25_begin_0"), val = tensor([6, 0, 0, 0])]; + tensor cache_25_end_0 = const()[name = string("cache_25_end_0"), val = tensor([7, 1, 42, 1024])]; + tensor cache_25_end_mask_0 = const()[name = string("cache_25_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_25_squeeze_mask_0 = const()[name = string("cache_25_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_25_cast_fp16 = slice_by_index(begin = cache_25_begin_0, end = cache_25_end_0, end_mask = cache_25_end_mask_0, squeeze_mask = cache_25_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_25_cast_fp16")]; + tensor cache_27_begin_0 = const()[name = string("cache_27_begin_0"), val = tensor([6, 0, 0, 0])]; + tensor cache_27_end_0 = const()[name = string("cache_27_end_0"), val = tensor([7, 1, 1024, 8])]; + tensor cache_27_end_mask_0 = const()[name = string("cache_27_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_27_squeeze_mask_0 = const()[name = string("cache_27_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_27_cast_fp16 = slice_by_index(begin = cache_27_begin_0, end = cache_27_end_0, end_mask = cache_27_end_mask_0, squeeze_mask = cache_27_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_27_cast_fp16")]; + tensor input_339_axes_0 = const()[name = string("input_339_axes_0"), val = tensor([-1])]; + tensor encoder_layers_6_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_6_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132449024)))]; + tensor encoder_layers_6_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_6_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132451136)))]; + tensor input_339_cast_fp16 = layer_norm(axes = input_339_axes_0, beta = encoder_layers_6_norm_feed_forward1_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_6_norm_feed_forward1_weight_to_fp16, x = input_337_cast_fp16)[name = string("input_339_cast_fp16")]; + tensor encoder_layers_6_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132453248))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135599040))))[name = string("encoder_layers_6_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_6_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_6_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135599232)))]; + tensor linear_55_cast_fp16 = linear(bias = encoder_layers_6_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_6_feed_forward1_linear1_weight_to_fp16_palettized, x = input_339_cast_fp16)[name = string("linear_55_cast_fp16")]; + tensor input_343_cast_fp16 = silu(x = linear_55_cast_fp16)[name = string("input_343_cast_fp16")]; + tensor encoder_layers_6_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135607488))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138753280))))[name = string("encoder_layers_6_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_6_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_6_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138753472)))]; + tensor linear_56_cast_fp16 = linear(bias = encoder_layers_6_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_6_feed_forward1_linear2_weight_to_fp16_palettized, x = input_343_cast_fp16)[name = string("linear_56_cast_fp16")]; + fp16 var_1732_to_fp16 = const()[name = string("op_1732_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1733_cast_fp16 = mul(x = linear_56_cast_fp16, y = var_1732_to_fp16)[name = string("op_1733_cast_fp16")]; + tensor input_349_cast_fp16 = add(x = input_337_cast_fp16, y = var_1733_cast_fp16)[name = string("input_349_cast_fp16")]; + tensor key_13_axes_0 = const()[name = string("key_13_axes_0"), val = tensor([-1])]; + tensor encoder_layers_6_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_6_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138755584)))]; + tensor encoder_layers_6_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_6_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138757696)))]; + tensor key_13_cast_fp16 = layer_norm(axes = key_13_axes_0, beta = encoder_layers_6_norm_self_att_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_6_norm_self_att_weight_to_fp16, x = input_349_cast_fp16)[name = string("key_13_cast_fp16")]; + bool input_351_interleave_0 = const()[name = string("input_351_interleave_0"), val = bool(false)]; + tensor input_351_cast_fp16 = concat(axis = var_67, interleave = input_351_interleave_0, values = (cache_25_cast_fp16, key_13_cast_fp16))[name = string("input_351_cast_fp16")]; + tensor var_1755_begin_0 = const()[name = string("op_1755_begin_0"), val = tensor([0, 14, 0])]; + tensor var_1755_end_0 = const()[name = string("op_1755_end_0"), val = tensor([1, 42, 1024])]; + tensor var_1755_end_mask_0 = const()[name = string("op_1755_end_mask_0"), val = tensor([true, true, true])]; + tensor var_1755_cast_fp16 = slice_by_index(begin = var_1755_begin_0, end = var_1755_end_0, end_mask = var_1755_end_mask_0, x = cache_25_cast_fp16)[name = string("op_1755_cast_fp16")]; + bool var_1761_interleave_0 = const()[name = string("op_1761_interleave_0"), val = bool(false)]; + tensor var_1761_cast_fp16 = concat(axis = var_67, interleave = var_1761_interleave_0, values = (var_1755_cast_fp16, key_13_cast_fp16))[name = string("op_1761_cast_fp16")]; + tensor encoder_layers_6_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138759808))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(139546304))))[name = string("encoder_layers_6_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_6_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_6_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(139546496)))]; + tensor linear_57_cast_fp16 = linear(bias = encoder_layers_6_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_6_self_attn_linear_q_weight_to_fp16_palettized, x = key_13_cast_fp16)[name = string("linear_57_cast_fp16")]; + tensor var_1766 = const()[name = string("op_1766"), val = tensor([1, -1, 8, 128])]; + tensor q_37_cast_fp16 = reshape(shape = var_1766, x = linear_57_cast_fp16)[name = string("q_37_cast_fp16")]; + tensor encoder_layers_6_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(139548608))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140335104))))[name = string("encoder_layers_6_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_6_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_6_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140335296)))]; + tensor linear_58_cast_fp16 = linear(bias = encoder_layers_6_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_6_self_attn_linear_k_weight_to_fp16_palettized, x = input_351_cast_fp16)[name = string("linear_58_cast_fp16")]; + tensor var_1771 = const()[name = string("op_1771"), val = tensor([1, -1, 8, 128])]; + tensor k_25_cast_fp16 = reshape(shape = var_1771, x = linear_58_cast_fp16)[name = string("k_25_cast_fp16")]; + tensor encoder_layers_6_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140337408))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141123904))))[name = string("encoder_layers_6_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_6_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_6_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141124096)))]; + tensor linear_59_cast_fp16 = linear(bias = encoder_layers_6_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_6_self_attn_linear_v_weight_to_fp16_palettized, x = input_351_cast_fp16)[name = string("linear_59_cast_fp16")]; + tensor var_1776 = const()[name = string("op_1776"), val = tensor([1, -1, 8, 128])]; + tensor v_13_cast_fp16 = reshape(shape = var_1776, x = linear_59_cast_fp16)[name = string("v_13_cast_fp16")]; + tensor value_21_perm_0 = const()[name = string("value_21_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_6_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_6_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141126208)))]; + tensor var_1789_cast_fp16 = add(x = q_37_cast_fp16, y = encoder_layers_6_self_attn_pos_bias_u_to_fp16)[name = string("op_1789_cast_fp16")]; + tensor encoder_layers_6_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_6_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141128320)))]; + tensor var_1791_cast_fp16 = add(x = q_37_cast_fp16, y = encoder_layers_6_self_attn_pos_bias_v_to_fp16)[name = string("op_1791_cast_fp16")]; + tensor q_with_bias_v_13_perm_0 = const()[name = string("q_with_bias_v_13_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_163_transpose_x_0 = const()[name = string("x_163_transpose_x_0"), val = bool(false)]; + bool x_163_transpose_y_0 = const()[name = string("x_163_transpose_y_0"), val = bool(false)]; + tensor op_1793_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141130432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141244160))))[name = string("op_1793_to_fp16_quantized")]; + tensor q_with_bias_v_13_cast_fp16 = transpose(perm = q_with_bias_v_13_perm_0, x = var_1791_cast_fp16)[name = string("transpose_308")]; + tensor x_163_cast_fp16 = matmul(transpose_x = x_163_transpose_x_0, transpose_y = x_163_transpose_y_0, x = q_with_bias_v_13_cast_fp16, y = op_1793_to_fp16_quantized)[name = string("x_163_cast_fp16")]; + tensor x_165_pad_0 = const()[name = string("x_165_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_165_mode_0 = const()[name = string("x_165_mode_0"), val = string("constant")]; + fp16 const_157_to_fp16 = const()[name = string("const_157_to_fp16"), val = fp16(0x0p+0)]; + tensor x_165_cast_fp16 = pad(constant_val = const_157_to_fp16, mode = x_165_mode_0, pad = x_165_pad_0, x = x_163_cast_fp16)[name = string("x_165_cast_fp16")]; + tensor var_1801 = const()[name = string("op_1801"), val = tensor([1, 8, -1, 14])]; + tensor x_167_cast_fp16 = reshape(shape = var_1801, x = x_165_cast_fp16)[name = string("x_167_cast_fp16")]; + tensor var_1805_begin_0 = const()[name = string("op_1805_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_1805_end_0 = const()[name = string("op_1805_end_0"), val = tensor([1, 8, 112, 14])]; + tensor var_1805_end_mask_0 = const()[name = string("op_1805_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1805_cast_fp16 = slice_by_index(begin = var_1805_begin_0, end = var_1805_end_0, end_mask = var_1805_end_mask_0, x = x_167_cast_fp16)[name = string("op_1805_cast_fp16")]; + tensor var_1806 = const()[name = string("op_1806"), val = tensor([1, 8, 14, 111])]; + tensor matrix_bd_25_cast_fp16 = reshape(shape = var_1806, x = var_1805_cast_fp16)[name = string("matrix_bd_25_cast_fp16")]; + bool matrix_ac_13_transpose_x_0 = const()[name = string("matrix_ac_13_transpose_x_0"), val = bool(false)]; + bool matrix_ac_13_transpose_y_0 = const()[name = string("matrix_ac_13_transpose_y_0"), val = bool(false)]; + tensor transpose_108_perm_0 = const()[name = string("transpose_108_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_109_perm_0 = const()[name = string("transpose_109_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_109 = transpose(perm = transpose_109_perm_0, x = k_25_cast_fp16)[name = string("transpose_306")]; + tensor transpose_108 = transpose(perm = transpose_108_perm_0, x = var_1789_cast_fp16)[name = string("transpose_307")]; + tensor matrix_ac_13_cast_fp16 = matmul(transpose_x = matrix_ac_13_transpose_x_0, transpose_y = matrix_ac_13_transpose_y_0, x = transpose_108, y = transpose_109)[name = string("matrix_ac_13_cast_fp16")]; + tensor matrix_bd_27_begin_0 = const()[name = string("matrix_bd_27_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_27_end_0 = const()[name = string("matrix_bd_27_end_0"), val = tensor([1, 8, 14, 56])]; + tensor matrix_bd_27_end_mask_0 = const()[name = string("matrix_bd_27_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_27_cast_fp16 = slice_by_index(begin = matrix_bd_27_begin_0, end = matrix_bd_27_end_0, end_mask = matrix_bd_27_end_mask_0, x = matrix_bd_25_cast_fp16)[name = string("matrix_bd_27_cast_fp16")]; + tensor var_1815_cast_fp16 = add(x = matrix_ac_13_cast_fp16, y = matrix_bd_27_cast_fp16)[name = string("op_1815_cast_fp16")]; + fp16 _inversed_scores_25_y_0_to_fp16 = const()[name = string("_inversed_scores_25_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_25_cast_fp16 = mul(x = var_1815_cast_fp16, y = _inversed_scores_25_y_0_to_fp16)[name = string("_inversed_scores_25_cast_fp16")]; + tensor scores_27_cast_fp16 = select(a = var_44_to_fp16, b = _inversed_scores_25_cast_fp16, cond = mask_11)[name = string("scores_27_cast_fp16")]; + tensor var_1821_cast_fp16 = softmax(axis = var_58, x = scores_27_cast_fp16)[name = string("op_1821_cast_fp16")]; + tensor input_353_cast_fp16 = select(a = var_43_to_fp16, b = var_1821_cast_fp16, cond = mask_11)[name = string("input_353_cast_fp16")]; + bool x_169_transpose_x_0 = const()[name = string("x_169_transpose_x_0"), val = bool(false)]; + bool x_169_transpose_y_0 = const()[name = string("x_169_transpose_y_0"), val = bool(false)]; + tensor value_21_cast_fp16 = transpose(perm = value_21_perm_0, x = v_13_cast_fp16)[name = string("transpose_305")]; + tensor x_169_cast_fp16 = matmul(transpose_x = x_169_transpose_x_0, transpose_y = x_169_transpose_y_0, x = input_353_cast_fp16, y = value_21_cast_fp16)[name = string("x_169_cast_fp16")]; + tensor var_1825_perm_0 = const()[name = string("op_1825_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1826 = const()[name = string("op_1826"), val = tensor([1, -1, 1024])]; + tensor var_1825_cast_fp16 = transpose(perm = var_1825_perm_0, x = x_169_cast_fp16)[name = string("transpose_304")]; + tensor input_355_cast_fp16 = reshape(shape = var_1826, x = var_1825_cast_fp16)[name = string("input_355_cast_fp16")]; + tensor encoder_layers_6_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141244480))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142030976))))[name = string("encoder_layers_6_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_6_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_6_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142031168)))]; + tensor linear_61_cast_fp16 = linear(bias = encoder_layers_6_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_6_self_attn_linear_out_weight_to_fp16_palettized, x = input_355_cast_fp16)[name = string("linear_61_cast_fp16")]; + tensor input_359_cast_fp16 = add(x = input_349_cast_fp16, y = linear_61_cast_fp16)[name = string("input_359_cast_fp16")]; + tensor x_173_axes_0 = const()[name = string("x_173_axes_0"), val = tensor([-1])]; + tensor encoder_layers_6_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_6_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142033280)))]; + tensor encoder_layers_6_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_6_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142035392)))]; + tensor x_173_cast_fp16 = layer_norm(axes = x_173_axes_0, beta = encoder_layers_6_norm_conv_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_6_norm_conv_weight_to_fp16, x = input_359_cast_fp16)[name = string("x_173_cast_fp16")]; + tensor input_361_perm_0 = const()[name = string("input_361_perm_0"), val = tensor([0, 2, 1])]; + string input_363_pad_type_0 = const()[name = string("input_363_pad_type_0"), val = string("valid")]; + tensor input_363_strides_0 = const()[name = string("input_363_strides_0"), val = tensor([1])]; + tensor input_363_pad_0 = const()[name = string("input_363_pad_0"), val = tensor([0, 0])]; + tensor input_363_dilations_0 = const()[name = string("input_363_dilations_0"), val = tensor([1])]; + int32 input_363_groups_0 = const()[name = string("input_363_groups_0"), val = int32(1)]; + tensor encoder_layers_6_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142037504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144134720))))[name = string("encoder_layers_6_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_361_cast_fp16 = transpose(perm = input_361_perm_0, x = x_173_cast_fp16)[name = string("transpose_303")]; + tensor input_363_cast_fp16 = conv(dilations = input_363_dilations_0, groups = input_363_groups_0, pad = input_363_pad_0, pad_type = input_363_pad_type_0, strides = input_363_strides_0, weight = encoder_layers_6_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_361_cast_fp16)[name = string("input_363_cast_fp16")]; + int32 x_175_split_num_splits_0 = const()[name = string("x_175_split_num_splits_0"), val = int32(2)]; + int32 x_175_split_axis_0 = const()[name = string("x_175_split_axis_0"), val = int32(1)]; + tensor x_175_split_cast_fp16_0, tensor x_175_split_cast_fp16_1 = split(axis = x_175_split_axis_0, num_splits = x_175_split_num_splits_0, x = input_363_cast_fp16)[name = string("x_175_split_cast_fp16")]; + tensor x_175_split_1_sigmoid_cast_fp16 = sigmoid(x = x_175_split_cast_fp16_1)[name = string("x_175_split_1_sigmoid_cast_fp16")]; + tensor x_175_cast_fp16 = mul(x = x_175_split_cast_fp16_0, y = x_175_split_1_sigmoid_cast_fp16)[name = string("x_175_cast_fp16")]; + tensor input_365_cast_fp16 = select(a = var_43_to_fp16, b = x_175_cast_fp16, cond = var_574)[name = string("input_365_cast_fp16")]; + bool new_x_27_interleave_0 = const()[name = string("new_x_27_interleave_0"), val = bool(false)]; + tensor new_x_27_cast_fp16 = concat(axis = var_58, interleave = new_x_27_interleave_0, values = (cache_27_cast_fp16, input_365_cast_fp16))[name = string("new_x_27_cast_fp16")]; + tensor var_1865_begin_0 = const()[name = string("op_1865_begin_0"), val = tensor([0, 0, 14])]; + tensor var_1865_end_0 = const()[name = string("op_1865_end_0"), val = tensor([1, 1024, 22])]; + tensor var_1865_end_mask_0 = const()[name = string("op_1865_end_mask_0"), val = tensor([true, true, true])]; + tensor var_1865_cast_fp16 = slice_by_index(begin = var_1865_begin_0, end = var_1865_end_0, end_mask = var_1865_end_mask_0, x = new_x_27_cast_fp16)[name = string("op_1865_cast_fp16")]; + string x_177_pad_type_0 = const()[name = string("x_177_pad_type_0"), val = string("valid")]; + int32 x_177_groups_0 = const()[name = string("x_177_groups_0"), val = int32(1024)]; + tensor x_177_strides_0 = const()[name = string("x_177_strides_0"), val = tensor([1])]; + tensor x_177_pad_0 = const()[name = string("x_177_pad_0"), val = tensor([0, 0])]; + tensor x_177_dilations_0 = const()[name = string("x_177_dilations_0"), val = tensor([1])]; + tensor encoder_layers_6_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144138880))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144148160))))[name = string("encoder_layers_6_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_177_cast_fp16 = conv(dilations = x_177_dilations_0, groups = x_177_groups_0, pad = x_177_pad_0, pad_type = x_177_pad_type_0, strides = x_177_strides_0, weight = encoder_layers_6_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_27_cast_fp16)[name = string("x_177_cast_fp16")]; + tensor input_367_perm_0 = const()[name = string("input_367_perm_0"), val = tensor([0, 2, 1])]; + tensor x_179_axes_0 = const()[name = string("x_179_axes_0"), val = tensor([-1])]; + tensor encoder_layers_6_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_6_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144150272)))]; + tensor encoder_layers_6_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_6_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144152384)))]; + tensor input_367_cast_fp16 = transpose(perm = input_367_perm_0, x = x_177_cast_fp16)[name = string("transpose_302")]; + tensor x_179_cast_fp16 = layer_norm(axes = x_179_axes_0, beta = encoder_layers_6_conv_batch_norm_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_6_conv_batch_norm_weight_to_fp16, x = input_367_cast_fp16)[name = string("x_179_cast_fp16")]; + tensor input_369_perm_0 = const()[name = string("input_369_perm_0"), val = tensor([0, 2, 1])]; + tensor input_369_cast_fp16 = transpose(perm = input_369_perm_0, x = x_179_cast_fp16)[name = string("transpose_301")]; + tensor input_371_cast_fp16 = silu(x = input_369_cast_fp16)[name = string("input_371_cast_fp16")]; + string x_181_pad_type_0 = const()[name = string("x_181_pad_type_0"), val = string("valid")]; + tensor x_181_strides_0 = const()[name = string("x_181_strides_0"), val = tensor([1])]; + tensor x_181_pad_0 = const()[name = string("x_181_pad_0"), val = tensor([0, 0])]; + tensor x_181_dilations_0 = const()[name = string("x_181_dilations_0"), val = tensor([1])]; + int32 x_181_groups_0 = const()[name = string("x_181_groups_0"), val = int32(1)]; + tensor encoder_layers_6_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144154496))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145203136))))[name = string("encoder_layers_6_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_181_cast_fp16 = conv(dilations = x_181_dilations_0, groups = x_181_groups_0, pad = x_181_pad_0, pad_type = x_181_pad_type_0, strides = x_181_strides_0, weight = encoder_layers_6_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_371_cast_fp16)[name = string("x_181_cast_fp16")]; + tensor input_373_perm_0 = const()[name = string("input_373_perm_0"), val = tensor([0, 2, 1])]; + tensor input_373_cast_fp16 = transpose(perm = input_373_perm_0, x = x_181_cast_fp16)[name = string("transpose_300")]; + tensor input_375_cast_fp16 = add(x = input_359_cast_fp16, y = input_373_cast_fp16)[name = string("input_375_cast_fp16")]; + tensor input_377_axes_0 = const()[name = string("input_377_axes_0"), val = tensor([-1])]; + tensor encoder_layers_6_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_6_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145205248)))]; + tensor encoder_layers_6_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_6_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145207360)))]; + tensor input_377_cast_fp16 = layer_norm(axes = input_377_axes_0, beta = encoder_layers_6_norm_feed_forward2_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_6_norm_feed_forward2_weight_to_fp16, x = input_375_cast_fp16)[name = string("input_377_cast_fp16")]; + tensor encoder_layers_6_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145209472))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(148355264))))[name = string("encoder_layers_6_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_6_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_6_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(148355456)))]; + tensor linear_62_cast_fp16 = linear(bias = encoder_layers_6_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_6_feed_forward2_linear1_weight_to_fp16_palettized, x = input_377_cast_fp16)[name = string("linear_62_cast_fp16")]; + tensor input_381_cast_fp16 = silu(x = linear_62_cast_fp16)[name = string("input_381_cast_fp16")]; + tensor encoder_layers_6_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(148363712))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(151509504))))[name = string("encoder_layers_6_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_6_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_6_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(151509696)))]; + tensor linear_63_cast_fp16 = linear(bias = encoder_layers_6_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_6_feed_forward2_linear2_weight_to_fp16_palettized, x = input_381_cast_fp16)[name = string("linear_63_cast_fp16")]; + fp16 var_1908_to_fp16 = const()[name = string("op_1908_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1909_cast_fp16 = mul(x = linear_63_cast_fp16, y = var_1908_to_fp16)[name = string("op_1909_cast_fp16")]; + tensor input_387_cast_fp16 = add(x = input_375_cast_fp16, y = var_1909_cast_fp16)[name = string("input_387_cast_fp16")]; + tensor input_389_axes_0 = const()[name = string("input_389_axes_0"), val = tensor([-1])]; + tensor encoder_layers_6_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_6_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(151511808)))]; + tensor encoder_layers_6_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_6_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(151513920)))]; + tensor input_389_cast_fp16 = layer_norm(axes = input_389_axes_0, beta = encoder_layers_6_norm_out_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_6_norm_out_weight_to_fp16, x = input_387_cast_fp16)[name = string("input_389_cast_fp16")]; + tensor cache_29_begin_0 = const()[name = string("cache_29_begin_0"), val = tensor([7, 0, 0, 0])]; + tensor cache_29_end_0 = const()[name = string("cache_29_end_0"), val = tensor([8, 1, 42, 1024])]; + tensor cache_29_end_mask_0 = const()[name = string("cache_29_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_29_squeeze_mask_0 = const()[name = string("cache_29_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_29_cast_fp16 = slice_by_index(begin = cache_29_begin_0, end = cache_29_end_0, end_mask = cache_29_end_mask_0, squeeze_mask = cache_29_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_29_cast_fp16")]; + tensor cache_31_begin_0 = const()[name = string("cache_31_begin_0"), val = tensor([7, 0, 0, 0])]; + tensor cache_31_end_0 = const()[name = string("cache_31_end_0"), val = tensor([8, 1, 1024, 8])]; + tensor cache_31_end_mask_0 = const()[name = string("cache_31_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_31_squeeze_mask_0 = const()[name = string("cache_31_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_31_cast_fp16 = slice_by_index(begin = cache_31_begin_0, end = cache_31_end_0, end_mask = cache_31_end_mask_0, squeeze_mask = cache_31_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_31_cast_fp16")]; + tensor input_391_axes_0 = const()[name = string("input_391_axes_0"), val = tensor([-1])]; + tensor encoder_layers_7_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_7_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(151516032)))]; + tensor encoder_layers_7_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_7_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(151518144)))]; + tensor input_391_cast_fp16 = layer_norm(axes = input_391_axes_0, beta = encoder_layers_7_norm_feed_forward1_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_7_norm_feed_forward1_weight_to_fp16, x = input_389_cast_fp16)[name = string("input_391_cast_fp16")]; + tensor encoder_layers_7_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(151520256))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154666048))))[name = string("encoder_layers_7_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_7_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_7_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154666240)))]; + tensor linear_64_cast_fp16 = linear(bias = encoder_layers_7_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_7_feed_forward1_linear1_weight_to_fp16_palettized, x = input_391_cast_fp16)[name = string("linear_64_cast_fp16")]; + tensor input_395_cast_fp16 = silu(x = linear_64_cast_fp16)[name = string("input_395_cast_fp16")]; + tensor encoder_layers_7_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154674496))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157820288))))[name = string("encoder_layers_7_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_7_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_7_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157820480)))]; + tensor linear_65_cast_fp16 = linear(bias = encoder_layers_7_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_7_feed_forward1_linear2_weight_to_fp16_palettized, x = input_395_cast_fp16)[name = string("linear_65_cast_fp16")]; + fp16 var_1945_to_fp16 = const()[name = string("op_1945_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1946_cast_fp16 = mul(x = linear_65_cast_fp16, y = var_1945_to_fp16)[name = string("op_1946_cast_fp16")]; + tensor input_401_cast_fp16 = add(x = input_389_cast_fp16, y = var_1946_cast_fp16)[name = string("input_401_cast_fp16")]; + tensor key_15_axes_0 = const()[name = string("key_15_axes_0"), val = tensor([-1])]; + tensor encoder_layers_7_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_7_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157822592)))]; + tensor encoder_layers_7_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_7_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157824704)))]; + tensor key_15_cast_fp16 = layer_norm(axes = key_15_axes_0, beta = encoder_layers_7_norm_self_att_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_7_norm_self_att_weight_to_fp16, x = input_401_cast_fp16)[name = string("key_15_cast_fp16")]; + bool input_403_interleave_0 = const()[name = string("input_403_interleave_0"), val = bool(false)]; + tensor input_403_cast_fp16 = concat(axis = var_67, interleave = input_403_interleave_0, values = (cache_29_cast_fp16, key_15_cast_fp16))[name = string("input_403_cast_fp16")]; + tensor var_1968_begin_0 = const()[name = string("op_1968_begin_0"), val = tensor([0, 14, 0])]; + tensor var_1968_end_0 = const()[name = string("op_1968_end_0"), val = tensor([1, 42, 1024])]; + tensor var_1968_end_mask_0 = const()[name = string("op_1968_end_mask_0"), val = tensor([true, true, true])]; + tensor var_1968_cast_fp16 = slice_by_index(begin = var_1968_begin_0, end = var_1968_end_0, end_mask = var_1968_end_mask_0, x = cache_29_cast_fp16)[name = string("op_1968_cast_fp16")]; + bool var_1974_interleave_0 = const()[name = string("op_1974_interleave_0"), val = bool(false)]; + tensor var_1974_cast_fp16 = concat(axis = var_67, interleave = var_1974_interleave_0, values = (var_1968_cast_fp16, key_15_cast_fp16))[name = string("op_1974_cast_fp16")]; + tensor encoder_layers_7_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157826816))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158613312))))[name = string("encoder_layers_7_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_7_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_7_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158613504)))]; + tensor linear_66_cast_fp16 = linear(bias = encoder_layers_7_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_7_self_attn_linear_q_weight_to_fp16_palettized, x = key_15_cast_fp16)[name = string("linear_66_cast_fp16")]; + tensor var_1979 = const()[name = string("op_1979"), val = tensor([1, -1, 8, 128])]; + tensor q_43_cast_fp16 = reshape(shape = var_1979, x = linear_66_cast_fp16)[name = string("q_43_cast_fp16")]; + tensor encoder_layers_7_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158615616))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(159402112))))[name = string("encoder_layers_7_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_7_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_7_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(159402304)))]; + tensor linear_67_cast_fp16 = linear(bias = encoder_layers_7_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_7_self_attn_linear_k_weight_to_fp16_palettized, x = input_403_cast_fp16)[name = string("linear_67_cast_fp16")]; + tensor var_1984 = const()[name = string("op_1984"), val = tensor([1, -1, 8, 128])]; + tensor k_29_cast_fp16 = reshape(shape = var_1984, x = linear_67_cast_fp16)[name = string("k_29_cast_fp16")]; + tensor encoder_layers_7_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(159404416))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160190912))))[name = string("encoder_layers_7_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_7_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_7_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160191104)))]; + tensor linear_68_cast_fp16 = linear(bias = encoder_layers_7_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_7_self_attn_linear_v_weight_to_fp16_palettized, x = input_403_cast_fp16)[name = string("linear_68_cast_fp16")]; + tensor var_1989 = const()[name = string("op_1989"), val = tensor([1, -1, 8, 128])]; + tensor v_15_cast_fp16 = reshape(shape = var_1989, x = linear_68_cast_fp16)[name = string("v_15_cast_fp16")]; + tensor value_23_perm_0 = const()[name = string("value_23_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_7_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_7_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160193216)))]; + tensor var_2002_cast_fp16 = add(x = q_43_cast_fp16, y = encoder_layers_7_self_attn_pos_bias_u_to_fp16)[name = string("op_2002_cast_fp16")]; + tensor encoder_layers_7_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_7_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160195328)))]; + tensor var_2004_cast_fp16 = add(x = q_43_cast_fp16, y = encoder_layers_7_self_attn_pos_bias_v_to_fp16)[name = string("op_2004_cast_fp16")]; + tensor q_with_bias_v_15_perm_0 = const()[name = string("q_with_bias_v_15_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_189_transpose_x_0 = const()[name = string("x_189_transpose_x_0"), val = bool(false)]; + bool x_189_transpose_y_0 = const()[name = string("x_189_transpose_y_0"), val = bool(false)]; + tensor op_2006_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160197440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160311168))))[name = string("op_2006_to_fp16_quantized")]; + tensor q_with_bias_v_15_cast_fp16 = transpose(perm = q_with_bias_v_15_perm_0, x = var_2004_cast_fp16)[name = string("transpose_299")]; + tensor x_189_cast_fp16 = matmul(transpose_x = x_189_transpose_x_0, transpose_y = x_189_transpose_y_0, x = q_with_bias_v_15_cast_fp16, y = op_2006_to_fp16_quantized)[name = string("x_189_cast_fp16")]; + tensor x_191_pad_0 = const()[name = string("x_191_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_191_mode_0 = const()[name = string("x_191_mode_0"), val = string("constant")]; + fp16 const_170_to_fp16 = const()[name = string("const_170_to_fp16"), val = fp16(0x0p+0)]; + tensor x_191_cast_fp16 = pad(constant_val = const_170_to_fp16, mode = x_191_mode_0, pad = x_191_pad_0, x = x_189_cast_fp16)[name = string("x_191_cast_fp16")]; + tensor var_2014 = const()[name = string("op_2014"), val = tensor([1, 8, -1, 14])]; + tensor x_193_cast_fp16 = reshape(shape = var_2014, x = x_191_cast_fp16)[name = string("x_193_cast_fp16")]; + tensor var_2018_begin_0 = const()[name = string("op_2018_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_2018_end_0 = const()[name = string("op_2018_end_0"), val = tensor([1, 8, 112, 14])]; + tensor var_2018_end_mask_0 = const()[name = string("op_2018_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_2018_cast_fp16 = slice_by_index(begin = var_2018_begin_0, end = var_2018_end_0, end_mask = var_2018_end_mask_0, x = x_193_cast_fp16)[name = string("op_2018_cast_fp16")]; + tensor var_2019 = const()[name = string("op_2019"), val = tensor([1, 8, 14, 111])]; + tensor matrix_bd_29_cast_fp16 = reshape(shape = var_2019, x = var_2018_cast_fp16)[name = string("matrix_bd_29_cast_fp16")]; + bool matrix_ac_15_transpose_x_0 = const()[name = string("matrix_ac_15_transpose_x_0"), val = bool(false)]; + bool matrix_ac_15_transpose_y_0 = const()[name = string("matrix_ac_15_transpose_y_0"), val = bool(false)]; + tensor transpose_110_perm_0 = const()[name = string("transpose_110_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_111_perm_0 = const()[name = string("transpose_111_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_111 = transpose(perm = transpose_111_perm_0, x = k_29_cast_fp16)[name = string("transpose_297")]; + tensor transpose_110 = transpose(perm = transpose_110_perm_0, x = var_2002_cast_fp16)[name = string("transpose_298")]; + tensor matrix_ac_15_cast_fp16 = matmul(transpose_x = matrix_ac_15_transpose_x_0, transpose_y = matrix_ac_15_transpose_y_0, x = transpose_110, y = transpose_111)[name = string("matrix_ac_15_cast_fp16")]; + tensor matrix_bd_31_begin_0 = const()[name = string("matrix_bd_31_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_31_end_0 = const()[name = string("matrix_bd_31_end_0"), val = tensor([1, 8, 14, 56])]; + tensor matrix_bd_31_end_mask_0 = const()[name = string("matrix_bd_31_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_31_cast_fp16 = slice_by_index(begin = matrix_bd_31_begin_0, end = matrix_bd_31_end_0, end_mask = matrix_bd_31_end_mask_0, x = matrix_bd_29_cast_fp16)[name = string("matrix_bd_31_cast_fp16")]; + tensor var_2028_cast_fp16 = add(x = matrix_ac_15_cast_fp16, y = matrix_bd_31_cast_fp16)[name = string("op_2028_cast_fp16")]; + fp16 _inversed_scores_29_y_0_to_fp16 = const()[name = string("_inversed_scores_29_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_29_cast_fp16 = mul(x = var_2028_cast_fp16, y = _inversed_scores_29_y_0_to_fp16)[name = string("_inversed_scores_29_cast_fp16")]; + tensor scores_31_cast_fp16 = select(a = var_44_to_fp16, b = _inversed_scores_29_cast_fp16, cond = mask_11)[name = string("scores_31_cast_fp16")]; + tensor var_2034_cast_fp16 = softmax(axis = var_58, x = scores_31_cast_fp16)[name = string("op_2034_cast_fp16")]; + tensor input_405_cast_fp16 = select(a = var_43_to_fp16, b = var_2034_cast_fp16, cond = mask_11)[name = string("input_405_cast_fp16")]; + bool x_195_transpose_x_0 = const()[name = string("x_195_transpose_x_0"), val = bool(false)]; + bool x_195_transpose_y_0 = const()[name = string("x_195_transpose_y_0"), val = bool(false)]; + tensor value_23_cast_fp16 = transpose(perm = value_23_perm_0, x = v_15_cast_fp16)[name = string("transpose_296")]; + tensor x_195_cast_fp16 = matmul(transpose_x = x_195_transpose_x_0, transpose_y = x_195_transpose_y_0, x = input_405_cast_fp16, y = value_23_cast_fp16)[name = string("x_195_cast_fp16")]; + tensor var_2038_perm_0 = const()[name = string("op_2038_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2039 = const()[name = string("op_2039"), val = tensor([1, -1, 1024])]; + tensor var_2038_cast_fp16 = transpose(perm = var_2038_perm_0, x = x_195_cast_fp16)[name = string("transpose_295")]; + tensor input_407_cast_fp16 = reshape(shape = var_2039, x = var_2038_cast_fp16)[name = string("input_407_cast_fp16")]; + tensor encoder_layers_7_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160311488))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(161097984))))[name = string("encoder_layers_7_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_7_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_7_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(161098176)))]; + tensor linear_70_cast_fp16 = linear(bias = encoder_layers_7_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_7_self_attn_linear_out_weight_to_fp16_palettized, x = input_407_cast_fp16)[name = string("linear_70_cast_fp16")]; + tensor input_411_cast_fp16 = add(x = input_401_cast_fp16, y = linear_70_cast_fp16)[name = string("input_411_cast_fp16")]; + tensor x_199_axes_0 = const()[name = string("x_199_axes_0"), val = tensor([-1])]; + tensor encoder_layers_7_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_7_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(161100288)))]; + tensor encoder_layers_7_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_7_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(161102400)))]; + tensor x_199_cast_fp16 = layer_norm(axes = x_199_axes_0, beta = encoder_layers_7_norm_conv_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_7_norm_conv_weight_to_fp16, x = input_411_cast_fp16)[name = string("x_199_cast_fp16")]; + tensor input_413_perm_0 = const()[name = string("input_413_perm_0"), val = tensor([0, 2, 1])]; + string input_415_pad_type_0 = const()[name = string("input_415_pad_type_0"), val = string("valid")]; + tensor input_415_strides_0 = const()[name = string("input_415_strides_0"), val = tensor([1])]; + tensor input_415_pad_0 = const()[name = string("input_415_pad_0"), val = tensor([0, 0])]; + tensor input_415_dilations_0 = const()[name = string("input_415_dilations_0"), val = tensor([1])]; + int32 input_415_groups_0 = const()[name = string("input_415_groups_0"), val = int32(1)]; + tensor encoder_layers_7_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(161104512))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163201728))))[name = string("encoder_layers_7_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_413_cast_fp16 = transpose(perm = input_413_perm_0, x = x_199_cast_fp16)[name = string("transpose_294")]; + tensor input_415_cast_fp16 = conv(dilations = input_415_dilations_0, groups = input_415_groups_0, pad = input_415_pad_0, pad_type = input_415_pad_type_0, strides = input_415_strides_0, weight = encoder_layers_7_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_413_cast_fp16)[name = string("input_415_cast_fp16")]; + int32 x_201_split_num_splits_0 = const()[name = string("x_201_split_num_splits_0"), val = int32(2)]; + int32 x_201_split_axis_0 = const()[name = string("x_201_split_axis_0"), val = int32(1)]; + tensor x_201_split_cast_fp16_0, tensor x_201_split_cast_fp16_1 = split(axis = x_201_split_axis_0, num_splits = x_201_split_num_splits_0, x = input_415_cast_fp16)[name = string("x_201_split_cast_fp16")]; + tensor x_201_split_1_sigmoid_cast_fp16 = sigmoid(x = x_201_split_cast_fp16_1)[name = string("x_201_split_1_sigmoid_cast_fp16")]; + tensor x_201_cast_fp16 = mul(x = x_201_split_cast_fp16_0, y = x_201_split_1_sigmoid_cast_fp16)[name = string("x_201_cast_fp16")]; + tensor input_417_cast_fp16 = select(a = var_43_to_fp16, b = x_201_cast_fp16, cond = var_574)[name = string("input_417_cast_fp16")]; + bool new_x_31_interleave_0 = const()[name = string("new_x_31_interleave_0"), val = bool(false)]; + tensor new_x_31_cast_fp16 = concat(axis = var_58, interleave = new_x_31_interleave_0, values = (cache_31_cast_fp16, input_417_cast_fp16))[name = string("new_x_31_cast_fp16")]; + tensor var_2078_begin_0 = const()[name = string("op_2078_begin_0"), val = tensor([0, 0, 14])]; + tensor var_2078_end_0 = const()[name = string("op_2078_end_0"), val = tensor([1, 1024, 22])]; + tensor var_2078_end_mask_0 = const()[name = string("op_2078_end_mask_0"), val = tensor([true, true, true])]; + tensor var_2078_cast_fp16 = slice_by_index(begin = var_2078_begin_0, end = var_2078_end_0, end_mask = var_2078_end_mask_0, x = new_x_31_cast_fp16)[name = string("op_2078_cast_fp16")]; + string x_203_pad_type_0 = const()[name = string("x_203_pad_type_0"), val = string("valid")]; + int32 x_203_groups_0 = const()[name = string("x_203_groups_0"), val = int32(1024)]; + tensor x_203_strides_0 = const()[name = string("x_203_strides_0"), val = tensor([1])]; + tensor x_203_pad_0 = const()[name = string("x_203_pad_0"), val = tensor([0, 0])]; + tensor x_203_dilations_0 = const()[name = string("x_203_dilations_0"), val = tensor([1])]; + tensor encoder_layers_7_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163205888))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163215168))))[name = string("encoder_layers_7_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_203_cast_fp16 = conv(dilations = x_203_dilations_0, groups = x_203_groups_0, pad = x_203_pad_0, pad_type = x_203_pad_type_0, strides = x_203_strides_0, weight = encoder_layers_7_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_31_cast_fp16)[name = string("x_203_cast_fp16")]; + tensor input_419_perm_0 = const()[name = string("input_419_perm_0"), val = tensor([0, 2, 1])]; + tensor x_205_axes_0 = const()[name = string("x_205_axes_0"), val = tensor([-1])]; + tensor encoder_layers_7_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_7_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163217280)))]; + tensor encoder_layers_7_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_7_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163219392)))]; + tensor input_419_cast_fp16 = transpose(perm = input_419_perm_0, x = x_203_cast_fp16)[name = string("transpose_293")]; + tensor x_205_cast_fp16 = layer_norm(axes = x_205_axes_0, beta = encoder_layers_7_conv_batch_norm_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_7_conv_batch_norm_weight_to_fp16, x = input_419_cast_fp16)[name = string("x_205_cast_fp16")]; + tensor input_421_perm_0 = const()[name = string("input_421_perm_0"), val = tensor([0, 2, 1])]; + tensor input_421_cast_fp16 = transpose(perm = input_421_perm_0, x = x_205_cast_fp16)[name = string("transpose_292")]; + tensor input_423_cast_fp16 = silu(x = input_421_cast_fp16)[name = string("input_423_cast_fp16")]; + string x_207_pad_type_0 = const()[name = string("x_207_pad_type_0"), val = string("valid")]; + tensor x_207_strides_0 = const()[name = string("x_207_strides_0"), val = tensor([1])]; + tensor x_207_pad_0 = const()[name = string("x_207_pad_0"), val = tensor([0, 0])]; + tensor x_207_dilations_0 = const()[name = string("x_207_dilations_0"), val = tensor([1])]; + int32 x_207_groups_0 = const()[name = string("x_207_groups_0"), val = int32(1)]; + tensor encoder_layers_7_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163221504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164270144))))[name = string("encoder_layers_7_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_207_cast_fp16 = conv(dilations = x_207_dilations_0, groups = x_207_groups_0, pad = x_207_pad_0, pad_type = x_207_pad_type_0, strides = x_207_strides_0, weight = encoder_layers_7_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_423_cast_fp16)[name = string("x_207_cast_fp16")]; + tensor input_425_perm_0 = const()[name = string("input_425_perm_0"), val = tensor([0, 2, 1])]; + tensor input_425_cast_fp16 = transpose(perm = input_425_perm_0, x = x_207_cast_fp16)[name = string("transpose_291")]; + tensor input_427_cast_fp16 = add(x = input_411_cast_fp16, y = input_425_cast_fp16)[name = string("input_427_cast_fp16")]; + tensor input_429_axes_0 = const()[name = string("input_429_axes_0"), val = tensor([-1])]; + tensor encoder_layers_7_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_7_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164272256)))]; + tensor encoder_layers_7_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_7_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164274368)))]; + tensor input_429_cast_fp16 = layer_norm(axes = input_429_axes_0, beta = encoder_layers_7_norm_feed_forward2_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_7_norm_feed_forward2_weight_to_fp16, x = input_427_cast_fp16)[name = string("input_429_cast_fp16")]; + tensor encoder_layers_7_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164276480))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167422272))))[name = string("encoder_layers_7_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_7_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_7_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167422464)))]; + tensor linear_71_cast_fp16 = linear(bias = encoder_layers_7_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_7_feed_forward2_linear1_weight_to_fp16_palettized, x = input_429_cast_fp16)[name = string("linear_71_cast_fp16")]; + tensor input_433_cast_fp16 = silu(x = linear_71_cast_fp16)[name = string("input_433_cast_fp16")]; + tensor encoder_layers_7_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167430720))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170576512))))[name = string("encoder_layers_7_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_7_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_7_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170576704)))]; + tensor linear_72_cast_fp16 = linear(bias = encoder_layers_7_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_7_feed_forward2_linear2_weight_to_fp16_palettized, x = input_433_cast_fp16)[name = string("linear_72_cast_fp16")]; + fp16 var_2121_to_fp16 = const()[name = string("op_2121_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2122_cast_fp16 = mul(x = linear_72_cast_fp16, y = var_2121_to_fp16)[name = string("op_2122_cast_fp16")]; + tensor input_439_cast_fp16 = add(x = input_427_cast_fp16, y = var_2122_cast_fp16)[name = string("input_439_cast_fp16")]; + tensor input_441_axes_0 = const()[name = string("input_441_axes_0"), val = tensor([-1])]; + tensor encoder_layers_7_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_7_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170578816)))]; + tensor encoder_layers_7_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_7_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170580928)))]; + tensor input_441_cast_fp16 = layer_norm(axes = input_441_axes_0, beta = encoder_layers_7_norm_out_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_7_norm_out_weight_to_fp16, x = input_439_cast_fp16)[name = string("input_441_cast_fp16")]; + tensor cache_33_begin_0 = const()[name = string("cache_33_begin_0"), val = tensor([8, 0, 0, 0])]; + tensor cache_33_end_0 = const()[name = string("cache_33_end_0"), val = tensor([9, 1, 42, 1024])]; + tensor cache_33_end_mask_0 = const()[name = string("cache_33_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_33_squeeze_mask_0 = const()[name = string("cache_33_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_33_cast_fp16 = slice_by_index(begin = cache_33_begin_0, end = cache_33_end_0, end_mask = cache_33_end_mask_0, squeeze_mask = cache_33_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_33_cast_fp16")]; + tensor cache_35_begin_0 = const()[name = string("cache_35_begin_0"), val = tensor([8, 0, 0, 0])]; + tensor cache_35_end_0 = const()[name = string("cache_35_end_0"), val = tensor([9, 1, 1024, 8])]; + tensor cache_35_end_mask_0 = const()[name = string("cache_35_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_35_squeeze_mask_0 = const()[name = string("cache_35_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_35_cast_fp16 = slice_by_index(begin = cache_35_begin_0, end = cache_35_end_0, end_mask = cache_35_end_mask_0, squeeze_mask = cache_35_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_35_cast_fp16")]; + tensor input_443_axes_0 = const()[name = string("input_443_axes_0"), val = tensor([-1])]; + tensor encoder_layers_8_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_8_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170583040)))]; + tensor encoder_layers_8_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_8_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170585152)))]; + tensor input_443_cast_fp16 = layer_norm(axes = input_443_axes_0, beta = encoder_layers_8_norm_feed_forward1_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_8_norm_feed_forward1_weight_to_fp16, x = input_441_cast_fp16)[name = string("input_443_cast_fp16")]; + tensor encoder_layers_8_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170587264))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(173733056))))[name = string("encoder_layers_8_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_8_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_8_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(173733248)))]; + tensor linear_73_cast_fp16 = linear(bias = encoder_layers_8_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_8_feed_forward1_linear1_weight_to_fp16_palettized, x = input_443_cast_fp16)[name = string("linear_73_cast_fp16")]; + tensor input_447_cast_fp16 = silu(x = linear_73_cast_fp16)[name = string("input_447_cast_fp16")]; + tensor encoder_layers_8_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(173741504))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176887296))))[name = string("encoder_layers_8_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_8_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_8_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176887488)))]; + tensor linear_74_cast_fp16 = linear(bias = encoder_layers_8_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_8_feed_forward1_linear2_weight_to_fp16_palettized, x = input_447_cast_fp16)[name = string("linear_74_cast_fp16")]; + fp16 var_2158_to_fp16 = const()[name = string("op_2158_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2159_cast_fp16 = mul(x = linear_74_cast_fp16, y = var_2158_to_fp16)[name = string("op_2159_cast_fp16")]; + tensor input_453_cast_fp16 = add(x = input_441_cast_fp16, y = var_2159_cast_fp16)[name = string("input_453_cast_fp16")]; + tensor key_17_axes_0 = const()[name = string("key_17_axes_0"), val = tensor([-1])]; + tensor encoder_layers_8_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_8_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176889600)))]; + tensor encoder_layers_8_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_8_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176891712)))]; + tensor key_17_cast_fp16 = layer_norm(axes = key_17_axes_0, beta = encoder_layers_8_norm_self_att_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_8_norm_self_att_weight_to_fp16, x = input_453_cast_fp16)[name = string("key_17_cast_fp16")]; + bool input_455_interleave_0 = const()[name = string("input_455_interleave_0"), val = bool(false)]; + tensor input_455_cast_fp16 = concat(axis = var_67, interleave = input_455_interleave_0, values = (cache_33_cast_fp16, key_17_cast_fp16))[name = string("input_455_cast_fp16")]; + tensor var_2181_begin_0 = const()[name = string("op_2181_begin_0"), val = tensor([0, 14, 0])]; + tensor var_2181_end_0 = const()[name = string("op_2181_end_0"), val = tensor([1, 42, 1024])]; + tensor var_2181_end_mask_0 = const()[name = string("op_2181_end_mask_0"), val = tensor([true, true, true])]; + tensor var_2181_cast_fp16 = slice_by_index(begin = var_2181_begin_0, end = var_2181_end_0, end_mask = var_2181_end_mask_0, x = cache_33_cast_fp16)[name = string("op_2181_cast_fp16")]; + bool var_2187_interleave_0 = const()[name = string("op_2187_interleave_0"), val = bool(false)]; + tensor var_2187_cast_fp16 = concat(axis = var_67, interleave = var_2187_interleave_0, values = (var_2181_cast_fp16, key_17_cast_fp16))[name = string("op_2187_cast_fp16")]; + tensor encoder_layers_8_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176893824))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177680320))))[name = string("encoder_layers_8_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_8_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_8_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177680512)))]; + tensor linear_75_cast_fp16 = linear(bias = encoder_layers_8_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_8_self_attn_linear_q_weight_to_fp16_palettized, x = key_17_cast_fp16)[name = string("linear_75_cast_fp16")]; + tensor var_2192 = const()[name = string("op_2192"), val = tensor([1, -1, 8, 128])]; + tensor q_49_cast_fp16 = reshape(shape = var_2192, x = linear_75_cast_fp16)[name = string("q_49_cast_fp16")]; + tensor encoder_layers_8_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177682624))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(178469120))))[name = string("encoder_layers_8_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_8_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_8_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(178469312)))]; + tensor linear_76_cast_fp16 = linear(bias = encoder_layers_8_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_8_self_attn_linear_k_weight_to_fp16_palettized, x = input_455_cast_fp16)[name = string("linear_76_cast_fp16")]; + tensor var_2197 = const()[name = string("op_2197"), val = tensor([1, -1, 8, 128])]; + tensor k_33_cast_fp16 = reshape(shape = var_2197, x = linear_76_cast_fp16)[name = string("k_33_cast_fp16")]; + tensor encoder_layers_8_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(178471424))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179257920))))[name = string("encoder_layers_8_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_8_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_8_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179258112)))]; + tensor linear_77_cast_fp16 = linear(bias = encoder_layers_8_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_8_self_attn_linear_v_weight_to_fp16_palettized, x = input_455_cast_fp16)[name = string("linear_77_cast_fp16")]; + tensor var_2202 = const()[name = string("op_2202"), val = tensor([1, -1, 8, 128])]; + tensor v_17_cast_fp16 = reshape(shape = var_2202, x = linear_77_cast_fp16)[name = string("v_17_cast_fp16")]; + tensor value_25_perm_0 = const()[name = string("value_25_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_8_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_8_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179260224)))]; + tensor var_2215_cast_fp16 = add(x = q_49_cast_fp16, y = encoder_layers_8_self_attn_pos_bias_u_to_fp16)[name = string("op_2215_cast_fp16")]; + tensor encoder_layers_8_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_8_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179262336)))]; + tensor var_2217_cast_fp16 = add(x = q_49_cast_fp16, y = encoder_layers_8_self_attn_pos_bias_v_to_fp16)[name = string("op_2217_cast_fp16")]; + tensor q_with_bias_v_17_perm_0 = const()[name = string("q_with_bias_v_17_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_215_transpose_x_0 = const()[name = string("x_215_transpose_x_0"), val = bool(false)]; + bool x_215_transpose_y_0 = const()[name = string("x_215_transpose_y_0"), val = bool(false)]; + tensor op_2219_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179264448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179378176))))[name = string("op_2219_to_fp16_quantized")]; + tensor q_with_bias_v_17_cast_fp16 = transpose(perm = q_with_bias_v_17_perm_0, x = var_2217_cast_fp16)[name = string("transpose_290")]; + tensor x_215_cast_fp16 = matmul(transpose_x = x_215_transpose_x_0, transpose_y = x_215_transpose_y_0, x = q_with_bias_v_17_cast_fp16, y = op_2219_to_fp16_quantized)[name = string("x_215_cast_fp16")]; + tensor x_217_pad_0 = const()[name = string("x_217_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_217_mode_0 = const()[name = string("x_217_mode_0"), val = string("constant")]; + fp16 const_183_to_fp16 = const()[name = string("const_183_to_fp16"), val = fp16(0x0p+0)]; + tensor x_217_cast_fp16 = pad(constant_val = const_183_to_fp16, mode = x_217_mode_0, pad = x_217_pad_0, x = x_215_cast_fp16)[name = string("x_217_cast_fp16")]; + tensor var_2227 = const()[name = string("op_2227"), val = tensor([1, 8, -1, 14])]; + tensor x_219_cast_fp16 = reshape(shape = var_2227, x = x_217_cast_fp16)[name = string("x_219_cast_fp16")]; + tensor var_2231_begin_0 = const()[name = string("op_2231_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_2231_end_0 = const()[name = string("op_2231_end_0"), val = tensor([1, 8, 112, 14])]; + tensor var_2231_end_mask_0 = const()[name = string("op_2231_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_2231_cast_fp16 = slice_by_index(begin = var_2231_begin_0, end = var_2231_end_0, end_mask = var_2231_end_mask_0, x = x_219_cast_fp16)[name = string("op_2231_cast_fp16")]; + tensor var_2232 = const()[name = string("op_2232"), val = tensor([1, 8, 14, 111])]; + tensor matrix_bd_33_cast_fp16 = reshape(shape = var_2232, x = var_2231_cast_fp16)[name = string("matrix_bd_33_cast_fp16")]; + bool matrix_ac_17_transpose_x_0 = const()[name = string("matrix_ac_17_transpose_x_0"), val = bool(false)]; + bool matrix_ac_17_transpose_y_0 = const()[name = string("matrix_ac_17_transpose_y_0"), val = bool(false)]; + tensor transpose_112_perm_0 = const()[name = string("transpose_112_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_113_perm_0 = const()[name = string("transpose_113_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_113 = transpose(perm = transpose_113_perm_0, x = k_33_cast_fp16)[name = string("transpose_288")]; + tensor transpose_112 = transpose(perm = transpose_112_perm_0, x = var_2215_cast_fp16)[name = string("transpose_289")]; + tensor matrix_ac_17_cast_fp16 = matmul(transpose_x = matrix_ac_17_transpose_x_0, transpose_y = matrix_ac_17_transpose_y_0, x = transpose_112, y = transpose_113)[name = string("matrix_ac_17_cast_fp16")]; + tensor matrix_bd_35_begin_0 = const()[name = string("matrix_bd_35_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_35_end_0 = const()[name = string("matrix_bd_35_end_0"), val = tensor([1, 8, 14, 56])]; + tensor matrix_bd_35_end_mask_0 = const()[name = string("matrix_bd_35_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_35_cast_fp16 = slice_by_index(begin = matrix_bd_35_begin_0, end = matrix_bd_35_end_0, end_mask = matrix_bd_35_end_mask_0, x = matrix_bd_33_cast_fp16)[name = string("matrix_bd_35_cast_fp16")]; + tensor var_2241_cast_fp16 = add(x = matrix_ac_17_cast_fp16, y = matrix_bd_35_cast_fp16)[name = string("op_2241_cast_fp16")]; + fp16 _inversed_scores_33_y_0_to_fp16 = const()[name = string("_inversed_scores_33_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_33_cast_fp16 = mul(x = var_2241_cast_fp16, y = _inversed_scores_33_y_0_to_fp16)[name = string("_inversed_scores_33_cast_fp16")]; + tensor scores_35_cast_fp16 = select(a = var_44_to_fp16, b = _inversed_scores_33_cast_fp16, cond = mask_11)[name = string("scores_35_cast_fp16")]; + tensor var_2247_cast_fp16 = softmax(axis = var_58, x = scores_35_cast_fp16)[name = string("op_2247_cast_fp16")]; + tensor input_457_cast_fp16 = select(a = var_43_to_fp16, b = var_2247_cast_fp16, cond = mask_11)[name = string("input_457_cast_fp16")]; + bool x_221_transpose_x_0 = const()[name = string("x_221_transpose_x_0"), val = bool(false)]; + bool x_221_transpose_y_0 = const()[name = string("x_221_transpose_y_0"), val = bool(false)]; + tensor value_25_cast_fp16 = transpose(perm = value_25_perm_0, x = v_17_cast_fp16)[name = string("transpose_287")]; + tensor x_221_cast_fp16 = matmul(transpose_x = x_221_transpose_x_0, transpose_y = x_221_transpose_y_0, x = input_457_cast_fp16, y = value_25_cast_fp16)[name = string("x_221_cast_fp16")]; + tensor var_2251_perm_0 = const()[name = string("op_2251_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2252 = const()[name = string("op_2252"), val = tensor([1, -1, 1024])]; + tensor var_2251_cast_fp16 = transpose(perm = var_2251_perm_0, x = x_221_cast_fp16)[name = string("transpose_286")]; + tensor input_459_cast_fp16 = reshape(shape = var_2252, x = var_2251_cast_fp16)[name = string("input_459_cast_fp16")]; + tensor encoder_layers_8_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179378496))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180164992))))[name = string("encoder_layers_8_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_8_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_8_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180165184)))]; + tensor linear_79_cast_fp16 = linear(bias = encoder_layers_8_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_8_self_attn_linear_out_weight_to_fp16_palettized, x = input_459_cast_fp16)[name = string("linear_79_cast_fp16")]; + tensor input_463_cast_fp16 = add(x = input_453_cast_fp16, y = linear_79_cast_fp16)[name = string("input_463_cast_fp16")]; + tensor x_225_axes_0 = const()[name = string("x_225_axes_0"), val = tensor([-1])]; + tensor encoder_layers_8_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_8_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180167296)))]; + tensor encoder_layers_8_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_8_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180169408)))]; + tensor x_225_cast_fp16 = layer_norm(axes = x_225_axes_0, beta = encoder_layers_8_norm_conv_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_8_norm_conv_weight_to_fp16, x = input_463_cast_fp16)[name = string("x_225_cast_fp16")]; + tensor input_465_perm_0 = const()[name = string("input_465_perm_0"), val = tensor([0, 2, 1])]; + string input_467_pad_type_0 = const()[name = string("input_467_pad_type_0"), val = string("valid")]; + tensor input_467_strides_0 = const()[name = string("input_467_strides_0"), val = tensor([1])]; + tensor input_467_pad_0 = const()[name = string("input_467_pad_0"), val = tensor([0, 0])]; + tensor input_467_dilations_0 = const()[name = string("input_467_dilations_0"), val = tensor([1])]; + int32 input_467_groups_0 = const()[name = string("input_467_groups_0"), val = int32(1)]; + tensor encoder_layers_8_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180171520))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(182268736))))[name = string("encoder_layers_8_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_465_cast_fp16 = transpose(perm = input_465_perm_0, x = x_225_cast_fp16)[name = string("transpose_285")]; + tensor input_467_cast_fp16 = conv(dilations = input_467_dilations_0, groups = input_467_groups_0, pad = input_467_pad_0, pad_type = input_467_pad_type_0, strides = input_467_strides_0, weight = encoder_layers_8_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_465_cast_fp16)[name = string("input_467_cast_fp16")]; + int32 x_227_split_num_splits_0 = const()[name = string("x_227_split_num_splits_0"), val = int32(2)]; + int32 x_227_split_axis_0 = const()[name = string("x_227_split_axis_0"), val = int32(1)]; + tensor x_227_split_cast_fp16_0, tensor x_227_split_cast_fp16_1 = split(axis = x_227_split_axis_0, num_splits = x_227_split_num_splits_0, x = input_467_cast_fp16)[name = string("x_227_split_cast_fp16")]; + tensor x_227_split_1_sigmoid_cast_fp16 = sigmoid(x = x_227_split_cast_fp16_1)[name = string("x_227_split_1_sigmoid_cast_fp16")]; + tensor x_227_cast_fp16 = mul(x = x_227_split_cast_fp16_0, y = x_227_split_1_sigmoid_cast_fp16)[name = string("x_227_cast_fp16")]; + tensor input_469_cast_fp16 = select(a = var_43_to_fp16, b = x_227_cast_fp16, cond = var_574)[name = string("input_469_cast_fp16")]; + bool new_x_35_interleave_0 = const()[name = string("new_x_35_interleave_0"), val = bool(false)]; + tensor new_x_35_cast_fp16 = concat(axis = var_58, interleave = new_x_35_interleave_0, values = (cache_35_cast_fp16, input_469_cast_fp16))[name = string("new_x_35_cast_fp16")]; + tensor var_2291_begin_0 = const()[name = string("op_2291_begin_0"), val = tensor([0, 0, 14])]; + tensor var_2291_end_0 = const()[name = string("op_2291_end_0"), val = tensor([1, 1024, 22])]; + tensor var_2291_end_mask_0 = const()[name = string("op_2291_end_mask_0"), val = tensor([true, true, true])]; + tensor var_2291_cast_fp16 = slice_by_index(begin = var_2291_begin_0, end = var_2291_end_0, end_mask = var_2291_end_mask_0, x = new_x_35_cast_fp16)[name = string("op_2291_cast_fp16")]; + string x_229_pad_type_0 = const()[name = string("x_229_pad_type_0"), val = string("valid")]; + int32 x_229_groups_0 = const()[name = string("x_229_groups_0"), val = int32(1024)]; + tensor x_229_strides_0 = const()[name = string("x_229_strides_0"), val = tensor([1])]; + tensor x_229_pad_0 = const()[name = string("x_229_pad_0"), val = tensor([0, 0])]; + tensor x_229_dilations_0 = const()[name = string("x_229_dilations_0"), val = tensor([1])]; + tensor encoder_layers_8_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(182272896))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(182282176))))[name = string("encoder_layers_8_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_229_cast_fp16 = conv(dilations = x_229_dilations_0, groups = x_229_groups_0, pad = x_229_pad_0, pad_type = x_229_pad_type_0, strides = x_229_strides_0, weight = encoder_layers_8_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_35_cast_fp16)[name = string("x_229_cast_fp16")]; + tensor input_471_perm_0 = const()[name = string("input_471_perm_0"), val = tensor([0, 2, 1])]; + tensor x_231_axes_0 = const()[name = string("x_231_axes_0"), val = tensor([-1])]; + tensor encoder_layers_8_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_8_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(182284288)))]; + tensor encoder_layers_8_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_8_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(182286400)))]; + tensor input_471_cast_fp16 = transpose(perm = input_471_perm_0, x = x_229_cast_fp16)[name = string("transpose_284")]; + tensor x_231_cast_fp16 = layer_norm(axes = x_231_axes_0, beta = encoder_layers_8_conv_batch_norm_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_8_conv_batch_norm_weight_to_fp16, x = input_471_cast_fp16)[name = string("x_231_cast_fp16")]; + tensor input_473_perm_0 = const()[name = string("input_473_perm_0"), val = tensor([0, 2, 1])]; + tensor input_473_cast_fp16 = transpose(perm = input_473_perm_0, x = x_231_cast_fp16)[name = string("transpose_283")]; + tensor input_475_cast_fp16 = silu(x = input_473_cast_fp16)[name = string("input_475_cast_fp16")]; + string x_233_pad_type_0 = const()[name = string("x_233_pad_type_0"), val = string("valid")]; + tensor x_233_strides_0 = const()[name = string("x_233_strides_0"), val = tensor([1])]; + tensor x_233_pad_0 = const()[name = string("x_233_pad_0"), val = tensor([0, 0])]; + tensor x_233_dilations_0 = const()[name = string("x_233_dilations_0"), val = tensor([1])]; + int32 x_233_groups_0 = const()[name = string("x_233_groups_0"), val = int32(1)]; + tensor encoder_layers_8_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(182288512))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(183337152))))[name = string("encoder_layers_8_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_233_cast_fp16 = conv(dilations = x_233_dilations_0, groups = x_233_groups_0, pad = x_233_pad_0, pad_type = x_233_pad_type_0, strides = x_233_strides_0, weight = encoder_layers_8_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_475_cast_fp16)[name = string("x_233_cast_fp16")]; + tensor input_477_perm_0 = const()[name = string("input_477_perm_0"), val = tensor([0, 2, 1])]; + tensor input_477_cast_fp16 = transpose(perm = input_477_perm_0, x = x_233_cast_fp16)[name = string("transpose_282")]; + tensor input_479_cast_fp16 = add(x = input_463_cast_fp16, y = input_477_cast_fp16)[name = string("input_479_cast_fp16")]; + tensor input_481_axes_0 = const()[name = string("input_481_axes_0"), val = tensor([-1])]; + tensor encoder_layers_8_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_8_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(183339264)))]; + tensor encoder_layers_8_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_8_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(183341376)))]; + tensor input_481_cast_fp16 = layer_norm(axes = input_481_axes_0, beta = encoder_layers_8_norm_feed_forward2_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_8_norm_feed_forward2_weight_to_fp16, x = input_479_cast_fp16)[name = string("input_481_cast_fp16")]; + tensor encoder_layers_8_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(183343488))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(186489280))))[name = string("encoder_layers_8_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_8_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_8_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(186489472)))]; + tensor linear_80_cast_fp16 = linear(bias = encoder_layers_8_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_8_feed_forward2_linear1_weight_to_fp16_palettized, x = input_481_cast_fp16)[name = string("linear_80_cast_fp16")]; + tensor input_485_cast_fp16 = silu(x = linear_80_cast_fp16)[name = string("input_485_cast_fp16")]; + tensor encoder_layers_8_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(186497728))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189643520))))[name = string("encoder_layers_8_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_8_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_8_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189643712)))]; + tensor linear_81_cast_fp16 = linear(bias = encoder_layers_8_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_8_feed_forward2_linear2_weight_to_fp16_palettized, x = input_485_cast_fp16)[name = string("linear_81_cast_fp16")]; + fp16 var_2334_to_fp16 = const()[name = string("op_2334_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2335_cast_fp16 = mul(x = linear_81_cast_fp16, y = var_2334_to_fp16)[name = string("op_2335_cast_fp16")]; + tensor input_491_cast_fp16 = add(x = input_479_cast_fp16, y = var_2335_cast_fp16)[name = string("input_491_cast_fp16")]; + tensor input_493_axes_0 = const()[name = string("input_493_axes_0"), val = tensor([-1])]; + tensor encoder_layers_8_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_8_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189645824)))]; + tensor encoder_layers_8_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_8_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189647936)))]; + tensor input_493_cast_fp16 = layer_norm(axes = input_493_axes_0, beta = encoder_layers_8_norm_out_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_8_norm_out_weight_to_fp16, x = input_491_cast_fp16)[name = string("input_493_cast_fp16")]; + tensor cache_37_begin_0 = const()[name = string("cache_37_begin_0"), val = tensor([9, 0, 0, 0])]; + tensor cache_37_end_0 = const()[name = string("cache_37_end_0"), val = tensor([10, 1, 42, 1024])]; + tensor cache_37_end_mask_0 = const()[name = string("cache_37_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_37_squeeze_mask_0 = const()[name = string("cache_37_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_37_cast_fp16 = slice_by_index(begin = cache_37_begin_0, end = cache_37_end_0, end_mask = cache_37_end_mask_0, squeeze_mask = cache_37_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_37_cast_fp16")]; + tensor cache_39_begin_0 = const()[name = string("cache_39_begin_0"), val = tensor([9, 0, 0, 0])]; + tensor cache_39_end_0 = const()[name = string("cache_39_end_0"), val = tensor([10, 1, 1024, 8])]; + tensor cache_39_end_mask_0 = const()[name = string("cache_39_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_39_squeeze_mask_0 = const()[name = string("cache_39_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_39_cast_fp16 = slice_by_index(begin = cache_39_begin_0, end = cache_39_end_0, end_mask = cache_39_end_mask_0, squeeze_mask = cache_39_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_39_cast_fp16")]; + tensor input_495_axes_0 = const()[name = string("input_495_axes_0"), val = tensor([-1])]; + tensor encoder_layers_9_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_9_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189650048)))]; + tensor encoder_layers_9_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_9_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189652160)))]; + tensor input_495_cast_fp16 = layer_norm(axes = input_495_axes_0, beta = encoder_layers_9_norm_feed_forward1_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_9_norm_feed_forward1_weight_to_fp16, x = input_493_cast_fp16)[name = string("input_495_cast_fp16")]; + tensor encoder_layers_9_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189654272))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(192800064))))[name = string("encoder_layers_9_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_9_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_9_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(192800256)))]; + tensor linear_82_cast_fp16 = linear(bias = encoder_layers_9_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_9_feed_forward1_linear1_weight_to_fp16_palettized, x = input_495_cast_fp16)[name = string("linear_82_cast_fp16")]; + tensor input_499_cast_fp16 = silu(x = linear_82_cast_fp16)[name = string("input_499_cast_fp16")]; + tensor encoder_layers_9_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(192808512))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(195954304))))[name = string("encoder_layers_9_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_9_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_9_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(195954496)))]; + tensor linear_83_cast_fp16 = linear(bias = encoder_layers_9_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_9_feed_forward1_linear2_weight_to_fp16_palettized, x = input_499_cast_fp16)[name = string("linear_83_cast_fp16")]; + fp16 var_2371_to_fp16 = const()[name = string("op_2371_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2372_cast_fp16 = mul(x = linear_83_cast_fp16, y = var_2371_to_fp16)[name = string("op_2372_cast_fp16")]; + tensor input_505_cast_fp16 = add(x = input_493_cast_fp16, y = var_2372_cast_fp16)[name = string("input_505_cast_fp16")]; + tensor key_19_axes_0 = const()[name = string("key_19_axes_0"), val = tensor([-1])]; + tensor encoder_layers_9_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_9_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(195956608)))]; + tensor encoder_layers_9_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_9_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(195958720)))]; + tensor key_19_cast_fp16 = layer_norm(axes = key_19_axes_0, beta = encoder_layers_9_norm_self_att_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_9_norm_self_att_weight_to_fp16, x = input_505_cast_fp16)[name = string("key_19_cast_fp16")]; + bool input_507_interleave_0 = const()[name = string("input_507_interleave_0"), val = bool(false)]; + tensor input_507_cast_fp16 = concat(axis = var_67, interleave = input_507_interleave_0, values = (cache_37_cast_fp16, key_19_cast_fp16))[name = string("input_507_cast_fp16")]; + tensor var_2394_begin_0 = const()[name = string("op_2394_begin_0"), val = tensor([0, 14, 0])]; + tensor var_2394_end_0 = const()[name = string("op_2394_end_0"), val = tensor([1, 42, 1024])]; + tensor var_2394_end_mask_0 = const()[name = string("op_2394_end_mask_0"), val = tensor([true, true, true])]; + tensor var_2394_cast_fp16 = slice_by_index(begin = var_2394_begin_0, end = var_2394_end_0, end_mask = var_2394_end_mask_0, x = cache_37_cast_fp16)[name = string("op_2394_cast_fp16")]; + bool var_2400_interleave_0 = const()[name = string("op_2400_interleave_0"), val = bool(false)]; + tensor var_2400_cast_fp16 = concat(axis = var_67, interleave = var_2400_interleave_0, values = (var_2394_cast_fp16, key_19_cast_fp16))[name = string("op_2400_cast_fp16")]; + tensor encoder_layers_9_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(195960832))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196747328))))[name = string("encoder_layers_9_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_9_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_9_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196747520)))]; + tensor linear_84_cast_fp16 = linear(bias = encoder_layers_9_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_9_self_attn_linear_q_weight_to_fp16_palettized, x = key_19_cast_fp16)[name = string("linear_84_cast_fp16")]; + tensor var_2405 = const()[name = string("op_2405"), val = tensor([1, -1, 8, 128])]; + tensor q_55_cast_fp16 = reshape(shape = var_2405, x = linear_84_cast_fp16)[name = string("q_55_cast_fp16")]; + tensor encoder_layers_9_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196749632))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197536128))))[name = string("encoder_layers_9_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_9_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_9_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197536320)))]; + tensor linear_85_cast_fp16 = linear(bias = encoder_layers_9_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_9_self_attn_linear_k_weight_to_fp16_palettized, x = input_507_cast_fp16)[name = string("linear_85_cast_fp16")]; + tensor var_2410 = const()[name = string("op_2410"), val = tensor([1, -1, 8, 128])]; + tensor k_37_cast_fp16 = reshape(shape = var_2410, x = linear_85_cast_fp16)[name = string("k_37_cast_fp16")]; + tensor encoder_layers_9_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197538432))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198324928))))[name = string("encoder_layers_9_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_9_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_9_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198325120)))]; + tensor linear_86_cast_fp16 = linear(bias = encoder_layers_9_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_9_self_attn_linear_v_weight_to_fp16_palettized, x = input_507_cast_fp16)[name = string("linear_86_cast_fp16")]; + tensor var_2415 = const()[name = string("op_2415"), val = tensor([1, -1, 8, 128])]; + tensor v_19_cast_fp16 = reshape(shape = var_2415, x = linear_86_cast_fp16)[name = string("v_19_cast_fp16")]; + tensor value_27_perm_0 = const()[name = string("value_27_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_9_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_9_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198327232)))]; + tensor var_2428_cast_fp16 = add(x = q_55_cast_fp16, y = encoder_layers_9_self_attn_pos_bias_u_to_fp16)[name = string("op_2428_cast_fp16")]; + tensor encoder_layers_9_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_9_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198329344)))]; + tensor var_2430_cast_fp16 = add(x = q_55_cast_fp16, y = encoder_layers_9_self_attn_pos_bias_v_to_fp16)[name = string("op_2430_cast_fp16")]; + tensor q_with_bias_v_19_perm_0 = const()[name = string("q_with_bias_v_19_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_241_transpose_x_0 = const()[name = string("x_241_transpose_x_0"), val = bool(false)]; + bool x_241_transpose_y_0 = const()[name = string("x_241_transpose_y_0"), val = bool(false)]; + tensor op_2432_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198331456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198445184))))[name = string("op_2432_to_fp16_quantized")]; + tensor q_with_bias_v_19_cast_fp16 = transpose(perm = q_with_bias_v_19_perm_0, x = var_2430_cast_fp16)[name = string("transpose_281")]; + tensor x_241_cast_fp16 = matmul(transpose_x = x_241_transpose_x_0, transpose_y = x_241_transpose_y_0, x = q_with_bias_v_19_cast_fp16, y = op_2432_to_fp16_quantized)[name = string("x_241_cast_fp16")]; + tensor x_243_pad_0 = const()[name = string("x_243_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_243_mode_0 = const()[name = string("x_243_mode_0"), val = string("constant")]; + fp16 const_196_to_fp16 = const()[name = string("const_196_to_fp16"), val = fp16(0x0p+0)]; + tensor x_243_cast_fp16 = pad(constant_val = const_196_to_fp16, mode = x_243_mode_0, pad = x_243_pad_0, x = x_241_cast_fp16)[name = string("x_243_cast_fp16")]; + tensor var_2440 = const()[name = string("op_2440"), val = tensor([1, 8, -1, 14])]; + tensor x_245_cast_fp16 = reshape(shape = var_2440, x = x_243_cast_fp16)[name = string("x_245_cast_fp16")]; + tensor var_2444_begin_0 = const()[name = string("op_2444_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_2444_end_0 = const()[name = string("op_2444_end_0"), val = tensor([1, 8, 112, 14])]; + tensor var_2444_end_mask_0 = const()[name = string("op_2444_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_2444_cast_fp16 = slice_by_index(begin = var_2444_begin_0, end = var_2444_end_0, end_mask = var_2444_end_mask_0, x = x_245_cast_fp16)[name = string("op_2444_cast_fp16")]; + tensor var_2445 = const()[name = string("op_2445"), val = tensor([1, 8, 14, 111])]; + tensor matrix_bd_37_cast_fp16 = reshape(shape = var_2445, x = var_2444_cast_fp16)[name = string("matrix_bd_37_cast_fp16")]; + bool matrix_ac_19_transpose_x_0 = const()[name = string("matrix_ac_19_transpose_x_0"), val = bool(false)]; + bool matrix_ac_19_transpose_y_0 = const()[name = string("matrix_ac_19_transpose_y_0"), val = bool(false)]; + tensor transpose_114_perm_0 = const()[name = string("transpose_114_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_115_perm_0 = const()[name = string("transpose_115_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_115 = transpose(perm = transpose_115_perm_0, x = k_37_cast_fp16)[name = string("transpose_279")]; + tensor transpose_114 = transpose(perm = transpose_114_perm_0, x = var_2428_cast_fp16)[name = string("transpose_280")]; + tensor matrix_ac_19_cast_fp16 = matmul(transpose_x = matrix_ac_19_transpose_x_0, transpose_y = matrix_ac_19_transpose_y_0, x = transpose_114, y = transpose_115)[name = string("matrix_ac_19_cast_fp16")]; + tensor matrix_bd_39_begin_0 = const()[name = string("matrix_bd_39_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_39_end_0 = const()[name = string("matrix_bd_39_end_0"), val = tensor([1, 8, 14, 56])]; + tensor matrix_bd_39_end_mask_0 = const()[name = string("matrix_bd_39_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_39_cast_fp16 = slice_by_index(begin = matrix_bd_39_begin_0, end = matrix_bd_39_end_0, end_mask = matrix_bd_39_end_mask_0, x = matrix_bd_37_cast_fp16)[name = string("matrix_bd_39_cast_fp16")]; + tensor var_2454_cast_fp16 = add(x = matrix_ac_19_cast_fp16, y = matrix_bd_39_cast_fp16)[name = string("op_2454_cast_fp16")]; + fp16 _inversed_scores_37_y_0_to_fp16 = const()[name = string("_inversed_scores_37_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_37_cast_fp16 = mul(x = var_2454_cast_fp16, y = _inversed_scores_37_y_0_to_fp16)[name = string("_inversed_scores_37_cast_fp16")]; + tensor scores_39_cast_fp16 = select(a = var_44_to_fp16, b = _inversed_scores_37_cast_fp16, cond = mask_11)[name = string("scores_39_cast_fp16")]; + tensor var_2460_cast_fp16 = softmax(axis = var_58, x = scores_39_cast_fp16)[name = string("op_2460_cast_fp16")]; + tensor input_509_cast_fp16 = select(a = var_43_to_fp16, b = var_2460_cast_fp16, cond = mask_11)[name = string("input_509_cast_fp16")]; + bool x_247_transpose_x_0 = const()[name = string("x_247_transpose_x_0"), val = bool(false)]; + bool x_247_transpose_y_0 = const()[name = string("x_247_transpose_y_0"), val = bool(false)]; + tensor value_27_cast_fp16 = transpose(perm = value_27_perm_0, x = v_19_cast_fp16)[name = string("transpose_278")]; + tensor x_247_cast_fp16 = matmul(transpose_x = x_247_transpose_x_0, transpose_y = x_247_transpose_y_0, x = input_509_cast_fp16, y = value_27_cast_fp16)[name = string("x_247_cast_fp16")]; + tensor var_2464_perm_0 = const()[name = string("op_2464_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2465 = const()[name = string("op_2465"), val = tensor([1, -1, 1024])]; + tensor var_2464_cast_fp16 = transpose(perm = var_2464_perm_0, x = x_247_cast_fp16)[name = string("transpose_277")]; + tensor input_511_cast_fp16 = reshape(shape = var_2465, x = var_2464_cast_fp16)[name = string("input_511_cast_fp16")]; + tensor encoder_layers_9_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198445504))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199232000))))[name = string("encoder_layers_9_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_9_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_9_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199232192)))]; + tensor linear_88_cast_fp16 = linear(bias = encoder_layers_9_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_9_self_attn_linear_out_weight_to_fp16_palettized, x = input_511_cast_fp16)[name = string("linear_88_cast_fp16")]; + tensor input_515_cast_fp16 = add(x = input_505_cast_fp16, y = linear_88_cast_fp16)[name = string("input_515_cast_fp16")]; + tensor x_251_axes_0 = const()[name = string("x_251_axes_0"), val = tensor([-1])]; + tensor encoder_layers_9_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_9_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199234304)))]; + tensor encoder_layers_9_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_9_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199236416)))]; + tensor x_251_cast_fp16 = layer_norm(axes = x_251_axes_0, beta = encoder_layers_9_norm_conv_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_9_norm_conv_weight_to_fp16, x = input_515_cast_fp16)[name = string("x_251_cast_fp16")]; + tensor input_517_perm_0 = const()[name = string("input_517_perm_0"), val = tensor([0, 2, 1])]; + string input_519_pad_type_0 = const()[name = string("input_519_pad_type_0"), val = string("valid")]; + tensor input_519_strides_0 = const()[name = string("input_519_strides_0"), val = tensor([1])]; + tensor input_519_pad_0 = const()[name = string("input_519_pad_0"), val = tensor([0, 0])]; + tensor input_519_dilations_0 = const()[name = string("input_519_dilations_0"), val = tensor([1])]; + int32 input_519_groups_0 = const()[name = string("input_519_groups_0"), val = int32(1)]; + tensor encoder_layers_9_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199238528))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201335744))))[name = string("encoder_layers_9_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_517_cast_fp16 = transpose(perm = input_517_perm_0, x = x_251_cast_fp16)[name = string("transpose_276")]; + tensor input_519_cast_fp16 = conv(dilations = input_519_dilations_0, groups = input_519_groups_0, pad = input_519_pad_0, pad_type = input_519_pad_type_0, strides = input_519_strides_0, weight = encoder_layers_9_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_517_cast_fp16)[name = string("input_519_cast_fp16")]; + int32 x_253_split_num_splits_0 = const()[name = string("x_253_split_num_splits_0"), val = int32(2)]; + int32 x_253_split_axis_0 = const()[name = string("x_253_split_axis_0"), val = int32(1)]; + tensor x_253_split_cast_fp16_0, tensor x_253_split_cast_fp16_1 = split(axis = x_253_split_axis_0, num_splits = x_253_split_num_splits_0, x = input_519_cast_fp16)[name = string("x_253_split_cast_fp16")]; + tensor x_253_split_1_sigmoid_cast_fp16 = sigmoid(x = x_253_split_cast_fp16_1)[name = string("x_253_split_1_sigmoid_cast_fp16")]; + tensor x_253_cast_fp16 = mul(x = x_253_split_cast_fp16_0, y = x_253_split_1_sigmoid_cast_fp16)[name = string("x_253_cast_fp16")]; + tensor input_521_cast_fp16 = select(a = var_43_to_fp16, b = x_253_cast_fp16, cond = var_574)[name = string("input_521_cast_fp16")]; + bool new_x_39_interleave_0 = const()[name = string("new_x_39_interleave_0"), val = bool(false)]; + tensor new_x_39_cast_fp16 = concat(axis = var_58, interleave = new_x_39_interleave_0, values = (cache_39_cast_fp16, input_521_cast_fp16))[name = string("new_x_39_cast_fp16")]; + tensor var_2504_begin_0 = const()[name = string("op_2504_begin_0"), val = tensor([0, 0, 14])]; + tensor var_2504_end_0 = const()[name = string("op_2504_end_0"), val = tensor([1, 1024, 22])]; + tensor var_2504_end_mask_0 = const()[name = string("op_2504_end_mask_0"), val = tensor([true, true, true])]; + tensor var_2504_cast_fp16 = slice_by_index(begin = var_2504_begin_0, end = var_2504_end_0, end_mask = var_2504_end_mask_0, x = new_x_39_cast_fp16)[name = string("op_2504_cast_fp16")]; + string x_255_pad_type_0 = const()[name = string("x_255_pad_type_0"), val = string("valid")]; + int32 x_255_groups_0 = const()[name = string("x_255_groups_0"), val = int32(1024)]; + tensor x_255_strides_0 = const()[name = string("x_255_strides_0"), val = tensor([1])]; + tensor x_255_pad_0 = const()[name = string("x_255_pad_0"), val = tensor([0, 0])]; + tensor x_255_dilations_0 = const()[name = string("x_255_dilations_0"), val = tensor([1])]; + tensor encoder_layers_9_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201339904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201349184))))[name = string("encoder_layers_9_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_255_cast_fp16 = conv(dilations = x_255_dilations_0, groups = x_255_groups_0, pad = x_255_pad_0, pad_type = x_255_pad_type_0, strides = x_255_strides_0, weight = encoder_layers_9_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_39_cast_fp16)[name = string("x_255_cast_fp16")]; + tensor input_523_perm_0 = const()[name = string("input_523_perm_0"), val = tensor([0, 2, 1])]; + tensor x_257_axes_0 = const()[name = string("x_257_axes_0"), val = tensor([-1])]; + tensor encoder_layers_9_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_9_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201351296)))]; + tensor encoder_layers_9_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_9_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201353408)))]; + tensor input_523_cast_fp16 = transpose(perm = input_523_perm_0, x = x_255_cast_fp16)[name = string("transpose_275")]; + tensor x_257_cast_fp16 = layer_norm(axes = x_257_axes_0, beta = encoder_layers_9_conv_batch_norm_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_9_conv_batch_norm_weight_to_fp16, x = input_523_cast_fp16)[name = string("x_257_cast_fp16")]; + tensor input_525_perm_0 = const()[name = string("input_525_perm_0"), val = tensor([0, 2, 1])]; + tensor input_525_cast_fp16 = transpose(perm = input_525_perm_0, x = x_257_cast_fp16)[name = string("transpose_274")]; + tensor input_527_cast_fp16 = silu(x = input_525_cast_fp16)[name = string("input_527_cast_fp16")]; + string x_259_pad_type_0 = const()[name = string("x_259_pad_type_0"), val = string("valid")]; + tensor x_259_strides_0 = const()[name = string("x_259_strides_0"), val = tensor([1])]; + tensor x_259_pad_0 = const()[name = string("x_259_pad_0"), val = tensor([0, 0])]; + tensor x_259_dilations_0 = const()[name = string("x_259_dilations_0"), val = tensor([1])]; + int32 x_259_groups_0 = const()[name = string("x_259_groups_0"), val = int32(1)]; + tensor encoder_layers_9_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201355520))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(202404160))))[name = string("encoder_layers_9_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_259_cast_fp16 = conv(dilations = x_259_dilations_0, groups = x_259_groups_0, pad = x_259_pad_0, pad_type = x_259_pad_type_0, strides = x_259_strides_0, weight = encoder_layers_9_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_527_cast_fp16)[name = string("x_259_cast_fp16")]; + tensor input_529_perm_0 = const()[name = string("input_529_perm_0"), val = tensor([0, 2, 1])]; + tensor input_529_cast_fp16 = transpose(perm = input_529_perm_0, x = x_259_cast_fp16)[name = string("transpose_273")]; + tensor input_531_cast_fp16 = add(x = input_515_cast_fp16, y = input_529_cast_fp16)[name = string("input_531_cast_fp16")]; + tensor input_533_axes_0 = const()[name = string("input_533_axes_0"), val = tensor([-1])]; + tensor encoder_layers_9_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_9_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(202406272)))]; + tensor encoder_layers_9_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_9_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(202408384)))]; + tensor input_533_cast_fp16 = layer_norm(axes = input_533_axes_0, beta = encoder_layers_9_norm_feed_forward2_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_9_norm_feed_forward2_weight_to_fp16, x = input_531_cast_fp16)[name = string("input_533_cast_fp16")]; + tensor encoder_layers_9_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(202410496))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205556288))))[name = string("encoder_layers_9_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_9_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_9_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205556480)))]; + tensor linear_89_cast_fp16 = linear(bias = encoder_layers_9_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_9_feed_forward2_linear1_weight_to_fp16_palettized, x = input_533_cast_fp16)[name = string("linear_89_cast_fp16")]; + tensor input_537_cast_fp16 = silu(x = linear_89_cast_fp16)[name = string("input_537_cast_fp16")]; + tensor encoder_layers_9_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205564736))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(208710528))))[name = string("encoder_layers_9_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_9_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_9_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(208710720)))]; + tensor linear_90_cast_fp16 = linear(bias = encoder_layers_9_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_9_feed_forward2_linear2_weight_to_fp16_palettized, x = input_537_cast_fp16)[name = string("linear_90_cast_fp16")]; + fp16 var_2547_to_fp16 = const()[name = string("op_2547_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2548_cast_fp16 = mul(x = linear_90_cast_fp16, y = var_2547_to_fp16)[name = string("op_2548_cast_fp16")]; + tensor input_543_cast_fp16 = add(x = input_531_cast_fp16, y = var_2548_cast_fp16)[name = string("input_543_cast_fp16")]; + tensor input_545_axes_0 = const()[name = string("input_545_axes_0"), val = tensor([-1])]; + tensor encoder_layers_9_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_9_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(208712832)))]; + tensor encoder_layers_9_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_9_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(208714944)))]; + tensor input_545_cast_fp16 = layer_norm(axes = input_545_axes_0, beta = encoder_layers_9_norm_out_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_9_norm_out_weight_to_fp16, x = input_543_cast_fp16)[name = string("input_545_cast_fp16")]; + tensor cache_41_begin_0 = const()[name = string("cache_41_begin_0"), val = tensor([10, 0, 0, 0])]; + tensor cache_41_end_0 = const()[name = string("cache_41_end_0"), val = tensor([11, 1, 42, 1024])]; + tensor cache_41_end_mask_0 = const()[name = string("cache_41_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_41_squeeze_mask_0 = const()[name = string("cache_41_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_41_cast_fp16 = slice_by_index(begin = cache_41_begin_0, end = cache_41_end_0, end_mask = cache_41_end_mask_0, squeeze_mask = cache_41_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_41_cast_fp16")]; + tensor cache_43_begin_0 = const()[name = string("cache_43_begin_0"), val = tensor([10, 0, 0, 0])]; + tensor cache_43_end_0 = const()[name = string("cache_43_end_0"), val = tensor([11, 1, 1024, 8])]; + tensor cache_43_end_mask_0 = const()[name = string("cache_43_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_43_squeeze_mask_0 = const()[name = string("cache_43_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_43_cast_fp16 = slice_by_index(begin = cache_43_begin_0, end = cache_43_end_0, end_mask = cache_43_end_mask_0, squeeze_mask = cache_43_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_43_cast_fp16")]; + tensor input_547_axes_0 = const()[name = string("input_547_axes_0"), val = tensor([-1])]; + tensor encoder_layers_10_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_10_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(208717056)))]; + tensor encoder_layers_10_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_10_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(208719168)))]; + tensor input_547_cast_fp16 = layer_norm(axes = input_547_axes_0, beta = encoder_layers_10_norm_feed_forward1_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_10_norm_feed_forward1_weight_to_fp16, x = input_545_cast_fp16)[name = string("input_547_cast_fp16")]; + tensor encoder_layers_10_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(208721280))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(211867072))))[name = string("encoder_layers_10_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_10_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_10_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(211867264)))]; + tensor linear_91_cast_fp16 = linear(bias = encoder_layers_10_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_10_feed_forward1_linear1_weight_to_fp16_palettized, x = input_547_cast_fp16)[name = string("linear_91_cast_fp16")]; + tensor input_551_cast_fp16 = silu(x = linear_91_cast_fp16)[name = string("input_551_cast_fp16")]; + tensor encoder_layers_10_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(211875520))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215021312))))[name = string("encoder_layers_10_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_10_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_10_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215021504)))]; + tensor linear_92_cast_fp16 = linear(bias = encoder_layers_10_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_10_feed_forward1_linear2_weight_to_fp16_palettized, x = input_551_cast_fp16)[name = string("linear_92_cast_fp16")]; + fp16 var_2584_to_fp16 = const()[name = string("op_2584_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2585_cast_fp16 = mul(x = linear_92_cast_fp16, y = var_2584_to_fp16)[name = string("op_2585_cast_fp16")]; + tensor input_557_cast_fp16 = add(x = input_545_cast_fp16, y = var_2585_cast_fp16)[name = string("input_557_cast_fp16")]; + tensor key_21_axes_0 = const()[name = string("key_21_axes_0"), val = tensor([-1])]; + tensor encoder_layers_10_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_10_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215023616)))]; + tensor encoder_layers_10_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_10_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215025728)))]; + tensor key_21_cast_fp16 = layer_norm(axes = key_21_axes_0, beta = encoder_layers_10_norm_self_att_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_10_norm_self_att_weight_to_fp16, x = input_557_cast_fp16)[name = string("key_21_cast_fp16")]; + bool input_559_interleave_0 = const()[name = string("input_559_interleave_0"), val = bool(false)]; + tensor input_559_cast_fp16 = concat(axis = var_67, interleave = input_559_interleave_0, values = (cache_41_cast_fp16, key_21_cast_fp16))[name = string("input_559_cast_fp16")]; + tensor var_2607_begin_0 = const()[name = string("op_2607_begin_0"), val = tensor([0, 14, 0])]; + tensor var_2607_end_0 = const()[name = string("op_2607_end_0"), val = tensor([1, 42, 1024])]; + tensor var_2607_end_mask_0 = const()[name = string("op_2607_end_mask_0"), val = tensor([true, true, true])]; + tensor var_2607_cast_fp16 = slice_by_index(begin = var_2607_begin_0, end = var_2607_end_0, end_mask = var_2607_end_mask_0, x = cache_41_cast_fp16)[name = string("op_2607_cast_fp16")]; + bool var_2613_interleave_0 = const()[name = string("op_2613_interleave_0"), val = bool(false)]; + tensor var_2613_cast_fp16 = concat(axis = var_67, interleave = var_2613_interleave_0, values = (var_2607_cast_fp16, key_21_cast_fp16))[name = string("op_2613_cast_fp16")]; + tensor encoder_layers_10_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215027840))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215814336))))[name = string("encoder_layers_10_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_10_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_10_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215814528)))]; + tensor linear_93_cast_fp16 = linear(bias = encoder_layers_10_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_10_self_attn_linear_q_weight_to_fp16_palettized, x = key_21_cast_fp16)[name = string("linear_93_cast_fp16")]; + tensor var_2618 = const()[name = string("op_2618"), val = tensor([1, -1, 8, 128])]; + tensor q_61_cast_fp16 = reshape(shape = var_2618, x = linear_93_cast_fp16)[name = string("q_61_cast_fp16")]; + tensor encoder_layers_10_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215816640))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216603136))))[name = string("encoder_layers_10_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_10_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_10_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216603328)))]; + tensor linear_94_cast_fp16 = linear(bias = encoder_layers_10_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_10_self_attn_linear_k_weight_to_fp16_palettized, x = input_559_cast_fp16)[name = string("linear_94_cast_fp16")]; + tensor var_2623 = const()[name = string("op_2623"), val = tensor([1, -1, 8, 128])]; + tensor k_41_cast_fp16 = reshape(shape = var_2623, x = linear_94_cast_fp16)[name = string("k_41_cast_fp16")]; + tensor encoder_layers_10_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216605440))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217391936))))[name = string("encoder_layers_10_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_10_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_10_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217392128)))]; + tensor linear_95_cast_fp16 = linear(bias = encoder_layers_10_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_10_self_attn_linear_v_weight_to_fp16_palettized, x = input_559_cast_fp16)[name = string("linear_95_cast_fp16")]; + tensor var_2628 = const()[name = string("op_2628"), val = tensor([1, -1, 8, 128])]; + tensor v_21_cast_fp16 = reshape(shape = var_2628, x = linear_95_cast_fp16)[name = string("v_21_cast_fp16")]; + tensor value_29_perm_0 = const()[name = string("value_29_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_10_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_10_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217394240)))]; + tensor var_2641_cast_fp16 = add(x = q_61_cast_fp16, y = encoder_layers_10_self_attn_pos_bias_u_to_fp16)[name = string("op_2641_cast_fp16")]; + tensor encoder_layers_10_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_10_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217396352)))]; + tensor var_2643_cast_fp16 = add(x = q_61_cast_fp16, y = encoder_layers_10_self_attn_pos_bias_v_to_fp16)[name = string("op_2643_cast_fp16")]; + tensor q_with_bias_v_21_perm_0 = const()[name = string("q_with_bias_v_21_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_267_transpose_x_0 = const()[name = string("x_267_transpose_x_0"), val = bool(false)]; + bool x_267_transpose_y_0 = const()[name = string("x_267_transpose_y_0"), val = bool(false)]; + tensor op_2645_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217398464))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217512192))))[name = string("op_2645_to_fp16_quantized")]; + tensor q_with_bias_v_21_cast_fp16 = transpose(perm = q_with_bias_v_21_perm_0, x = var_2643_cast_fp16)[name = string("transpose_272")]; + tensor x_267_cast_fp16 = matmul(transpose_x = x_267_transpose_x_0, transpose_y = x_267_transpose_y_0, x = q_with_bias_v_21_cast_fp16, y = op_2645_to_fp16_quantized)[name = string("x_267_cast_fp16")]; + tensor x_269_pad_0 = const()[name = string("x_269_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_269_mode_0 = const()[name = string("x_269_mode_0"), val = string("constant")]; + fp16 const_209_to_fp16 = const()[name = string("const_209_to_fp16"), val = fp16(0x0p+0)]; + tensor x_269_cast_fp16 = pad(constant_val = const_209_to_fp16, mode = x_269_mode_0, pad = x_269_pad_0, x = x_267_cast_fp16)[name = string("x_269_cast_fp16")]; + tensor var_2653 = const()[name = string("op_2653"), val = tensor([1, 8, -1, 14])]; + tensor x_271_cast_fp16 = reshape(shape = var_2653, x = x_269_cast_fp16)[name = string("x_271_cast_fp16")]; + tensor var_2657_begin_0 = const()[name = string("op_2657_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_2657_end_0 = const()[name = string("op_2657_end_0"), val = tensor([1, 8, 112, 14])]; + tensor var_2657_end_mask_0 = const()[name = string("op_2657_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_2657_cast_fp16 = slice_by_index(begin = var_2657_begin_0, end = var_2657_end_0, end_mask = var_2657_end_mask_0, x = x_271_cast_fp16)[name = string("op_2657_cast_fp16")]; + tensor var_2658 = const()[name = string("op_2658"), val = tensor([1, 8, 14, 111])]; + tensor matrix_bd_41_cast_fp16 = reshape(shape = var_2658, x = var_2657_cast_fp16)[name = string("matrix_bd_41_cast_fp16")]; + bool matrix_ac_21_transpose_x_0 = const()[name = string("matrix_ac_21_transpose_x_0"), val = bool(false)]; + bool matrix_ac_21_transpose_y_0 = const()[name = string("matrix_ac_21_transpose_y_0"), val = bool(false)]; + tensor transpose_116_perm_0 = const()[name = string("transpose_116_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_117_perm_0 = const()[name = string("transpose_117_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_117 = transpose(perm = transpose_117_perm_0, x = k_41_cast_fp16)[name = string("transpose_270")]; + tensor transpose_116 = transpose(perm = transpose_116_perm_0, x = var_2641_cast_fp16)[name = string("transpose_271")]; + tensor matrix_ac_21_cast_fp16 = matmul(transpose_x = matrix_ac_21_transpose_x_0, transpose_y = matrix_ac_21_transpose_y_0, x = transpose_116, y = transpose_117)[name = string("matrix_ac_21_cast_fp16")]; + tensor matrix_bd_43_begin_0 = const()[name = string("matrix_bd_43_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_43_end_0 = const()[name = string("matrix_bd_43_end_0"), val = tensor([1, 8, 14, 56])]; + tensor matrix_bd_43_end_mask_0 = const()[name = string("matrix_bd_43_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_43_cast_fp16 = slice_by_index(begin = matrix_bd_43_begin_0, end = matrix_bd_43_end_0, end_mask = matrix_bd_43_end_mask_0, x = matrix_bd_41_cast_fp16)[name = string("matrix_bd_43_cast_fp16")]; + tensor var_2667_cast_fp16 = add(x = matrix_ac_21_cast_fp16, y = matrix_bd_43_cast_fp16)[name = string("op_2667_cast_fp16")]; + fp16 _inversed_scores_41_y_0_to_fp16 = const()[name = string("_inversed_scores_41_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_41_cast_fp16 = mul(x = var_2667_cast_fp16, y = _inversed_scores_41_y_0_to_fp16)[name = string("_inversed_scores_41_cast_fp16")]; + tensor scores_43_cast_fp16 = select(a = var_44_to_fp16, b = _inversed_scores_41_cast_fp16, cond = mask_11)[name = string("scores_43_cast_fp16")]; + tensor var_2673_cast_fp16 = softmax(axis = var_58, x = scores_43_cast_fp16)[name = string("op_2673_cast_fp16")]; + tensor input_561_cast_fp16 = select(a = var_43_to_fp16, b = var_2673_cast_fp16, cond = mask_11)[name = string("input_561_cast_fp16")]; + bool x_273_transpose_x_0 = const()[name = string("x_273_transpose_x_0"), val = bool(false)]; + bool x_273_transpose_y_0 = const()[name = string("x_273_transpose_y_0"), val = bool(false)]; + tensor value_29_cast_fp16 = transpose(perm = value_29_perm_0, x = v_21_cast_fp16)[name = string("transpose_269")]; + tensor x_273_cast_fp16 = matmul(transpose_x = x_273_transpose_x_0, transpose_y = x_273_transpose_y_0, x = input_561_cast_fp16, y = value_29_cast_fp16)[name = string("x_273_cast_fp16")]; + tensor var_2677_perm_0 = const()[name = string("op_2677_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2678 = const()[name = string("op_2678"), val = tensor([1, -1, 1024])]; + tensor var_2677_cast_fp16 = transpose(perm = var_2677_perm_0, x = x_273_cast_fp16)[name = string("transpose_268")]; + tensor input_563_cast_fp16 = reshape(shape = var_2678, x = var_2677_cast_fp16)[name = string("input_563_cast_fp16")]; + tensor encoder_layers_10_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217512512))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218299008))))[name = string("encoder_layers_10_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_10_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_10_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218299200)))]; + tensor linear_97_cast_fp16 = linear(bias = encoder_layers_10_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_10_self_attn_linear_out_weight_to_fp16_palettized, x = input_563_cast_fp16)[name = string("linear_97_cast_fp16")]; + tensor input_567_cast_fp16 = add(x = input_557_cast_fp16, y = linear_97_cast_fp16)[name = string("input_567_cast_fp16")]; + tensor x_277_axes_0 = const()[name = string("x_277_axes_0"), val = tensor([-1])]; + tensor encoder_layers_10_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_10_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218301312)))]; + tensor encoder_layers_10_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_10_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218303424)))]; + tensor x_277_cast_fp16 = layer_norm(axes = x_277_axes_0, beta = encoder_layers_10_norm_conv_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_10_norm_conv_weight_to_fp16, x = input_567_cast_fp16)[name = string("x_277_cast_fp16")]; + tensor input_569_perm_0 = const()[name = string("input_569_perm_0"), val = tensor([0, 2, 1])]; + string input_571_pad_type_0 = const()[name = string("input_571_pad_type_0"), val = string("valid")]; + tensor input_571_strides_0 = const()[name = string("input_571_strides_0"), val = tensor([1])]; + tensor input_571_pad_0 = const()[name = string("input_571_pad_0"), val = tensor([0, 0])]; + tensor input_571_dilations_0 = const()[name = string("input_571_dilations_0"), val = tensor([1])]; + int32 input_571_groups_0 = const()[name = string("input_571_groups_0"), val = int32(1)]; + tensor encoder_layers_10_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218305536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220402752))))[name = string("encoder_layers_10_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_569_cast_fp16 = transpose(perm = input_569_perm_0, x = x_277_cast_fp16)[name = string("transpose_267")]; + tensor input_571_cast_fp16 = conv(dilations = input_571_dilations_0, groups = input_571_groups_0, pad = input_571_pad_0, pad_type = input_571_pad_type_0, strides = input_571_strides_0, weight = encoder_layers_10_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_569_cast_fp16)[name = string("input_571_cast_fp16")]; + int32 x_279_split_num_splits_0 = const()[name = string("x_279_split_num_splits_0"), val = int32(2)]; + int32 x_279_split_axis_0 = const()[name = string("x_279_split_axis_0"), val = int32(1)]; + tensor x_279_split_cast_fp16_0, tensor x_279_split_cast_fp16_1 = split(axis = x_279_split_axis_0, num_splits = x_279_split_num_splits_0, x = input_571_cast_fp16)[name = string("x_279_split_cast_fp16")]; + tensor x_279_split_1_sigmoid_cast_fp16 = sigmoid(x = x_279_split_cast_fp16_1)[name = string("x_279_split_1_sigmoid_cast_fp16")]; + tensor x_279_cast_fp16 = mul(x = x_279_split_cast_fp16_0, y = x_279_split_1_sigmoid_cast_fp16)[name = string("x_279_cast_fp16")]; + tensor input_573_cast_fp16 = select(a = var_43_to_fp16, b = x_279_cast_fp16, cond = var_574)[name = string("input_573_cast_fp16")]; + bool new_x_43_interleave_0 = const()[name = string("new_x_43_interleave_0"), val = bool(false)]; + tensor new_x_43_cast_fp16 = concat(axis = var_58, interleave = new_x_43_interleave_0, values = (cache_43_cast_fp16, input_573_cast_fp16))[name = string("new_x_43_cast_fp16")]; + tensor var_2717_begin_0 = const()[name = string("op_2717_begin_0"), val = tensor([0, 0, 14])]; + tensor var_2717_end_0 = const()[name = string("op_2717_end_0"), val = tensor([1, 1024, 22])]; + tensor var_2717_end_mask_0 = const()[name = string("op_2717_end_mask_0"), val = tensor([true, true, true])]; + tensor var_2717_cast_fp16 = slice_by_index(begin = var_2717_begin_0, end = var_2717_end_0, end_mask = var_2717_end_mask_0, x = new_x_43_cast_fp16)[name = string("op_2717_cast_fp16")]; + string x_281_pad_type_0 = const()[name = string("x_281_pad_type_0"), val = string("valid")]; + int32 x_281_groups_0 = const()[name = string("x_281_groups_0"), val = int32(1024)]; + tensor x_281_strides_0 = const()[name = string("x_281_strides_0"), val = tensor([1])]; + tensor x_281_pad_0 = const()[name = string("x_281_pad_0"), val = tensor([0, 0])]; + tensor x_281_dilations_0 = const()[name = string("x_281_dilations_0"), val = tensor([1])]; + tensor encoder_layers_10_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220406912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220416192))))[name = string("encoder_layers_10_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_281_cast_fp16 = conv(dilations = x_281_dilations_0, groups = x_281_groups_0, pad = x_281_pad_0, pad_type = x_281_pad_type_0, strides = x_281_strides_0, weight = encoder_layers_10_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_43_cast_fp16)[name = string("x_281_cast_fp16")]; + tensor input_575_perm_0 = const()[name = string("input_575_perm_0"), val = tensor([0, 2, 1])]; + tensor x_283_axes_0 = const()[name = string("x_283_axes_0"), val = tensor([-1])]; + tensor encoder_layers_10_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_10_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220418304)))]; + tensor encoder_layers_10_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_10_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220420416)))]; + tensor input_575_cast_fp16 = transpose(perm = input_575_perm_0, x = x_281_cast_fp16)[name = string("transpose_266")]; + tensor x_283_cast_fp16 = layer_norm(axes = x_283_axes_0, beta = encoder_layers_10_conv_batch_norm_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_10_conv_batch_norm_weight_to_fp16, x = input_575_cast_fp16)[name = string("x_283_cast_fp16")]; + tensor input_577_perm_0 = const()[name = string("input_577_perm_0"), val = tensor([0, 2, 1])]; + tensor input_577_cast_fp16 = transpose(perm = input_577_perm_0, x = x_283_cast_fp16)[name = string("transpose_265")]; + tensor input_579_cast_fp16 = silu(x = input_577_cast_fp16)[name = string("input_579_cast_fp16")]; + string x_285_pad_type_0 = const()[name = string("x_285_pad_type_0"), val = string("valid")]; + tensor x_285_strides_0 = const()[name = string("x_285_strides_0"), val = tensor([1])]; + tensor x_285_pad_0 = const()[name = string("x_285_pad_0"), val = tensor([0, 0])]; + tensor x_285_dilations_0 = const()[name = string("x_285_dilations_0"), val = tensor([1])]; + int32 x_285_groups_0 = const()[name = string("x_285_groups_0"), val = int32(1)]; + tensor encoder_layers_10_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220422528))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(221471168))))[name = string("encoder_layers_10_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_285_cast_fp16 = conv(dilations = x_285_dilations_0, groups = x_285_groups_0, pad = x_285_pad_0, pad_type = x_285_pad_type_0, strides = x_285_strides_0, weight = encoder_layers_10_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_579_cast_fp16)[name = string("x_285_cast_fp16")]; + tensor input_581_perm_0 = const()[name = string("input_581_perm_0"), val = tensor([0, 2, 1])]; + tensor input_581_cast_fp16 = transpose(perm = input_581_perm_0, x = x_285_cast_fp16)[name = string("transpose_264")]; + tensor input_583_cast_fp16 = add(x = input_567_cast_fp16, y = input_581_cast_fp16)[name = string("input_583_cast_fp16")]; + tensor input_585_axes_0 = const()[name = string("input_585_axes_0"), val = tensor([-1])]; + tensor encoder_layers_10_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_10_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(221473280)))]; + tensor encoder_layers_10_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_10_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(221475392)))]; + tensor input_585_cast_fp16 = layer_norm(axes = input_585_axes_0, beta = encoder_layers_10_norm_feed_forward2_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_10_norm_feed_forward2_weight_to_fp16, x = input_583_cast_fp16)[name = string("input_585_cast_fp16")]; + tensor encoder_layers_10_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(221477504))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224623296))))[name = string("encoder_layers_10_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_10_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_10_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224623488)))]; + tensor linear_98_cast_fp16 = linear(bias = encoder_layers_10_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_10_feed_forward2_linear1_weight_to_fp16_palettized, x = input_585_cast_fp16)[name = string("linear_98_cast_fp16")]; + tensor input_589_cast_fp16 = silu(x = linear_98_cast_fp16)[name = string("input_589_cast_fp16")]; + tensor encoder_layers_10_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224631744))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(227777536))))[name = string("encoder_layers_10_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_10_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_10_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(227777728)))]; + tensor linear_99_cast_fp16 = linear(bias = encoder_layers_10_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_10_feed_forward2_linear2_weight_to_fp16_palettized, x = input_589_cast_fp16)[name = string("linear_99_cast_fp16")]; + fp16 var_2760_to_fp16 = const()[name = string("op_2760_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2761_cast_fp16 = mul(x = linear_99_cast_fp16, y = var_2760_to_fp16)[name = string("op_2761_cast_fp16")]; + tensor input_595_cast_fp16 = add(x = input_583_cast_fp16, y = var_2761_cast_fp16)[name = string("input_595_cast_fp16")]; + tensor input_597_axes_0 = const()[name = string("input_597_axes_0"), val = tensor([-1])]; + tensor encoder_layers_10_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_10_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(227779840)))]; + tensor encoder_layers_10_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_10_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(227781952)))]; + tensor input_597_cast_fp16 = layer_norm(axes = input_597_axes_0, beta = encoder_layers_10_norm_out_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_10_norm_out_weight_to_fp16, x = input_595_cast_fp16)[name = string("input_597_cast_fp16")]; + tensor cache_45_begin_0 = const()[name = string("cache_45_begin_0"), val = tensor([11, 0, 0, 0])]; + tensor cache_45_end_0 = const()[name = string("cache_45_end_0"), val = tensor([12, 1, 42, 1024])]; + tensor cache_45_end_mask_0 = const()[name = string("cache_45_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_45_squeeze_mask_0 = const()[name = string("cache_45_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_45_cast_fp16 = slice_by_index(begin = cache_45_begin_0, end = cache_45_end_0, end_mask = cache_45_end_mask_0, squeeze_mask = cache_45_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_45_cast_fp16")]; + tensor cache_47_begin_0 = const()[name = string("cache_47_begin_0"), val = tensor([11, 0, 0, 0])]; + tensor cache_47_end_0 = const()[name = string("cache_47_end_0"), val = tensor([12, 1, 1024, 8])]; + tensor cache_47_end_mask_0 = const()[name = string("cache_47_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_47_squeeze_mask_0 = const()[name = string("cache_47_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_47_cast_fp16 = slice_by_index(begin = cache_47_begin_0, end = cache_47_end_0, end_mask = cache_47_end_mask_0, squeeze_mask = cache_47_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_47_cast_fp16")]; + tensor input_599_axes_0 = const()[name = string("input_599_axes_0"), val = tensor([-1])]; + tensor encoder_layers_11_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_11_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(227784064)))]; + tensor encoder_layers_11_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_11_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(227786176)))]; + tensor input_599_cast_fp16 = layer_norm(axes = input_599_axes_0, beta = encoder_layers_11_norm_feed_forward1_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_11_norm_feed_forward1_weight_to_fp16, x = input_597_cast_fp16)[name = string("input_599_cast_fp16")]; + tensor encoder_layers_11_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(227788288))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230934080))))[name = string("encoder_layers_11_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_11_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_11_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230934272)))]; + tensor linear_100_cast_fp16 = linear(bias = encoder_layers_11_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_11_feed_forward1_linear1_weight_to_fp16_palettized, x = input_599_cast_fp16)[name = string("linear_100_cast_fp16")]; + tensor input_603_cast_fp16 = silu(x = linear_100_cast_fp16)[name = string("input_603_cast_fp16")]; + tensor encoder_layers_11_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230942528))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(234088320))))[name = string("encoder_layers_11_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_11_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_11_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(234088512)))]; + tensor linear_101_cast_fp16 = linear(bias = encoder_layers_11_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_11_feed_forward1_linear2_weight_to_fp16_palettized, x = input_603_cast_fp16)[name = string("linear_101_cast_fp16")]; + fp16 var_2797_to_fp16 = const()[name = string("op_2797_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2798_cast_fp16 = mul(x = linear_101_cast_fp16, y = var_2797_to_fp16)[name = string("op_2798_cast_fp16")]; + tensor input_609_cast_fp16 = add(x = input_597_cast_fp16, y = var_2798_cast_fp16)[name = string("input_609_cast_fp16")]; + tensor key_23_axes_0 = const()[name = string("key_23_axes_0"), val = tensor([-1])]; + tensor encoder_layers_11_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_11_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(234090624)))]; + tensor encoder_layers_11_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_11_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(234092736)))]; + tensor key_23_cast_fp16 = layer_norm(axes = key_23_axes_0, beta = encoder_layers_11_norm_self_att_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_11_norm_self_att_weight_to_fp16, x = input_609_cast_fp16)[name = string("key_23_cast_fp16")]; + bool input_611_interleave_0 = const()[name = string("input_611_interleave_0"), val = bool(false)]; + tensor input_611_cast_fp16 = concat(axis = var_67, interleave = input_611_interleave_0, values = (cache_45_cast_fp16, key_23_cast_fp16))[name = string("input_611_cast_fp16")]; + tensor var_2820_begin_0 = const()[name = string("op_2820_begin_0"), val = tensor([0, 14, 0])]; + tensor var_2820_end_0 = const()[name = string("op_2820_end_0"), val = tensor([1, 42, 1024])]; + tensor var_2820_end_mask_0 = const()[name = string("op_2820_end_mask_0"), val = tensor([true, true, true])]; + tensor var_2820_cast_fp16 = slice_by_index(begin = var_2820_begin_0, end = var_2820_end_0, end_mask = var_2820_end_mask_0, x = cache_45_cast_fp16)[name = string("op_2820_cast_fp16")]; + bool var_2826_interleave_0 = const()[name = string("op_2826_interleave_0"), val = bool(false)]; + tensor var_2826_cast_fp16 = concat(axis = var_67, interleave = var_2826_interleave_0, values = (var_2820_cast_fp16, key_23_cast_fp16))[name = string("op_2826_cast_fp16")]; + tensor encoder_layers_11_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(234094848))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(234881344))))[name = string("encoder_layers_11_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_11_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_11_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(234881536)))]; + tensor linear_102_cast_fp16 = linear(bias = encoder_layers_11_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_11_self_attn_linear_q_weight_to_fp16_palettized, x = key_23_cast_fp16)[name = string("linear_102_cast_fp16")]; + tensor var_2831 = const()[name = string("op_2831"), val = tensor([1, -1, 8, 128])]; + tensor q_67_cast_fp16 = reshape(shape = var_2831, x = linear_102_cast_fp16)[name = string("q_67_cast_fp16")]; + tensor encoder_layers_11_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(234883648))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235670144))))[name = string("encoder_layers_11_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_11_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_11_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235670336)))]; + tensor linear_103_cast_fp16 = linear(bias = encoder_layers_11_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_11_self_attn_linear_k_weight_to_fp16_palettized, x = input_611_cast_fp16)[name = string("linear_103_cast_fp16")]; + tensor var_2836 = const()[name = string("op_2836"), val = tensor([1, -1, 8, 128])]; + tensor k_45_cast_fp16 = reshape(shape = var_2836, x = linear_103_cast_fp16)[name = string("k_45_cast_fp16")]; + tensor encoder_layers_11_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235672448))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236458944))))[name = string("encoder_layers_11_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_11_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_11_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236459136)))]; + tensor linear_104_cast_fp16 = linear(bias = encoder_layers_11_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_11_self_attn_linear_v_weight_to_fp16_palettized, x = input_611_cast_fp16)[name = string("linear_104_cast_fp16")]; + tensor var_2841 = const()[name = string("op_2841"), val = tensor([1, -1, 8, 128])]; + tensor v_23_cast_fp16 = reshape(shape = var_2841, x = linear_104_cast_fp16)[name = string("v_23_cast_fp16")]; + tensor value_31_perm_0 = const()[name = string("value_31_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_11_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_11_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236461248)))]; + tensor var_2854_cast_fp16 = add(x = q_67_cast_fp16, y = encoder_layers_11_self_attn_pos_bias_u_to_fp16)[name = string("op_2854_cast_fp16")]; + tensor encoder_layers_11_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_11_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236463360)))]; + tensor var_2856_cast_fp16 = add(x = q_67_cast_fp16, y = encoder_layers_11_self_attn_pos_bias_v_to_fp16)[name = string("op_2856_cast_fp16")]; + tensor q_with_bias_v_23_perm_0 = const()[name = string("q_with_bias_v_23_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_293_transpose_x_0 = const()[name = string("x_293_transpose_x_0"), val = bool(false)]; + bool x_293_transpose_y_0 = const()[name = string("x_293_transpose_y_0"), val = bool(false)]; + tensor op_2858_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236465472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236579200))))[name = string("op_2858_to_fp16_quantized")]; + tensor q_with_bias_v_23_cast_fp16 = transpose(perm = q_with_bias_v_23_perm_0, x = var_2856_cast_fp16)[name = string("transpose_263")]; + tensor x_293_cast_fp16 = matmul(transpose_x = x_293_transpose_x_0, transpose_y = x_293_transpose_y_0, x = q_with_bias_v_23_cast_fp16, y = op_2858_to_fp16_quantized)[name = string("x_293_cast_fp16")]; + tensor x_295_pad_0 = const()[name = string("x_295_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_295_mode_0 = const()[name = string("x_295_mode_0"), val = string("constant")]; + fp16 const_222_to_fp16 = const()[name = string("const_222_to_fp16"), val = fp16(0x0p+0)]; + tensor x_295_cast_fp16 = pad(constant_val = const_222_to_fp16, mode = x_295_mode_0, pad = x_295_pad_0, x = x_293_cast_fp16)[name = string("x_295_cast_fp16")]; + tensor var_2866 = const()[name = string("op_2866"), val = tensor([1, 8, -1, 14])]; + tensor x_297_cast_fp16 = reshape(shape = var_2866, x = x_295_cast_fp16)[name = string("x_297_cast_fp16")]; + tensor var_2870_begin_0 = const()[name = string("op_2870_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_2870_end_0 = const()[name = string("op_2870_end_0"), val = tensor([1, 8, 112, 14])]; + tensor var_2870_end_mask_0 = const()[name = string("op_2870_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_2870_cast_fp16 = slice_by_index(begin = var_2870_begin_0, end = var_2870_end_0, end_mask = var_2870_end_mask_0, x = x_297_cast_fp16)[name = string("op_2870_cast_fp16")]; + tensor var_2871 = const()[name = string("op_2871"), val = tensor([1, 8, 14, 111])]; + tensor matrix_bd_45_cast_fp16 = reshape(shape = var_2871, x = var_2870_cast_fp16)[name = string("matrix_bd_45_cast_fp16")]; + bool matrix_ac_23_transpose_x_0 = const()[name = string("matrix_ac_23_transpose_x_0"), val = bool(false)]; + bool matrix_ac_23_transpose_y_0 = const()[name = string("matrix_ac_23_transpose_y_0"), val = bool(false)]; + tensor transpose_118_perm_0 = const()[name = string("transpose_118_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_119_perm_0 = const()[name = string("transpose_119_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_119 = transpose(perm = transpose_119_perm_0, x = k_45_cast_fp16)[name = string("transpose_261")]; + tensor transpose_118 = transpose(perm = transpose_118_perm_0, x = var_2854_cast_fp16)[name = string("transpose_262")]; + tensor matrix_ac_23_cast_fp16 = matmul(transpose_x = matrix_ac_23_transpose_x_0, transpose_y = matrix_ac_23_transpose_y_0, x = transpose_118, y = transpose_119)[name = string("matrix_ac_23_cast_fp16")]; + tensor matrix_bd_47_begin_0 = const()[name = string("matrix_bd_47_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_47_end_0 = const()[name = string("matrix_bd_47_end_0"), val = tensor([1, 8, 14, 56])]; + tensor matrix_bd_47_end_mask_0 = const()[name = string("matrix_bd_47_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_47_cast_fp16 = slice_by_index(begin = matrix_bd_47_begin_0, end = matrix_bd_47_end_0, end_mask = matrix_bd_47_end_mask_0, x = matrix_bd_45_cast_fp16)[name = string("matrix_bd_47_cast_fp16")]; + tensor var_2880_cast_fp16 = add(x = matrix_ac_23_cast_fp16, y = matrix_bd_47_cast_fp16)[name = string("op_2880_cast_fp16")]; + fp16 _inversed_scores_45_y_0_to_fp16 = const()[name = string("_inversed_scores_45_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_45_cast_fp16 = mul(x = var_2880_cast_fp16, y = _inversed_scores_45_y_0_to_fp16)[name = string("_inversed_scores_45_cast_fp16")]; + tensor scores_47_cast_fp16 = select(a = var_44_to_fp16, b = _inversed_scores_45_cast_fp16, cond = mask_11)[name = string("scores_47_cast_fp16")]; + tensor var_2886_cast_fp16 = softmax(axis = var_58, x = scores_47_cast_fp16)[name = string("op_2886_cast_fp16")]; + tensor input_613_cast_fp16 = select(a = var_43_to_fp16, b = var_2886_cast_fp16, cond = mask_11)[name = string("input_613_cast_fp16")]; + bool x_299_transpose_x_0 = const()[name = string("x_299_transpose_x_0"), val = bool(false)]; + bool x_299_transpose_y_0 = const()[name = string("x_299_transpose_y_0"), val = bool(false)]; + tensor value_31_cast_fp16 = transpose(perm = value_31_perm_0, x = v_23_cast_fp16)[name = string("transpose_260")]; + tensor x_299_cast_fp16 = matmul(transpose_x = x_299_transpose_x_0, transpose_y = x_299_transpose_y_0, x = input_613_cast_fp16, y = value_31_cast_fp16)[name = string("x_299_cast_fp16")]; + tensor var_2890_perm_0 = const()[name = string("op_2890_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2891 = const()[name = string("op_2891"), val = tensor([1, -1, 1024])]; + tensor var_2890_cast_fp16 = transpose(perm = var_2890_perm_0, x = x_299_cast_fp16)[name = string("transpose_259")]; + tensor input_615_cast_fp16 = reshape(shape = var_2891, x = var_2890_cast_fp16)[name = string("input_615_cast_fp16")]; + tensor encoder_layers_11_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236579520))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(237366016))))[name = string("encoder_layers_11_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_11_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_11_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(237366208)))]; + tensor linear_106_cast_fp16 = linear(bias = encoder_layers_11_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_11_self_attn_linear_out_weight_to_fp16_palettized, x = input_615_cast_fp16)[name = string("linear_106_cast_fp16")]; + tensor input_619_cast_fp16 = add(x = input_609_cast_fp16, y = linear_106_cast_fp16)[name = string("input_619_cast_fp16")]; + tensor x_303_axes_0 = const()[name = string("x_303_axes_0"), val = tensor([-1])]; + tensor encoder_layers_11_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_11_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(237368320)))]; + tensor encoder_layers_11_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_11_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(237370432)))]; + tensor x_303_cast_fp16 = layer_norm(axes = x_303_axes_0, beta = encoder_layers_11_norm_conv_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_11_norm_conv_weight_to_fp16, x = input_619_cast_fp16)[name = string("x_303_cast_fp16")]; + tensor input_621_perm_0 = const()[name = string("input_621_perm_0"), val = tensor([0, 2, 1])]; + string input_623_pad_type_0 = const()[name = string("input_623_pad_type_0"), val = string("valid")]; + tensor input_623_strides_0 = const()[name = string("input_623_strides_0"), val = tensor([1])]; + tensor input_623_pad_0 = const()[name = string("input_623_pad_0"), val = tensor([0, 0])]; + tensor input_623_dilations_0 = const()[name = string("input_623_dilations_0"), val = tensor([1])]; + int32 input_623_groups_0 = const()[name = string("input_623_groups_0"), val = int32(1)]; + tensor encoder_layers_11_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(237372544))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(239469760))))[name = string("encoder_layers_11_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_621_cast_fp16 = transpose(perm = input_621_perm_0, x = x_303_cast_fp16)[name = string("transpose_258")]; + tensor input_623_cast_fp16 = conv(dilations = input_623_dilations_0, groups = input_623_groups_0, pad = input_623_pad_0, pad_type = input_623_pad_type_0, strides = input_623_strides_0, weight = encoder_layers_11_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_621_cast_fp16)[name = string("input_623_cast_fp16")]; + int32 x_305_split_num_splits_0 = const()[name = string("x_305_split_num_splits_0"), val = int32(2)]; + int32 x_305_split_axis_0 = const()[name = string("x_305_split_axis_0"), val = int32(1)]; + tensor x_305_split_cast_fp16_0, tensor x_305_split_cast_fp16_1 = split(axis = x_305_split_axis_0, num_splits = x_305_split_num_splits_0, x = input_623_cast_fp16)[name = string("x_305_split_cast_fp16")]; + tensor x_305_split_1_sigmoid_cast_fp16 = sigmoid(x = x_305_split_cast_fp16_1)[name = string("x_305_split_1_sigmoid_cast_fp16")]; + tensor x_305_cast_fp16 = mul(x = x_305_split_cast_fp16_0, y = x_305_split_1_sigmoid_cast_fp16)[name = string("x_305_cast_fp16")]; + tensor input_625_cast_fp16 = select(a = var_43_to_fp16, b = x_305_cast_fp16, cond = var_574)[name = string("input_625_cast_fp16")]; + bool new_x_47_interleave_0 = const()[name = string("new_x_47_interleave_0"), val = bool(false)]; + tensor new_x_47_cast_fp16 = concat(axis = var_58, interleave = new_x_47_interleave_0, values = (cache_47_cast_fp16, input_625_cast_fp16))[name = string("new_x_47_cast_fp16")]; + tensor var_2930_begin_0 = const()[name = string("op_2930_begin_0"), val = tensor([0, 0, 14])]; + tensor var_2930_end_0 = const()[name = string("op_2930_end_0"), val = tensor([1, 1024, 22])]; + tensor var_2930_end_mask_0 = const()[name = string("op_2930_end_mask_0"), val = tensor([true, true, true])]; + tensor var_2930_cast_fp16 = slice_by_index(begin = var_2930_begin_0, end = var_2930_end_0, end_mask = var_2930_end_mask_0, x = new_x_47_cast_fp16)[name = string("op_2930_cast_fp16")]; + string x_307_pad_type_0 = const()[name = string("x_307_pad_type_0"), val = string("valid")]; + int32 x_307_groups_0 = const()[name = string("x_307_groups_0"), val = int32(1024)]; + tensor x_307_strides_0 = const()[name = string("x_307_strides_0"), val = tensor([1])]; + tensor x_307_pad_0 = const()[name = string("x_307_pad_0"), val = tensor([0, 0])]; + tensor x_307_dilations_0 = const()[name = string("x_307_dilations_0"), val = tensor([1])]; + tensor encoder_layers_11_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(239473920))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(239483200))))[name = string("encoder_layers_11_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_307_cast_fp16 = conv(dilations = x_307_dilations_0, groups = x_307_groups_0, pad = x_307_pad_0, pad_type = x_307_pad_type_0, strides = x_307_strides_0, weight = encoder_layers_11_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_47_cast_fp16)[name = string("x_307_cast_fp16")]; + tensor input_627_perm_0 = const()[name = string("input_627_perm_0"), val = tensor([0, 2, 1])]; + tensor x_309_axes_0 = const()[name = string("x_309_axes_0"), val = tensor([-1])]; + tensor encoder_layers_11_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_11_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(239485312)))]; + tensor encoder_layers_11_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_11_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(239487424)))]; + tensor input_627_cast_fp16 = transpose(perm = input_627_perm_0, x = x_307_cast_fp16)[name = string("transpose_257")]; + tensor x_309_cast_fp16 = layer_norm(axes = x_309_axes_0, beta = encoder_layers_11_conv_batch_norm_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_11_conv_batch_norm_weight_to_fp16, x = input_627_cast_fp16)[name = string("x_309_cast_fp16")]; + tensor input_629_perm_0 = const()[name = string("input_629_perm_0"), val = tensor([0, 2, 1])]; + tensor input_629_cast_fp16 = transpose(perm = input_629_perm_0, x = x_309_cast_fp16)[name = string("transpose_256")]; + tensor input_631_cast_fp16 = silu(x = input_629_cast_fp16)[name = string("input_631_cast_fp16")]; + string x_311_pad_type_0 = const()[name = string("x_311_pad_type_0"), val = string("valid")]; + tensor x_311_strides_0 = const()[name = string("x_311_strides_0"), val = tensor([1])]; + tensor x_311_pad_0 = const()[name = string("x_311_pad_0"), val = tensor([0, 0])]; + tensor x_311_dilations_0 = const()[name = string("x_311_dilations_0"), val = tensor([1])]; + int32 x_311_groups_0 = const()[name = string("x_311_groups_0"), val = int32(1)]; + tensor encoder_layers_11_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(239489536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240538176))))[name = string("encoder_layers_11_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_311_cast_fp16 = conv(dilations = x_311_dilations_0, groups = x_311_groups_0, pad = x_311_pad_0, pad_type = x_311_pad_type_0, strides = x_311_strides_0, weight = encoder_layers_11_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_631_cast_fp16)[name = string("x_311_cast_fp16")]; + tensor input_633_perm_0 = const()[name = string("input_633_perm_0"), val = tensor([0, 2, 1])]; + tensor input_633_cast_fp16 = transpose(perm = input_633_perm_0, x = x_311_cast_fp16)[name = string("transpose_255")]; + tensor input_635_cast_fp16 = add(x = input_619_cast_fp16, y = input_633_cast_fp16)[name = string("input_635_cast_fp16")]; + tensor input_637_axes_0 = const()[name = string("input_637_axes_0"), val = tensor([-1])]; + tensor encoder_layers_11_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_11_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240540288)))]; + tensor encoder_layers_11_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_11_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240542400)))]; + tensor input_637_cast_fp16 = layer_norm(axes = input_637_axes_0, beta = encoder_layers_11_norm_feed_forward2_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_11_norm_feed_forward2_weight_to_fp16, x = input_635_cast_fp16)[name = string("input_637_cast_fp16")]; + tensor encoder_layers_11_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240544512))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(243690304))))[name = string("encoder_layers_11_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_11_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_11_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(243690496)))]; + tensor linear_107_cast_fp16 = linear(bias = encoder_layers_11_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_11_feed_forward2_linear1_weight_to_fp16_palettized, x = input_637_cast_fp16)[name = string("linear_107_cast_fp16")]; + tensor input_641_cast_fp16 = silu(x = linear_107_cast_fp16)[name = string("input_641_cast_fp16")]; + tensor encoder_layers_11_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(243698752))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(246844544))))[name = string("encoder_layers_11_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_11_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_11_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(246844736)))]; + tensor linear_108_cast_fp16 = linear(bias = encoder_layers_11_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_11_feed_forward2_linear2_weight_to_fp16_palettized, x = input_641_cast_fp16)[name = string("linear_108_cast_fp16")]; + fp16 var_2973_to_fp16 = const()[name = string("op_2973_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2974_cast_fp16 = mul(x = linear_108_cast_fp16, y = var_2973_to_fp16)[name = string("op_2974_cast_fp16")]; + tensor input_647_cast_fp16 = add(x = input_635_cast_fp16, y = var_2974_cast_fp16)[name = string("input_647_cast_fp16")]; + tensor input_649_axes_0 = const()[name = string("input_649_axes_0"), val = tensor([-1])]; + tensor encoder_layers_11_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_11_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(246846848)))]; + tensor encoder_layers_11_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_11_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(246848960)))]; + tensor input_649_cast_fp16 = layer_norm(axes = input_649_axes_0, beta = encoder_layers_11_norm_out_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_11_norm_out_weight_to_fp16, x = input_647_cast_fp16)[name = string("input_649_cast_fp16")]; + tensor cache_49_begin_0 = const()[name = string("cache_49_begin_0"), val = tensor([12, 0, 0, 0])]; + tensor cache_49_end_0 = const()[name = string("cache_49_end_0"), val = tensor([13, 1, 42, 1024])]; + tensor cache_49_end_mask_0 = const()[name = string("cache_49_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_49_squeeze_mask_0 = const()[name = string("cache_49_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_49_cast_fp16 = slice_by_index(begin = cache_49_begin_0, end = cache_49_end_0, end_mask = cache_49_end_mask_0, squeeze_mask = cache_49_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_49_cast_fp16")]; + tensor cache_51_begin_0 = const()[name = string("cache_51_begin_0"), val = tensor([12, 0, 0, 0])]; + tensor cache_51_end_0 = const()[name = string("cache_51_end_0"), val = tensor([13, 1, 1024, 8])]; + tensor cache_51_end_mask_0 = const()[name = string("cache_51_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_51_squeeze_mask_0 = const()[name = string("cache_51_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_51_cast_fp16 = slice_by_index(begin = cache_51_begin_0, end = cache_51_end_0, end_mask = cache_51_end_mask_0, squeeze_mask = cache_51_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_51_cast_fp16")]; + tensor input_651_axes_0 = const()[name = string("input_651_axes_0"), val = tensor([-1])]; + tensor encoder_layers_12_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_12_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(246851072)))]; + tensor encoder_layers_12_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_12_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(246853184)))]; + tensor input_651_cast_fp16 = layer_norm(axes = input_651_axes_0, beta = encoder_layers_12_norm_feed_forward1_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_12_norm_feed_forward1_weight_to_fp16, x = input_649_cast_fp16)[name = string("input_651_cast_fp16")]; + tensor encoder_layers_12_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(246855296))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(250001088))))[name = string("encoder_layers_12_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_12_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_12_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(250001280)))]; + tensor linear_109_cast_fp16 = linear(bias = encoder_layers_12_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_12_feed_forward1_linear1_weight_to_fp16_palettized, x = input_651_cast_fp16)[name = string("linear_109_cast_fp16")]; + tensor input_655_cast_fp16 = silu(x = linear_109_cast_fp16)[name = string("input_655_cast_fp16")]; + tensor encoder_layers_12_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(250009536))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(253155328))))[name = string("encoder_layers_12_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_12_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_12_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(253155520)))]; + tensor linear_110_cast_fp16 = linear(bias = encoder_layers_12_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_12_feed_forward1_linear2_weight_to_fp16_palettized, x = input_655_cast_fp16)[name = string("linear_110_cast_fp16")]; + fp16 var_3010_to_fp16 = const()[name = string("op_3010_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3011_cast_fp16 = mul(x = linear_110_cast_fp16, y = var_3010_to_fp16)[name = string("op_3011_cast_fp16")]; + tensor input_661_cast_fp16 = add(x = input_649_cast_fp16, y = var_3011_cast_fp16)[name = string("input_661_cast_fp16")]; + tensor key_25_axes_0 = const()[name = string("key_25_axes_0"), val = tensor([-1])]; + tensor encoder_layers_12_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_12_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(253157632)))]; + tensor encoder_layers_12_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_12_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(253159744)))]; + tensor key_25_cast_fp16 = layer_norm(axes = key_25_axes_0, beta = encoder_layers_12_norm_self_att_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_12_norm_self_att_weight_to_fp16, x = input_661_cast_fp16)[name = string("key_25_cast_fp16")]; + bool input_663_interleave_0 = const()[name = string("input_663_interleave_0"), val = bool(false)]; + tensor input_663_cast_fp16 = concat(axis = var_67, interleave = input_663_interleave_0, values = (cache_49_cast_fp16, key_25_cast_fp16))[name = string("input_663_cast_fp16")]; + tensor var_3033_begin_0 = const()[name = string("op_3033_begin_0"), val = tensor([0, 14, 0])]; + tensor var_3033_end_0 = const()[name = string("op_3033_end_0"), val = tensor([1, 42, 1024])]; + tensor var_3033_end_mask_0 = const()[name = string("op_3033_end_mask_0"), val = tensor([true, true, true])]; + tensor var_3033_cast_fp16 = slice_by_index(begin = var_3033_begin_0, end = var_3033_end_0, end_mask = var_3033_end_mask_0, x = cache_49_cast_fp16)[name = string("op_3033_cast_fp16")]; + bool var_3039_interleave_0 = const()[name = string("op_3039_interleave_0"), val = bool(false)]; + tensor var_3039_cast_fp16 = concat(axis = var_67, interleave = var_3039_interleave_0, values = (var_3033_cast_fp16, key_25_cast_fp16))[name = string("op_3039_cast_fp16")]; + tensor encoder_layers_12_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(253161856))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(253948352))))[name = string("encoder_layers_12_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_12_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_12_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(253948544)))]; + tensor linear_111_cast_fp16 = linear(bias = encoder_layers_12_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_12_self_attn_linear_q_weight_to_fp16_palettized, x = key_25_cast_fp16)[name = string("linear_111_cast_fp16")]; + tensor var_3044 = const()[name = string("op_3044"), val = tensor([1, -1, 8, 128])]; + tensor q_73_cast_fp16 = reshape(shape = var_3044, x = linear_111_cast_fp16)[name = string("q_73_cast_fp16")]; + tensor encoder_layers_12_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(253950656))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(254737152))))[name = string("encoder_layers_12_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_12_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_12_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(254737344)))]; + tensor linear_112_cast_fp16 = linear(bias = encoder_layers_12_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_12_self_attn_linear_k_weight_to_fp16_palettized, x = input_663_cast_fp16)[name = string("linear_112_cast_fp16")]; + tensor var_3049 = const()[name = string("op_3049"), val = tensor([1, -1, 8, 128])]; + tensor k_49_cast_fp16 = reshape(shape = var_3049, x = linear_112_cast_fp16)[name = string("k_49_cast_fp16")]; + tensor encoder_layers_12_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(254739456))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(255525952))))[name = string("encoder_layers_12_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_12_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_12_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(255526144)))]; + tensor linear_113_cast_fp16 = linear(bias = encoder_layers_12_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_12_self_attn_linear_v_weight_to_fp16_palettized, x = input_663_cast_fp16)[name = string("linear_113_cast_fp16")]; + tensor var_3054 = const()[name = string("op_3054"), val = tensor([1, -1, 8, 128])]; + tensor v_25_cast_fp16 = reshape(shape = var_3054, x = linear_113_cast_fp16)[name = string("v_25_cast_fp16")]; + tensor value_33_perm_0 = const()[name = string("value_33_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_12_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_12_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(255528256)))]; + tensor var_3067_cast_fp16 = add(x = q_73_cast_fp16, y = encoder_layers_12_self_attn_pos_bias_u_to_fp16)[name = string("op_3067_cast_fp16")]; + tensor encoder_layers_12_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_12_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(255530368)))]; + tensor var_3069_cast_fp16 = add(x = q_73_cast_fp16, y = encoder_layers_12_self_attn_pos_bias_v_to_fp16)[name = string("op_3069_cast_fp16")]; + tensor q_with_bias_v_25_perm_0 = const()[name = string("q_with_bias_v_25_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_319_transpose_x_0 = const()[name = string("x_319_transpose_x_0"), val = bool(false)]; + bool x_319_transpose_y_0 = const()[name = string("x_319_transpose_y_0"), val = bool(false)]; + tensor op_3071_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(255532480))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(255646208))))[name = string("op_3071_to_fp16_quantized")]; + tensor q_with_bias_v_25_cast_fp16 = transpose(perm = q_with_bias_v_25_perm_0, x = var_3069_cast_fp16)[name = string("transpose_254")]; + tensor x_319_cast_fp16 = matmul(transpose_x = x_319_transpose_x_0, transpose_y = x_319_transpose_y_0, x = q_with_bias_v_25_cast_fp16, y = op_3071_to_fp16_quantized)[name = string("x_319_cast_fp16")]; + tensor x_321_pad_0 = const()[name = string("x_321_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_321_mode_0 = const()[name = string("x_321_mode_0"), val = string("constant")]; + fp16 const_235_to_fp16 = const()[name = string("const_235_to_fp16"), val = fp16(0x0p+0)]; + tensor x_321_cast_fp16 = pad(constant_val = const_235_to_fp16, mode = x_321_mode_0, pad = x_321_pad_0, x = x_319_cast_fp16)[name = string("x_321_cast_fp16")]; + tensor var_3079 = const()[name = string("op_3079"), val = tensor([1, 8, -1, 14])]; + tensor x_323_cast_fp16 = reshape(shape = var_3079, x = x_321_cast_fp16)[name = string("x_323_cast_fp16")]; + tensor var_3083_begin_0 = const()[name = string("op_3083_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_3083_end_0 = const()[name = string("op_3083_end_0"), val = tensor([1, 8, 112, 14])]; + tensor var_3083_end_mask_0 = const()[name = string("op_3083_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_3083_cast_fp16 = slice_by_index(begin = var_3083_begin_0, end = var_3083_end_0, end_mask = var_3083_end_mask_0, x = x_323_cast_fp16)[name = string("op_3083_cast_fp16")]; + tensor var_3084 = const()[name = string("op_3084"), val = tensor([1, 8, 14, 111])]; + tensor matrix_bd_49_cast_fp16 = reshape(shape = var_3084, x = var_3083_cast_fp16)[name = string("matrix_bd_49_cast_fp16")]; + bool matrix_ac_25_transpose_x_0 = const()[name = string("matrix_ac_25_transpose_x_0"), val = bool(false)]; + bool matrix_ac_25_transpose_y_0 = const()[name = string("matrix_ac_25_transpose_y_0"), val = bool(false)]; + tensor transpose_120_perm_0 = const()[name = string("transpose_120_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_121_perm_0 = const()[name = string("transpose_121_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_121 = transpose(perm = transpose_121_perm_0, x = k_49_cast_fp16)[name = string("transpose_252")]; + tensor transpose_120 = transpose(perm = transpose_120_perm_0, x = var_3067_cast_fp16)[name = string("transpose_253")]; + tensor matrix_ac_25_cast_fp16 = matmul(transpose_x = matrix_ac_25_transpose_x_0, transpose_y = matrix_ac_25_transpose_y_0, x = transpose_120, y = transpose_121)[name = string("matrix_ac_25_cast_fp16")]; + tensor matrix_bd_51_begin_0 = const()[name = string("matrix_bd_51_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_51_end_0 = const()[name = string("matrix_bd_51_end_0"), val = tensor([1, 8, 14, 56])]; + tensor matrix_bd_51_end_mask_0 = const()[name = string("matrix_bd_51_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_51_cast_fp16 = slice_by_index(begin = matrix_bd_51_begin_0, end = matrix_bd_51_end_0, end_mask = matrix_bd_51_end_mask_0, x = matrix_bd_49_cast_fp16)[name = string("matrix_bd_51_cast_fp16")]; + tensor var_3093_cast_fp16 = add(x = matrix_ac_25_cast_fp16, y = matrix_bd_51_cast_fp16)[name = string("op_3093_cast_fp16")]; + fp16 _inversed_scores_49_y_0_to_fp16 = const()[name = string("_inversed_scores_49_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_49_cast_fp16 = mul(x = var_3093_cast_fp16, y = _inversed_scores_49_y_0_to_fp16)[name = string("_inversed_scores_49_cast_fp16")]; + tensor scores_51_cast_fp16 = select(a = var_44_to_fp16, b = _inversed_scores_49_cast_fp16, cond = mask_11)[name = string("scores_51_cast_fp16")]; + tensor var_3099_cast_fp16 = softmax(axis = var_58, x = scores_51_cast_fp16)[name = string("op_3099_cast_fp16")]; + tensor input_665_cast_fp16 = select(a = var_43_to_fp16, b = var_3099_cast_fp16, cond = mask_11)[name = string("input_665_cast_fp16")]; + bool x_325_transpose_x_0 = const()[name = string("x_325_transpose_x_0"), val = bool(false)]; + bool x_325_transpose_y_0 = const()[name = string("x_325_transpose_y_0"), val = bool(false)]; + tensor value_33_cast_fp16 = transpose(perm = value_33_perm_0, x = v_25_cast_fp16)[name = string("transpose_251")]; + tensor x_325_cast_fp16 = matmul(transpose_x = x_325_transpose_x_0, transpose_y = x_325_transpose_y_0, x = input_665_cast_fp16, y = value_33_cast_fp16)[name = string("x_325_cast_fp16")]; + tensor var_3103_perm_0 = const()[name = string("op_3103_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3104 = const()[name = string("op_3104"), val = tensor([1, -1, 1024])]; + tensor var_3103_cast_fp16 = transpose(perm = var_3103_perm_0, x = x_325_cast_fp16)[name = string("transpose_250")]; + tensor input_667_cast_fp16 = reshape(shape = var_3104, x = var_3103_cast_fp16)[name = string("input_667_cast_fp16")]; + tensor encoder_layers_12_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(255646528))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(256433024))))[name = string("encoder_layers_12_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_12_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_12_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(256433216)))]; + tensor linear_115_cast_fp16 = linear(bias = encoder_layers_12_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_12_self_attn_linear_out_weight_to_fp16_palettized, x = input_667_cast_fp16)[name = string("linear_115_cast_fp16")]; + tensor input_671_cast_fp16 = add(x = input_661_cast_fp16, y = linear_115_cast_fp16)[name = string("input_671_cast_fp16")]; + tensor x_329_axes_0 = const()[name = string("x_329_axes_0"), val = tensor([-1])]; + tensor encoder_layers_12_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_12_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(256435328)))]; + tensor encoder_layers_12_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_12_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(256437440)))]; + tensor x_329_cast_fp16 = layer_norm(axes = x_329_axes_0, beta = encoder_layers_12_norm_conv_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_12_norm_conv_weight_to_fp16, x = input_671_cast_fp16)[name = string("x_329_cast_fp16")]; + tensor input_673_perm_0 = const()[name = string("input_673_perm_0"), val = tensor([0, 2, 1])]; + string input_675_pad_type_0 = const()[name = string("input_675_pad_type_0"), val = string("valid")]; + tensor input_675_strides_0 = const()[name = string("input_675_strides_0"), val = tensor([1])]; + tensor input_675_pad_0 = const()[name = string("input_675_pad_0"), val = tensor([0, 0])]; + tensor input_675_dilations_0 = const()[name = string("input_675_dilations_0"), val = tensor([1])]; + int32 input_675_groups_0 = const()[name = string("input_675_groups_0"), val = int32(1)]; + tensor encoder_layers_12_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(256439552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(258536768))))[name = string("encoder_layers_12_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_673_cast_fp16 = transpose(perm = input_673_perm_0, x = x_329_cast_fp16)[name = string("transpose_249")]; + tensor input_675_cast_fp16 = conv(dilations = input_675_dilations_0, groups = input_675_groups_0, pad = input_675_pad_0, pad_type = input_675_pad_type_0, strides = input_675_strides_0, weight = encoder_layers_12_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_673_cast_fp16)[name = string("input_675_cast_fp16")]; + int32 x_331_split_num_splits_0 = const()[name = string("x_331_split_num_splits_0"), val = int32(2)]; + int32 x_331_split_axis_0 = const()[name = string("x_331_split_axis_0"), val = int32(1)]; + tensor x_331_split_cast_fp16_0, tensor x_331_split_cast_fp16_1 = split(axis = x_331_split_axis_0, num_splits = x_331_split_num_splits_0, x = input_675_cast_fp16)[name = string("x_331_split_cast_fp16")]; + tensor x_331_split_1_sigmoid_cast_fp16 = sigmoid(x = x_331_split_cast_fp16_1)[name = string("x_331_split_1_sigmoid_cast_fp16")]; + tensor x_331_cast_fp16 = mul(x = x_331_split_cast_fp16_0, y = x_331_split_1_sigmoid_cast_fp16)[name = string("x_331_cast_fp16")]; + tensor input_677_cast_fp16 = select(a = var_43_to_fp16, b = x_331_cast_fp16, cond = var_574)[name = string("input_677_cast_fp16")]; + bool new_x_51_interleave_0 = const()[name = string("new_x_51_interleave_0"), val = bool(false)]; + tensor new_x_51_cast_fp16 = concat(axis = var_58, interleave = new_x_51_interleave_0, values = (cache_51_cast_fp16, input_677_cast_fp16))[name = string("new_x_51_cast_fp16")]; + tensor var_3143_begin_0 = const()[name = string("op_3143_begin_0"), val = tensor([0, 0, 14])]; + tensor var_3143_end_0 = const()[name = string("op_3143_end_0"), val = tensor([1, 1024, 22])]; + tensor var_3143_end_mask_0 = const()[name = string("op_3143_end_mask_0"), val = tensor([true, true, true])]; + tensor var_3143_cast_fp16 = slice_by_index(begin = var_3143_begin_0, end = var_3143_end_0, end_mask = var_3143_end_mask_0, x = new_x_51_cast_fp16)[name = string("op_3143_cast_fp16")]; + string x_333_pad_type_0 = const()[name = string("x_333_pad_type_0"), val = string("valid")]; + int32 x_333_groups_0 = const()[name = string("x_333_groups_0"), val = int32(1024)]; + tensor x_333_strides_0 = const()[name = string("x_333_strides_0"), val = tensor([1])]; + tensor x_333_pad_0 = const()[name = string("x_333_pad_0"), val = tensor([0, 0])]; + tensor x_333_dilations_0 = const()[name = string("x_333_dilations_0"), val = tensor([1])]; + tensor encoder_layers_12_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(258540928))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(258550208))))[name = string("encoder_layers_12_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_333_cast_fp16 = conv(dilations = x_333_dilations_0, groups = x_333_groups_0, pad = x_333_pad_0, pad_type = x_333_pad_type_0, strides = x_333_strides_0, weight = encoder_layers_12_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_51_cast_fp16)[name = string("x_333_cast_fp16")]; + tensor input_679_perm_0 = const()[name = string("input_679_perm_0"), val = tensor([0, 2, 1])]; + tensor x_335_axes_0 = const()[name = string("x_335_axes_0"), val = tensor([-1])]; + tensor encoder_layers_12_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_12_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(258552320)))]; + tensor encoder_layers_12_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_12_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(258554432)))]; + tensor input_679_cast_fp16 = transpose(perm = input_679_perm_0, x = x_333_cast_fp16)[name = string("transpose_248")]; + tensor x_335_cast_fp16 = layer_norm(axes = x_335_axes_0, beta = encoder_layers_12_conv_batch_norm_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_12_conv_batch_norm_weight_to_fp16, x = input_679_cast_fp16)[name = string("x_335_cast_fp16")]; + tensor input_681_perm_0 = const()[name = string("input_681_perm_0"), val = tensor([0, 2, 1])]; + tensor input_681_cast_fp16 = transpose(perm = input_681_perm_0, x = x_335_cast_fp16)[name = string("transpose_247")]; + tensor input_683_cast_fp16 = silu(x = input_681_cast_fp16)[name = string("input_683_cast_fp16")]; + string x_337_pad_type_0 = const()[name = string("x_337_pad_type_0"), val = string("valid")]; + tensor x_337_strides_0 = const()[name = string("x_337_strides_0"), val = tensor([1])]; + tensor x_337_pad_0 = const()[name = string("x_337_pad_0"), val = tensor([0, 0])]; + tensor x_337_dilations_0 = const()[name = string("x_337_dilations_0"), val = tensor([1])]; + int32 x_337_groups_0 = const()[name = string("x_337_groups_0"), val = int32(1)]; + tensor encoder_layers_12_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(258556544))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259605184))))[name = string("encoder_layers_12_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_337_cast_fp16 = conv(dilations = x_337_dilations_0, groups = x_337_groups_0, pad = x_337_pad_0, pad_type = x_337_pad_type_0, strides = x_337_strides_0, weight = encoder_layers_12_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_683_cast_fp16)[name = string("x_337_cast_fp16")]; + tensor input_685_perm_0 = const()[name = string("input_685_perm_0"), val = tensor([0, 2, 1])]; + tensor input_685_cast_fp16 = transpose(perm = input_685_perm_0, x = x_337_cast_fp16)[name = string("transpose_246")]; + tensor input_687_cast_fp16 = add(x = input_671_cast_fp16, y = input_685_cast_fp16)[name = string("input_687_cast_fp16")]; + tensor input_689_axes_0 = const()[name = string("input_689_axes_0"), val = tensor([-1])]; + tensor encoder_layers_12_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_12_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259607296)))]; + tensor encoder_layers_12_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_12_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259609408)))]; + tensor input_689_cast_fp16 = layer_norm(axes = input_689_axes_0, beta = encoder_layers_12_norm_feed_forward2_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_12_norm_feed_forward2_weight_to_fp16, x = input_687_cast_fp16)[name = string("input_689_cast_fp16")]; + tensor encoder_layers_12_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259611520))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(262757312))))[name = string("encoder_layers_12_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_12_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_12_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(262757504)))]; + tensor linear_116_cast_fp16 = linear(bias = encoder_layers_12_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_12_feed_forward2_linear1_weight_to_fp16_palettized, x = input_689_cast_fp16)[name = string("linear_116_cast_fp16")]; + tensor input_693_cast_fp16 = silu(x = linear_116_cast_fp16)[name = string("input_693_cast_fp16")]; + tensor encoder_layers_12_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(262765760))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265911552))))[name = string("encoder_layers_12_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_12_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_12_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265911744)))]; + tensor linear_117_cast_fp16 = linear(bias = encoder_layers_12_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_12_feed_forward2_linear2_weight_to_fp16_palettized, x = input_693_cast_fp16)[name = string("linear_117_cast_fp16")]; + fp16 var_3186_to_fp16 = const()[name = string("op_3186_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3187_cast_fp16 = mul(x = linear_117_cast_fp16, y = var_3186_to_fp16)[name = string("op_3187_cast_fp16")]; + tensor input_699_cast_fp16 = add(x = input_687_cast_fp16, y = var_3187_cast_fp16)[name = string("input_699_cast_fp16")]; + tensor input_701_axes_0 = const()[name = string("input_701_axes_0"), val = tensor([-1])]; + tensor encoder_layers_12_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_12_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265913856)))]; + tensor encoder_layers_12_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_12_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265915968)))]; + tensor input_701_cast_fp16 = layer_norm(axes = input_701_axes_0, beta = encoder_layers_12_norm_out_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_12_norm_out_weight_to_fp16, x = input_699_cast_fp16)[name = string("input_701_cast_fp16")]; + tensor cache_53_begin_0 = const()[name = string("cache_53_begin_0"), val = tensor([13, 0, 0, 0])]; + tensor cache_53_end_0 = const()[name = string("cache_53_end_0"), val = tensor([14, 1, 42, 1024])]; + tensor cache_53_end_mask_0 = const()[name = string("cache_53_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_53_squeeze_mask_0 = const()[name = string("cache_53_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_53_cast_fp16 = slice_by_index(begin = cache_53_begin_0, end = cache_53_end_0, end_mask = cache_53_end_mask_0, squeeze_mask = cache_53_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_53_cast_fp16")]; + tensor cache_55_begin_0 = const()[name = string("cache_55_begin_0"), val = tensor([13, 0, 0, 0])]; + tensor cache_55_end_0 = const()[name = string("cache_55_end_0"), val = tensor([14, 1, 1024, 8])]; + tensor cache_55_end_mask_0 = const()[name = string("cache_55_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_55_squeeze_mask_0 = const()[name = string("cache_55_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_55_cast_fp16 = slice_by_index(begin = cache_55_begin_0, end = cache_55_end_0, end_mask = cache_55_end_mask_0, squeeze_mask = cache_55_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_55_cast_fp16")]; + tensor input_703_axes_0 = const()[name = string("input_703_axes_0"), val = tensor([-1])]; + tensor encoder_layers_13_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_13_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265918080)))]; + tensor encoder_layers_13_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_13_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265920192)))]; + tensor input_703_cast_fp16 = layer_norm(axes = input_703_axes_0, beta = encoder_layers_13_norm_feed_forward1_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_13_norm_feed_forward1_weight_to_fp16, x = input_701_cast_fp16)[name = string("input_703_cast_fp16")]; + tensor encoder_layers_13_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265922304))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(269068096))))[name = string("encoder_layers_13_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_13_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_13_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(269068288)))]; + tensor linear_118_cast_fp16 = linear(bias = encoder_layers_13_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_13_feed_forward1_linear1_weight_to_fp16_palettized, x = input_703_cast_fp16)[name = string("linear_118_cast_fp16")]; + tensor input_707_cast_fp16 = silu(x = linear_118_cast_fp16)[name = string("input_707_cast_fp16")]; + tensor encoder_layers_13_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(269076544))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(272222336))))[name = string("encoder_layers_13_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_13_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_13_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(272222528)))]; + tensor linear_119_cast_fp16 = linear(bias = encoder_layers_13_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_13_feed_forward1_linear2_weight_to_fp16_palettized, x = input_707_cast_fp16)[name = string("linear_119_cast_fp16")]; + fp16 var_3223_to_fp16 = const()[name = string("op_3223_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3224_cast_fp16 = mul(x = linear_119_cast_fp16, y = var_3223_to_fp16)[name = string("op_3224_cast_fp16")]; + tensor input_713_cast_fp16 = add(x = input_701_cast_fp16, y = var_3224_cast_fp16)[name = string("input_713_cast_fp16")]; + tensor key_27_axes_0 = const()[name = string("key_27_axes_0"), val = tensor([-1])]; + tensor encoder_layers_13_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_13_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(272224640)))]; + tensor encoder_layers_13_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_13_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(272226752)))]; + tensor key_27_cast_fp16 = layer_norm(axes = key_27_axes_0, beta = encoder_layers_13_norm_self_att_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_13_norm_self_att_weight_to_fp16, x = input_713_cast_fp16)[name = string("key_27_cast_fp16")]; + bool input_715_interleave_0 = const()[name = string("input_715_interleave_0"), val = bool(false)]; + tensor input_715_cast_fp16 = concat(axis = var_67, interleave = input_715_interleave_0, values = (cache_53_cast_fp16, key_27_cast_fp16))[name = string("input_715_cast_fp16")]; + tensor var_3246_begin_0 = const()[name = string("op_3246_begin_0"), val = tensor([0, 14, 0])]; + tensor var_3246_end_0 = const()[name = string("op_3246_end_0"), val = tensor([1, 42, 1024])]; + tensor var_3246_end_mask_0 = const()[name = string("op_3246_end_mask_0"), val = tensor([true, true, true])]; + tensor var_3246_cast_fp16 = slice_by_index(begin = var_3246_begin_0, end = var_3246_end_0, end_mask = var_3246_end_mask_0, x = cache_53_cast_fp16)[name = string("op_3246_cast_fp16")]; + bool var_3252_interleave_0 = const()[name = string("op_3252_interleave_0"), val = bool(false)]; + tensor var_3252_cast_fp16 = concat(axis = var_67, interleave = var_3252_interleave_0, values = (var_3246_cast_fp16, key_27_cast_fp16))[name = string("op_3252_cast_fp16")]; + tensor encoder_layers_13_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(272228864))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(273015360))))[name = string("encoder_layers_13_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_13_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_13_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(273015552)))]; + tensor linear_120_cast_fp16 = linear(bias = encoder_layers_13_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_13_self_attn_linear_q_weight_to_fp16_palettized, x = key_27_cast_fp16)[name = string("linear_120_cast_fp16")]; + tensor var_3257 = const()[name = string("op_3257"), val = tensor([1, -1, 8, 128])]; + tensor q_79_cast_fp16 = reshape(shape = var_3257, x = linear_120_cast_fp16)[name = string("q_79_cast_fp16")]; + tensor encoder_layers_13_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(273017664))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(273804160))))[name = string("encoder_layers_13_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_13_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_13_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(273804352)))]; + tensor linear_121_cast_fp16 = linear(bias = encoder_layers_13_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_13_self_attn_linear_k_weight_to_fp16_palettized, x = input_715_cast_fp16)[name = string("linear_121_cast_fp16")]; + tensor var_3262 = const()[name = string("op_3262"), val = tensor([1, -1, 8, 128])]; + tensor k_53_cast_fp16 = reshape(shape = var_3262, x = linear_121_cast_fp16)[name = string("k_53_cast_fp16")]; + tensor encoder_layers_13_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(273806464))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(274592960))))[name = string("encoder_layers_13_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_13_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_13_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(274593152)))]; + tensor linear_122_cast_fp16 = linear(bias = encoder_layers_13_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_13_self_attn_linear_v_weight_to_fp16_palettized, x = input_715_cast_fp16)[name = string("linear_122_cast_fp16")]; + tensor var_3267 = const()[name = string("op_3267"), val = tensor([1, -1, 8, 128])]; + tensor v_27_cast_fp16 = reshape(shape = var_3267, x = linear_122_cast_fp16)[name = string("v_27_cast_fp16")]; + tensor value_35_perm_0 = const()[name = string("value_35_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_13_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_13_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(274595264)))]; + tensor var_3280_cast_fp16 = add(x = q_79_cast_fp16, y = encoder_layers_13_self_attn_pos_bias_u_to_fp16)[name = string("op_3280_cast_fp16")]; + tensor encoder_layers_13_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_13_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(274597376)))]; + tensor var_3282_cast_fp16 = add(x = q_79_cast_fp16, y = encoder_layers_13_self_attn_pos_bias_v_to_fp16)[name = string("op_3282_cast_fp16")]; + tensor q_with_bias_v_27_perm_0 = const()[name = string("q_with_bias_v_27_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_345_transpose_x_0 = const()[name = string("x_345_transpose_x_0"), val = bool(false)]; + bool x_345_transpose_y_0 = const()[name = string("x_345_transpose_y_0"), val = bool(false)]; + tensor op_3284_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(274599488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(274713216))))[name = string("op_3284_to_fp16_quantized")]; + tensor q_with_bias_v_27_cast_fp16 = transpose(perm = q_with_bias_v_27_perm_0, x = var_3282_cast_fp16)[name = string("transpose_245")]; + tensor x_345_cast_fp16 = matmul(transpose_x = x_345_transpose_x_0, transpose_y = x_345_transpose_y_0, x = q_with_bias_v_27_cast_fp16, y = op_3284_to_fp16_quantized)[name = string("x_345_cast_fp16")]; + tensor x_347_pad_0 = const()[name = string("x_347_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_347_mode_0 = const()[name = string("x_347_mode_0"), val = string("constant")]; + fp16 const_248_to_fp16 = const()[name = string("const_248_to_fp16"), val = fp16(0x0p+0)]; + tensor x_347_cast_fp16 = pad(constant_val = const_248_to_fp16, mode = x_347_mode_0, pad = x_347_pad_0, x = x_345_cast_fp16)[name = string("x_347_cast_fp16")]; + tensor var_3292 = const()[name = string("op_3292"), val = tensor([1, 8, -1, 14])]; + tensor x_349_cast_fp16 = reshape(shape = var_3292, x = x_347_cast_fp16)[name = string("x_349_cast_fp16")]; + tensor var_3296_begin_0 = const()[name = string("op_3296_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_3296_end_0 = const()[name = string("op_3296_end_0"), val = tensor([1, 8, 112, 14])]; + tensor var_3296_end_mask_0 = const()[name = string("op_3296_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_3296_cast_fp16 = slice_by_index(begin = var_3296_begin_0, end = var_3296_end_0, end_mask = var_3296_end_mask_0, x = x_349_cast_fp16)[name = string("op_3296_cast_fp16")]; + tensor var_3297 = const()[name = string("op_3297"), val = tensor([1, 8, 14, 111])]; + tensor matrix_bd_53_cast_fp16 = reshape(shape = var_3297, x = var_3296_cast_fp16)[name = string("matrix_bd_53_cast_fp16")]; + bool matrix_ac_27_transpose_x_0 = const()[name = string("matrix_ac_27_transpose_x_0"), val = bool(false)]; + bool matrix_ac_27_transpose_y_0 = const()[name = string("matrix_ac_27_transpose_y_0"), val = bool(false)]; + tensor transpose_122_perm_0 = const()[name = string("transpose_122_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_123_perm_0 = const()[name = string("transpose_123_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_123 = transpose(perm = transpose_123_perm_0, x = k_53_cast_fp16)[name = string("transpose_243")]; + tensor transpose_122 = transpose(perm = transpose_122_perm_0, x = var_3280_cast_fp16)[name = string("transpose_244")]; + tensor matrix_ac_27_cast_fp16 = matmul(transpose_x = matrix_ac_27_transpose_x_0, transpose_y = matrix_ac_27_transpose_y_0, x = transpose_122, y = transpose_123)[name = string("matrix_ac_27_cast_fp16")]; + tensor matrix_bd_55_begin_0 = const()[name = string("matrix_bd_55_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_55_end_0 = const()[name = string("matrix_bd_55_end_0"), val = tensor([1, 8, 14, 56])]; + tensor matrix_bd_55_end_mask_0 = const()[name = string("matrix_bd_55_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_55_cast_fp16 = slice_by_index(begin = matrix_bd_55_begin_0, end = matrix_bd_55_end_0, end_mask = matrix_bd_55_end_mask_0, x = matrix_bd_53_cast_fp16)[name = string("matrix_bd_55_cast_fp16")]; + tensor var_3306_cast_fp16 = add(x = matrix_ac_27_cast_fp16, y = matrix_bd_55_cast_fp16)[name = string("op_3306_cast_fp16")]; + fp16 _inversed_scores_53_y_0_to_fp16 = const()[name = string("_inversed_scores_53_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_53_cast_fp16 = mul(x = var_3306_cast_fp16, y = _inversed_scores_53_y_0_to_fp16)[name = string("_inversed_scores_53_cast_fp16")]; + tensor scores_55_cast_fp16 = select(a = var_44_to_fp16, b = _inversed_scores_53_cast_fp16, cond = mask_11)[name = string("scores_55_cast_fp16")]; + tensor var_3312_cast_fp16 = softmax(axis = var_58, x = scores_55_cast_fp16)[name = string("op_3312_cast_fp16")]; + tensor input_717_cast_fp16 = select(a = var_43_to_fp16, b = var_3312_cast_fp16, cond = mask_11)[name = string("input_717_cast_fp16")]; + bool x_351_transpose_x_0 = const()[name = string("x_351_transpose_x_0"), val = bool(false)]; + bool x_351_transpose_y_0 = const()[name = string("x_351_transpose_y_0"), val = bool(false)]; + tensor value_35_cast_fp16 = transpose(perm = value_35_perm_0, x = v_27_cast_fp16)[name = string("transpose_242")]; + tensor x_351_cast_fp16 = matmul(transpose_x = x_351_transpose_x_0, transpose_y = x_351_transpose_y_0, x = input_717_cast_fp16, y = value_35_cast_fp16)[name = string("x_351_cast_fp16")]; + tensor var_3316_perm_0 = const()[name = string("op_3316_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3317 = const()[name = string("op_3317"), val = tensor([1, -1, 1024])]; + tensor var_3316_cast_fp16 = transpose(perm = var_3316_perm_0, x = x_351_cast_fp16)[name = string("transpose_241")]; + tensor input_719_cast_fp16 = reshape(shape = var_3317, x = var_3316_cast_fp16)[name = string("input_719_cast_fp16")]; + tensor encoder_layers_13_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(274713536))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275500032))))[name = string("encoder_layers_13_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_13_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_13_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275500224)))]; + tensor linear_124_cast_fp16 = linear(bias = encoder_layers_13_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_13_self_attn_linear_out_weight_to_fp16_palettized, x = input_719_cast_fp16)[name = string("linear_124_cast_fp16")]; + tensor input_723_cast_fp16 = add(x = input_713_cast_fp16, y = linear_124_cast_fp16)[name = string("input_723_cast_fp16")]; + tensor x_355_axes_0 = const()[name = string("x_355_axes_0"), val = tensor([-1])]; + tensor encoder_layers_13_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_13_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275502336)))]; + tensor encoder_layers_13_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_13_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275504448)))]; + tensor x_355_cast_fp16 = layer_norm(axes = x_355_axes_0, beta = encoder_layers_13_norm_conv_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_13_norm_conv_weight_to_fp16, x = input_723_cast_fp16)[name = string("x_355_cast_fp16")]; + tensor input_725_perm_0 = const()[name = string("input_725_perm_0"), val = tensor([0, 2, 1])]; + string input_727_pad_type_0 = const()[name = string("input_727_pad_type_0"), val = string("valid")]; + tensor input_727_strides_0 = const()[name = string("input_727_strides_0"), val = tensor([1])]; + tensor input_727_pad_0 = const()[name = string("input_727_pad_0"), val = tensor([0, 0])]; + tensor input_727_dilations_0 = const()[name = string("input_727_dilations_0"), val = tensor([1])]; + int32 input_727_groups_0 = const()[name = string("input_727_groups_0"), val = int32(1)]; + tensor encoder_layers_13_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275506560))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(277603776))))[name = string("encoder_layers_13_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_725_cast_fp16 = transpose(perm = input_725_perm_0, x = x_355_cast_fp16)[name = string("transpose_240")]; + tensor input_727_cast_fp16 = conv(dilations = input_727_dilations_0, groups = input_727_groups_0, pad = input_727_pad_0, pad_type = input_727_pad_type_0, strides = input_727_strides_0, weight = encoder_layers_13_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_725_cast_fp16)[name = string("input_727_cast_fp16")]; + int32 x_357_split_num_splits_0 = const()[name = string("x_357_split_num_splits_0"), val = int32(2)]; + int32 x_357_split_axis_0 = const()[name = string("x_357_split_axis_0"), val = int32(1)]; + tensor x_357_split_cast_fp16_0, tensor x_357_split_cast_fp16_1 = split(axis = x_357_split_axis_0, num_splits = x_357_split_num_splits_0, x = input_727_cast_fp16)[name = string("x_357_split_cast_fp16")]; + tensor x_357_split_1_sigmoid_cast_fp16 = sigmoid(x = x_357_split_cast_fp16_1)[name = string("x_357_split_1_sigmoid_cast_fp16")]; + tensor x_357_cast_fp16 = mul(x = x_357_split_cast_fp16_0, y = x_357_split_1_sigmoid_cast_fp16)[name = string("x_357_cast_fp16")]; + tensor input_729_cast_fp16 = select(a = var_43_to_fp16, b = x_357_cast_fp16, cond = var_574)[name = string("input_729_cast_fp16")]; + bool new_x_55_interleave_0 = const()[name = string("new_x_55_interleave_0"), val = bool(false)]; + tensor new_x_55_cast_fp16 = concat(axis = var_58, interleave = new_x_55_interleave_0, values = (cache_55_cast_fp16, input_729_cast_fp16))[name = string("new_x_55_cast_fp16")]; + tensor var_3356_begin_0 = const()[name = string("op_3356_begin_0"), val = tensor([0, 0, 14])]; + tensor var_3356_end_0 = const()[name = string("op_3356_end_0"), val = tensor([1, 1024, 22])]; + tensor var_3356_end_mask_0 = const()[name = string("op_3356_end_mask_0"), val = tensor([true, true, true])]; + tensor var_3356_cast_fp16 = slice_by_index(begin = var_3356_begin_0, end = var_3356_end_0, end_mask = var_3356_end_mask_0, x = new_x_55_cast_fp16)[name = string("op_3356_cast_fp16")]; + string x_359_pad_type_0 = const()[name = string("x_359_pad_type_0"), val = string("valid")]; + int32 x_359_groups_0 = const()[name = string("x_359_groups_0"), val = int32(1024)]; + tensor x_359_strides_0 = const()[name = string("x_359_strides_0"), val = tensor([1])]; + tensor x_359_pad_0 = const()[name = string("x_359_pad_0"), val = tensor([0, 0])]; + tensor x_359_dilations_0 = const()[name = string("x_359_dilations_0"), val = tensor([1])]; + tensor encoder_layers_13_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(277607936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(277617216))))[name = string("encoder_layers_13_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_359_cast_fp16 = conv(dilations = x_359_dilations_0, groups = x_359_groups_0, pad = x_359_pad_0, pad_type = x_359_pad_type_0, strides = x_359_strides_0, weight = encoder_layers_13_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_55_cast_fp16)[name = string("x_359_cast_fp16")]; + tensor input_731_perm_0 = const()[name = string("input_731_perm_0"), val = tensor([0, 2, 1])]; + tensor x_361_axes_0 = const()[name = string("x_361_axes_0"), val = tensor([-1])]; + tensor encoder_layers_13_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_13_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(277619328)))]; + tensor encoder_layers_13_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_13_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(277621440)))]; + tensor input_731_cast_fp16 = transpose(perm = input_731_perm_0, x = x_359_cast_fp16)[name = string("transpose_239")]; + tensor x_361_cast_fp16 = layer_norm(axes = x_361_axes_0, beta = encoder_layers_13_conv_batch_norm_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_13_conv_batch_norm_weight_to_fp16, x = input_731_cast_fp16)[name = string("x_361_cast_fp16")]; + tensor input_733_perm_0 = const()[name = string("input_733_perm_0"), val = tensor([0, 2, 1])]; + tensor input_733_cast_fp16 = transpose(perm = input_733_perm_0, x = x_361_cast_fp16)[name = string("transpose_238")]; + tensor input_735_cast_fp16 = silu(x = input_733_cast_fp16)[name = string("input_735_cast_fp16")]; + string x_363_pad_type_0 = const()[name = string("x_363_pad_type_0"), val = string("valid")]; + tensor x_363_strides_0 = const()[name = string("x_363_strides_0"), val = tensor([1])]; + tensor x_363_pad_0 = const()[name = string("x_363_pad_0"), val = tensor([0, 0])]; + tensor x_363_dilations_0 = const()[name = string("x_363_dilations_0"), val = tensor([1])]; + int32 x_363_groups_0 = const()[name = string("x_363_groups_0"), val = int32(1)]; + tensor encoder_layers_13_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(277623552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278672192))))[name = string("encoder_layers_13_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_363_cast_fp16 = conv(dilations = x_363_dilations_0, groups = x_363_groups_0, pad = x_363_pad_0, pad_type = x_363_pad_type_0, strides = x_363_strides_0, weight = encoder_layers_13_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_735_cast_fp16)[name = string("x_363_cast_fp16")]; + tensor input_737_perm_0 = const()[name = string("input_737_perm_0"), val = tensor([0, 2, 1])]; + tensor input_737_cast_fp16 = transpose(perm = input_737_perm_0, x = x_363_cast_fp16)[name = string("transpose_237")]; + tensor input_739_cast_fp16 = add(x = input_723_cast_fp16, y = input_737_cast_fp16)[name = string("input_739_cast_fp16")]; + tensor input_741_axes_0 = const()[name = string("input_741_axes_0"), val = tensor([-1])]; + tensor encoder_layers_13_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_13_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278674304)))]; + tensor encoder_layers_13_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_13_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278676416)))]; + tensor input_741_cast_fp16 = layer_norm(axes = input_741_axes_0, beta = encoder_layers_13_norm_feed_forward2_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_13_norm_feed_forward2_weight_to_fp16, x = input_739_cast_fp16)[name = string("input_741_cast_fp16")]; + tensor encoder_layers_13_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278678528))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(281824320))))[name = string("encoder_layers_13_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_13_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_13_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(281824512)))]; + tensor linear_125_cast_fp16 = linear(bias = encoder_layers_13_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_13_feed_forward2_linear1_weight_to_fp16_palettized, x = input_741_cast_fp16)[name = string("linear_125_cast_fp16")]; + tensor input_745_cast_fp16 = silu(x = linear_125_cast_fp16)[name = string("input_745_cast_fp16")]; + tensor encoder_layers_13_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(281832768))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284978560))))[name = string("encoder_layers_13_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_13_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_13_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284978752)))]; + tensor linear_126_cast_fp16 = linear(bias = encoder_layers_13_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_13_feed_forward2_linear2_weight_to_fp16_palettized, x = input_745_cast_fp16)[name = string("linear_126_cast_fp16")]; + fp16 var_3399_to_fp16 = const()[name = string("op_3399_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3400_cast_fp16 = mul(x = linear_126_cast_fp16, y = var_3399_to_fp16)[name = string("op_3400_cast_fp16")]; + tensor input_751_cast_fp16 = add(x = input_739_cast_fp16, y = var_3400_cast_fp16)[name = string("input_751_cast_fp16")]; + tensor input_753_axes_0 = const()[name = string("input_753_axes_0"), val = tensor([-1])]; + tensor encoder_layers_13_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_13_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284980864)))]; + tensor encoder_layers_13_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_13_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284982976)))]; + tensor input_753_cast_fp16 = layer_norm(axes = input_753_axes_0, beta = encoder_layers_13_norm_out_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_13_norm_out_weight_to_fp16, x = input_751_cast_fp16)[name = string("input_753_cast_fp16")]; + tensor cache_57_begin_0 = const()[name = string("cache_57_begin_0"), val = tensor([14, 0, 0, 0])]; + tensor cache_57_end_0 = const()[name = string("cache_57_end_0"), val = tensor([15, 1, 42, 1024])]; + tensor cache_57_end_mask_0 = const()[name = string("cache_57_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_57_squeeze_mask_0 = const()[name = string("cache_57_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_57_cast_fp16 = slice_by_index(begin = cache_57_begin_0, end = cache_57_end_0, end_mask = cache_57_end_mask_0, squeeze_mask = cache_57_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_57_cast_fp16")]; + tensor cache_59_begin_0 = const()[name = string("cache_59_begin_0"), val = tensor([14, 0, 0, 0])]; + tensor cache_59_end_0 = const()[name = string("cache_59_end_0"), val = tensor([15, 1, 1024, 8])]; + tensor cache_59_end_mask_0 = const()[name = string("cache_59_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_59_squeeze_mask_0 = const()[name = string("cache_59_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_59_cast_fp16 = slice_by_index(begin = cache_59_begin_0, end = cache_59_end_0, end_mask = cache_59_end_mask_0, squeeze_mask = cache_59_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_59_cast_fp16")]; + tensor input_755_axes_0 = const()[name = string("input_755_axes_0"), val = tensor([-1])]; + tensor encoder_layers_14_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_14_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284985088)))]; + tensor encoder_layers_14_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_14_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284987200)))]; + tensor input_755_cast_fp16 = layer_norm(axes = input_755_axes_0, beta = encoder_layers_14_norm_feed_forward1_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_14_norm_feed_forward1_weight_to_fp16, x = input_753_cast_fp16)[name = string("input_755_cast_fp16")]; + tensor encoder_layers_14_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284989312))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(288135104))))[name = string("encoder_layers_14_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_14_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_14_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(288135296)))]; + tensor linear_127_cast_fp16 = linear(bias = encoder_layers_14_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_14_feed_forward1_linear1_weight_to_fp16_palettized, x = input_755_cast_fp16)[name = string("linear_127_cast_fp16")]; + tensor input_759_cast_fp16 = silu(x = linear_127_cast_fp16)[name = string("input_759_cast_fp16")]; + tensor encoder_layers_14_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(288143552))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291289344))))[name = string("encoder_layers_14_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_14_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_14_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291289536)))]; + tensor linear_128_cast_fp16 = linear(bias = encoder_layers_14_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_14_feed_forward1_linear2_weight_to_fp16_palettized, x = input_759_cast_fp16)[name = string("linear_128_cast_fp16")]; + fp16 var_3436_to_fp16 = const()[name = string("op_3436_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3437_cast_fp16 = mul(x = linear_128_cast_fp16, y = var_3436_to_fp16)[name = string("op_3437_cast_fp16")]; + tensor input_765_cast_fp16 = add(x = input_753_cast_fp16, y = var_3437_cast_fp16)[name = string("input_765_cast_fp16")]; + tensor key_29_axes_0 = const()[name = string("key_29_axes_0"), val = tensor([-1])]; + tensor encoder_layers_14_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_14_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291291648)))]; + tensor encoder_layers_14_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_14_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291293760)))]; + tensor key_29_cast_fp16 = layer_norm(axes = key_29_axes_0, beta = encoder_layers_14_norm_self_att_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_14_norm_self_att_weight_to_fp16, x = input_765_cast_fp16)[name = string("key_29_cast_fp16")]; + bool input_767_interleave_0 = const()[name = string("input_767_interleave_0"), val = bool(false)]; + tensor input_767_cast_fp16 = concat(axis = var_67, interleave = input_767_interleave_0, values = (cache_57_cast_fp16, key_29_cast_fp16))[name = string("input_767_cast_fp16")]; + tensor var_3459_begin_0 = const()[name = string("op_3459_begin_0"), val = tensor([0, 14, 0])]; + tensor var_3459_end_0 = const()[name = string("op_3459_end_0"), val = tensor([1, 42, 1024])]; + tensor var_3459_end_mask_0 = const()[name = string("op_3459_end_mask_0"), val = tensor([true, true, true])]; + tensor var_3459_cast_fp16 = slice_by_index(begin = var_3459_begin_0, end = var_3459_end_0, end_mask = var_3459_end_mask_0, x = cache_57_cast_fp16)[name = string("op_3459_cast_fp16")]; + bool var_3465_interleave_0 = const()[name = string("op_3465_interleave_0"), val = bool(false)]; + tensor var_3465_cast_fp16 = concat(axis = var_67, interleave = var_3465_interleave_0, values = (var_3459_cast_fp16, key_29_cast_fp16))[name = string("op_3465_cast_fp16")]; + tensor encoder_layers_14_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291295872))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292082368))))[name = string("encoder_layers_14_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_14_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_14_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292082560)))]; + tensor linear_129_cast_fp16 = linear(bias = encoder_layers_14_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_14_self_attn_linear_q_weight_to_fp16_palettized, x = key_29_cast_fp16)[name = string("linear_129_cast_fp16")]; + tensor var_3470 = const()[name = string("op_3470"), val = tensor([1, -1, 8, 128])]; + tensor q_85_cast_fp16 = reshape(shape = var_3470, x = linear_129_cast_fp16)[name = string("q_85_cast_fp16")]; + tensor encoder_layers_14_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292084672))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292871168))))[name = string("encoder_layers_14_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_14_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_14_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292871360)))]; + tensor linear_130_cast_fp16 = linear(bias = encoder_layers_14_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_14_self_attn_linear_k_weight_to_fp16_palettized, x = input_767_cast_fp16)[name = string("linear_130_cast_fp16")]; + tensor var_3475 = const()[name = string("op_3475"), val = tensor([1, -1, 8, 128])]; + tensor k_57_cast_fp16 = reshape(shape = var_3475, x = linear_130_cast_fp16)[name = string("k_57_cast_fp16")]; + tensor encoder_layers_14_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292873472))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(293659968))))[name = string("encoder_layers_14_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_14_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_14_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(293660160)))]; + tensor linear_131_cast_fp16 = linear(bias = encoder_layers_14_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_14_self_attn_linear_v_weight_to_fp16_palettized, x = input_767_cast_fp16)[name = string("linear_131_cast_fp16")]; + tensor var_3480 = const()[name = string("op_3480"), val = tensor([1, -1, 8, 128])]; + tensor v_29_cast_fp16 = reshape(shape = var_3480, x = linear_131_cast_fp16)[name = string("v_29_cast_fp16")]; + tensor value_37_perm_0 = const()[name = string("value_37_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_14_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_14_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(293662272)))]; + tensor var_3493_cast_fp16 = add(x = q_85_cast_fp16, y = encoder_layers_14_self_attn_pos_bias_u_to_fp16)[name = string("op_3493_cast_fp16")]; + tensor encoder_layers_14_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_14_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(293664384)))]; + tensor var_3495_cast_fp16 = add(x = q_85_cast_fp16, y = encoder_layers_14_self_attn_pos_bias_v_to_fp16)[name = string("op_3495_cast_fp16")]; + tensor q_with_bias_v_29_perm_0 = const()[name = string("q_with_bias_v_29_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_371_transpose_x_0 = const()[name = string("x_371_transpose_x_0"), val = bool(false)]; + bool x_371_transpose_y_0 = const()[name = string("x_371_transpose_y_0"), val = bool(false)]; + tensor op_3497_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(293666496))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(293780224))))[name = string("op_3497_to_fp16_quantized")]; + tensor q_with_bias_v_29_cast_fp16 = transpose(perm = q_with_bias_v_29_perm_0, x = var_3495_cast_fp16)[name = string("transpose_236")]; + tensor x_371_cast_fp16 = matmul(transpose_x = x_371_transpose_x_0, transpose_y = x_371_transpose_y_0, x = q_with_bias_v_29_cast_fp16, y = op_3497_to_fp16_quantized)[name = string("x_371_cast_fp16")]; + tensor x_373_pad_0 = const()[name = string("x_373_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_373_mode_0 = const()[name = string("x_373_mode_0"), val = string("constant")]; + fp16 const_261_to_fp16 = const()[name = string("const_261_to_fp16"), val = fp16(0x0p+0)]; + tensor x_373_cast_fp16 = pad(constant_val = const_261_to_fp16, mode = x_373_mode_0, pad = x_373_pad_0, x = x_371_cast_fp16)[name = string("x_373_cast_fp16")]; + tensor var_3505 = const()[name = string("op_3505"), val = tensor([1, 8, -1, 14])]; + tensor x_375_cast_fp16 = reshape(shape = var_3505, x = x_373_cast_fp16)[name = string("x_375_cast_fp16")]; + tensor var_3509_begin_0 = const()[name = string("op_3509_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_3509_end_0 = const()[name = string("op_3509_end_0"), val = tensor([1, 8, 112, 14])]; + tensor var_3509_end_mask_0 = const()[name = string("op_3509_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_3509_cast_fp16 = slice_by_index(begin = var_3509_begin_0, end = var_3509_end_0, end_mask = var_3509_end_mask_0, x = x_375_cast_fp16)[name = string("op_3509_cast_fp16")]; + tensor var_3510 = const()[name = string("op_3510"), val = tensor([1, 8, 14, 111])]; + tensor matrix_bd_57_cast_fp16 = reshape(shape = var_3510, x = var_3509_cast_fp16)[name = string("matrix_bd_57_cast_fp16")]; + bool matrix_ac_29_transpose_x_0 = const()[name = string("matrix_ac_29_transpose_x_0"), val = bool(false)]; + bool matrix_ac_29_transpose_y_0 = const()[name = string("matrix_ac_29_transpose_y_0"), val = bool(false)]; + tensor transpose_124_perm_0 = const()[name = string("transpose_124_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_125_perm_0 = const()[name = string("transpose_125_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_125 = transpose(perm = transpose_125_perm_0, x = k_57_cast_fp16)[name = string("transpose_234")]; + tensor transpose_124 = transpose(perm = transpose_124_perm_0, x = var_3493_cast_fp16)[name = string("transpose_235")]; + tensor matrix_ac_29_cast_fp16 = matmul(transpose_x = matrix_ac_29_transpose_x_0, transpose_y = matrix_ac_29_transpose_y_0, x = transpose_124, y = transpose_125)[name = string("matrix_ac_29_cast_fp16")]; + tensor matrix_bd_59_begin_0 = const()[name = string("matrix_bd_59_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_59_end_0 = const()[name = string("matrix_bd_59_end_0"), val = tensor([1, 8, 14, 56])]; + tensor matrix_bd_59_end_mask_0 = const()[name = string("matrix_bd_59_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_59_cast_fp16 = slice_by_index(begin = matrix_bd_59_begin_0, end = matrix_bd_59_end_0, end_mask = matrix_bd_59_end_mask_0, x = matrix_bd_57_cast_fp16)[name = string("matrix_bd_59_cast_fp16")]; + tensor var_3519_cast_fp16 = add(x = matrix_ac_29_cast_fp16, y = matrix_bd_59_cast_fp16)[name = string("op_3519_cast_fp16")]; + fp16 _inversed_scores_57_y_0_to_fp16 = const()[name = string("_inversed_scores_57_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_57_cast_fp16 = mul(x = var_3519_cast_fp16, y = _inversed_scores_57_y_0_to_fp16)[name = string("_inversed_scores_57_cast_fp16")]; + tensor scores_59_cast_fp16 = select(a = var_44_to_fp16, b = _inversed_scores_57_cast_fp16, cond = mask_11)[name = string("scores_59_cast_fp16")]; + tensor var_3525_cast_fp16 = softmax(axis = var_58, x = scores_59_cast_fp16)[name = string("op_3525_cast_fp16")]; + tensor input_769_cast_fp16 = select(a = var_43_to_fp16, b = var_3525_cast_fp16, cond = mask_11)[name = string("input_769_cast_fp16")]; + bool x_377_transpose_x_0 = const()[name = string("x_377_transpose_x_0"), val = bool(false)]; + bool x_377_transpose_y_0 = const()[name = string("x_377_transpose_y_0"), val = bool(false)]; + tensor value_37_cast_fp16 = transpose(perm = value_37_perm_0, x = v_29_cast_fp16)[name = string("transpose_233")]; + tensor x_377_cast_fp16 = matmul(transpose_x = x_377_transpose_x_0, transpose_y = x_377_transpose_y_0, x = input_769_cast_fp16, y = value_37_cast_fp16)[name = string("x_377_cast_fp16")]; + tensor var_3529_perm_0 = const()[name = string("op_3529_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3530 = const()[name = string("op_3530"), val = tensor([1, -1, 1024])]; + tensor var_3529_cast_fp16 = transpose(perm = var_3529_perm_0, x = x_377_cast_fp16)[name = string("transpose_232")]; + tensor input_771_cast_fp16 = reshape(shape = var_3530, x = var_3529_cast_fp16)[name = string("input_771_cast_fp16")]; + tensor encoder_layers_14_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(293780544))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(294567040))))[name = string("encoder_layers_14_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_14_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_14_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(294567232)))]; + tensor linear_133_cast_fp16 = linear(bias = encoder_layers_14_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_14_self_attn_linear_out_weight_to_fp16_palettized, x = input_771_cast_fp16)[name = string("linear_133_cast_fp16")]; + tensor input_775_cast_fp16 = add(x = input_765_cast_fp16, y = linear_133_cast_fp16)[name = string("input_775_cast_fp16")]; + tensor x_381_axes_0 = const()[name = string("x_381_axes_0"), val = tensor([-1])]; + tensor encoder_layers_14_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_14_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(294569344)))]; + tensor encoder_layers_14_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_14_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(294571456)))]; + tensor x_381_cast_fp16 = layer_norm(axes = x_381_axes_0, beta = encoder_layers_14_norm_conv_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_14_norm_conv_weight_to_fp16, x = input_775_cast_fp16)[name = string("x_381_cast_fp16")]; + tensor input_777_perm_0 = const()[name = string("input_777_perm_0"), val = tensor([0, 2, 1])]; + string input_779_pad_type_0 = const()[name = string("input_779_pad_type_0"), val = string("valid")]; + tensor input_779_strides_0 = const()[name = string("input_779_strides_0"), val = tensor([1])]; + tensor input_779_pad_0 = const()[name = string("input_779_pad_0"), val = tensor([0, 0])]; + tensor input_779_dilations_0 = const()[name = string("input_779_dilations_0"), val = tensor([1])]; + int32 input_779_groups_0 = const()[name = string("input_779_groups_0"), val = int32(1)]; + tensor encoder_layers_14_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(294573568))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(296670784))))[name = string("encoder_layers_14_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_777_cast_fp16 = transpose(perm = input_777_perm_0, x = x_381_cast_fp16)[name = string("transpose_231")]; + tensor input_779_cast_fp16 = conv(dilations = input_779_dilations_0, groups = input_779_groups_0, pad = input_779_pad_0, pad_type = input_779_pad_type_0, strides = input_779_strides_0, weight = encoder_layers_14_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_777_cast_fp16)[name = string("input_779_cast_fp16")]; + int32 x_383_split_num_splits_0 = const()[name = string("x_383_split_num_splits_0"), val = int32(2)]; + int32 x_383_split_axis_0 = const()[name = string("x_383_split_axis_0"), val = int32(1)]; + tensor x_383_split_cast_fp16_0, tensor x_383_split_cast_fp16_1 = split(axis = x_383_split_axis_0, num_splits = x_383_split_num_splits_0, x = input_779_cast_fp16)[name = string("x_383_split_cast_fp16")]; + tensor x_383_split_1_sigmoid_cast_fp16 = sigmoid(x = x_383_split_cast_fp16_1)[name = string("x_383_split_1_sigmoid_cast_fp16")]; + tensor x_383_cast_fp16 = mul(x = x_383_split_cast_fp16_0, y = x_383_split_1_sigmoid_cast_fp16)[name = string("x_383_cast_fp16")]; + tensor input_781_cast_fp16 = select(a = var_43_to_fp16, b = x_383_cast_fp16, cond = var_574)[name = string("input_781_cast_fp16")]; + bool new_x_59_interleave_0 = const()[name = string("new_x_59_interleave_0"), val = bool(false)]; + tensor new_x_59_cast_fp16 = concat(axis = var_58, interleave = new_x_59_interleave_0, values = (cache_59_cast_fp16, input_781_cast_fp16))[name = string("new_x_59_cast_fp16")]; + tensor var_3569_begin_0 = const()[name = string("op_3569_begin_0"), val = tensor([0, 0, 14])]; + tensor var_3569_end_0 = const()[name = string("op_3569_end_0"), val = tensor([1, 1024, 22])]; + tensor var_3569_end_mask_0 = const()[name = string("op_3569_end_mask_0"), val = tensor([true, true, true])]; + tensor var_3569_cast_fp16 = slice_by_index(begin = var_3569_begin_0, end = var_3569_end_0, end_mask = var_3569_end_mask_0, x = new_x_59_cast_fp16)[name = string("op_3569_cast_fp16")]; + string x_385_pad_type_0 = const()[name = string("x_385_pad_type_0"), val = string("valid")]; + int32 x_385_groups_0 = const()[name = string("x_385_groups_0"), val = int32(1024)]; + tensor x_385_strides_0 = const()[name = string("x_385_strides_0"), val = tensor([1])]; + tensor x_385_pad_0 = const()[name = string("x_385_pad_0"), val = tensor([0, 0])]; + tensor x_385_dilations_0 = const()[name = string("x_385_dilations_0"), val = tensor([1])]; + tensor encoder_layers_14_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(296674944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(296684224))))[name = string("encoder_layers_14_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_385_cast_fp16 = conv(dilations = x_385_dilations_0, groups = x_385_groups_0, pad = x_385_pad_0, pad_type = x_385_pad_type_0, strides = x_385_strides_0, weight = encoder_layers_14_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_59_cast_fp16)[name = string("x_385_cast_fp16")]; + tensor input_783_perm_0 = const()[name = string("input_783_perm_0"), val = tensor([0, 2, 1])]; + tensor x_387_axes_0 = const()[name = string("x_387_axes_0"), val = tensor([-1])]; + tensor encoder_layers_14_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_14_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(296686336)))]; + tensor encoder_layers_14_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_14_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(296688448)))]; + tensor input_783_cast_fp16 = transpose(perm = input_783_perm_0, x = x_385_cast_fp16)[name = string("transpose_230")]; + tensor x_387_cast_fp16 = layer_norm(axes = x_387_axes_0, beta = encoder_layers_14_conv_batch_norm_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_14_conv_batch_norm_weight_to_fp16, x = input_783_cast_fp16)[name = string("x_387_cast_fp16")]; + tensor input_785_perm_0 = const()[name = string("input_785_perm_0"), val = tensor([0, 2, 1])]; + tensor input_785_cast_fp16 = transpose(perm = input_785_perm_0, x = x_387_cast_fp16)[name = string("transpose_229")]; + tensor input_787_cast_fp16 = silu(x = input_785_cast_fp16)[name = string("input_787_cast_fp16")]; + string x_389_pad_type_0 = const()[name = string("x_389_pad_type_0"), val = string("valid")]; + tensor x_389_strides_0 = const()[name = string("x_389_strides_0"), val = tensor([1])]; + tensor x_389_pad_0 = const()[name = string("x_389_pad_0"), val = tensor([0, 0])]; + tensor x_389_dilations_0 = const()[name = string("x_389_dilations_0"), val = tensor([1])]; + int32 x_389_groups_0 = const()[name = string("x_389_groups_0"), val = int32(1)]; + tensor encoder_layers_14_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(296690560))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(297739200))))[name = string("encoder_layers_14_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_389_cast_fp16 = conv(dilations = x_389_dilations_0, groups = x_389_groups_0, pad = x_389_pad_0, pad_type = x_389_pad_type_0, strides = x_389_strides_0, weight = encoder_layers_14_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_787_cast_fp16)[name = string("x_389_cast_fp16")]; + tensor input_789_perm_0 = const()[name = string("input_789_perm_0"), val = tensor([0, 2, 1])]; + tensor input_789_cast_fp16 = transpose(perm = input_789_perm_0, x = x_389_cast_fp16)[name = string("transpose_228")]; + tensor input_791_cast_fp16 = add(x = input_775_cast_fp16, y = input_789_cast_fp16)[name = string("input_791_cast_fp16")]; + tensor input_793_axes_0 = const()[name = string("input_793_axes_0"), val = tensor([-1])]; + tensor encoder_layers_14_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_14_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(297741312)))]; + tensor encoder_layers_14_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_14_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(297743424)))]; + tensor input_793_cast_fp16 = layer_norm(axes = input_793_axes_0, beta = encoder_layers_14_norm_feed_forward2_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_14_norm_feed_forward2_weight_to_fp16, x = input_791_cast_fp16)[name = string("input_793_cast_fp16")]; + tensor encoder_layers_14_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(297745536))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(300891328))))[name = string("encoder_layers_14_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_14_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_14_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(300891520)))]; + tensor linear_134_cast_fp16 = linear(bias = encoder_layers_14_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_14_feed_forward2_linear1_weight_to_fp16_palettized, x = input_793_cast_fp16)[name = string("linear_134_cast_fp16")]; + tensor input_797_cast_fp16 = silu(x = linear_134_cast_fp16)[name = string("input_797_cast_fp16")]; + tensor encoder_layers_14_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(300899776))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(304045568))))[name = string("encoder_layers_14_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_14_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_14_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(304045760)))]; + tensor linear_135_cast_fp16 = linear(bias = encoder_layers_14_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_14_feed_forward2_linear2_weight_to_fp16_palettized, x = input_797_cast_fp16)[name = string("linear_135_cast_fp16")]; + fp16 var_3612_to_fp16 = const()[name = string("op_3612_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3613_cast_fp16 = mul(x = linear_135_cast_fp16, y = var_3612_to_fp16)[name = string("op_3613_cast_fp16")]; + tensor input_803_cast_fp16 = add(x = input_791_cast_fp16, y = var_3613_cast_fp16)[name = string("input_803_cast_fp16")]; + tensor input_805_axes_0 = const()[name = string("input_805_axes_0"), val = tensor([-1])]; + tensor encoder_layers_14_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_14_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(304047872)))]; + tensor encoder_layers_14_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_14_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(304049984)))]; + tensor input_805_cast_fp16 = layer_norm(axes = input_805_axes_0, beta = encoder_layers_14_norm_out_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_14_norm_out_weight_to_fp16, x = input_803_cast_fp16)[name = string("input_805_cast_fp16")]; + tensor cache_61_begin_0 = const()[name = string("cache_61_begin_0"), val = tensor([15, 0, 0, 0])]; + tensor cache_61_end_0 = const()[name = string("cache_61_end_0"), val = tensor([16, 1, 42, 1024])]; + tensor cache_61_end_mask_0 = const()[name = string("cache_61_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_61_squeeze_mask_0 = const()[name = string("cache_61_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_61_cast_fp16 = slice_by_index(begin = cache_61_begin_0, end = cache_61_end_0, end_mask = cache_61_end_mask_0, squeeze_mask = cache_61_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_61_cast_fp16")]; + tensor cache_63_begin_0 = const()[name = string("cache_63_begin_0"), val = tensor([15, 0, 0, 0])]; + tensor cache_63_end_0 = const()[name = string("cache_63_end_0"), val = tensor([16, 1, 1024, 8])]; + tensor cache_63_end_mask_0 = const()[name = string("cache_63_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_63_squeeze_mask_0 = const()[name = string("cache_63_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_63_cast_fp16 = slice_by_index(begin = cache_63_begin_0, end = cache_63_end_0, end_mask = cache_63_end_mask_0, squeeze_mask = cache_63_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_63_cast_fp16")]; + tensor input_807_axes_0 = const()[name = string("input_807_axes_0"), val = tensor([-1])]; + tensor encoder_layers_15_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_15_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(304052096)))]; + tensor encoder_layers_15_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_15_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(304054208)))]; + tensor input_807_cast_fp16 = layer_norm(axes = input_807_axes_0, beta = encoder_layers_15_norm_feed_forward1_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_15_norm_feed_forward1_weight_to_fp16, x = input_805_cast_fp16)[name = string("input_807_cast_fp16")]; + tensor encoder_layers_15_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(304056320))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307202112))))[name = string("encoder_layers_15_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_15_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_15_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307202304)))]; + tensor linear_136_cast_fp16 = linear(bias = encoder_layers_15_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_15_feed_forward1_linear1_weight_to_fp16_palettized, x = input_807_cast_fp16)[name = string("linear_136_cast_fp16")]; + tensor input_811_cast_fp16 = silu(x = linear_136_cast_fp16)[name = string("input_811_cast_fp16")]; + tensor encoder_layers_15_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307210560))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(310356352))))[name = string("encoder_layers_15_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_15_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_15_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(310356544)))]; + tensor linear_137_cast_fp16 = linear(bias = encoder_layers_15_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_15_feed_forward1_linear2_weight_to_fp16_palettized, x = input_811_cast_fp16)[name = string("linear_137_cast_fp16")]; + fp16 var_3649_to_fp16 = const()[name = string("op_3649_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3650_cast_fp16 = mul(x = linear_137_cast_fp16, y = var_3649_to_fp16)[name = string("op_3650_cast_fp16")]; + tensor input_817_cast_fp16 = add(x = input_805_cast_fp16, y = var_3650_cast_fp16)[name = string("input_817_cast_fp16")]; + tensor key_31_axes_0 = const()[name = string("key_31_axes_0"), val = tensor([-1])]; + tensor encoder_layers_15_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_15_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(310358656)))]; + tensor encoder_layers_15_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_15_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(310360768)))]; + tensor key_31_cast_fp16 = layer_norm(axes = key_31_axes_0, beta = encoder_layers_15_norm_self_att_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_15_norm_self_att_weight_to_fp16, x = input_817_cast_fp16)[name = string("key_31_cast_fp16")]; + bool input_819_interleave_0 = const()[name = string("input_819_interleave_0"), val = bool(false)]; + tensor input_819_cast_fp16 = concat(axis = var_67, interleave = input_819_interleave_0, values = (cache_61_cast_fp16, key_31_cast_fp16))[name = string("input_819_cast_fp16")]; + tensor var_3672_begin_0 = const()[name = string("op_3672_begin_0"), val = tensor([0, 14, 0])]; + tensor var_3672_end_0 = const()[name = string("op_3672_end_0"), val = tensor([1, 42, 1024])]; + tensor var_3672_end_mask_0 = const()[name = string("op_3672_end_mask_0"), val = tensor([true, true, true])]; + tensor var_3672_cast_fp16 = slice_by_index(begin = var_3672_begin_0, end = var_3672_end_0, end_mask = var_3672_end_mask_0, x = cache_61_cast_fp16)[name = string("op_3672_cast_fp16")]; + bool var_3678_interleave_0 = const()[name = string("op_3678_interleave_0"), val = bool(false)]; + tensor var_3678_cast_fp16 = concat(axis = var_67, interleave = var_3678_interleave_0, values = (var_3672_cast_fp16, key_31_cast_fp16))[name = string("op_3678_cast_fp16")]; + tensor encoder_layers_15_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(310362880))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(311149376))))[name = string("encoder_layers_15_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_15_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_15_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(311149568)))]; + tensor linear_138_cast_fp16 = linear(bias = encoder_layers_15_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_15_self_attn_linear_q_weight_to_fp16_palettized, x = key_31_cast_fp16)[name = string("linear_138_cast_fp16")]; + tensor var_3683 = const()[name = string("op_3683"), val = tensor([1, -1, 8, 128])]; + tensor q_91_cast_fp16 = reshape(shape = var_3683, x = linear_138_cast_fp16)[name = string("q_91_cast_fp16")]; + tensor encoder_layers_15_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(311151680))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(311938176))))[name = string("encoder_layers_15_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_15_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_15_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(311938368)))]; + tensor linear_139_cast_fp16 = linear(bias = encoder_layers_15_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_15_self_attn_linear_k_weight_to_fp16_palettized, x = input_819_cast_fp16)[name = string("linear_139_cast_fp16")]; + tensor var_3688 = const()[name = string("op_3688"), val = tensor([1, -1, 8, 128])]; + tensor k_61_cast_fp16 = reshape(shape = var_3688, x = linear_139_cast_fp16)[name = string("k_61_cast_fp16")]; + tensor encoder_layers_15_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(311940480))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312726976))))[name = string("encoder_layers_15_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_15_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_15_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312727168)))]; + tensor linear_140_cast_fp16 = linear(bias = encoder_layers_15_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_15_self_attn_linear_v_weight_to_fp16_palettized, x = input_819_cast_fp16)[name = string("linear_140_cast_fp16")]; + tensor var_3693 = const()[name = string("op_3693"), val = tensor([1, -1, 8, 128])]; + tensor v_31_cast_fp16 = reshape(shape = var_3693, x = linear_140_cast_fp16)[name = string("v_31_cast_fp16")]; + tensor value_39_perm_0 = const()[name = string("value_39_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_15_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_15_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312729280)))]; + tensor var_3706_cast_fp16 = add(x = q_91_cast_fp16, y = encoder_layers_15_self_attn_pos_bias_u_to_fp16)[name = string("op_3706_cast_fp16")]; + tensor encoder_layers_15_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_15_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312731392)))]; + tensor var_3708_cast_fp16 = add(x = q_91_cast_fp16, y = encoder_layers_15_self_attn_pos_bias_v_to_fp16)[name = string("op_3708_cast_fp16")]; + tensor q_with_bias_v_31_perm_0 = const()[name = string("q_with_bias_v_31_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_397_transpose_x_0 = const()[name = string("x_397_transpose_x_0"), val = bool(false)]; + bool x_397_transpose_y_0 = const()[name = string("x_397_transpose_y_0"), val = bool(false)]; + tensor op_3710_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312733504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312847232))))[name = string("op_3710_to_fp16_quantized")]; + tensor q_with_bias_v_31_cast_fp16 = transpose(perm = q_with_bias_v_31_perm_0, x = var_3708_cast_fp16)[name = string("transpose_227")]; + tensor x_397_cast_fp16 = matmul(transpose_x = x_397_transpose_x_0, transpose_y = x_397_transpose_y_0, x = q_with_bias_v_31_cast_fp16, y = op_3710_to_fp16_quantized)[name = string("x_397_cast_fp16")]; + tensor x_399_pad_0 = const()[name = string("x_399_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_399_mode_0 = const()[name = string("x_399_mode_0"), val = string("constant")]; + fp16 const_274_to_fp16 = const()[name = string("const_274_to_fp16"), val = fp16(0x0p+0)]; + tensor x_399_cast_fp16 = pad(constant_val = const_274_to_fp16, mode = x_399_mode_0, pad = x_399_pad_0, x = x_397_cast_fp16)[name = string("x_399_cast_fp16")]; + tensor var_3718 = const()[name = string("op_3718"), val = tensor([1, 8, -1, 14])]; + tensor x_401_cast_fp16 = reshape(shape = var_3718, x = x_399_cast_fp16)[name = string("x_401_cast_fp16")]; + tensor var_3722_begin_0 = const()[name = string("op_3722_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_3722_end_0 = const()[name = string("op_3722_end_0"), val = tensor([1, 8, 112, 14])]; + tensor var_3722_end_mask_0 = const()[name = string("op_3722_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_3722_cast_fp16 = slice_by_index(begin = var_3722_begin_0, end = var_3722_end_0, end_mask = var_3722_end_mask_0, x = x_401_cast_fp16)[name = string("op_3722_cast_fp16")]; + tensor var_3723 = const()[name = string("op_3723"), val = tensor([1, 8, 14, 111])]; + tensor matrix_bd_61_cast_fp16 = reshape(shape = var_3723, x = var_3722_cast_fp16)[name = string("matrix_bd_61_cast_fp16")]; + bool matrix_ac_31_transpose_x_0 = const()[name = string("matrix_ac_31_transpose_x_0"), val = bool(false)]; + bool matrix_ac_31_transpose_y_0 = const()[name = string("matrix_ac_31_transpose_y_0"), val = bool(false)]; + tensor transpose_126_perm_0 = const()[name = string("transpose_126_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_127_perm_0 = const()[name = string("transpose_127_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_127 = transpose(perm = transpose_127_perm_0, x = k_61_cast_fp16)[name = string("transpose_225")]; + tensor transpose_126 = transpose(perm = transpose_126_perm_0, x = var_3706_cast_fp16)[name = string("transpose_226")]; + tensor matrix_ac_31_cast_fp16 = matmul(transpose_x = matrix_ac_31_transpose_x_0, transpose_y = matrix_ac_31_transpose_y_0, x = transpose_126, y = transpose_127)[name = string("matrix_ac_31_cast_fp16")]; + tensor matrix_bd_63_begin_0 = const()[name = string("matrix_bd_63_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_63_end_0 = const()[name = string("matrix_bd_63_end_0"), val = tensor([1, 8, 14, 56])]; + tensor matrix_bd_63_end_mask_0 = const()[name = string("matrix_bd_63_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_63_cast_fp16 = slice_by_index(begin = matrix_bd_63_begin_0, end = matrix_bd_63_end_0, end_mask = matrix_bd_63_end_mask_0, x = matrix_bd_61_cast_fp16)[name = string("matrix_bd_63_cast_fp16")]; + tensor var_3732_cast_fp16 = add(x = matrix_ac_31_cast_fp16, y = matrix_bd_63_cast_fp16)[name = string("op_3732_cast_fp16")]; + fp16 _inversed_scores_61_y_0_to_fp16 = const()[name = string("_inversed_scores_61_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_61_cast_fp16 = mul(x = var_3732_cast_fp16, y = _inversed_scores_61_y_0_to_fp16)[name = string("_inversed_scores_61_cast_fp16")]; + tensor scores_63_cast_fp16 = select(a = var_44_to_fp16, b = _inversed_scores_61_cast_fp16, cond = mask_11)[name = string("scores_63_cast_fp16")]; + tensor var_3738_cast_fp16 = softmax(axis = var_58, x = scores_63_cast_fp16)[name = string("op_3738_cast_fp16")]; + tensor input_821_cast_fp16 = select(a = var_43_to_fp16, b = var_3738_cast_fp16, cond = mask_11)[name = string("input_821_cast_fp16")]; + bool x_403_transpose_x_0 = const()[name = string("x_403_transpose_x_0"), val = bool(false)]; + bool x_403_transpose_y_0 = const()[name = string("x_403_transpose_y_0"), val = bool(false)]; + tensor value_39_cast_fp16 = transpose(perm = value_39_perm_0, x = v_31_cast_fp16)[name = string("transpose_224")]; + tensor x_403_cast_fp16 = matmul(transpose_x = x_403_transpose_x_0, transpose_y = x_403_transpose_y_0, x = input_821_cast_fp16, y = value_39_cast_fp16)[name = string("x_403_cast_fp16")]; + tensor var_3742_perm_0 = const()[name = string("op_3742_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3743 = const()[name = string("op_3743"), val = tensor([1, -1, 1024])]; + tensor var_3742_cast_fp16 = transpose(perm = var_3742_perm_0, x = x_403_cast_fp16)[name = string("transpose_223")]; + tensor input_823_cast_fp16 = reshape(shape = var_3743, x = var_3742_cast_fp16)[name = string("input_823_cast_fp16")]; + tensor encoder_layers_15_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312847552))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(313634048))))[name = string("encoder_layers_15_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_15_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_15_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(313634240)))]; + tensor linear_142_cast_fp16 = linear(bias = encoder_layers_15_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_15_self_attn_linear_out_weight_to_fp16_palettized, x = input_823_cast_fp16)[name = string("linear_142_cast_fp16")]; + tensor input_827_cast_fp16 = add(x = input_817_cast_fp16, y = linear_142_cast_fp16)[name = string("input_827_cast_fp16")]; + tensor x_407_axes_0 = const()[name = string("x_407_axes_0"), val = tensor([-1])]; + tensor encoder_layers_15_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_15_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(313636352)))]; + tensor encoder_layers_15_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_15_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(313638464)))]; + tensor x_407_cast_fp16 = layer_norm(axes = x_407_axes_0, beta = encoder_layers_15_norm_conv_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_15_norm_conv_weight_to_fp16, x = input_827_cast_fp16)[name = string("x_407_cast_fp16")]; + tensor input_829_perm_0 = const()[name = string("input_829_perm_0"), val = tensor([0, 2, 1])]; + string input_831_pad_type_0 = const()[name = string("input_831_pad_type_0"), val = string("valid")]; + tensor input_831_strides_0 = const()[name = string("input_831_strides_0"), val = tensor([1])]; + tensor input_831_pad_0 = const()[name = string("input_831_pad_0"), val = tensor([0, 0])]; + tensor input_831_dilations_0 = const()[name = string("input_831_dilations_0"), val = tensor([1])]; + int32 input_831_groups_0 = const()[name = string("input_831_groups_0"), val = int32(1)]; + tensor encoder_layers_15_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(313640576))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(315737792))))[name = string("encoder_layers_15_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_829_cast_fp16 = transpose(perm = input_829_perm_0, x = x_407_cast_fp16)[name = string("transpose_222")]; + tensor input_831_cast_fp16 = conv(dilations = input_831_dilations_0, groups = input_831_groups_0, pad = input_831_pad_0, pad_type = input_831_pad_type_0, strides = input_831_strides_0, weight = encoder_layers_15_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_829_cast_fp16)[name = string("input_831_cast_fp16")]; + int32 x_409_split_num_splits_0 = const()[name = string("x_409_split_num_splits_0"), val = int32(2)]; + int32 x_409_split_axis_0 = const()[name = string("x_409_split_axis_0"), val = int32(1)]; + tensor x_409_split_cast_fp16_0, tensor x_409_split_cast_fp16_1 = split(axis = x_409_split_axis_0, num_splits = x_409_split_num_splits_0, x = input_831_cast_fp16)[name = string("x_409_split_cast_fp16")]; + tensor x_409_split_1_sigmoid_cast_fp16 = sigmoid(x = x_409_split_cast_fp16_1)[name = string("x_409_split_1_sigmoid_cast_fp16")]; + tensor x_409_cast_fp16 = mul(x = x_409_split_cast_fp16_0, y = x_409_split_1_sigmoid_cast_fp16)[name = string("x_409_cast_fp16")]; + tensor input_833_cast_fp16 = select(a = var_43_to_fp16, b = x_409_cast_fp16, cond = var_574)[name = string("input_833_cast_fp16")]; + bool new_x_63_interleave_0 = const()[name = string("new_x_63_interleave_0"), val = bool(false)]; + tensor new_x_63_cast_fp16 = concat(axis = var_58, interleave = new_x_63_interleave_0, values = (cache_63_cast_fp16, input_833_cast_fp16))[name = string("new_x_63_cast_fp16")]; + tensor var_3782_begin_0 = const()[name = string("op_3782_begin_0"), val = tensor([0, 0, 14])]; + tensor var_3782_end_0 = const()[name = string("op_3782_end_0"), val = tensor([1, 1024, 22])]; + tensor var_3782_end_mask_0 = const()[name = string("op_3782_end_mask_0"), val = tensor([true, true, true])]; + tensor var_3782_cast_fp16 = slice_by_index(begin = var_3782_begin_0, end = var_3782_end_0, end_mask = var_3782_end_mask_0, x = new_x_63_cast_fp16)[name = string("op_3782_cast_fp16")]; + string x_411_pad_type_0 = const()[name = string("x_411_pad_type_0"), val = string("valid")]; + int32 x_411_groups_0 = const()[name = string("x_411_groups_0"), val = int32(1024)]; + tensor x_411_strides_0 = const()[name = string("x_411_strides_0"), val = tensor([1])]; + tensor x_411_pad_0 = const()[name = string("x_411_pad_0"), val = tensor([0, 0])]; + tensor x_411_dilations_0 = const()[name = string("x_411_dilations_0"), val = tensor([1])]; + tensor encoder_layers_15_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(315741952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(315751232))))[name = string("encoder_layers_15_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_411_cast_fp16 = conv(dilations = x_411_dilations_0, groups = x_411_groups_0, pad = x_411_pad_0, pad_type = x_411_pad_type_0, strides = x_411_strides_0, weight = encoder_layers_15_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_63_cast_fp16)[name = string("x_411_cast_fp16")]; + tensor input_835_perm_0 = const()[name = string("input_835_perm_0"), val = tensor([0, 2, 1])]; + tensor x_413_axes_0 = const()[name = string("x_413_axes_0"), val = tensor([-1])]; + tensor encoder_layers_15_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_15_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(315753344)))]; + tensor encoder_layers_15_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_15_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(315755456)))]; + tensor input_835_cast_fp16 = transpose(perm = input_835_perm_0, x = x_411_cast_fp16)[name = string("transpose_221")]; + tensor x_413_cast_fp16 = layer_norm(axes = x_413_axes_0, beta = encoder_layers_15_conv_batch_norm_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_15_conv_batch_norm_weight_to_fp16, x = input_835_cast_fp16)[name = string("x_413_cast_fp16")]; + tensor input_837_perm_0 = const()[name = string("input_837_perm_0"), val = tensor([0, 2, 1])]; + tensor input_837_cast_fp16 = transpose(perm = input_837_perm_0, x = x_413_cast_fp16)[name = string("transpose_220")]; + tensor input_839_cast_fp16 = silu(x = input_837_cast_fp16)[name = string("input_839_cast_fp16")]; + string x_415_pad_type_0 = const()[name = string("x_415_pad_type_0"), val = string("valid")]; + tensor x_415_strides_0 = const()[name = string("x_415_strides_0"), val = tensor([1])]; + tensor x_415_pad_0 = const()[name = string("x_415_pad_0"), val = tensor([0, 0])]; + tensor x_415_dilations_0 = const()[name = string("x_415_dilations_0"), val = tensor([1])]; + int32 x_415_groups_0 = const()[name = string("x_415_groups_0"), val = int32(1)]; + tensor encoder_layers_15_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(315757568))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(316806208))))[name = string("encoder_layers_15_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_415_cast_fp16 = conv(dilations = x_415_dilations_0, groups = x_415_groups_0, pad = x_415_pad_0, pad_type = x_415_pad_type_0, strides = x_415_strides_0, weight = encoder_layers_15_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_839_cast_fp16)[name = string("x_415_cast_fp16")]; + tensor input_841_perm_0 = const()[name = string("input_841_perm_0"), val = tensor([0, 2, 1])]; + tensor input_841_cast_fp16 = transpose(perm = input_841_perm_0, x = x_415_cast_fp16)[name = string("transpose_219")]; + tensor input_843_cast_fp16 = add(x = input_827_cast_fp16, y = input_841_cast_fp16)[name = string("input_843_cast_fp16")]; + tensor input_845_axes_0 = const()[name = string("input_845_axes_0"), val = tensor([-1])]; + tensor encoder_layers_15_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_15_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(316808320)))]; + tensor encoder_layers_15_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_15_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(316810432)))]; + tensor input_845_cast_fp16 = layer_norm(axes = input_845_axes_0, beta = encoder_layers_15_norm_feed_forward2_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_15_norm_feed_forward2_weight_to_fp16, x = input_843_cast_fp16)[name = string("input_845_cast_fp16")]; + tensor encoder_layers_15_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(316812544))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(319958336))))[name = string("encoder_layers_15_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_15_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_15_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(319958528)))]; + tensor linear_143_cast_fp16 = linear(bias = encoder_layers_15_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_15_feed_forward2_linear1_weight_to_fp16_palettized, x = input_845_cast_fp16)[name = string("linear_143_cast_fp16")]; + tensor input_849_cast_fp16 = silu(x = linear_143_cast_fp16)[name = string("input_849_cast_fp16")]; + tensor encoder_layers_15_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(319966784))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(323112576))))[name = string("encoder_layers_15_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_15_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_15_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(323112768)))]; + tensor linear_144_cast_fp16 = linear(bias = encoder_layers_15_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_15_feed_forward2_linear2_weight_to_fp16_palettized, x = input_849_cast_fp16)[name = string("linear_144_cast_fp16")]; + fp16 var_3825_to_fp16 = const()[name = string("op_3825_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3826_cast_fp16 = mul(x = linear_144_cast_fp16, y = var_3825_to_fp16)[name = string("op_3826_cast_fp16")]; + tensor input_855_cast_fp16 = add(x = input_843_cast_fp16, y = var_3826_cast_fp16)[name = string("input_855_cast_fp16")]; + tensor input_857_axes_0 = const()[name = string("input_857_axes_0"), val = tensor([-1])]; + tensor encoder_layers_15_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_15_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(323114880)))]; + tensor encoder_layers_15_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_15_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(323116992)))]; + tensor input_857_cast_fp16 = layer_norm(axes = input_857_axes_0, beta = encoder_layers_15_norm_out_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_15_norm_out_weight_to_fp16, x = input_855_cast_fp16)[name = string("input_857_cast_fp16")]; + tensor cache_65_begin_0 = const()[name = string("cache_65_begin_0"), val = tensor([16, 0, 0, 0])]; + tensor cache_65_end_0 = const()[name = string("cache_65_end_0"), val = tensor([17, 1, 42, 1024])]; + tensor cache_65_end_mask_0 = const()[name = string("cache_65_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_65_squeeze_mask_0 = const()[name = string("cache_65_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_65_cast_fp16 = slice_by_index(begin = cache_65_begin_0, end = cache_65_end_0, end_mask = cache_65_end_mask_0, squeeze_mask = cache_65_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_65_cast_fp16")]; + tensor cache_67_begin_0 = const()[name = string("cache_67_begin_0"), val = tensor([16, 0, 0, 0])]; + tensor cache_67_end_0 = const()[name = string("cache_67_end_0"), val = tensor([17, 1, 1024, 8])]; + tensor cache_67_end_mask_0 = const()[name = string("cache_67_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_67_squeeze_mask_0 = const()[name = string("cache_67_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_67_cast_fp16 = slice_by_index(begin = cache_67_begin_0, end = cache_67_end_0, end_mask = cache_67_end_mask_0, squeeze_mask = cache_67_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_67_cast_fp16")]; + tensor input_859_axes_0 = const()[name = string("input_859_axes_0"), val = tensor([-1])]; + tensor encoder_layers_16_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_16_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(323119104)))]; + tensor encoder_layers_16_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_16_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(323121216)))]; + tensor input_859_cast_fp16 = layer_norm(axes = input_859_axes_0, beta = encoder_layers_16_norm_feed_forward1_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_16_norm_feed_forward1_weight_to_fp16, x = input_857_cast_fp16)[name = string("input_859_cast_fp16")]; + tensor encoder_layers_16_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(323123328))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(326269120))))[name = string("encoder_layers_16_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_16_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_16_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(326269312)))]; + tensor linear_145_cast_fp16 = linear(bias = encoder_layers_16_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_16_feed_forward1_linear1_weight_to_fp16_palettized, x = input_859_cast_fp16)[name = string("linear_145_cast_fp16")]; + tensor input_863_cast_fp16 = silu(x = linear_145_cast_fp16)[name = string("input_863_cast_fp16")]; + tensor encoder_layers_16_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(326277568))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(329423360))))[name = string("encoder_layers_16_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_16_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_16_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(329423552)))]; + tensor linear_146_cast_fp16 = linear(bias = encoder_layers_16_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_16_feed_forward1_linear2_weight_to_fp16_palettized, x = input_863_cast_fp16)[name = string("linear_146_cast_fp16")]; + fp16 var_3862_to_fp16 = const()[name = string("op_3862_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3863_cast_fp16 = mul(x = linear_146_cast_fp16, y = var_3862_to_fp16)[name = string("op_3863_cast_fp16")]; + tensor input_869_cast_fp16 = add(x = input_857_cast_fp16, y = var_3863_cast_fp16)[name = string("input_869_cast_fp16")]; + tensor key_33_axes_0 = const()[name = string("key_33_axes_0"), val = tensor([-1])]; + tensor encoder_layers_16_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_16_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(329425664)))]; + tensor encoder_layers_16_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_16_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(329427776)))]; + tensor key_33_cast_fp16 = layer_norm(axes = key_33_axes_0, beta = encoder_layers_16_norm_self_att_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_16_norm_self_att_weight_to_fp16, x = input_869_cast_fp16)[name = string("key_33_cast_fp16")]; + bool input_871_interleave_0 = const()[name = string("input_871_interleave_0"), val = bool(false)]; + tensor input_871_cast_fp16 = concat(axis = var_67, interleave = input_871_interleave_0, values = (cache_65_cast_fp16, key_33_cast_fp16))[name = string("input_871_cast_fp16")]; + tensor var_3885_begin_0 = const()[name = string("op_3885_begin_0"), val = tensor([0, 14, 0])]; + tensor var_3885_end_0 = const()[name = string("op_3885_end_0"), val = tensor([1, 42, 1024])]; + tensor var_3885_end_mask_0 = const()[name = string("op_3885_end_mask_0"), val = tensor([true, true, true])]; + tensor var_3885_cast_fp16 = slice_by_index(begin = var_3885_begin_0, end = var_3885_end_0, end_mask = var_3885_end_mask_0, x = cache_65_cast_fp16)[name = string("op_3885_cast_fp16")]; + bool var_3891_interleave_0 = const()[name = string("op_3891_interleave_0"), val = bool(false)]; + tensor var_3891_cast_fp16 = concat(axis = var_67, interleave = var_3891_interleave_0, values = (var_3885_cast_fp16, key_33_cast_fp16))[name = string("op_3891_cast_fp16")]; + tensor encoder_layers_16_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(329429888))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(330216384))))[name = string("encoder_layers_16_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_16_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_16_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(330216576)))]; + tensor linear_147_cast_fp16 = linear(bias = encoder_layers_16_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_16_self_attn_linear_q_weight_to_fp16_palettized, x = key_33_cast_fp16)[name = string("linear_147_cast_fp16")]; + tensor var_3896 = const()[name = string("op_3896"), val = tensor([1, -1, 8, 128])]; + tensor q_97_cast_fp16 = reshape(shape = var_3896, x = linear_147_cast_fp16)[name = string("q_97_cast_fp16")]; + tensor encoder_layers_16_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(330218688))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(331005184))))[name = string("encoder_layers_16_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_16_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_16_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(331005376)))]; + tensor linear_148_cast_fp16 = linear(bias = encoder_layers_16_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_16_self_attn_linear_k_weight_to_fp16_palettized, x = input_871_cast_fp16)[name = string("linear_148_cast_fp16")]; + tensor var_3901 = const()[name = string("op_3901"), val = tensor([1, -1, 8, 128])]; + tensor k_65_cast_fp16 = reshape(shape = var_3901, x = linear_148_cast_fp16)[name = string("k_65_cast_fp16")]; + tensor encoder_layers_16_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(331007488))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(331793984))))[name = string("encoder_layers_16_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_16_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_16_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(331794176)))]; + tensor linear_149_cast_fp16 = linear(bias = encoder_layers_16_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_16_self_attn_linear_v_weight_to_fp16_palettized, x = input_871_cast_fp16)[name = string("linear_149_cast_fp16")]; + tensor var_3906 = const()[name = string("op_3906"), val = tensor([1, -1, 8, 128])]; + tensor v_33_cast_fp16 = reshape(shape = var_3906, x = linear_149_cast_fp16)[name = string("v_33_cast_fp16")]; + tensor value_41_perm_0 = const()[name = string("value_41_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_16_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_16_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(331796288)))]; + tensor var_3919_cast_fp16 = add(x = q_97_cast_fp16, y = encoder_layers_16_self_attn_pos_bias_u_to_fp16)[name = string("op_3919_cast_fp16")]; + tensor encoder_layers_16_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_16_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(331798400)))]; + tensor var_3921_cast_fp16 = add(x = q_97_cast_fp16, y = encoder_layers_16_self_attn_pos_bias_v_to_fp16)[name = string("op_3921_cast_fp16")]; + tensor q_with_bias_v_33_perm_0 = const()[name = string("q_with_bias_v_33_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_423_transpose_x_0 = const()[name = string("x_423_transpose_x_0"), val = bool(false)]; + bool x_423_transpose_y_0 = const()[name = string("x_423_transpose_y_0"), val = bool(false)]; + tensor op_3923_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(331800512))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(331914240))))[name = string("op_3923_to_fp16_quantized")]; + tensor q_with_bias_v_33_cast_fp16 = transpose(perm = q_with_bias_v_33_perm_0, x = var_3921_cast_fp16)[name = string("transpose_218")]; + tensor x_423_cast_fp16 = matmul(transpose_x = x_423_transpose_x_0, transpose_y = x_423_transpose_y_0, x = q_with_bias_v_33_cast_fp16, y = op_3923_to_fp16_quantized)[name = string("x_423_cast_fp16")]; + tensor x_425_pad_0 = const()[name = string("x_425_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_425_mode_0 = const()[name = string("x_425_mode_0"), val = string("constant")]; + fp16 const_287_to_fp16 = const()[name = string("const_287_to_fp16"), val = fp16(0x0p+0)]; + tensor x_425_cast_fp16 = pad(constant_val = const_287_to_fp16, mode = x_425_mode_0, pad = x_425_pad_0, x = x_423_cast_fp16)[name = string("x_425_cast_fp16")]; + tensor var_3931 = const()[name = string("op_3931"), val = tensor([1, 8, -1, 14])]; + tensor x_427_cast_fp16 = reshape(shape = var_3931, x = x_425_cast_fp16)[name = string("x_427_cast_fp16")]; + tensor var_3935_begin_0 = const()[name = string("op_3935_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_3935_end_0 = const()[name = string("op_3935_end_0"), val = tensor([1, 8, 112, 14])]; + tensor var_3935_end_mask_0 = const()[name = string("op_3935_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_3935_cast_fp16 = slice_by_index(begin = var_3935_begin_0, end = var_3935_end_0, end_mask = var_3935_end_mask_0, x = x_427_cast_fp16)[name = string("op_3935_cast_fp16")]; + tensor var_3936 = const()[name = string("op_3936"), val = tensor([1, 8, 14, 111])]; + tensor matrix_bd_65_cast_fp16 = reshape(shape = var_3936, x = var_3935_cast_fp16)[name = string("matrix_bd_65_cast_fp16")]; + bool matrix_ac_33_transpose_x_0 = const()[name = string("matrix_ac_33_transpose_x_0"), val = bool(false)]; + bool matrix_ac_33_transpose_y_0 = const()[name = string("matrix_ac_33_transpose_y_0"), val = bool(false)]; + tensor transpose_128_perm_0 = const()[name = string("transpose_128_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_129_perm_0 = const()[name = string("transpose_129_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_129 = transpose(perm = transpose_129_perm_0, x = k_65_cast_fp16)[name = string("transpose_216")]; + tensor transpose_128 = transpose(perm = transpose_128_perm_0, x = var_3919_cast_fp16)[name = string("transpose_217")]; + tensor matrix_ac_33_cast_fp16 = matmul(transpose_x = matrix_ac_33_transpose_x_0, transpose_y = matrix_ac_33_transpose_y_0, x = transpose_128, y = transpose_129)[name = string("matrix_ac_33_cast_fp16")]; + tensor matrix_bd_67_begin_0 = const()[name = string("matrix_bd_67_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_67_end_0 = const()[name = string("matrix_bd_67_end_0"), val = tensor([1, 8, 14, 56])]; + tensor matrix_bd_67_end_mask_0 = const()[name = string("matrix_bd_67_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_67_cast_fp16 = slice_by_index(begin = matrix_bd_67_begin_0, end = matrix_bd_67_end_0, end_mask = matrix_bd_67_end_mask_0, x = matrix_bd_65_cast_fp16)[name = string("matrix_bd_67_cast_fp16")]; + tensor var_3945_cast_fp16 = add(x = matrix_ac_33_cast_fp16, y = matrix_bd_67_cast_fp16)[name = string("op_3945_cast_fp16")]; + fp16 _inversed_scores_65_y_0_to_fp16 = const()[name = string("_inversed_scores_65_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_65_cast_fp16 = mul(x = var_3945_cast_fp16, y = _inversed_scores_65_y_0_to_fp16)[name = string("_inversed_scores_65_cast_fp16")]; + tensor scores_67_cast_fp16 = select(a = var_44_to_fp16, b = _inversed_scores_65_cast_fp16, cond = mask_11)[name = string("scores_67_cast_fp16")]; + tensor var_3951_cast_fp16 = softmax(axis = var_58, x = scores_67_cast_fp16)[name = string("op_3951_cast_fp16")]; + tensor input_873_cast_fp16 = select(a = var_43_to_fp16, b = var_3951_cast_fp16, cond = mask_11)[name = string("input_873_cast_fp16")]; + bool x_429_transpose_x_0 = const()[name = string("x_429_transpose_x_0"), val = bool(false)]; + bool x_429_transpose_y_0 = const()[name = string("x_429_transpose_y_0"), val = bool(false)]; + tensor value_41_cast_fp16 = transpose(perm = value_41_perm_0, x = v_33_cast_fp16)[name = string("transpose_215")]; + tensor x_429_cast_fp16 = matmul(transpose_x = x_429_transpose_x_0, transpose_y = x_429_transpose_y_0, x = input_873_cast_fp16, y = value_41_cast_fp16)[name = string("x_429_cast_fp16")]; + tensor var_3955_perm_0 = const()[name = string("op_3955_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3956 = const()[name = string("op_3956"), val = tensor([1, -1, 1024])]; + tensor var_3955_cast_fp16 = transpose(perm = var_3955_perm_0, x = x_429_cast_fp16)[name = string("transpose_214")]; + tensor input_875_cast_fp16 = reshape(shape = var_3956, x = var_3955_cast_fp16)[name = string("input_875_cast_fp16")]; + tensor encoder_layers_16_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(331914560))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(332701056))))[name = string("encoder_layers_16_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_16_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_16_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(332701248)))]; + tensor linear_151_cast_fp16 = linear(bias = encoder_layers_16_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_16_self_attn_linear_out_weight_to_fp16_palettized, x = input_875_cast_fp16)[name = string("linear_151_cast_fp16")]; + tensor input_879_cast_fp16 = add(x = input_869_cast_fp16, y = linear_151_cast_fp16)[name = string("input_879_cast_fp16")]; + tensor x_433_axes_0 = const()[name = string("x_433_axes_0"), val = tensor([-1])]; + tensor encoder_layers_16_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_16_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(332703360)))]; + tensor encoder_layers_16_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_16_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(332705472)))]; + tensor x_433_cast_fp16 = layer_norm(axes = x_433_axes_0, beta = encoder_layers_16_norm_conv_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_16_norm_conv_weight_to_fp16, x = input_879_cast_fp16)[name = string("x_433_cast_fp16")]; + tensor input_881_perm_0 = const()[name = string("input_881_perm_0"), val = tensor([0, 2, 1])]; + string input_883_pad_type_0 = const()[name = string("input_883_pad_type_0"), val = string("valid")]; + tensor input_883_strides_0 = const()[name = string("input_883_strides_0"), val = tensor([1])]; + tensor input_883_pad_0 = const()[name = string("input_883_pad_0"), val = tensor([0, 0])]; + tensor input_883_dilations_0 = const()[name = string("input_883_dilations_0"), val = tensor([1])]; + int32 input_883_groups_0 = const()[name = string("input_883_groups_0"), val = int32(1)]; + tensor encoder_layers_16_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(332707584))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(334804800))))[name = string("encoder_layers_16_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_881_cast_fp16 = transpose(perm = input_881_perm_0, x = x_433_cast_fp16)[name = string("transpose_213")]; + tensor input_883_cast_fp16 = conv(dilations = input_883_dilations_0, groups = input_883_groups_0, pad = input_883_pad_0, pad_type = input_883_pad_type_0, strides = input_883_strides_0, weight = encoder_layers_16_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_881_cast_fp16)[name = string("input_883_cast_fp16")]; + int32 x_435_split_num_splits_0 = const()[name = string("x_435_split_num_splits_0"), val = int32(2)]; + int32 x_435_split_axis_0 = const()[name = string("x_435_split_axis_0"), val = int32(1)]; + tensor x_435_split_cast_fp16_0, tensor x_435_split_cast_fp16_1 = split(axis = x_435_split_axis_0, num_splits = x_435_split_num_splits_0, x = input_883_cast_fp16)[name = string("x_435_split_cast_fp16")]; + tensor x_435_split_1_sigmoid_cast_fp16 = sigmoid(x = x_435_split_cast_fp16_1)[name = string("x_435_split_1_sigmoid_cast_fp16")]; + tensor x_435_cast_fp16 = mul(x = x_435_split_cast_fp16_0, y = x_435_split_1_sigmoid_cast_fp16)[name = string("x_435_cast_fp16")]; + tensor input_885_cast_fp16 = select(a = var_43_to_fp16, b = x_435_cast_fp16, cond = var_574)[name = string("input_885_cast_fp16")]; + bool new_x_67_interleave_0 = const()[name = string("new_x_67_interleave_0"), val = bool(false)]; + tensor new_x_67_cast_fp16 = concat(axis = var_58, interleave = new_x_67_interleave_0, values = (cache_67_cast_fp16, input_885_cast_fp16))[name = string("new_x_67_cast_fp16")]; + tensor var_3995_begin_0 = const()[name = string("op_3995_begin_0"), val = tensor([0, 0, 14])]; + tensor var_3995_end_0 = const()[name = string("op_3995_end_0"), val = tensor([1, 1024, 22])]; + tensor var_3995_end_mask_0 = const()[name = string("op_3995_end_mask_0"), val = tensor([true, true, true])]; + tensor var_3995_cast_fp16 = slice_by_index(begin = var_3995_begin_0, end = var_3995_end_0, end_mask = var_3995_end_mask_0, x = new_x_67_cast_fp16)[name = string("op_3995_cast_fp16")]; + string x_437_pad_type_0 = const()[name = string("x_437_pad_type_0"), val = string("valid")]; + int32 x_437_groups_0 = const()[name = string("x_437_groups_0"), val = int32(1024)]; + tensor x_437_strides_0 = const()[name = string("x_437_strides_0"), val = tensor([1])]; + tensor x_437_pad_0 = const()[name = string("x_437_pad_0"), val = tensor([0, 0])]; + tensor x_437_dilations_0 = const()[name = string("x_437_dilations_0"), val = tensor([1])]; + tensor encoder_layers_16_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(334808960))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(334818240))))[name = string("encoder_layers_16_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_437_cast_fp16 = conv(dilations = x_437_dilations_0, groups = x_437_groups_0, pad = x_437_pad_0, pad_type = x_437_pad_type_0, strides = x_437_strides_0, weight = encoder_layers_16_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_67_cast_fp16)[name = string("x_437_cast_fp16")]; + tensor input_887_perm_0 = const()[name = string("input_887_perm_0"), val = tensor([0, 2, 1])]; + tensor x_439_axes_0 = const()[name = string("x_439_axes_0"), val = tensor([-1])]; + tensor encoder_layers_16_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_16_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(334820352)))]; + tensor encoder_layers_16_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_16_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(334822464)))]; + tensor input_887_cast_fp16 = transpose(perm = input_887_perm_0, x = x_437_cast_fp16)[name = string("transpose_212")]; + tensor x_439_cast_fp16 = layer_norm(axes = x_439_axes_0, beta = encoder_layers_16_conv_batch_norm_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_16_conv_batch_norm_weight_to_fp16, x = input_887_cast_fp16)[name = string("x_439_cast_fp16")]; + tensor input_889_perm_0 = const()[name = string("input_889_perm_0"), val = tensor([0, 2, 1])]; + tensor input_889_cast_fp16 = transpose(perm = input_889_perm_0, x = x_439_cast_fp16)[name = string("transpose_211")]; + tensor input_891_cast_fp16 = silu(x = input_889_cast_fp16)[name = string("input_891_cast_fp16")]; + string x_441_pad_type_0 = const()[name = string("x_441_pad_type_0"), val = string("valid")]; + tensor x_441_strides_0 = const()[name = string("x_441_strides_0"), val = tensor([1])]; + tensor x_441_pad_0 = const()[name = string("x_441_pad_0"), val = tensor([0, 0])]; + tensor x_441_dilations_0 = const()[name = string("x_441_dilations_0"), val = tensor([1])]; + int32 x_441_groups_0 = const()[name = string("x_441_groups_0"), val = int32(1)]; + tensor encoder_layers_16_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(334824576))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335873216))))[name = string("encoder_layers_16_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_441_cast_fp16 = conv(dilations = x_441_dilations_0, groups = x_441_groups_0, pad = x_441_pad_0, pad_type = x_441_pad_type_0, strides = x_441_strides_0, weight = encoder_layers_16_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_891_cast_fp16)[name = string("x_441_cast_fp16")]; + tensor input_893_perm_0 = const()[name = string("input_893_perm_0"), val = tensor([0, 2, 1])]; + tensor input_893_cast_fp16 = transpose(perm = input_893_perm_0, x = x_441_cast_fp16)[name = string("transpose_210")]; + tensor input_895_cast_fp16 = add(x = input_879_cast_fp16, y = input_893_cast_fp16)[name = string("input_895_cast_fp16")]; + tensor input_897_axes_0 = const()[name = string("input_897_axes_0"), val = tensor([-1])]; + tensor encoder_layers_16_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_16_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335875328)))]; + tensor encoder_layers_16_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_16_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335877440)))]; + tensor input_897_cast_fp16 = layer_norm(axes = input_897_axes_0, beta = encoder_layers_16_norm_feed_forward2_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_16_norm_feed_forward2_weight_to_fp16, x = input_895_cast_fp16)[name = string("input_897_cast_fp16")]; + tensor encoder_layers_16_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335879552))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(339025344))))[name = string("encoder_layers_16_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_16_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_16_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(339025536)))]; + tensor linear_152_cast_fp16 = linear(bias = encoder_layers_16_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_16_feed_forward2_linear1_weight_to_fp16_palettized, x = input_897_cast_fp16)[name = string("linear_152_cast_fp16")]; + tensor input_901_cast_fp16 = silu(x = linear_152_cast_fp16)[name = string("input_901_cast_fp16")]; + tensor encoder_layers_16_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(339033792))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(342179584))))[name = string("encoder_layers_16_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_16_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_16_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(342179776)))]; + tensor linear_153_cast_fp16 = linear(bias = encoder_layers_16_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_16_feed_forward2_linear2_weight_to_fp16_palettized, x = input_901_cast_fp16)[name = string("linear_153_cast_fp16")]; + fp16 var_4038_to_fp16 = const()[name = string("op_4038_to_fp16"), val = fp16(0x1p-1)]; + tensor var_4039_cast_fp16 = mul(x = linear_153_cast_fp16, y = var_4038_to_fp16)[name = string("op_4039_cast_fp16")]; + tensor input_907_cast_fp16 = add(x = input_895_cast_fp16, y = var_4039_cast_fp16)[name = string("input_907_cast_fp16")]; + tensor input_909_axes_0 = const()[name = string("input_909_axes_0"), val = tensor([-1])]; + tensor encoder_layers_16_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_16_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(342181888)))]; + tensor encoder_layers_16_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_16_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(342184000)))]; + tensor input_909_cast_fp16 = layer_norm(axes = input_909_axes_0, beta = encoder_layers_16_norm_out_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_16_norm_out_weight_to_fp16, x = input_907_cast_fp16)[name = string("input_909_cast_fp16")]; + tensor cache_69_begin_0 = const()[name = string("cache_69_begin_0"), val = tensor([17, 0, 0, 0])]; + tensor cache_69_end_0 = const()[name = string("cache_69_end_0"), val = tensor([18, 1, 42, 1024])]; + tensor cache_69_end_mask_0 = const()[name = string("cache_69_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_69_squeeze_mask_0 = const()[name = string("cache_69_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_69_cast_fp16 = slice_by_index(begin = cache_69_begin_0, end = cache_69_end_0, end_mask = cache_69_end_mask_0, squeeze_mask = cache_69_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_69_cast_fp16")]; + tensor cache_71_begin_0 = const()[name = string("cache_71_begin_0"), val = tensor([17, 0, 0, 0])]; + tensor cache_71_end_0 = const()[name = string("cache_71_end_0"), val = tensor([18, 1, 1024, 8])]; + tensor cache_71_end_mask_0 = const()[name = string("cache_71_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_71_squeeze_mask_0 = const()[name = string("cache_71_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_71_cast_fp16 = slice_by_index(begin = cache_71_begin_0, end = cache_71_end_0, end_mask = cache_71_end_mask_0, squeeze_mask = cache_71_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_71_cast_fp16")]; + tensor input_911_axes_0 = const()[name = string("input_911_axes_0"), val = tensor([-1])]; + tensor encoder_layers_17_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_17_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(342186112)))]; + tensor encoder_layers_17_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_17_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(342188224)))]; + tensor input_911_cast_fp16 = layer_norm(axes = input_911_axes_0, beta = encoder_layers_17_norm_feed_forward1_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_17_norm_feed_forward1_weight_to_fp16, x = input_909_cast_fp16)[name = string("input_911_cast_fp16")]; + tensor encoder_layers_17_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(342190336))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(345336128))))[name = string("encoder_layers_17_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_17_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_17_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(345336320)))]; + tensor linear_154_cast_fp16 = linear(bias = encoder_layers_17_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_17_feed_forward1_linear1_weight_to_fp16_palettized, x = input_911_cast_fp16)[name = string("linear_154_cast_fp16")]; + tensor input_915_cast_fp16 = silu(x = linear_154_cast_fp16)[name = string("input_915_cast_fp16")]; + tensor encoder_layers_17_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(345344576))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(348490368))))[name = string("encoder_layers_17_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_17_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_17_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(348490560)))]; + tensor linear_155_cast_fp16 = linear(bias = encoder_layers_17_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_17_feed_forward1_linear2_weight_to_fp16_palettized, x = input_915_cast_fp16)[name = string("linear_155_cast_fp16")]; + fp16 var_4075_to_fp16 = const()[name = string("op_4075_to_fp16"), val = fp16(0x1p-1)]; + tensor var_4076_cast_fp16 = mul(x = linear_155_cast_fp16, y = var_4075_to_fp16)[name = string("op_4076_cast_fp16")]; + tensor input_921_cast_fp16 = add(x = input_909_cast_fp16, y = var_4076_cast_fp16)[name = string("input_921_cast_fp16")]; + tensor key_35_axes_0 = const()[name = string("key_35_axes_0"), val = tensor([-1])]; + tensor encoder_layers_17_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_17_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(348492672)))]; + tensor encoder_layers_17_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_17_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(348494784)))]; + tensor key_35_cast_fp16 = layer_norm(axes = key_35_axes_0, beta = encoder_layers_17_norm_self_att_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_17_norm_self_att_weight_to_fp16, x = input_921_cast_fp16)[name = string("key_35_cast_fp16")]; + bool input_923_interleave_0 = const()[name = string("input_923_interleave_0"), val = bool(false)]; + tensor input_923_cast_fp16 = concat(axis = var_67, interleave = input_923_interleave_0, values = (cache_69_cast_fp16, key_35_cast_fp16))[name = string("input_923_cast_fp16")]; + tensor var_4098_begin_0 = const()[name = string("op_4098_begin_0"), val = tensor([0, 14, 0])]; + tensor var_4098_end_0 = const()[name = string("op_4098_end_0"), val = tensor([1, 42, 1024])]; + tensor var_4098_end_mask_0 = const()[name = string("op_4098_end_mask_0"), val = tensor([true, true, true])]; + tensor var_4098_cast_fp16 = slice_by_index(begin = var_4098_begin_0, end = var_4098_end_0, end_mask = var_4098_end_mask_0, x = cache_69_cast_fp16)[name = string("op_4098_cast_fp16")]; + bool var_4104_interleave_0 = const()[name = string("op_4104_interleave_0"), val = bool(false)]; + tensor var_4104_cast_fp16 = concat(axis = var_67, interleave = var_4104_interleave_0, values = (var_4098_cast_fp16, key_35_cast_fp16))[name = string("op_4104_cast_fp16")]; + tensor encoder_layers_17_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(348496896))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(349283392))))[name = string("encoder_layers_17_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_17_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_17_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(349283584)))]; + tensor linear_156_cast_fp16 = linear(bias = encoder_layers_17_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_17_self_attn_linear_q_weight_to_fp16_palettized, x = key_35_cast_fp16)[name = string("linear_156_cast_fp16")]; + tensor var_4109 = const()[name = string("op_4109"), val = tensor([1, -1, 8, 128])]; + tensor q_103_cast_fp16 = reshape(shape = var_4109, x = linear_156_cast_fp16)[name = string("q_103_cast_fp16")]; + tensor encoder_layers_17_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(349285696))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350072192))))[name = string("encoder_layers_17_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_17_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_17_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350072384)))]; + tensor linear_157_cast_fp16 = linear(bias = encoder_layers_17_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_17_self_attn_linear_k_weight_to_fp16_palettized, x = input_923_cast_fp16)[name = string("linear_157_cast_fp16")]; + tensor var_4114 = const()[name = string("op_4114"), val = tensor([1, -1, 8, 128])]; + tensor k_69_cast_fp16 = reshape(shape = var_4114, x = linear_157_cast_fp16)[name = string("k_69_cast_fp16")]; + tensor encoder_layers_17_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350074496))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350860992))))[name = string("encoder_layers_17_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_17_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_17_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350861184)))]; + tensor linear_158_cast_fp16 = linear(bias = encoder_layers_17_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_17_self_attn_linear_v_weight_to_fp16_palettized, x = input_923_cast_fp16)[name = string("linear_158_cast_fp16")]; + tensor var_4119 = const()[name = string("op_4119"), val = tensor([1, -1, 8, 128])]; + tensor v_35_cast_fp16 = reshape(shape = var_4119, x = linear_158_cast_fp16)[name = string("v_35_cast_fp16")]; + tensor value_43_perm_0 = const()[name = string("value_43_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_17_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_17_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350863296)))]; + tensor var_4132_cast_fp16 = add(x = q_103_cast_fp16, y = encoder_layers_17_self_attn_pos_bias_u_to_fp16)[name = string("op_4132_cast_fp16")]; + tensor encoder_layers_17_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_17_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350865408)))]; + tensor var_4134_cast_fp16 = add(x = q_103_cast_fp16, y = encoder_layers_17_self_attn_pos_bias_v_to_fp16)[name = string("op_4134_cast_fp16")]; + tensor q_with_bias_v_35_perm_0 = const()[name = string("q_with_bias_v_35_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_449_transpose_x_0 = const()[name = string("x_449_transpose_x_0"), val = bool(false)]; + bool x_449_transpose_y_0 = const()[name = string("x_449_transpose_y_0"), val = bool(false)]; + tensor op_4136_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350867520))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350981248))))[name = string("op_4136_to_fp16_quantized")]; + tensor q_with_bias_v_35_cast_fp16 = transpose(perm = q_with_bias_v_35_perm_0, x = var_4134_cast_fp16)[name = string("transpose_209")]; + tensor x_449_cast_fp16 = matmul(transpose_x = x_449_transpose_x_0, transpose_y = x_449_transpose_y_0, x = q_with_bias_v_35_cast_fp16, y = op_4136_to_fp16_quantized)[name = string("x_449_cast_fp16")]; + tensor x_451_pad_0 = const()[name = string("x_451_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_451_mode_0 = const()[name = string("x_451_mode_0"), val = string("constant")]; + fp16 const_300_to_fp16 = const()[name = string("const_300_to_fp16"), val = fp16(0x0p+0)]; + tensor x_451_cast_fp16 = pad(constant_val = const_300_to_fp16, mode = x_451_mode_0, pad = x_451_pad_0, x = x_449_cast_fp16)[name = string("x_451_cast_fp16")]; + tensor var_4144 = const()[name = string("op_4144"), val = tensor([1, 8, -1, 14])]; + tensor x_453_cast_fp16 = reshape(shape = var_4144, x = x_451_cast_fp16)[name = string("x_453_cast_fp16")]; + tensor var_4148_begin_0 = const()[name = string("op_4148_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_4148_end_0 = const()[name = string("op_4148_end_0"), val = tensor([1, 8, 112, 14])]; + tensor var_4148_end_mask_0 = const()[name = string("op_4148_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_4148_cast_fp16 = slice_by_index(begin = var_4148_begin_0, end = var_4148_end_0, end_mask = var_4148_end_mask_0, x = x_453_cast_fp16)[name = string("op_4148_cast_fp16")]; + tensor var_4149 = const()[name = string("op_4149"), val = tensor([1, 8, 14, 111])]; + tensor matrix_bd_69_cast_fp16 = reshape(shape = var_4149, x = var_4148_cast_fp16)[name = string("matrix_bd_69_cast_fp16")]; + bool matrix_ac_35_transpose_x_0 = const()[name = string("matrix_ac_35_transpose_x_0"), val = bool(false)]; + bool matrix_ac_35_transpose_y_0 = const()[name = string("matrix_ac_35_transpose_y_0"), val = bool(false)]; + tensor transpose_130_perm_0 = const()[name = string("transpose_130_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_131_perm_0 = const()[name = string("transpose_131_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_131 = transpose(perm = transpose_131_perm_0, x = k_69_cast_fp16)[name = string("transpose_207")]; + tensor transpose_130 = transpose(perm = transpose_130_perm_0, x = var_4132_cast_fp16)[name = string("transpose_208")]; + tensor matrix_ac_35_cast_fp16 = matmul(transpose_x = matrix_ac_35_transpose_x_0, transpose_y = matrix_ac_35_transpose_y_0, x = transpose_130, y = transpose_131)[name = string("matrix_ac_35_cast_fp16")]; + tensor matrix_bd_71_begin_0 = const()[name = string("matrix_bd_71_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_71_end_0 = const()[name = string("matrix_bd_71_end_0"), val = tensor([1, 8, 14, 56])]; + tensor matrix_bd_71_end_mask_0 = const()[name = string("matrix_bd_71_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_71_cast_fp16 = slice_by_index(begin = matrix_bd_71_begin_0, end = matrix_bd_71_end_0, end_mask = matrix_bd_71_end_mask_0, x = matrix_bd_69_cast_fp16)[name = string("matrix_bd_71_cast_fp16")]; + tensor var_4158_cast_fp16 = add(x = matrix_ac_35_cast_fp16, y = matrix_bd_71_cast_fp16)[name = string("op_4158_cast_fp16")]; + fp16 _inversed_scores_69_y_0_to_fp16 = const()[name = string("_inversed_scores_69_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_69_cast_fp16 = mul(x = var_4158_cast_fp16, y = _inversed_scores_69_y_0_to_fp16)[name = string("_inversed_scores_69_cast_fp16")]; + tensor scores_71_cast_fp16 = select(a = var_44_to_fp16, b = _inversed_scores_69_cast_fp16, cond = mask_11)[name = string("scores_71_cast_fp16")]; + tensor var_4164_cast_fp16 = softmax(axis = var_58, x = scores_71_cast_fp16)[name = string("op_4164_cast_fp16")]; + tensor input_925_cast_fp16 = select(a = var_43_to_fp16, b = var_4164_cast_fp16, cond = mask_11)[name = string("input_925_cast_fp16")]; + bool x_455_transpose_x_0 = const()[name = string("x_455_transpose_x_0"), val = bool(false)]; + bool x_455_transpose_y_0 = const()[name = string("x_455_transpose_y_0"), val = bool(false)]; + tensor value_43_cast_fp16 = transpose(perm = value_43_perm_0, x = v_35_cast_fp16)[name = string("transpose_206")]; + tensor x_455_cast_fp16 = matmul(transpose_x = x_455_transpose_x_0, transpose_y = x_455_transpose_y_0, x = input_925_cast_fp16, y = value_43_cast_fp16)[name = string("x_455_cast_fp16")]; + tensor var_4168_perm_0 = const()[name = string("op_4168_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_4169 = const()[name = string("op_4169"), val = tensor([1, -1, 1024])]; + tensor var_4168_cast_fp16 = transpose(perm = var_4168_perm_0, x = x_455_cast_fp16)[name = string("transpose_205")]; + tensor input_927_cast_fp16 = reshape(shape = var_4169, x = var_4168_cast_fp16)[name = string("input_927_cast_fp16")]; + tensor encoder_layers_17_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350981568))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(351768064))))[name = string("encoder_layers_17_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_17_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_17_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(351768256)))]; + tensor linear_160_cast_fp16 = linear(bias = encoder_layers_17_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_17_self_attn_linear_out_weight_to_fp16_palettized, x = input_927_cast_fp16)[name = string("linear_160_cast_fp16")]; + tensor input_931_cast_fp16 = add(x = input_921_cast_fp16, y = linear_160_cast_fp16)[name = string("input_931_cast_fp16")]; + tensor x_459_axes_0 = const()[name = string("x_459_axes_0"), val = tensor([-1])]; + tensor encoder_layers_17_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_17_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(351770368)))]; + tensor encoder_layers_17_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_17_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(351772480)))]; + tensor x_459_cast_fp16 = layer_norm(axes = x_459_axes_0, beta = encoder_layers_17_norm_conv_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_17_norm_conv_weight_to_fp16, x = input_931_cast_fp16)[name = string("x_459_cast_fp16")]; + tensor input_933_perm_0 = const()[name = string("input_933_perm_0"), val = tensor([0, 2, 1])]; + string input_935_pad_type_0 = const()[name = string("input_935_pad_type_0"), val = string("valid")]; + tensor input_935_strides_0 = const()[name = string("input_935_strides_0"), val = tensor([1])]; + tensor input_935_pad_0 = const()[name = string("input_935_pad_0"), val = tensor([0, 0])]; + tensor input_935_dilations_0 = const()[name = string("input_935_dilations_0"), val = tensor([1])]; + int32 input_935_groups_0 = const()[name = string("input_935_groups_0"), val = int32(1)]; + tensor encoder_layers_17_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(351774592))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(353871808))))[name = string("encoder_layers_17_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_933_cast_fp16 = transpose(perm = input_933_perm_0, x = x_459_cast_fp16)[name = string("transpose_204")]; + tensor input_935_cast_fp16 = conv(dilations = input_935_dilations_0, groups = input_935_groups_0, pad = input_935_pad_0, pad_type = input_935_pad_type_0, strides = input_935_strides_0, weight = encoder_layers_17_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_933_cast_fp16)[name = string("input_935_cast_fp16")]; + int32 x_461_split_num_splits_0 = const()[name = string("x_461_split_num_splits_0"), val = int32(2)]; + int32 x_461_split_axis_0 = const()[name = string("x_461_split_axis_0"), val = int32(1)]; + tensor x_461_split_cast_fp16_0, tensor x_461_split_cast_fp16_1 = split(axis = x_461_split_axis_0, num_splits = x_461_split_num_splits_0, x = input_935_cast_fp16)[name = string("x_461_split_cast_fp16")]; + tensor x_461_split_1_sigmoid_cast_fp16 = sigmoid(x = x_461_split_cast_fp16_1)[name = string("x_461_split_1_sigmoid_cast_fp16")]; + tensor x_461_cast_fp16 = mul(x = x_461_split_cast_fp16_0, y = x_461_split_1_sigmoid_cast_fp16)[name = string("x_461_cast_fp16")]; + tensor input_937_cast_fp16 = select(a = var_43_to_fp16, b = x_461_cast_fp16, cond = var_574)[name = string("input_937_cast_fp16")]; + bool new_x_71_interleave_0 = const()[name = string("new_x_71_interleave_0"), val = bool(false)]; + tensor new_x_71_cast_fp16 = concat(axis = var_58, interleave = new_x_71_interleave_0, values = (cache_71_cast_fp16, input_937_cast_fp16))[name = string("new_x_71_cast_fp16")]; + tensor var_4208_begin_0 = const()[name = string("op_4208_begin_0"), val = tensor([0, 0, 14])]; + tensor var_4208_end_0 = const()[name = string("op_4208_end_0"), val = tensor([1, 1024, 22])]; + tensor var_4208_end_mask_0 = const()[name = string("op_4208_end_mask_0"), val = tensor([true, true, true])]; + tensor var_4208_cast_fp16 = slice_by_index(begin = var_4208_begin_0, end = var_4208_end_0, end_mask = var_4208_end_mask_0, x = new_x_71_cast_fp16)[name = string("op_4208_cast_fp16")]; + string x_463_pad_type_0 = const()[name = string("x_463_pad_type_0"), val = string("valid")]; + int32 x_463_groups_0 = const()[name = string("x_463_groups_0"), val = int32(1024)]; + tensor x_463_strides_0 = const()[name = string("x_463_strides_0"), val = tensor([1])]; + tensor x_463_pad_0 = const()[name = string("x_463_pad_0"), val = tensor([0, 0])]; + tensor x_463_dilations_0 = const()[name = string("x_463_dilations_0"), val = tensor([1])]; + tensor encoder_layers_17_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(353875968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(353885248))))[name = string("encoder_layers_17_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_463_cast_fp16 = conv(dilations = x_463_dilations_0, groups = x_463_groups_0, pad = x_463_pad_0, pad_type = x_463_pad_type_0, strides = x_463_strides_0, weight = encoder_layers_17_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_71_cast_fp16)[name = string("x_463_cast_fp16")]; + tensor input_939_perm_0 = const()[name = string("input_939_perm_0"), val = tensor([0, 2, 1])]; + tensor x_465_axes_0 = const()[name = string("x_465_axes_0"), val = tensor([-1])]; + tensor encoder_layers_17_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_17_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(353887360)))]; + tensor encoder_layers_17_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_17_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(353889472)))]; + tensor input_939_cast_fp16 = transpose(perm = input_939_perm_0, x = x_463_cast_fp16)[name = string("transpose_203")]; + tensor x_465_cast_fp16 = layer_norm(axes = x_465_axes_0, beta = encoder_layers_17_conv_batch_norm_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_17_conv_batch_norm_weight_to_fp16, x = input_939_cast_fp16)[name = string("x_465_cast_fp16")]; + tensor input_941_perm_0 = const()[name = string("input_941_perm_0"), val = tensor([0, 2, 1])]; + tensor input_941_cast_fp16 = transpose(perm = input_941_perm_0, x = x_465_cast_fp16)[name = string("transpose_202")]; + tensor input_943_cast_fp16 = silu(x = input_941_cast_fp16)[name = string("input_943_cast_fp16")]; + string x_467_pad_type_0 = const()[name = string("x_467_pad_type_0"), val = string("valid")]; + tensor x_467_strides_0 = const()[name = string("x_467_strides_0"), val = tensor([1])]; + tensor x_467_pad_0 = const()[name = string("x_467_pad_0"), val = tensor([0, 0])]; + tensor x_467_dilations_0 = const()[name = string("x_467_dilations_0"), val = tensor([1])]; + int32 x_467_groups_0 = const()[name = string("x_467_groups_0"), val = int32(1)]; + tensor encoder_layers_17_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(353891584))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(354940224))))[name = string("encoder_layers_17_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_467_cast_fp16 = conv(dilations = x_467_dilations_0, groups = x_467_groups_0, pad = x_467_pad_0, pad_type = x_467_pad_type_0, strides = x_467_strides_0, weight = encoder_layers_17_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_943_cast_fp16)[name = string("x_467_cast_fp16")]; + tensor input_945_perm_0 = const()[name = string("input_945_perm_0"), val = tensor([0, 2, 1])]; + tensor input_945_cast_fp16 = transpose(perm = input_945_perm_0, x = x_467_cast_fp16)[name = string("transpose_201")]; + tensor input_947_cast_fp16 = add(x = input_931_cast_fp16, y = input_945_cast_fp16)[name = string("input_947_cast_fp16")]; + tensor input_949_axes_0 = const()[name = string("input_949_axes_0"), val = tensor([-1])]; + tensor encoder_layers_17_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_17_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(354942336)))]; + tensor encoder_layers_17_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_17_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(354944448)))]; + tensor input_949_cast_fp16 = layer_norm(axes = input_949_axes_0, beta = encoder_layers_17_norm_feed_forward2_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_17_norm_feed_forward2_weight_to_fp16, x = input_947_cast_fp16)[name = string("input_949_cast_fp16")]; + tensor encoder_layers_17_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(354946560))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(358092352))))[name = string("encoder_layers_17_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_17_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_17_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(358092544)))]; + tensor linear_161_cast_fp16 = linear(bias = encoder_layers_17_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_17_feed_forward2_linear1_weight_to_fp16_palettized, x = input_949_cast_fp16)[name = string("linear_161_cast_fp16")]; + tensor input_953_cast_fp16 = silu(x = linear_161_cast_fp16)[name = string("input_953_cast_fp16")]; + tensor encoder_layers_17_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(358100800))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(361246592))))[name = string("encoder_layers_17_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_17_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_17_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(361246784)))]; + tensor linear_162_cast_fp16 = linear(bias = encoder_layers_17_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_17_feed_forward2_linear2_weight_to_fp16_palettized, x = input_953_cast_fp16)[name = string("linear_162_cast_fp16")]; + fp16 var_4251_to_fp16 = const()[name = string("op_4251_to_fp16"), val = fp16(0x1p-1)]; + tensor var_4252_cast_fp16 = mul(x = linear_162_cast_fp16, y = var_4251_to_fp16)[name = string("op_4252_cast_fp16")]; + tensor input_959_cast_fp16 = add(x = input_947_cast_fp16, y = var_4252_cast_fp16)[name = string("input_959_cast_fp16")]; + tensor input_961_axes_0 = const()[name = string("input_961_axes_0"), val = tensor([-1])]; + tensor encoder_layers_17_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_17_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(361248896)))]; + tensor encoder_layers_17_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_17_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(361251008)))]; + tensor input_961_cast_fp16 = layer_norm(axes = input_961_axes_0, beta = encoder_layers_17_norm_out_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_17_norm_out_weight_to_fp16, x = input_959_cast_fp16)[name = string("input_961_cast_fp16")]; + tensor cache_73_begin_0 = const()[name = string("cache_73_begin_0"), val = tensor([18, 0, 0, 0])]; + tensor cache_73_end_0 = const()[name = string("cache_73_end_0"), val = tensor([19, 1, 42, 1024])]; + tensor cache_73_end_mask_0 = const()[name = string("cache_73_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_73_squeeze_mask_0 = const()[name = string("cache_73_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_73_cast_fp16 = slice_by_index(begin = cache_73_begin_0, end = cache_73_end_0, end_mask = cache_73_end_mask_0, squeeze_mask = cache_73_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_73_cast_fp16")]; + tensor cache_75_begin_0 = const()[name = string("cache_75_begin_0"), val = tensor([18, 0, 0, 0])]; + tensor cache_75_end_0 = const()[name = string("cache_75_end_0"), val = tensor([19, 1, 1024, 8])]; + tensor cache_75_end_mask_0 = const()[name = string("cache_75_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_75_squeeze_mask_0 = const()[name = string("cache_75_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_75_cast_fp16 = slice_by_index(begin = cache_75_begin_0, end = cache_75_end_0, end_mask = cache_75_end_mask_0, squeeze_mask = cache_75_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_75_cast_fp16")]; + tensor input_963_axes_0 = const()[name = string("input_963_axes_0"), val = tensor([-1])]; + tensor encoder_layers_18_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_18_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(361253120)))]; + tensor encoder_layers_18_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_18_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(361255232)))]; + tensor input_963_cast_fp16 = layer_norm(axes = input_963_axes_0, beta = encoder_layers_18_norm_feed_forward1_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_18_norm_feed_forward1_weight_to_fp16, x = input_961_cast_fp16)[name = string("input_963_cast_fp16")]; + tensor encoder_layers_18_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(361257344))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(364403136))))[name = string("encoder_layers_18_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_18_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_18_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(364403328)))]; + tensor linear_163_cast_fp16 = linear(bias = encoder_layers_18_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_18_feed_forward1_linear1_weight_to_fp16_palettized, x = input_963_cast_fp16)[name = string("linear_163_cast_fp16")]; + tensor input_967_cast_fp16 = silu(x = linear_163_cast_fp16)[name = string("input_967_cast_fp16")]; + tensor encoder_layers_18_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(364411584))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(367557376))))[name = string("encoder_layers_18_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_18_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_18_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(367557568)))]; + tensor linear_164_cast_fp16 = linear(bias = encoder_layers_18_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_18_feed_forward1_linear2_weight_to_fp16_palettized, x = input_967_cast_fp16)[name = string("linear_164_cast_fp16")]; + fp16 var_4288_to_fp16 = const()[name = string("op_4288_to_fp16"), val = fp16(0x1p-1)]; + tensor var_4289_cast_fp16 = mul(x = linear_164_cast_fp16, y = var_4288_to_fp16)[name = string("op_4289_cast_fp16")]; + tensor input_973_cast_fp16 = add(x = input_961_cast_fp16, y = var_4289_cast_fp16)[name = string("input_973_cast_fp16")]; + tensor key_37_axes_0 = const()[name = string("key_37_axes_0"), val = tensor([-1])]; + tensor encoder_layers_18_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_18_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(367559680)))]; + tensor encoder_layers_18_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_18_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(367561792)))]; + tensor key_37_cast_fp16 = layer_norm(axes = key_37_axes_0, beta = encoder_layers_18_norm_self_att_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_18_norm_self_att_weight_to_fp16, x = input_973_cast_fp16)[name = string("key_37_cast_fp16")]; + bool input_975_interleave_0 = const()[name = string("input_975_interleave_0"), val = bool(false)]; + tensor input_975_cast_fp16 = concat(axis = var_67, interleave = input_975_interleave_0, values = (cache_73_cast_fp16, key_37_cast_fp16))[name = string("input_975_cast_fp16")]; + tensor var_4311_begin_0 = const()[name = string("op_4311_begin_0"), val = tensor([0, 14, 0])]; + tensor var_4311_end_0 = const()[name = string("op_4311_end_0"), val = tensor([1, 42, 1024])]; + tensor var_4311_end_mask_0 = const()[name = string("op_4311_end_mask_0"), val = tensor([true, true, true])]; + tensor var_4311_cast_fp16 = slice_by_index(begin = var_4311_begin_0, end = var_4311_end_0, end_mask = var_4311_end_mask_0, x = cache_73_cast_fp16)[name = string("op_4311_cast_fp16")]; + bool var_4317_interleave_0 = const()[name = string("op_4317_interleave_0"), val = bool(false)]; + tensor var_4317_cast_fp16 = concat(axis = var_67, interleave = var_4317_interleave_0, values = (var_4311_cast_fp16, key_37_cast_fp16))[name = string("op_4317_cast_fp16")]; + tensor encoder_layers_18_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(367563904))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(368350400))))[name = string("encoder_layers_18_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_18_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_18_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(368350592)))]; + tensor linear_165_cast_fp16 = linear(bias = encoder_layers_18_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_18_self_attn_linear_q_weight_to_fp16_palettized, x = key_37_cast_fp16)[name = string("linear_165_cast_fp16")]; + tensor var_4322 = const()[name = string("op_4322"), val = tensor([1, -1, 8, 128])]; + tensor q_109_cast_fp16 = reshape(shape = var_4322, x = linear_165_cast_fp16)[name = string("q_109_cast_fp16")]; + tensor encoder_layers_18_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(368352704))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(369139200))))[name = string("encoder_layers_18_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_18_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_18_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(369139392)))]; + tensor linear_166_cast_fp16 = linear(bias = encoder_layers_18_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_18_self_attn_linear_k_weight_to_fp16_palettized, x = input_975_cast_fp16)[name = string("linear_166_cast_fp16")]; + tensor var_4327 = const()[name = string("op_4327"), val = tensor([1, -1, 8, 128])]; + tensor k_73_cast_fp16 = reshape(shape = var_4327, x = linear_166_cast_fp16)[name = string("k_73_cast_fp16")]; + tensor encoder_layers_18_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(369141504))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(369928000))))[name = string("encoder_layers_18_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_18_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_18_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(369928192)))]; + tensor linear_167_cast_fp16 = linear(bias = encoder_layers_18_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_18_self_attn_linear_v_weight_to_fp16_palettized, x = input_975_cast_fp16)[name = string("linear_167_cast_fp16")]; + tensor var_4332 = const()[name = string("op_4332"), val = tensor([1, -1, 8, 128])]; + tensor v_37_cast_fp16 = reshape(shape = var_4332, x = linear_167_cast_fp16)[name = string("v_37_cast_fp16")]; + tensor value_45_perm_0 = const()[name = string("value_45_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_18_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_18_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(369930304)))]; + tensor var_4345_cast_fp16 = add(x = q_109_cast_fp16, y = encoder_layers_18_self_attn_pos_bias_u_to_fp16)[name = string("op_4345_cast_fp16")]; + tensor encoder_layers_18_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_18_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(369932416)))]; + tensor var_4347_cast_fp16 = add(x = q_109_cast_fp16, y = encoder_layers_18_self_attn_pos_bias_v_to_fp16)[name = string("op_4347_cast_fp16")]; + tensor q_with_bias_v_37_perm_0 = const()[name = string("q_with_bias_v_37_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_475_transpose_x_0 = const()[name = string("x_475_transpose_x_0"), val = bool(false)]; + bool x_475_transpose_y_0 = const()[name = string("x_475_transpose_y_0"), val = bool(false)]; + tensor op_4349_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(369934528))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(370048256))))[name = string("op_4349_to_fp16_quantized")]; + tensor q_with_bias_v_37_cast_fp16 = transpose(perm = q_with_bias_v_37_perm_0, x = var_4347_cast_fp16)[name = string("transpose_200")]; + tensor x_475_cast_fp16 = matmul(transpose_x = x_475_transpose_x_0, transpose_y = x_475_transpose_y_0, x = q_with_bias_v_37_cast_fp16, y = op_4349_to_fp16_quantized)[name = string("x_475_cast_fp16")]; + tensor x_477_pad_0 = const()[name = string("x_477_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_477_mode_0 = const()[name = string("x_477_mode_0"), val = string("constant")]; + fp16 const_313_to_fp16 = const()[name = string("const_313_to_fp16"), val = fp16(0x0p+0)]; + tensor x_477_cast_fp16 = pad(constant_val = const_313_to_fp16, mode = x_477_mode_0, pad = x_477_pad_0, x = x_475_cast_fp16)[name = string("x_477_cast_fp16")]; + tensor var_4357 = const()[name = string("op_4357"), val = tensor([1, 8, -1, 14])]; + tensor x_479_cast_fp16 = reshape(shape = var_4357, x = x_477_cast_fp16)[name = string("x_479_cast_fp16")]; + tensor var_4361_begin_0 = const()[name = string("op_4361_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_4361_end_0 = const()[name = string("op_4361_end_0"), val = tensor([1, 8, 112, 14])]; + tensor var_4361_end_mask_0 = const()[name = string("op_4361_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_4361_cast_fp16 = slice_by_index(begin = var_4361_begin_0, end = var_4361_end_0, end_mask = var_4361_end_mask_0, x = x_479_cast_fp16)[name = string("op_4361_cast_fp16")]; + tensor var_4362 = const()[name = string("op_4362"), val = tensor([1, 8, 14, 111])]; + tensor matrix_bd_73_cast_fp16 = reshape(shape = var_4362, x = var_4361_cast_fp16)[name = string("matrix_bd_73_cast_fp16")]; + bool matrix_ac_37_transpose_x_0 = const()[name = string("matrix_ac_37_transpose_x_0"), val = bool(false)]; + bool matrix_ac_37_transpose_y_0 = const()[name = string("matrix_ac_37_transpose_y_0"), val = bool(false)]; + tensor transpose_132_perm_0 = const()[name = string("transpose_132_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_133_perm_0 = const()[name = string("transpose_133_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_133 = transpose(perm = transpose_133_perm_0, x = k_73_cast_fp16)[name = string("transpose_198")]; + tensor transpose_132 = transpose(perm = transpose_132_perm_0, x = var_4345_cast_fp16)[name = string("transpose_199")]; + tensor matrix_ac_37_cast_fp16 = matmul(transpose_x = matrix_ac_37_transpose_x_0, transpose_y = matrix_ac_37_transpose_y_0, x = transpose_132, y = transpose_133)[name = string("matrix_ac_37_cast_fp16")]; + tensor matrix_bd_75_begin_0 = const()[name = string("matrix_bd_75_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_75_end_0 = const()[name = string("matrix_bd_75_end_0"), val = tensor([1, 8, 14, 56])]; + tensor matrix_bd_75_end_mask_0 = const()[name = string("matrix_bd_75_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_75_cast_fp16 = slice_by_index(begin = matrix_bd_75_begin_0, end = matrix_bd_75_end_0, end_mask = matrix_bd_75_end_mask_0, x = matrix_bd_73_cast_fp16)[name = string("matrix_bd_75_cast_fp16")]; + tensor var_4371_cast_fp16 = add(x = matrix_ac_37_cast_fp16, y = matrix_bd_75_cast_fp16)[name = string("op_4371_cast_fp16")]; + fp16 _inversed_scores_73_y_0_to_fp16 = const()[name = string("_inversed_scores_73_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_73_cast_fp16 = mul(x = var_4371_cast_fp16, y = _inversed_scores_73_y_0_to_fp16)[name = string("_inversed_scores_73_cast_fp16")]; + tensor scores_75_cast_fp16 = select(a = var_44_to_fp16, b = _inversed_scores_73_cast_fp16, cond = mask_11)[name = string("scores_75_cast_fp16")]; + tensor var_4377_cast_fp16 = softmax(axis = var_58, x = scores_75_cast_fp16)[name = string("op_4377_cast_fp16")]; + tensor input_977_cast_fp16 = select(a = var_43_to_fp16, b = var_4377_cast_fp16, cond = mask_11)[name = string("input_977_cast_fp16")]; + bool x_481_transpose_x_0 = const()[name = string("x_481_transpose_x_0"), val = bool(false)]; + bool x_481_transpose_y_0 = const()[name = string("x_481_transpose_y_0"), val = bool(false)]; + tensor value_45_cast_fp16 = transpose(perm = value_45_perm_0, x = v_37_cast_fp16)[name = string("transpose_197")]; + tensor x_481_cast_fp16 = matmul(transpose_x = x_481_transpose_x_0, transpose_y = x_481_transpose_y_0, x = input_977_cast_fp16, y = value_45_cast_fp16)[name = string("x_481_cast_fp16")]; + tensor var_4381_perm_0 = const()[name = string("op_4381_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_4382 = const()[name = string("op_4382"), val = tensor([1, -1, 1024])]; + tensor var_4381_cast_fp16 = transpose(perm = var_4381_perm_0, x = x_481_cast_fp16)[name = string("transpose_196")]; + tensor input_979_cast_fp16 = reshape(shape = var_4382, x = var_4381_cast_fp16)[name = string("input_979_cast_fp16")]; + tensor encoder_layers_18_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(370048576))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(371097216))))[name = string("encoder_layers_18_self_attn_linear_out_weight_to_fp16_quantized")]; + tensor encoder_layers_18_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_18_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(371099328)))]; + tensor linear_169_cast_fp16 = linear(bias = encoder_layers_18_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_18_self_attn_linear_out_weight_to_fp16_quantized, x = input_979_cast_fp16)[name = string("linear_169_cast_fp16")]; + tensor input_983_cast_fp16 = add(x = input_973_cast_fp16, y = linear_169_cast_fp16)[name = string("input_983_cast_fp16")]; + tensor x_485_axes_0 = const()[name = string("x_485_axes_0"), val = tensor([-1])]; + tensor encoder_layers_18_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_18_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(371101440)))]; + tensor encoder_layers_18_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_18_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(371103552)))]; + tensor x_485_cast_fp16 = layer_norm(axes = x_485_axes_0, beta = encoder_layers_18_norm_conv_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_18_norm_conv_weight_to_fp16, x = input_983_cast_fp16)[name = string("x_485_cast_fp16")]; + tensor input_985_perm_0 = const()[name = string("input_985_perm_0"), val = tensor([0, 2, 1])]; + string input_987_pad_type_0 = const()[name = string("input_987_pad_type_0"), val = string("valid")]; + tensor input_987_strides_0 = const()[name = string("input_987_strides_0"), val = tensor([1])]; + tensor input_987_pad_0 = const()[name = string("input_987_pad_0"), val = tensor([0, 0])]; + tensor input_987_dilations_0 = const()[name = string("input_987_dilations_0"), val = tensor([1])]; + int32 input_987_groups_0 = const()[name = string("input_987_groups_0"), val = int32(1)]; + tensor encoder_layers_18_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(371105664))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(373202880))))[name = string("encoder_layers_18_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_985_cast_fp16 = transpose(perm = input_985_perm_0, x = x_485_cast_fp16)[name = string("transpose_195")]; + tensor input_987_cast_fp16 = conv(dilations = input_987_dilations_0, groups = input_987_groups_0, pad = input_987_pad_0, pad_type = input_987_pad_type_0, strides = input_987_strides_0, weight = encoder_layers_18_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_985_cast_fp16)[name = string("input_987_cast_fp16")]; + int32 x_487_split_num_splits_0 = const()[name = string("x_487_split_num_splits_0"), val = int32(2)]; + int32 x_487_split_axis_0 = const()[name = string("x_487_split_axis_0"), val = int32(1)]; + tensor x_487_split_cast_fp16_0, tensor x_487_split_cast_fp16_1 = split(axis = x_487_split_axis_0, num_splits = x_487_split_num_splits_0, x = input_987_cast_fp16)[name = string("x_487_split_cast_fp16")]; + tensor x_487_split_1_sigmoid_cast_fp16 = sigmoid(x = x_487_split_cast_fp16_1)[name = string("x_487_split_1_sigmoid_cast_fp16")]; + tensor x_487_cast_fp16 = mul(x = x_487_split_cast_fp16_0, y = x_487_split_1_sigmoid_cast_fp16)[name = string("x_487_cast_fp16")]; + tensor input_989_cast_fp16 = select(a = var_43_to_fp16, b = x_487_cast_fp16, cond = var_574)[name = string("input_989_cast_fp16")]; + bool new_x_75_interleave_0 = const()[name = string("new_x_75_interleave_0"), val = bool(false)]; + tensor new_x_75_cast_fp16 = concat(axis = var_58, interleave = new_x_75_interleave_0, values = (cache_75_cast_fp16, input_989_cast_fp16))[name = string("new_x_75_cast_fp16")]; + tensor var_4421_begin_0 = const()[name = string("op_4421_begin_0"), val = tensor([0, 0, 14])]; + tensor var_4421_end_0 = const()[name = string("op_4421_end_0"), val = tensor([1, 1024, 22])]; + tensor var_4421_end_mask_0 = const()[name = string("op_4421_end_mask_0"), val = tensor([true, true, true])]; + tensor var_4421_cast_fp16 = slice_by_index(begin = var_4421_begin_0, end = var_4421_end_0, end_mask = var_4421_end_mask_0, x = new_x_75_cast_fp16)[name = string("op_4421_cast_fp16")]; + string x_489_pad_type_0 = const()[name = string("x_489_pad_type_0"), val = string("valid")]; + int32 x_489_groups_0 = const()[name = string("x_489_groups_0"), val = int32(1024)]; + tensor x_489_strides_0 = const()[name = string("x_489_strides_0"), val = tensor([1])]; + tensor x_489_pad_0 = const()[name = string("x_489_pad_0"), val = tensor([0, 0])]; + tensor x_489_dilations_0 = const()[name = string("x_489_dilations_0"), val = tensor([1])]; + tensor encoder_layers_18_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(373207040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(373216320))))[name = string("encoder_layers_18_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_489_cast_fp16 = conv(dilations = x_489_dilations_0, groups = x_489_groups_0, pad = x_489_pad_0, pad_type = x_489_pad_type_0, strides = x_489_strides_0, weight = encoder_layers_18_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_75_cast_fp16)[name = string("x_489_cast_fp16")]; + tensor input_991_perm_0 = const()[name = string("input_991_perm_0"), val = tensor([0, 2, 1])]; + tensor x_491_axes_0 = const()[name = string("x_491_axes_0"), val = tensor([-1])]; + tensor encoder_layers_18_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_18_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(373218432)))]; + tensor encoder_layers_18_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_18_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(373220544)))]; + tensor input_991_cast_fp16 = transpose(perm = input_991_perm_0, x = x_489_cast_fp16)[name = string("transpose_194")]; + tensor x_491_cast_fp16 = layer_norm(axes = x_491_axes_0, beta = encoder_layers_18_conv_batch_norm_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_18_conv_batch_norm_weight_to_fp16, x = input_991_cast_fp16)[name = string("x_491_cast_fp16")]; + tensor input_993_perm_0 = const()[name = string("input_993_perm_0"), val = tensor([0, 2, 1])]; + tensor input_993_cast_fp16 = transpose(perm = input_993_perm_0, x = x_491_cast_fp16)[name = string("transpose_193")]; + tensor input_995_cast_fp16 = silu(x = input_993_cast_fp16)[name = string("input_995_cast_fp16")]; + string x_493_pad_type_0 = const()[name = string("x_493_pad_type_0"), val = string("valid")]; + tensor x_493_strides_0 = const()[name = string("x_493_strides_0"), val = tensor([1])]; + tensor x_493_pad_0 = const()[name = string("x_493_pad_0"), val = tensor([0, 0])]; + tensor x_493_dilations_0 = const()[name = string("x_493_dilations_0"), val = tensor([1])]; + int32 x_493_groups_0 = const()[name = string("x_493_groups_0"), val = int32(1)]; + tensor encoder_layers_18_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(373222656))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(374271296))))[name = string("encoder_layers_18_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_493_cast_fp16 = conv(dilations = x_493_dilations_0, groups = x_493_groups_0, pad = x_493_pad_0, pad_type = x_493_pad_type_0, strides = x_493_strides_0, weight = encoder_layers_18_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_995_cast_fp16)[name = string("x_493_cast_fp16")]; + tensor input_997_perm_0 = const()[name = string("input_997_perm_0"), val = tensor([0, 2, 1])]; + tensor input_997_cast_fp16 = transpose(perm = input_997_perm_0, x = x_493_cast_fp16)[name = string("transpose_192")]; + tensor input_999_cast_fp16 = add(x = input_983_cast_fp16, y = input_997_cast_fp16)[name = string("input_999_cast_fp16")]; + tensor input_1001_axes_0 = const()[name = string("input_1001_axes_0"), val = tensor([-1])]; + tensor encoder_layers_18_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_18_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(374273408)))]; + tensor encoder_layers_18_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_18_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(374275520)))]; + tensor input_1001_cast_fp16 = layer_norm(axes = input_1001_axes_0, beta = encoder_layers_18_norm_feed_forward2_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_18_norm_feed_forward2_weight_to_fp16, x = input_999_cast_fp16)[name = string("input_1001_cast_fp16")]; + tensor encoder_layers_18_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(374277632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(378472000))))[name = string("encoder_layers_18_feed_forward2_linear1_weight_to_fp16_quantized")]; + tensor encoder_layers_18_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_18_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(378480256)))]; + tensor linear_170_cast_fp16 = linear(bias = encoder_layers_18_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_18_feed_forward2_linear1_weight_to_fp16_quantized, x = input_1001_cast_fp16)[name = string("linear_170_cast_fp16")]; + tensor input_1005_cast_fp16 = silu(x = linear_170_cast_fp16)[name = string("input_1005_cast_fp16")]; + tensor encoder_layers_18_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(378488512))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(382682880))))[name = string("encoder_layers_18_feed_forward2_linear2_weight_to_fp16_quantized")]; + tensor encoder_layers_18_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_18_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(382684992)))]; + tensor linear_171_cast_fp16 = linear(bias = encoder_layers_18_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_18_feed_forward2_linear2_weight_to_fp16_quantized, x = input_1005_cast_fp16)[name = string("linear_171_cast_fp16")]; + fp16 var_4464_to_fp16 = const()[name = string("op_4464_to_fp16"), val = fp16(0x1p-1)]; + tensor var_4465_cast_fp16 = mul(x = linear_171_cast_fp16, y = var_4464_to_fp16)[name = string("op_4465_cast_fp16")]; + tensor input_1011_cast_fp16 = add(x = input_999_cast_fp16, y = var_4465_cast_fp16)[name = string("input_1011_cast_fp16")]; + tensor input_1013_axes_0 = const()[name = string("input_1013_axes_0"), val = tensor([-1])]; + tensor encoder_layers_18_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_18_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(382687104)))]; + tensor encoder_layers_18_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_18_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(382689216)))]; + tensor input_1013_cast_fp16 = layer_norm(axes = input_1013_axes_0, beta = encoder_layers_18_norm_out_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_18_norm_out_weight_to_fp16, x = input_1011_cast_fp16)[name = string("input_1013_cast_fp16")]; + tensor cache_77_begin_0 = const()[name = string("cache_77_begin_0"), val = tensor([19, 0, 0, 0])]; + tensor cache_77_end_0 = const()[name = string("cache_77_end_0"), val = tensor([20, 1, 42, 1024])]; + tensor cache_77_end_mask_0 = const()[name = string("cache_77_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_77_squeeze_mask_0 = const()[name = string("cache_77_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_77_cast_fp16 = slice_by_index(begin = cache_77_begin_0, end = cache_77_end_0, end_mask = cache_77_end_mask_0, squeeze_mask = cache_77_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_77_cast_fp16")]; + tensor cache_79_begin_0 = const()[name = string("cache_79_begin_0"), val = tensor([19, 0, 0, 0])]; + tensor cache_79_end_0 = const()[name = string("cache_79_end_0"), val = tensor([20, 1, 1024, 8])]; + tensor cache_79_end_mask_0 = const()[name = string("cache_79_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_79_squeeze_mask_0 = const()[name = string("cache_79_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_79_cast_fp16 = slice_by_index(begin = cache_79_begin_0, end = cache_79_end_0, end_mask = cache_79_end_mask_0, squeeze_mask = cache_79_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_79_cast_fp16")]; + tensor input_1015_axes_0 = const()[name = string("input_1015_axes_0"), val = tensor([-1])]; + tensor encoder_layers_19_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_19_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(382691328)))]; + tensor encoder_layers_19_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_19_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(382693440)))]; + tensor input_1015_cast_fp16 = layer_norm(axes = input_1015_axes_0, beta = encoder_layers_19_norm_feed_forward1_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_19_norm_feed_forward1_weight_to_fp16, x = input_1013_cast_fp16)[name = string("input_1015_cast_fp16")]; + tensor encoder_layers_19_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(382695552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(386889920))))[name = string("encoder_layers_19_feed_forward1_linear1_weight_to_fp16_quantized")]; + tensor encoder_layers_19_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_19_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(386898176)))]; + tensor linear_172_cast_fp16 = linear(bias = encoder_layers_19_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_19_feed_forward1_linear1_weight_to_fp16_quantized, x = input_1015_cast_fp16)[name = string("linear_172_cast_fp16")]; + tensor input_1019_cast_fp16 = silu(x = linear_172_cast_fp16)[name = string("input_1019_cast_fp16")]; + tensor encoder_layers_19_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(386906432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(391100800))))[name = string("encoder_layers_19_feed_forward1_linear2_weight_to_fp16_quantized")]; + tensor encoder_layers_19_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_19_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(391102912)))]; + tensor linear_173_cast_fp16 = linear(bias = encoder_layers_19_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_19_feed_forward1_linear2_weight_to_fp16_quantized, x = input_1019_cast_fp16)[name = string("linear_173_cast_fp16")]; + fp16 var_4501_to_fp16 = const()[name = string("op_4501_to_fp16"), val = fp16(0x1p-1)]; + tensor var_4502_cast_fp16 = mul(x = linear_173_cast_fp16, y = var_4501_to_fp16)[name = string("op_4502_cast_fp16")]; + tensor input_1025_cast_fp16 = add(x = input_1013_cast_fp16, y = var_4502_cast_fp16)[name = string("input_1025_cast_fp16")]; + tensor key_39_axes_0 = const()[name = string("key_39_axes_0"), val = tensor([-1])]; + tensor encoder_layers_19_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_19_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(391105024)))]; + tensor encoder_layers_19_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_19_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(391107136)))]; + tensor key_39_cast_fp16 = layer_norm(axes = key_39_axes_0, beta = encoder_layers_19_norm_self_att_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_19_norm_self_att_weight_to_fp16, x = input_1025_cast_fp16)[name = string("key_39_cast_fp16")]; + bool input_1027_interleave_0 = const()[name = string("input_1027_interleave_0"), val = bool(false)]; + tensor input_1027_cast_fp16 = concat(axis = var_67, interleave = input_1027_interleave_0, values = (cache_77_cast_fp16, key_39_cast_fp16))[name = string("input_1027_cast_fp16")]; + tensor var_4524_begin_0 = const()[name = string("op_4524_begin_0"), val = tensor([0, 14, 0])]; + tensor var_4524_end_0 = const()[name = string("op_4524_end_0"), val = tensor([1, 42, 1024])]; + tensor var_4524_end_mask_0 = const()[name = string("op_4524_end_mask_0"), val = tensor([true, true, true])]; + tensor var_4524_cast_fp16 = slice_by_index(begin = var_4524_begin_0, end = var_4524_end_0, end_mask = var_4524_end_mask_0, x = cache_77_cast_fp16)[name = string("op_4524_cast_fp16")]; + bool var_4530_interleave_0 = const()[name = string("op_4530_interleave_0"), val = bool(false)]; + tensor var_4530_cast_fp16 = concat(axis = var_67, interleave = var_4530_interleave_0, values = (var_4524_cast_fp16, key_39_cast_fp16))[name = string("op_4530_cast_fp16")]; + tensor encoder_layers_19_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(391109248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(392157888))))[name = string("encoder_layers_19_self_attn_linear_q_weight_to_fp16_quantized")]; + tensor encoder_layers_19_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_19_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(392160000)))]; + tensor linear_174_cast_fp16 = linear(bias = encoder_layers_19_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_19_self_attn_linear_q_weight_to_fp16_quantized, x = key_39_cast_fp16)[name = string("linear_174_cast_fp16")]; + tensor var_4535 = const()[name = string("op_4535"), val = tensor([1, -1, 8, 128])]; + tensor q_115_cast_fp16 = reshape(shape = var_4535, x = linear_174_cast_fp16)[name = string("q_115_cast_fp16")]; + tensor encoder_layers_19_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(392162112))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(393210752))))[name = string("encoder_layers_19_self_attn_linear_k_weight_to_fp16_quantized")]; + tensor encoder_layers_19_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_19_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(393212864)))]; + tensor linear_175_cast_fp16 = linear(bias = encoder_layers_19_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_19_self_attn_linear_k_weight_to_fp16_quantized, x = input_1027_cast_fp16)[name = string("linear_175_cast_fp16")]; + tensor var_4540 = const()[name = string("op_4540"), val = tensor([1, -1, 8, 128])]; + tensor k_77_cast_fp16 = reshape(shape = var_4540, x = linear_175_cast_fp16)[name = string("k_77_cast_fp16")]; + tensor encoder_layers_19_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(393214976))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(394263616))))[name = string("encoder_layers_19_self_attn_linear_v_weight_to_fp16_quantized")]; + tensor encoder_layers_19_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_19_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(394265728)))]; + tensor linear_176_cast_fp16 = linear(bias = encoder_layers_19_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_19_self_attn_linear_v_weight_to_fp16_quantized, x = input_1027_cast_fp16)[name = string("linear_176_cast_fp16")]; + tensor var_4545 = const()[name = string("op_4545"), val = tensor([1, -1, 8, 128])]; + tensor v_39_cast_fp16 = reshape(shape = var_4545, x = linear_176_cast_fp16)[name = string("v_39_cast_fp16")]; + tensor value_47_perm_0 = const()[name = string("value_47_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_19_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_19_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(394267840)))]; + tensor var_4558_cast_fp16 = add(x = q_115_cast_fp16, y = encoder_layers_19_self_attn_pos_bias_u_to_fp16)[name = string("op_4558_cast_fp16")]; + tensor encoder_layers_19_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_19_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(394269952)))]; + tensor var_4560_cast_fp16 = add(x = q_115_cast_fp16, y = encoder_layers_19_self_attn_pos_bias_v_to_fp16)[name = string("op_4560_cast_fp16")]; + tensor q_with_bias_v_39_perm_0 = const()[name = string("q_with_bias_v_39_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_501_transpose_x_0 = const()[name = string("x_501_transpose_x_0"), val = bool(false)]; + bool x_501_transpose_y_0 = const()[name = string("x_501_transpose_y_0"), val = bool(false)]; + tensor op_4562_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(394272064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(394385792))))[name = string("op_4562_to_fp16_quantized")]; + tensor q_with_bias_v_39_cast_fp16 = transpose(perm = q_with_bias_v_39_perm_0, x = var_4560_cast_fp16)[name = string("transpose_191")]; + tensor x_501_cast_fp16 = matmul(transpose_x = x_501_transpose_x_0, transpose_y = x_501_transpose_y_0, x = q_with_bias_v_39_cast_fp16, y = op_4562_to_fp16_quantized)[name = string("x_501_cast_fp16")]; + tensor x_503_pad_0 = const()[name = string("x_503_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_503_mode_0 = const()[name = string("x_503_mode_0"), val = string("constant")]; + fp16 const_326_to_fp16 = const()[name = string("const_326_to_fp16"), val = fp16(0x0p+0)]; + tensor x_503_cast_fp16 = pad(constant_val = const_326_to_fp16, mode = x_503_mode_0, pad = x_503_pad_0, x = x_501_cast_fp16)[name = string("x_503_cast_fp16")]; + tensor var_4570 = const()[name = string("op_4570"), val = tensor([1, 8, -1, 14])]; + tensor x_505_cast_fp16 = reshape(shape = var_4570, x = x_503_cast_fp16)[name = string("x_505_cast_fp16")]; + tensor var_4574_begin_0 = const()[name = string("op_4574_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_4574_end_0 = const()[name = string("op_4574_end_0"), val = tensor([1, 8, 112, 14])]; + tensor var_4574_end_mask_0 = const()[name = string("op_4574_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_4574_cast_fp16 = slice_by_index(begin = var_4574_begin_0, end = var_4574_end_0, end_mask = var_4574_end_mask_0, x = x_505_cast_fp16)[name = string("op_4574_cast_fp16")]; + tensor var_4575 = const()[name = string("op_4575"), val = tensor([1, 8, 14, 111])]; + tensor matrix_bd_77_cast_fp16 = reshape(shape = var_4575, x = var_4574_cast_fp16)[name = string("matrix_bd_77_cast_fp16")]; + bool matrix_ac_39_transpose_x_0 = const()[name = string("matrix_ac_39_transpose_x_0"), val = bool(false)]; + bool matrix_ac_39_transpose_y_0 = const()[name = string("matrix_ac_39_transpose_y_0"), val = bool(false)]; + tensor transpose_134_perm_0 = const()[name = string("transpose_134_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_135_perm_0 = const()[name = string("transpose_135_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_135 = transpose(perm = transpose_135_perm_0, x = k_77_cast_fp16)[name = string("transpose_189")]; + tensor transpose_134 = transpose(perm = transpose_134_perm_0, x = var_4558_cast_fp16)[name = string("transpose_190")]; + tensor matrix_ac_39_cast_fp16 = matmul(transpose_x = matrix_ac_39_transpose_x_0, transpose_y = matrix_ac_39_transpose_y_0, x = transpose_134, y = transpose_135)[name = string("matrix_ac_39_cast_fp16")]; + tensor matrix_bd_79_begin_0 = const()[name = string("matrix_bd_79_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_79_end_0 = const()[name = string("matrix_bd_79_end_0"), val = tensor([1, 8, 14, 56])]; + tensor matrix_bd_79_end_mask_0 = const()[name = string("matrix_bd_79_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_79_cast_fp16 = slice_by_index(begin = matrix_bd_79_begin_0, end = matrix_bd_79_end_0, end_mask = matrix_bd_79_end_mask_0, x = matrix_bd_77_cast_fp16)[name = string("matrix_bd_79_cast_fp16")]; + tensor var_4584_cast_fp16 = add(x = matrix_ac_39_cast_fp16, y = matrix_bd_79_cast_fp16)[name = string("op_4584_cast_fp16")]; + fp16 _inversed_scores_77_y_0_to_fp16 = const()[name = string("_inversed_scores_77_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_77_cast_fp16 = mul(x = var_4584_cast_fp16, y = _inversed_scores_77_y_0_to_fp16)[name = string("_inversed_scores_77_cast_fp16")]; + tensor scores_79_cast_fp16 = select(a = var_44_to_fp16, b = _inversed_scores_77_cast_fp16, cond = mask_11)[name = string("scores_79_cast_fp16")]; + tensor var_4590_cast_fp16 = softmax(axis = var_58, x = scores_79_cast_fp16)[name = string("op_4590_cast_fp16")]; + tensor input_1029_cast_fp16 = select(a = var_43_to_fp16, b = var_4590_cast_fp16, cond = mask_11)[name = string("input_1029_cast_fp16")]; + bool x_507_transpose_x_0 = const()[name = string("x_507_transpose_x_0"), val = bool(false)]; + bool x_507_transpose_y_0 = const()[name = string("x_507_transpose_y_0"), val = bool(false)]; + tensor value_47_cast_fp16 = transpose(perm = value_47_perm_0, x = v_39_cast_fp16)[name = string("transpose_188")]; + tensor x_507_cast_fp16 = matmul(transpose_x = x_507_transpose_x_0, transpose_y = x_507_transpose_y_0, x = input_1029_cast_fp16, y = value_47_cast_fp16)[name = string("x_507_cast_fp16")]; + tensor var_4594_perm_0 = const()[name = string("op_4594_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_4595 = const()[name = string("op_4595"), val = tensor([1, -1, 1024])]; + tensor var_4594_cast_fp16 = transpose(perm = var_4594_perm_0, x = x_507_cast_fp16)[name = string("transpose_187")]; + tensor input_1031_cast_fp16 = reshape(shape = var_4595, x = var_4594_cast_fp16)[name = string("input_1031_cast_fp16")]; + tensor encoder_layers_19_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(394386112))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(395434752))))[name = string("encoder_layers_19_self_attn_linear_out_weight_to_fp16_quantized")]; + tensor encoder_layers_19_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_19_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(395436864)))]; + tensor linear_178_cast_fp16 = linear(bias = encoder_layers_19_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_19_self_attn_linear_out_weight_to_fp16_quantized, x = input_1031_cast_fp16)[name = string("linear_178_cast_fp16")]; + tensor input_1035_cast_fp16 = add(x = input_1025_cast_fp16, y = linear_178_cast_fp16)[name = string("input_1035_cast_fp16")]; + tensor x_511_axes_0 = const()[name = string("x_511_axes_0"), val = tensor([-1])]; + tensor encoder_layers_19_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_19_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(395438976)))]; + tensor encoder_layers_19_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_19_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(395441088)))]; + tensor x_511_cast_fp16 = layer_norm(axes = x_511_axes_0, beta = encoder_layers_19_norm_conv_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_19_norm_conv_weight_to_fp16, x = input_1035_cast_fp16)[name = string("x_511_cast_fp16")]; + tensor input_1037_perm_0 = const()[name = string("input_1037_perm_0"), val = tensor([0, 2, 1])]; + string input_1039_pad_type_0 = const()[name = string("input_1039_pad_type_0"), val = string("valid")]; + tensor input_1039_strides_0 = const()[name = string("input_1039_strides_0"), val = tensor([1])]; + tensor input_1039_pad_0 = const()[name = string("input_1039_pad_0"), val = tensor([0, 0])]; + tensor input_1039_dilations_0 = const()[name = string("input_1039_dilations_0"), val = tensor([1])]; + int32 input_1039_groups_0 = const()[name = string("input_1039_groups_0"), val = int32(1)]; + tensor encoder_layers_19_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(395443200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397540416))))[name = string("encoder_layers_19_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_1037_cast_fp16 = transpose(perm = input_1037_perm_0, x = x_511_cast_fp16)[name = string("transpose_186")]; + tensor input_1039_cast_fp16 = conv(dilations = input_1039_dilations_0, groups = input_1039_groups_0, pad = input_1039_pad_0, pad_type = input_1039_pad_type_0, strides = input_1039_strides_0, weight = encoder_layers_19_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1037_cast_fp16)[name = string("input_1039_cast_fp16")]; + int32 x_513_split_num_splits_0 = const()[name = string("x_513_split_num_splits_0"), val = int32(2)]; + int32 x_513_split_axis_0 = const()[name = string("x_513_split_axis_0"), val = int32(1)]; + tensor x_513_split_cast_fp16_0, tensor x_513_split_cast_fp16_1 = split(axis = x_513_split_axis_0, num_splits = x_513_split_num_splits_0, x = input_1039_cast_fp16)[name = string("x_513_split_cast_fp16")]; + tensor x_513_split_1_sigmoid_cast_fp16 = sigmoid(x = x_513_split_cast_fp16_1)[name = string("x_513_split_1_sigmoid_cast_fp16")]; + tensor x_513_cast_fp16 = mul(x = x_513_split_cast_fp16_0, y = x_513_split_1_sigmoid_cast_fp16)[name = string("x_513_cast_fp16")]; + tensor input_1041_cast_fp16 = select(a = var_43_to_fp16, b = x_513_cast_fp16, cond = var_574)[name = string("input_1041_cast_fp16")]; + bool new_x_79_interleave_0 = const()[name = string("new_x_79_interleave_0"), val = bool(false)]; + tensor new_x_79_cast_fp16 = concat(axis = var_58, interleave = new_x_79_interleave_0, values = (cache_79_cast_fp16, input_1041_cast_fp16))[name = string("new_x_79_cast_fp16")]; + tensor var_4634_begin_0 = const()[name = string("op_4634_begin_0"), val = tensor([0, 0, 14])]; + tensor var_4634_end_0 = const()[name = string("op_4634_end_0"), val = tensor([1, 1024, 22])]; + tensor var_4634_end_mask_0 = const()[name = string("op_4634_end_mask_0"), val = tensor([true, true, true])]; + tensor var_4634_cast_fp16 = slice_by_index(begin = var_4634_begin_0, end = var_4634_end_0, end_mask = var_4634_end_mask_0, x = new_x_79_cast_fp16)[name = string("op_4634_cast_fp16")]; + string x_515_pad_type_0 = const()[name = string("x_515_pad_type_0"), val = string("valid")]; + int32 x_515_groups_0 = const()[name = string("x_515_groups_0"), val = int32(1024)]; + tensor x_515_strides_0 = const()[name = string("x_515_strides_0"), val = tensor([1])]; + tensor x_515_pad_0 = const()[name = string("x_515_pad_0"), val = tensor([0, 0])]; + tensor x_515_dilations_0 = const()[name = string("x_515_dilations_0"), val = tensor([1])]; + tensor encoder_layers_19_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397544576))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397553856))))[name = string("encoder_layers_19_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_515_cast_fp16 = conv(dilations = x_515_dilations_0, groups = x_515_groups_0, pad = x_515_pad_0, pad_type = x_515_pad_type_0, strides = x_515_strides_0, weight = encoder_layers_19_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_79_cast_fp16)[name = string("x_515_cast_fp16")]; + tensor input_1043_perm_0 = const()[name = string("input_1043_perm_0"), val = tensor([0, 2, 1])]; + tensor x_517_axes_0 = const()[name = string("x_517_axes_0"), val = tensor([-1])]; + tensor encoder_layers_19_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_19_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397555968)))]; + tensor encoder_layers_19_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_19_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397558080)))]; + tensor input_1043_cast_fp16 = transpose(perm = input_1043_perm_0, x = x_515_cast_fp16)[name = string("transpose_185")]; + tensor x_517_cast_fp16 = layer_norm(axes = x_517_axes_0, beta = encoder_layers_19_conv_batch_norm_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_19_conv_batch_norm_weight_to_fp16, x = input_1043_cast_fp16)[name = string("x_517_cast_fp16")]; + tensor input_1045_perm_0 = const()[name = string("input_1045_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1045_cast_fp16 = transpose(perm = input_1045_perm_0, x = x_517_cast_fp16)[name = string("transpose_184")]; + tensor input_1047_cast_fp16 = silu(x = input_1045_cast_fp16)[name = string("input_1047_cast_fp16")]; + string x_519_pad_type_0 = const()[name = string("x_519_pad_type_0"), val = string("valid")]; + tensor x_519_strides_0 = const()[name = string("x_519_strides_0"), val = tensor([1])]; + tensor x_519_pad_0 = const()[name = string("x_519_pad_0"), val = tensor([0, 0])]; + tensor x_519_dilations_0 = const()[name = string("x_519_dilations_0"), val = tensor([1])]; + int32 x_519_groups_0 = const()[name = string("x_519_groups_0"), val = int32(1)]; + tensor encoder_layers_19_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397560192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(398608832))))[name = string("encoder_layers_19_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_519_cast_fp16 = conv(dilations = x_519_dilations_0, groups = x_519_groups_0, pad = x_519_pad_0, pad_type = x_519_pad_type_0, strides = x_519_strides_0, weight = encoder_layers_19_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1047_cast_fp16)[name = string("x_519_cast_fp16")]; + tensor input_1049_perm_0 = const()[name = string("input_1049_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1049_cast_fp16 = transpose(perm = input_1049_perm_0, x = x_519_cast_fp16)[name = string("transpose_183")]; + tensor input_1051_cast_fp16 = add(x = input_1035_cast_fp16, y = input_1049_cast_fp16)[name = string("input_1051_cast_fp16")]; + tensor input_1053_axes_0 = const()[name = string("input_1053_axes_0"), val = tensor([-1])]; + tensor encoder_layers_19_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_19_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(398610944)))]; + tensor encoder_layers_19_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_19_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(398613056)))]; + tensor input_1053_cast_fp16 = layer_norm(axes = input_1053_axes_0, beta = encoder_layers_19_norm_feed_forward2_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_19_norm_feed_forward2_weight_to_fp16, x = input_1051_cast_fp16)[name = string("input_1053_cast_fp16")]; + tensor encoder_layers_19_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(398615168))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402809536))))[name = string("encoder_layers_19_feed_forward2_linear1_weight_to_fp16_quantized")]; + tensor encoder_layers_19_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_19_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402817792)))]; + tensor linear_179_cast_fp16 = linear(bias = encoder_layers_19_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_19_feed_forward2_linear1_weight_to_fp16_quantized, x = input_1053_cast_fp16)[name = string("linear_179_cast_fp16")]; + tensor input_1057_cast_fp16 = silu(x = linear_179_cast_fp16)[name = string("input_1057_cast_fp16")]; + tensor encoder_layers_19_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402826048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(407020416))))[name = string("encoder_layers_19_feed_forward2_linear2_weight_to_fp16_quantized")]; + tensor encoder_layers_19_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_19_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(407022528)))]; + tensor linear_180_cast_fp16 = linear(bias = encoder_layers_19_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_19_feed_forward2_linear2_weight_to_fp16_quantized, x = input_1057_cast_fp16)[name = string("linear_180_cast_fp16")]; + fp16 var_4677_to_fp16 = const()[name = string("op_4677_to_fp16"), val = fp16(0x1p-1)]; + tensor var_4678_cast_fp16 = mul(x = linear_180_cast_fp16, y = var_4677_to_fp16)[name = string("op_4678_cast_fp16")]; + tensor input_1063_cast_fp16 = add(x = input_1051_cast_fp16, y = var_4678_cast_fp16)[name = string("input_1063_cast_fp16")]; + tensor input_1065_axes_0 = const()[name = string("input_1065_axes_0"), val = tensor([-1])]; + tensor encoder_layers_19_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_19_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(407024640)))]; + tensor encoder_layers_19_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_19_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(407026752)))]; + tensor input_1065_cast_fp16 = layer_norm(axes = input_1065_axes_0, beta = encoder_layers_19_norm_out_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_19_norm_out_weight_to_fp16, x = input_1063_cast_fp16)[name = string("input_1065_cast_fp16")]; + tensor cache_81_begin_0 = const()[name = string("cache_81_begin_0"), val = tensor([20, 0, 0, 0])]; + tensor cache_81_end_0 = const()[name = string("cache_81_end_0"), val = tensor([21, 1, 42, 1024])]; + tensor cache_81_end_mask_0 = const()[name = string("cache_81_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_81_squeeze_mask_0 = const()[name = string("cache_81_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_81_cast_fp16 = slice_by_index(begin = cache_81_begin_0, end = cache_81_end_0, end_mask = cache_81_end_mask_0, squeeze_mask = cache_81_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_81_cast_fp16")]; + tensor cache_83_begin_0 = const()[name = string("cache_83_begin_0"), val = tensor([20, 0, 0, 0])]; + tensor cache_83_end_0 = const()[name = string("cache_83_end_0"), val = tensor([21, 1, 1024, 8])]; + tensor cache_83_end_mask_0 = const()[name = string("cache_83_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_83_squeeze_mask_0 = const()[name = string("cache_83_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_83_cast_fp16 = slice_by_index(begin = cache_83_begin_0, end = cache_83_end_0, end_mask = cache_83_end_mask_0, squeeze_mask = cache_83_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_83_cast_fp16")]; + tensor input_1067_axes_0 = const()[name = string("input_1067_axes_0"), val = tensor([-1])]; + tensor encoder_layers_20_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_20_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(407028864)))]; + tensor encoder_layers_20_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_20_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(407030976)))]; + tensor input_1067_cast_fp16 = layer_norm(axes = input_1067_axes_0, beta = encoder_layers_20_norm_feed_forward1_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_20_norm_feed_forward1_weight_to_fp16, x = input_1065_cast_fp16)[name = string("input_1067_cast_fp16")]; + tensor encoder_layers_20_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(407033088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(411227456))))[name = string("encoder_layers_20_feed_forward1_linear1_weight_to_fp16_quantized")]; + tensor encoder_layers_20_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_20_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(411235712)))]; + tensor linear_181_cast_fp16 = linear(bias = encoder_layers_20_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_20_feed_forward1_linear1_weight_to_fp16_quantized, x = input_1067_cast_fp16)[name = string("linear_181_cast_fp16")]; + tensor input_1071_cast_fp16 = silu(x = linear_181_cast_fp16)[name = string("input_1071_cast_fp16")]; + tensor encoder_layers_20_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(411243968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(415438336))))[name = string("encoder_layers_20_feed_forward1_linear2_weight_to_fp16_quantized")]; + tensor encoder_layers_20_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_20_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(415440448)))]; + tensor linear_182_cast_fp16 = linear(bias = encoder_layers_20_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_20_feed_forward1_linear2_weight_to_fp16_quantized, x = input_1071_cast_fp16)[name = string("linear_182_cast_fp16")]; + fp16 var_4714_to_fp16 = const()[name = string("op_4714_to_fp16"), val = fp16(0x1p-1)]; + tensor var_4715_cast_fp16 = mul(x = linear_182_cast_fp16, y = var_4714_to_fp16)[name = string("op_4715_cast_fp16")]; + tensor input_1077_cast_fp16 = add(x = input_1065_cast_fp16, y = var_4715_cast_fp16)[name = string("input_1077_cast_fp16")]; + tensor key_41_axes_0 = const()[name = string("key_41_axes_0"), val = tensor([-1])]; + tensor encoder_layers_20_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_20_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(415442560)))]; + tensor encoder_layers_20_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_20_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(415444672)))]; + tensor key_41_cast_fp16 = layer_norm(axes = key_41_axes_0, beta = encoder_layers_20_norm_self_att_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_20_norm_self_att_weight_to_fp16, x = input_1077_cast_fp16)[name = string("key_41_cast_fp16")]; + bool input_1079_interleave_0 = const()[name = string("input_1079_interleave_0"), val = bool(false)]; + tensor input_1079_cast_fp16 = concat(axis = var_67, interleave = input_1079_interleave_0, values = (cache_81_cast_fp16, key_41_cast_fp16))[name = string("input_1079_cast_fp16")]; + tensor var_4737_begin_0 = const()[name = string("op_4737_begin_0"), val = tensor([0, 14, 0])]; + tensor var_4737_end_0 = const()[name = string("op_4737_end_0"), val = tensor([1, 42, 1024])]; + tensor var_4737_end_mask_0 = const()[name = string("op_4737_end_mask_0"), val = tensor([true, true, true])]; + tensor var_4737_cast_fp16 = slice_by_index(begin = var_4737_begin_0, end = var_4737_end_0, end_mask = var_4737_end_mask_0, x = cache_81_cast_fp16)[name = string("op_4737_cast_fp16")]; + bool var_4743_interleave_0 = const()[name = string("op_4743_interleave_0"), val = bool(false)]; + tensor var_4743_cast_fp16 = concat(axis = var_67, interleave = var_4743_interleave_0, values = (var_4737_cast_fp16, key_41_cast_fp16))[name = string("op_4743_cast_fp16")]; + tensor encoder_layers_20_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(415446784))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(416495424))))[name = string("encoder_layers_20_self_attn_linear_q_weight_to_fp16_quantized")]; + tensor encoder_layers_20_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_20_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(416497536)))]; + tensor linear_183_cast_fp16 = linear(bias = encoder_layers_20_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_20_self_attn_linear_q_weight_to_fp16_quantized, x = key_41_cast_fp16)[name = string("linear_183_cast_fp16")]; + tensor var_4748 = const()[name = string("op_4748"), val = tensor([1, -1, 8, 128])]; + tensor q_121_cast_fp16 = reshape(shape = var_4748, x = linear_183_cast_fp16)[name = string("q_121_cast_fp16")]; + tensor encoder_layers_20_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(416499648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(417548288))))[name = string("encoder_layers_20_self_attn_linear_k_weight_to_fp16_quantized")]; + tensor encoder_layers_20_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_20_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(417550400)))]; + tensor linear_184_cast_fp16 = linear(bias = encoder_layers_20_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_20_self_attn_linear_k_weight_to_fp16_quantized, x = input_1079_cast_fp16)[name = string("linear_184_cast_fp16")]; + tensor var_4753 = const()[name = string("op_4753"), val = tensor([1, -1, 8, 128])]; + tensor k_81_cast_fp16 = reshape(shape = var_4753, x = linear_184_cast_fp16)[name = string("k_81_cast_fp16")]; + tensor encoder_layers_20_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(417552512))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(418601152))))[name = string("encoder_layers_20_self_attn_linear_v_weight_to_fp16_quantized")]; + tensor encoder_layers_20_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_20_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(418603264)))]; + tensor linear_185_cast_fp16 = linear(bias = encoder_layers_20_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_20_self_attn_linear_v_weight_to_fp16_quantized, x = input_1079_cast_fp16)[name = string("linear_185_cast_fp16")]; + tensor var_4758 = const()[name = string("op_4758"), val = tensor([1, -1, 8, 128])]; + tensor v_41_cast_fp16 = reshape(shape = var_4758, x = linear_185_cast_fp16)[name = string("v_41_cast_fp16")]; + tensor value_49_perm_0 = const()[name = string("value_49_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_20_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_20_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(418605376)))]; + tensor var_4771_cast_fp16 = add(x = q_121_cast_fp16, y = encoder_layers_20_self_attn_pos_bias_u_to_fp16)[name = string("op_4771_cast_fp16")]; + tensor encoder_layers_20_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_20_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(418607488)))]; + tensor var_4773_cast_fp16 = add(x = q_121_cast_fp16, y = encoder_layers_20_self_attn_pos_bias_v_to_fp16)[name = string("op_4773_cast_fp16")]; + tensor q_with_bias_v_41_perm_0 = const()[name = string("q_with_bias_v_41_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_527_transpose_x_0 = const()[name = string("x_527_transpose_x_0"), val = bool(false)]; + bool x_527_transpose_y_0 = const()[name = string("x_527_transpose_y_0"), val = bool(false)]; + tensor op_4775_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(418609600))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(418723328))))[name = string("op_4775_to_fp16_quantized")]; + tensor q_with_bias_v_41_cast_fp16 = transpose(perm = q_with_bias_v_41_perm_0, x = var_4773_cast_fp16)[name = string("transpose_182")]; + tensor x_527_cast_fp16 = matmul(transpose_x = x_527_transpose_x_0, transpose_y = x_527_transpose_y_0, x = q_with_bias_v_41_cast_fp16, y = op_4775_to_fp16_quantized)[name = string("x_527_cast_fp16")]; + tensor x_529_pad_0 = const()[name = string("x_529_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_529_mode_0 = const()[name = string("x_529_mode_0"), val = string("constant")]; + fp16 const_339_to_fp16 = const()[name = string("const_339_to_fp16"), val = fp16(0x0p+0)]; + tensor x_529_cast_fp16 = pad(constant_val = const_339_to_fp16, mode = x_529_mode_0, pad = x_529_pad_0, x = x_527_cast_fp16)[name = string("x_529_cast_fp16")]; + tensor var_4783 = const()[name = string("op_4783"), val = tensor([1, 8, -1, 14])]; + tensor x_531_cast_fp16 = reshape(shape = var_4783, x = x_529_cast_fp16)[name = string("x_531_cast_fp16")]; + tensor var_4787_begin_0 = const()[name = string("op_4787_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_4787_end_0 = const()[name = string("op_4787_end_0"), val = tensor([1, 8, 112, 14])]; + tensor var_4787_end_mask_0 = const()[name = string("op_4787_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_4787_cast_fp16 = slice_by_index(begin = var_4787_begin_0, end = var_4787_end_0, end_mask = var_4787_end_mask_0, x = x_531_cast_fp16)[name = string("op_4787_cast_fp16")]; + tensor var_4788 = const()[name = string("op_4788"), val = tensor([1, 8, 14, 111])]; + tensor matrix_bd_81_cast_fp16 = reshape(shape = var_4788, x = var_4787_cast_fp16)[name = string("matrix_bd_81_cast_fp16")]; + bool matrix_ac_41_transpose_x_0 = const()[name = string("matrix_ac_41_transpose_x_0"), val = bool(false)]; + bool matrix_ac_41_transpose_y_0 = const()[name = string("matrix_ac_41_transpose_y_0"), val = bool(false)]; + tensor transpose_136_perm_0 = const()[name = string("transpose_136_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_137_perm_0 = const()[name = string("transpose_137_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_137 = transpose(perm = transpose_137_perm_0, x = k_81_cast_fp16)[name = string("transpose_180")]; + tensor transpose_136 = transpose(perm = transpose_136_perm_0, x = var_4771_cast_fp16)[name = string("transpose_181")]; + tensor matrix_ac_41_cast_fp16 = matmul(transpose_x = matrix_ac_41_transpose_x_0, transpose_y = matrix_ac_41_transpose_y_0, x = transpose_136, y = transpose_137)[name = string("matrix_ac_41_cast_fp16")]; + tensor matrix_bd_83_begin_0 = const()[name = string("matrix_bd_83_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_83_end_0 = const()[name = string("matrix_bd_83_end_0"), val = tensor([1, 8, 14, 56])]; + tensor matrix_bd_83_end_mask_0 = const()[name = string("matrix_bd_83_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_83_cast_fp16 = slice_by_index(begin = matrix_bd_83_begin_0, end = matrix_bd_83_end_0, end_mask = matrix_bd_83_end_mask_0, x = matrix_bd_81_cast_fp16)[name = string("matrix_bd_83_cast_fp16")]; + tensor var_4797_cast_fp16 = add(x = matrix_ac_41_cast_fp16, y = matrix_bd_83_cast_fp16)[name = string("op_4797_cast_fp16")]; + fp16 _inversed_scores_81_y_0_to_fp16 = const()[name = string("_inversed_scores_81_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_81_cast_fp16 = mul(x = var_4797_cast_fp16, y = _inversed_scores_81_y_0_to_fp16)[name = string("_inversed_scores_81_cast_fp16")]; + tensor scores_83_cast_fp16 = select(a = var_44_to_fp16, b = _inversed_scores_81_cast_fp16, cond = mask_11)[name = string("scores_83_cast_fp16")]; + tensor var_4803_cast_fp16 = softmax(axis = var_58, x = scores_83_cast_fp16)[name = string("op_4803_cast_fp16")]; + tensor input_1081_cast_fp16 = select(a = var_43_to_fp16, b = var_4803_cast_fp16, cond = mask_11)[name = string("input_1081_cast_fp16")]; + bool x_533_transpose_x_0 = const()[name = string("x_533_transpose_x_0"), val = bool(false)]; + bool x_533_transpose_y_0 = const()[name = string("x_533_transpose_y_0"), val = bool(false)]; + tensor value_49_cast_fp16 = transpose(perm = value_49_perm_0, x = v_41_cast_fp16)[name = string("transpose_179")]; + tensor x_533_cast_fp16 = matmul(transpose_x = x_533_transpose_x_0, transpose_y = x_533_transpose_y_0, x = input_1081_cast_fp16, y = value_49_cast_fp16)[name = string("x_533_cast_fp16")]; + tensor var_4807_perm_0 = const()[name = string("op_4807_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_4808 = const()[name = string("op_4808"), val = tensor([1, -1, 1024])]; + tensor var_4807_cast_fp16 = transpose(perm = var_4807_perm_0, x = x_533_cast_fp16)[name = string("transpose_178")]; + tensor input_1083_cast_fp16 = reshape(shape = var_4808, x = var_4807_cast_fp16)[name = string("input_1083_cast_fp16")]; + tensor encoder_layers_20_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(418723648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(419772288))))[name = string("encoder_layers_20_self_attn_linear_out_weight_to_fp16_quantized")]; + tensor encoder_layers_20_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_20_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(419774400)))]; + tensor linear_187_cast_fp16 = linear(bias = encoder_layers_20_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_20_self_attn_linear_out_weight_to_fp16_quantized, x = input_1083_cast_fp16)[name = string("linear_187_cast_fp16")]; + tensor input_1087_cast_fp16 = add(x = input_1077_cast_fp16, y = linear_187_cast_fp16)[name = string("input_1087_cast_fp16")]; + tensor x_537_axes_0 = const()[name = string("x_537_axes_0"), val = tensor([-1])]; + tensor encoder_layers_20_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_20_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(419776512)))]; + tensor encoder_layers_20_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_20_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(419778624)))]; + tensor x_537_cast_fp16 = layer_norm(axes = x_537_axes_0, beta = encoder_layers_20_norm_conv_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_20_norm_conv_weight_to_fp16, x = input_1087_cast_fp16)[name = string("x_537_cast_fp16")]; + tensor input_1089_perm_0 = const()[name = string("input_1089_perm_0"), val = tensor([0, 2, 1])]; + string input_1091_pad_type_0 = const()[name = string("input_1091_pad_type_0"), val = string("valid")]; + tensor input_1091_strides_0 = const()[name = string("input_1091_strides_0"), val = tensor([1])]; + tensor input_1091_pad_0 = const()[name = string("input_1091_pad_0"), val = tensor([0, 0])]; + tensor input_1091_dilations_0 = const()[name = string("input_1091_dilations_0"), val = tensor([1])]; + int32 input_1091_groups_0 = const()[name = string("input_1091_groups_0"), val = int32(1)]; + tensor encoder_layers_20_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(419780736))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(421877952))))[name = string("encoder_layers_20_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_1089_cast_fp16 = transpose(perm = input_1089_perm_0, x = x_537_cast_fp16)[name = string("transpose_177")]; + tensor input_1091_cast_fp16 = conv(dilations = input_1091_dilations_0, groups = input_1091_groups_0, pad = input_1091_pad_0, pad_type = input_1091_pad_type_0, strides = input_1091_strides_0, weight = encoder_layers_20_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1089_cast_fp16)[name = string("input_1091_cast_fp16")]; + int32 x_539_split_num_splits_0 = const()[name = string("x_539_split_num_splits_0"), val = int32(2)]; + int32 x_539_split_axis_0 = const()[name = string("x_539_split_axis_0"), val = int32(1)]; + tensor x_539_split_cast_fp16_0, tensor x_539_split_cast_fp16_1 = split(axis = x_539_split_axis_0, num_splits = x_539_split_num_splits_0, x = input_1091_cast_fp16)[name = string("x_539_split_cast_fp16")]; + tensor x_539_split_1_sigmoid_cast_fp16 = sigmoid(x = x_539_split_cast_fp16_1)[name = string("x_539_split_1_sigmoid_cast_fp16")]; + tensor x_539_cast_fp16 = mul(x = x_539_split_cast_fp16_0, y = x_539_split_1_sigmoid_cast_fp16)[name = string("x_539_cast_fp16")]; + tensor input_1093_cast_fp16 = select(a = var_43_to_fp16, b = x_539_cast_fp16, cond = var_574)[name = string("input_1093_cast_fp16")]; + bool new_x_83_interleave_0 = const()[name = string("new_x_83_interleave_0"), val = bool(false)]; + tensor new_x_83_cast_fp16 = concat(axis = var_58, interleave = new_x_83_interleave_0, values = (cache_83_cast_fp16, input_1093_cast_fp16))[name = string("new_x_83_cast_fp16")]; + tensor var_4847_begin_0 = const()[name = string("op_4847_begin_0"), val = tensor([0, 0, 14])]; + tensor var_4847_end_0 = const()[name = string("op_4847_end_0"), val = tensor([1, 1024, 22])]; + tensor var_4847_end_mask_0 = const()[name = string("op_4847_end_mask_0"), val = tensor([true, true, true])]; + tensor var_4847_cast_fp16 = slice_by_index(begin = var_4847_begin_0, end = var_4847_end_0, end_mask = var_4847_end_mask_0, x = new_x_83_cast_fp16)[name = string("op_4847_cast_fp16")]; + string x_541_pad_type_0 = const()[name = string("x_541_pad_type_0"), val = string("valid")]; + int32 x_541_groups_0 = const()[name = string("x_541_groups_0"), val = int32(1024)]; + tensor x_541_strides_0 = const()[name = string("x_541_strides_0"), val = tensor([1])]; + tensor x_541_pad_0 = const()[name = string("x_541_pad_0"), val = tensor([0, 0])]; + tensor x_541_dilations_0 = const()[name = string("x_541_dilations_0"), val = tensor([1])]; + tensor encoder_layers_20_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(421882112))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(421891392))))[name = string("encoder_layers_20_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_541_cast_fp16 = conv(dilations = x_541_dilations_0, groups = x_541_groups_0, pad = x_541_pad_0, pad_type = x_541_pad_type_0, strides = x_541_strides_0, weight = encoder_layers_20_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_83_cast_fp16)[name = string("x_541_cast_fp16")]; + tensor input_1095_perm_0 = const()[name = string("input_1095_perm_0"), val = tensor([0, 2, 1])]; + tensor x_543_axes_0 = const()[name = string("x_543_axes_0"), val = tensor([-1])]; + tensor encoder_layers_20_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_20_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(421893504)))]; + tensor encoder_layers_20_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_20_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(421895616)))]; + tensor input_1095_cast_fp16 = transpose(perm = input_1095_perm_0, x = x_541_cast_fp16)[name = string("transpose_176")]; + tensor x_543_cast_fp16 = layer_norm(axes = x_543_axes_0, beta = encoder_layers_20_conv_batch_norm_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_20_conv_batch_norm_weight_to_fp16, x = input_1095_cast_fp16)[name = string("x_543_cast_fp16")]; + tensor input_1097_perm_0 = const()[name = string("input_1097_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1097_cast_fp16 = transpose(perm = input_1097_perm_0, x = x_543_cast_fp16)[name = string("transpose_175")]; + tensor input_1099_cast_fp16 = silu(x = input_1097_cast_fp16)[name = string("input_1099_cast_fp16")]; + string x_545_pad_type_0 = const()[name = string("x_545_pad_type_0"), val = string("valid")]; + tensor x_545_strides_0 = const()[name = string("x_545_strides_0"), val = tensor([1])]; + tensor x_545_pad_0 = const()[name = string("x_545_pad_0"), val = tensor([0, 0])]; + tensor x_545_dilations_0 = const()[name = string("x_545_dilations_0"), val = tensor([1])]; + int32 x_545_groups_0 = const()[name = string("x_545_groups_0"), val = int32(1)]; + tensor encoder_layers_20_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(421897728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(422946368))))[name = string("encoder_layers_20_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_545_cast_fp16 = conv(dilations = x_545_dilations_0, groups = x_545_groups_0, pad = x_545_pad_0, pad_type = x_545_pad_type_0, strides = x_545_strides_0, weight = encoder_layers_20_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1099_cast_fp16)[name = string("x_545_cast_fp16")]; + tensor input_1101_perm_0 = const()[name = string("input_1101_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1101_cast_fp16 = transpose(perm = input_1101_perm_0, x = x_545_cast_fp16)[name = string("transpose_174")]; + tensor input_1103_cast_fp16 = add(x = input_1087_cast_fp16, y = input_1101_cast_fp16)[name = string("input_1103_cast_fp16")]; + tensor input_1105_axes_0 = const()[name = string("input_1105_axes_0"), val = tensor([-1])]; + tensor encoder_layers_20_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_20_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(422948480)))]; + tensor encoder_layers_20_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_20_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(422950592)))]; + tensor input_1105_cast_fp16 = layer_norm(axes = input_1105_axes_0, beta = encoder_layers_20_norm_feed_forward2_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_20_norm_feed_forward2_weight_to_fp16, x = input_1103_cast_fp16)[name = string("input_1105_cast_fp16")]; + tensor encoder_layers_20_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(422952704))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(427147072))))[name = string("encoder_layers_20_feed_forward2_linear1_weight_to_fp16_quantized")]; + tensor encoder_layers_20_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_20_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(427155328)))]; + tensor linear_188_cast_fp16 = linear(bias = encoder_layers_20_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_20_feed_forward2_linear1_weight_to_fp16_quantized, x = input_1105_cast_fp16)[name = string("linear_188_cast_fp16")]; + tensor input_1109_cast_fp16 = silu(x = linear_188_cast_fp16)[name = string("input_1109_cast_fp16")]; + tensor encoder_layers_20_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(427163584))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(431357952))))[name = string("encoder_layers_20_feed_forward2_linear2_weight_to_fp16_quantized")]; + tensor encoder_layers_20_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_20_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(431360064)))]; + tensor linear_189_cast_fp16 = linear(bias = encoder_layers_20_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_20_feed_forward2_linear2_weight_to_fp16_quantized, x = input_1109_cast_fp16)[name = string("linear_189_cast_fp16")]; + fp16 var_4890_to_fp16 = const()[name = string("op_4890_to_fp16"), val = fp16(0x1p-1)]; + tensor var_4891_cast_fp16 = mul(x = linear_189_cast_fp16, y = var_4890_to_fp16)[name = string("op_4891_cast_fp16")]; + tensor input_1115_cast_fp16 = add(x = input_1103_cast_fp16, y = var_4891_cast_fp16)[name = string("input_1115_cast_fp16")]; + tensor input_1117_axes_0 = const()[name = string("input_1117_axes_0"), val = tensor([-1])]; + tensor encoder_layers_20_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_20_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(431362176)))]; + tensor encoder_layers_20_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_20_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(431364288)))]; + tensor input_1117_cast_fp16 = layer_norm(axes = input_1117_axes_0, beta = encoder_layers_20_norm_out_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_20_norm_out_weight_to_fp16, x = input_1115_cast_fp16)[name = string("input_1117_cast_fp16")]; + tensor cache_85_begin_0 = const()[name = string("cache_85_begin_0"), val = tensor([21, 0, 0, 0])]; + tensor cache_85_end_0 = const()[name = string("cache_85_end_0"), val = tensor([22, 1, 42, 1024])]; + tensor cache_85_end_mask_0 = const()[name = string("cache_85_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_85_squeeze_mask_0 = const()[name = string("cache_85_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_85_cast_fp16 = slice_by_index(begin = cache_85_begin_0, end = cache_85_end_0, end_mask = cache_85_end_mask_0, squeeze_mask = cache_85_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_85_cast_fp16")]; + tensor cache_87_begin_0 = const()[name = string("cache_87_begin_0"), val = tensor([21, 0, 0, 0])]; + tensor cache_87_end_0 = const()[name = string("cache_87_end_0"), val = tensor([22, 1, 1024, 8])]; + tensor cache_87_end_mask_0 = const()[name = string("cache_87_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_87_squeeze_mask_0 = const()[name = string("cache_87_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_87_cast_fp16 = slice_by_index(begin = cache_87_begin_0, end = cache_87_end_0, end_mask = cache_87_end_mask_0, squeeze_mask = cache_87_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_87_cast_fp16")]; + tensor input_1119_axes_0 = const()[name = string("input_1119_axes_0"), val = tensor([-1])]; + tensor encoder_layers_21_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_21_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(431366400)))]; + tensor encoder_layers_21_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_21_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(431368512)))]; + tensor input_1119_cast_fp16 = layer_norm(axes = input_1119_axes_0, beta = encoder_layers_21_norm_feed_forward1_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_21_norm_feed_forward1_weight_to_fp16, x = input_1117_cast_fp16)[name = string("input_1119_cast_fp16")]; + tensor encoder_layers_21_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(431370624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(435564992))))[name = string("encoder_layers_21_feed_forward1_linear1_weight_to_fp16_quantized")]; + tensor encoder_layers_21_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_21_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(435573248)))]; + tensor linear_190_cast_fp16 = linear(bias = encoder_layers_21_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_21_feed_forward1_linear1_weight_to_fp16_quantized, x = input_1119_cast_fp16)[name = string("linear_190_cast_fp16")]; + tensor input_1123_cast_fp16 = silu(x = linear_190_cast_fp16)[name = string("input_1123_cast_fp16")]; + tensor encoder_layers_21_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(435581504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(439775872))))[name = string("encoder_layers_21_feed_forward1_linear2_weight_to_fp16_quantized")]; + tensor encoder_layers_21_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_21_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(439777984)))]; + tensor linear_191_cast_fp16 = linear(bias = encoder_layers_21_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_21_feed_forward1_linear2_weight_to_fp16_quantized, x = input_1123_cast_fp16)[name = string("linear_191_cast_fp16")]; + fp16 var_4927_to_fp16 = const()[name = string("op_4927_to_fp16"), val = fp16(0x1p-1)]; + tensor var_4928_cast_fp16 = mul(x = linear_191_cast_fp16, y = var_4927_to_fp16)[name = string("op_4928_cast_fp16")]; + tensor input_1129_cast_fp16 = add(x = input_1117_cast_fp16, y = var_4928_cast_fp16)[name = string("input_1129_cast_fp16")]; + tensor key_43_axes_0 = const()[name = string("key_43_axes_0"), val = tensor([-1])]; + tensor encoder_layers_21_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_21_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(439780096)))]; + tensor encoder_layers_21_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_21_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(439782208)))]; + tensor key_43_cast_fp16 = layer_norm(axes = key_43_axes_0, beta = encoder_layers_21_norm_self_att_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_21_norm_self_att_weight_to_fp16, x = input_1129_cast_fp16)[name = string("key_43_cast_fp16")]; + bool input_1131_interleave_0 = const()[name = string("input_1131_interleave_0"), val = bool(false)]; + tensor input_1131_cast_fp16 = concat(axis = var_67, interleave = input_1131_interleave_0, values = (cache_85_cast_fp16, key_43_cast_fp16))[name = string("input_1131_cast_fp16")]; + tensor var_4950_begin_0 = const()[name = string("op_4950_begin_0"), val = tensor([0, 14, 0])]; + tensor var_4950_end_0 = const()[name = string("op_4950_end_0"), val = tensor([1, 42, 1024])]; + tensor var_4950_end_mask_0 = const()[name = string("op_4950_end_mask_0"), val = tensor([true, true, true])]; + tensor var_4950_cast_fp16 = slice_by_index(begin = var_4950_begin_0, end = var_4950_end_0, end_mask = var_4950_end_mask_0, x = cache_85_cast_fp16)[name = string("op_4950_cast_fp16")]; + bool var_4956_interleave_0 = const()[name = string("op_4956_interleave_0"), val = bool(false)]; + tensor var_4956_cast_fp16 = concat(axis = var_67, interleave = var_4956_interleave_0, values = (var_4950_cast_fp16, key_43_cast_fp16))[name = string("op_4956_cast_fp16")]; + tensor encoder_layers_21_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(439784320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(440832960))))[name = string("encoder_layers_21_self_attn_linear_q_weight_to_fp16_quantized")]; + tensor encoder_layers_21_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_21_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(440835072)))]; + tensor linear_192_cast_fp16 = linear(bias = encoder_layers_21_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_21_self_attn_linear_q_weight_to_fp16_quantized, x = key_43_cast_fp16)[name = string("linear_192_cast_fp16")]; + tensor var_4961 = const()[name = string("op_4961"), val = tensor([1, -1, 8, 128])]; + tensor q_127_cast_fp16 = reshape(shape = var_4961, x = linear_192_cast_fp16)[name = string("q_127_cast_fp16")]; + tensor encoder_layers_21_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(440837184))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(441885824))))[name = string("encoder_layers_21_self_attn_linear_k_weight_to_fp16_quantized")]; + tensor encoder_layers_21_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_21_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(441887936)))]; + tensor linear_193_cast_fp16 = linear(bias = encoder_layers_21_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_21_self_attn_linear_k_weight_to_fp16_quantized, x = input_1131_cast_fp16)[name = string("linear_193_cast_fp16")]; + tensor var_4966 = const()[name = string("op_4966"), val = tensor([1, -1, 8, 128])]; + tensor k_85_cast_fp16 = reshape(shape = var_4966, x = linear_193_cast_fp16)[name = string("k_85_cast_fp16")]; + tensor encoder_layers_21_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(441890048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(442938688))))[name = string("encoder_layers_21_self_attn_linear_v_weight_to_fp16_quantized")]; + tensor encoder_layers_21_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_21_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(442940800)))]; + tensor linear_194_cast_fp16 = linear(bias = encoder_layers_21_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_21_self_attn_linear_v_weight_to_fp16_quantized, x = input_1131_cast_fp16)[name = string("linear_194_cast_fp16")]; + tensor var_4971 = const()[name = string("op_4971"), val = tensor([1, -1, 8, 128])]; + tensor v_43_cast_fp16 = reshape(shape = var_4971, x = linear_194_cast_fp16)[name = string("v_43_cast_fp16")]; + tensor value_51_perm_0 = const()[name = string("value_51_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_21_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_21_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(442942912)))]; + tensor var_4984_cast_fp16 = add(x = q_127_cast_fp16, y = encoder_layers_21_self_attn_pos_bias_u_to_fp16)[name = string("op_4984_cast_fp16")]; + tensor encoder_layers_21_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_21_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(442945024)))]; + tensor var_4986_cast_fp16 = add(x = q_127_cast_fp16, y = encoder_layers_21_self_attn_pos_bias_v_to_fp16)[name = string("op_4986_cast_fp16")]; + tensor q_with_bias_v_43_perm_0 = const()[name = string("q_with_bias_v_43_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_553_transpose_x_0 = const()[name = string("x_553_transpose_x_0"), val = bool(false)]; + bool x_553_transpose_y_0 = const()[name = string("x_553_transpose_y_0"), val = bool(false)]; + tensor op_4988_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(442947136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(443060864))))[name = string("op_4988_to_fp16_quantized")]; + tensor q_with_bias_v_43_cast_fp16 = transpose(perm = q_with_bias_v_43_perm_0, x = var_4986_cast_fp16)[name = string("transpose_173")]; + tensor x_553_cast_fp16 = matmul(transpose_x = x_553_transpose_x_0, transpose_y = x_553_transpose_y_0, x = q_with_bias_v_43_cast_fp16, y = op_4988_to_fp16_quantized)[name = string("x_553_cast_fp16")]; + tensor x_555_pad_0 = const()[name = string("x_555_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_555_mode_0 = const()[name = string("x_555_mode_0"), val = string("constant")]; + fp16 const_352_to_fp16 = const()[name = string("const_352_to_fp16"), val = fp16(0x0p+0)]; + tensor x_555_cast_fp16 = pad(constant_val = const_352_to_fp16, mode = x_555_mode_0, pad = x_555_pad_0, x = x_553_cast_fp16)[name = string("x_555_cast_fp16")]; + tensor var_4996 = const()[name = string("op_4996"), val = tensor([1, 8, -1, 14])]; + tensor x_557_cast_fp16 = reshape(shape = var_4996, x = x_555_cast_fp16)[name = string("x_557_cast_fp16")]; + tensor var_5000_begin_0 = const()[name = string("op_5000_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_5000_end_0 = const()[name = string("op_5000_end_0"), val = tensor([1, 8, 112, 14])]; + tensor var_5000_end_mask_0 = const()[name = string("op_5000_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_5000_cast_fp16 = slice_by_index(begin = var_5000_begin_0, end = var_5000_end_0, end_mask = var_5000_end_mask_0, x = x_557_cast_fp16)[name = string("op_5000_cast_fp16")]; + tensor var_5001 = const()[name = string("op_5001"), val = tensor([1, 8, 14, 111])]; + tensor matrix_bd_85_cast_fp16 = reshape(shape = var_5001, x = var_5000_cast_fp16)[name = string("matrix_bd_85_cast_fp16")]; + bool matrix_ac_43_transpose_x_0 = const()[name = string("matrix_ac_43_transpose_x_0"), val = bool(false)]; + bool matrix_ac_43_transpose_y_0 = const()[name = string("matrix_ac_43_transpose_y_0"), val = bool(false)]; + tensor transpose_138_perm_0 = const()[name = string("transpose_138_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_139_perm_0 = const()[name = string("transpose_139_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_139 = transpose(perm = transpose_139_perm_0, x = k_85_cast_fp16)[name = string("transpose_171")]; + tensor transpose_138 = transpose(perm = transpose_138_perm_0, x = var_4984_cast_fp16)[name = string("transpose_172")]; + tensor matrix_ac_43_cast_fp16 = matmul(transpose_x = matrix_ac_43_transpose_x_0, transpose_y = matrix_ac_43_transpose_y_0, x = transpose_138, y = transpose_139)[name = string("matrix_ac_43_cast_fp16")]; + tensor matrix_bd_87_begin_0 = const()[name = string("matrix_bd_87_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_87_end_0 = const()[name = string("matrix_bd_87_end_0"), val = tensor([1, 8, 14, 56])]; + tensor matrix_bd_87_end_mask_0 = const()[name = string("matrix_bd_87_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_87_cast_fp16 = slice_by_index(begin = matrix_bd_87_begin_0, end = matrix_bd_87_end_0, end_mask = matrix_bd_87_end_mask_0, x = matrix_bd_85_cast_fp16)[name = string("matrix_bd_87_cast_fp16")]; + tensor var_5010_cast_fp16 = add(x = matrix_ac_43_cast_fp16, y = matrix_bd_87_cast_fp16)[name = string("op_5010_cast_fp16")]; + fp16 _inversed_scores_85_y_0_to_fp16 = const()[name = string("_inversed_scores_85_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_85_cast_fp16 = mul(x = var_5010_cast_fp16, y = _inversed_scores_85_y_0_to_fp16)[name = string("_inversed_scores_85_cast_fp16")]; + tensor scores_87_cast_fp16 = select(a = var_44_to_fp16, b = _inversed_scores_85_cast_fp16, cond = mask_11)[name = string("scores_87_cast_fp16")]; + tensor var_5016_cast_fp16 = softmax(axis = var_58, x = scores_87_cast_fp16)[name = string("op_5016_cast_fp16")]; + tensor input_1133_cast_fp16 = select(a = var_43_to_fp16, b = var_5016_cast_fp16, cond = mask_11)[name = string("input_1133_cast_fp16")]; + bool x_559_transpose_x_0 = const()[name = string("x_559_transpose_x_0"), val = bool(false)]; + bool x_559_transpose_y_0 = const()[name = string("x_559_transpose_y_0"), val = bool(false)]; + tensor value_51_cast_fp16 = transpose(perm = value_51_perm_0, x = v_43_cast_fp16)[name = string("transpose_170")]; + tensor x_559_cast_fp16 = matmul(transpose_x = x_559_transpose_x_0, transpose_y = x_559_transpose_y_0, x = input_1133_cast_fp16, y = value_51_cast_fp16)[name = string("x_559_cast_fp16")]; + tensor var_5020_perm_0 = const()[name = string("op_5020_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_5021 = const()[name = string("op_5021"), val = tensor([1, -1, 1024])]; + tensor var_5020_cast_fp16 = transpose(perm = var_5020_perm_0, x = x_559_cast_fp16)[name = string("transpose_169")]; + tensor input_1135_cast_fp16 = reshape(shape = var_5021, x = var_5020_cast_fp16)[name = string("input_1135_cast_fp16")]; + tensor encoder_layers_21_self_attn_linear_out_weight_to_fp16 = const()[name = string("encoder_layers_21_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(443061184)))]; + tensor encoder_layers_21_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_21_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(445158400)))]; + tensor linear_196_cast_fp16 = linear(bias = encoder_layers_21_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_21_self_attn_linear_out_weight_to_fp16, x = input_1135_cast_fp16)[name = string("linear_196_cast_fp16")]; + tensor input_1139_cast_fp16 = add(x = input_1129_cast_fp16, y = linear_196_cast_fp16)[name = string("input_1139_cast_fp16")]; + tensor x_563_axes_0 = const()[name = string("x_563_axes_0"), val = tensor([-1])]; + tensor encoder_layers_21_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_21_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(445160512)))]; + tensor encoder_layers_21_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_21_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(445162624)))]; + tensor x_563_cast_fp16 = layer_norm(axes = x_563_axes_0, beta = encoder_layers_21_norm_conv_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_21_norm_conv_weight_to_fp16, x = input_1139_cast_fp16)[name = string("x_563_cast_fp16")]; + tensor input_1141_perm_0 = const()[name = string("input_1141_perm_0"), val = tensor([0, 2, 1])]; + string input_1143_pad_type_0 = const()[name = string("input_1143_pad_type_0"), val = string("valid")]; + tensor input_1143_strides_0 = const()[name = string("input_1143_strides_0"), val = tensor([1])]; + tensor input_1143_pad_0 = const()[name = string("input_1143_pad_0"), val = tensor([0, 0])]; + tensor input_1143_dilations_0 = const()[name = string("input_1143_dilations_0"), val = tensor([1])]; + int32 input_1143_groups_0 = const()[name = string("input_1143_groups_0"), val = int32(1)]; + tensor encoder_layers_21_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(445164736))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(447261952))))[name = string("encoder_layers_21_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_1141_cast_fp16 = transpose(perm = input_1141_perm_0, x = x_563_cast_fp16)[name = string("transpose_168")]; + tensor input_1143_cast_fp16 = conv(dilations = input_1143_dilations_0, groups = input_1143_groups_0, pad = input_1143_pad_0, pad_type = input_1143_pad_type_0, strides = input_1143_strides_0, weight = encoder_layers_21_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1141_cast_fp16)[name = string("input_1143_cast_fp16")]; + int32 x_565_split_num_splits_0 = const()[name = string("x_565_split_num_splits_0"), val = int32(2)]; + int32 x_565_split_axis_0 = const()[name = string("x_565_split_axis_0"), val = int32(1)]; + tensor x_565_split_cast_fp16_0, tensor x_565_split_cast_fp16_1 = split(axis = x_565_split_axis_0, num_splits = x_565_split_num_splits_0, x = input_1143_cast_fp16)[name = string("x_565_split_cast_fp16")]; + tensor x_565_split_1_sigmoid_cast_fp16 = sigmoid(x = x_565_split_cast_fp16_1)[name = string("x_565_split_1_sigmoid_cast_fp16")]; + tensor x_565_cast_fp16 = mul(x = x_565_split_cast_fp16_0, y = x_565_split_1_sigmoid_cast_fp16)[name = string("x_565_cast_fp16")]; + tensor input_1145_cast_fp16 = select(a = var_43_to_fp16, b = x_565_cast_fp16, cond = var_574)[name = string("input_1145_cast_fp16")]; + bool new_x_87_interleave_0 = const()[name = string("new_x_87_interleave_0"), val = bool(false)]; + tensor new_x_87_cast_fp16 = concat(axis = var_58, interleave = new_x_87_interleave_0, values = (cache_87_cast_fp16, input_1145_cast_fp16))[name = string("new_x_87_cast_fp16")]; + tensor var_5060_begin_0 = const()[name = string("op_5060_begin_0"), val = tensor([0, 0, 14])]; + tensor var_5060_end_0 = const()[name = string("op_5060_end_0"), val = tensor([1, 1024, 22])]; + tensor var_5060_end_mask_0 = const()[name = string("op_5060_end_mask_0"), val = tensor([true, true, true])]; + tensor var_5060_cast_fp16 = slice_by_index(begin = var_5060_begin_0, end = var_5060_end_0, end_mask = var_5060_end_mask_0, x = new_x_87_cast_fp16)[name = string("op_5060_cast_fp16")]; + string x_567_pad_type_0 = const()[name = string("x_567_pad_type_0"), val = string("valid")]; + int32 x_567_groups_0 = const()[name = string("x_567_groups_0"), val = int32(1024)]; + tensor x_567_strides_0 = const()[name = string("x_567_strides_0"), val = tensor([1])]; + tensor x_567_pad_0 = const()[name = string("x_567_pad_0"), val = tensor([0, 0])]; + tensor x_567_dilations_0 = const()[name = string("x_567_dilations_0"), val = tensor([1])]; + tensor encoder_layers_21_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(447266112))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(447275392))))[name = string("encoder_layers_21_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_567_cast_fp16 = conv(dilations = x_567_dilations_0, groups = x_567_groups_0, pad = x_567_pad_0, pad_type = x_567_pad_type_0, strides = x_567_strides_0, weight = encoder_layers_21_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_87_cast_fp16)[name = string("x_567_cast_fp16")]; + tensor input_1147_perm_0 = const()[name = string("input_1147_perm_0"), val = tensor([0, 2, 1])]; + tensor x_569_axes_0 = const()[name = string("x_569_axes_0"), val = tensor([-1])]; + tensor encoder_layers_21_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_21_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(447277504)))]; + tensor encoder_layers_21_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_21_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(447279616)))]; + tensor input_1147_cast_fp16 = transpose(perm = input_1147_perm_0, x = x_567_cast_fp16)[name = string("transpose_167")]; + tensor x_569_cast_fp16 = layer_norm(axes = x_569_axes_0, beta = encoder_layers_21_conv_batch_norm_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_21_conv_batch_norm_weight_to_fp16, x = input_1147_cast_fp16)[name = string("x_569_cast_fp16")]; + tensor input_1149_perm_0 = const()[name = string("input_1149_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1149_cast_fp16 = transpose(perm = input_1149_perm_0, x = x_569_cast_fp16)[name = string("transpose_166")]; + tensor input_1151_cast_fp16 = silu(x = input_1149_cast_fp16)[name = string("input_1151_cast_fp16")]; + string x_571_pad_type_0 = const()[name = string("x_571_pad_type_0"), val = string("valid")]; + tensor x_571_strides_0 = const()[name = string("x_571_strides_0"), val = tensor([1])]; + tensor x_571_pad_0 = const()[name = string("x_571_pad_0"), val = tensor([0, 0])]; + tensor x_571_dilations_0 = const()[name = string("x_571_dilations_0"), val = tensor([1])]; + int32 x_571_groups_0 = const()[name = string("x_571_groups_0"), val = int32(1)]; + tensor encoder_layers_21_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(447281728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(448330368))))[name = string("encoder_layers_21_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_571_cast_fp16 = conv(dilations = x_571_dilations_0, groups = x_571_groups_0, pad = x_571_pad_0, pad_type = x_571_pad_type_0, strides = x_571_strides_0, weight = encoder_layers_21_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1151_cast_fp16)[name = string("x_571_cast_fp16")]; + tensor input_1153_perm_0 = const()[name = string("input_1153_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1153_cast_fp16 = transpose(perm = input_1153_perm_0, x = x_571_cast_fp16)[name = string("transpose_165")]; + tensor input_1155_cast_fp16 = add(x = input_1139_cast_fp16, y = input_1153_cast_fp16)[name = string("input_1155_cast_fp16")]; + tensor input_1157_axes_0 = const()[name = string("input_1157_axes_0"), val = tensor([-1])]; + tensor encoder_layers_21_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_21_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(448332480)))]; + tensor encoder_layers_21_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_21_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(448334592)))]; + tensor input_1157_cast_fp16 = layer_norm(axes = input_1157_axes_0, beta = encoder_layers_21_norm_feed_forward2_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_21_norm_feed_forward2_weight_to_fp16, x = input_1155_cast_fp16)[name = string("input_1157_cast_fp16")]; + tensor encoder_layers_21_feed_forward2_linear1_weight_to_fp16 = const()[name = string("encoder_layers_21_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(448336704)))]; + tensor encoder_layers_21_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_21_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(456725376)))]; + tensor linear_197_cast_fp16 = linear(bias = encoder_layers_21_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_21_feed_forward2_linear1_weight_to_fp16, x = input_1157_cast_fp16)[name = string("linear_197_cast_fp16")]; + tensor input_1161_cast_fp16 = silu(x = linear_197_cast_fp16)[name = string("input_1161_cast_fp16")]; + tensor encoder_layers_21_feed_forward2_linear2_weight_to_fp16 = const()[name = string("encoder_layers_21_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(456733632)))]; + tensor encoder_layers_21_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_21_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(465122304)))]; + tensor linear_198_cast_fp16 = linear(bias = encoder_layers_21_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_21_feed_forward2_linear2_weight_to_fp16, x = input_1161_cast_fp16)[name = string("linear_198_cast_fp16")]; + fp16 var_5103_to_fp16 = const()[name = string("op_5103_to_fp16"), val = fp16(0x1p-1)]; + tensor var_5104_cast_fp16 = mul(x = linear_198_cast_fp16, y = var_5103_to_fp16)[name = string("op_5104_cast_fp16")]; + tensor input_1167_cast_fp16 = add(x = input_1155_cast_fp16, y = var_5104_cast_fp16)[name = string("input_1167_cast_fp16")]; + tensor input_1169_axes_0 = const()[name = string("input_1169_axes_0"), val = tensor([-1])]; + tensor encoder_layers_21_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_21_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(465124416)))]; + tensor encoder_layers_21_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_21_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(465126528)))]; + tensor input_1169_cast_fp16 = layer_norm(axes = input_1169_axes_0, beta = encoder_layers_21_norm_out_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_21_norm_out_weight_to_fp16, x = input_1167_cast_fp16)[name = string("input_1169_cast_fp16")]; + tensor cache_89_begin_0 = const()[name = string("cache_89_begin_0"), val = tensor([22, 0, 0, 0])]; + tensor cache_89_end_0 = const()[name = string("cache_89_end_0"), val = tensor([23, 1, 42, 1024])]; + tensor cache_89_end_mask_0 = const()[name = string("cache_89_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_89_squeeze_mask_0 = const()[name = string("cache_89_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_89_cast_fp16 = slice_by_index(begin = cache_89_begin_0, end = cache_89_end_0, end_mask = cache_89_end_mask_0, squeeze_mask = cache_89_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_89_cast_fp16")]; + tensor cache_91_begin_0 = const()[name = string("cache_91_begin_0"), val = tensor([22, 0, 0, 0])]; + tensor cache_91_end_0 = const()[name = string("cache_91_end_0"), val = tensor([23, 1, 1024, 8])]; + tensor cache_91_end_mask_0 = const()[name = string("cache_91_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_91_squeeze_mask_0 = const()[name = string("cache_91_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_91_cast_fp16 = slice_by_index(begin = cache_91_begin_0, end = cache_91_end_0, end_mask = cache_91_end_mask_0, squeeze_mask = cache_91_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_91_cast_fp16")]; + tensor input_1171_axes_0 = const()[name = string("input_1171_axes_0"), val = tensor([-1])]; + tensor encoder_layers_22_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_22_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(465128640)))]; + tensor encoder_layers_22_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_22_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(465130752)))]; + tensor input_1171_cast_fp16 = layer_norm(axes = input_1171_axes_0, beta = encoder_layers_22_norm_feed_forward1_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_22_norm_feed_forward1_weight_to_fp16, x = input_1169_cast_fp16)[name = string("input_1171_cast_fp16")]; + tensor encoder_layers_22_feed_forward1_linear1_weight_to_fp16 = const()[name = string("encoder_layers_22_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(465132864)))]; + tensor encoder_layers_22_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_22_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(473521536)))]; + tensor linear_199_cast_fp16 = linear(bias = encoder_layers_22_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_22_feed_forward1_linear1_weight_to_fp16, x = input_1171_cast_fp16)[name = string("linear_199_cast_fp16")]; + tensor input_1175_cast_fp16 = silu(x = linear_199_cast_fp16)[name = string("input_1175_cast_fp16")]; + tensor encoder_layers_22_feed_forward1_linear2_weight_to_fp16 = const()[name = string("encoder_layers_22_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(473529792)))]; + tensor encoder_layers_22_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_22_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(481918464)))]; + tensor linear_200_cast_fp16 = linear(bias = encoder_layers_22_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_22_feed_forward1_linear2_weight_to_fp16, x = input_1175_cast_fp16)[name = string("linear_200_cast_fp16")]; + fp16 var_5140_to_fp16 = const()[name = string("op_5140_to_fp16"), val = fp16(0x1p-1)]; + tensor var_5141_cast_fp16 = mul(x = linear_200_cast_fp16, y = var_5140_to_fp16)[name = string("op_5141_cast_fp16")]; + tensor input_1181_cast_fp16 = add(x = input_1169_cast_fp16, y = var_5141_cast_fp16)[name = string("input_1181_cast_fp16")]; + tensor key_45_axes_0 = const()[name = string("key_45_axes_0"), val = tensor([-1])]; + tensor encoder_layers_22_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_22_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(481920576)))]; + tensor encoder_layers_22_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_22_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(481922688)))]; + tensor key_45_cast_fp16 = layer_norm(axes = key_45_axes_0, beta = encoder_layers_22_norm_self_att_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_22_norm_self_att_weight_to_fp16, x = input_1181_cast_fp16)[name = string("key_45_cast_fp16")]; + bool input_1183_interleave_0 = const()[name = string("input_1183_interleave_0"), val = bool(false)]; + tensor input_1183_cast_fp16 = concat(axis = var_67, interleave = input_1183_interleave_0, values = (cache_89_cast_fp16, key_45_cast_fp16))[name = string("input_1183_cast_fp16")]; + tensor var_5163_begin_0 = const()[name = string("op_5163_begin_0"), val = tensor([0, 14, 0])]; + tensor var_5163_end_0 = const()[name = string("op_5163_end_0"), val = tensor([1, 42, 1024])]; + tensor var_5163_end_mask_0 = const()[name = string("op_5163_end_mask_0"), val = tensor([true, true, true])]; + tensor var_5163_cast_fp16 = slice_by_index(begin = var_5163_begin_0, end = var_5163_end_0, end_mask = var_5163_end_mask_0, x = cache_89_cast_fp16)[name = string("op_5163_cast_fp16")]; + bool var_5169_interleave_0 = const()[name = string("op_5169_interleave_0"), val = bool(false)]; + tensor var_5169_cast_fp16 = concat(axis = var_67, interleave = var_5169_interleave_0, values = (var_5163_cast_fp16, key_45_cast_fp16))[name = string("op_5169_cast_fp16")]; + tensor encoder_layers_22_self_attn_linear_q_weight_to_fp16 = const()[name = string("encoder_layers_22_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(481924800)))]; + tensor encoder_layers_22_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_22_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(484022016)))]; + tensor linear_201_cast_fp16 = linear(bias = encoder_layers_22_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_22_self_attn_linear_q_weight_to_fp16, x = key_45_cast_fp16)[name = string("linear_201_cast_fp16")]; + tensor var_5174 = const()[name = string("op_5174"), val = tensor([1, -1, 8, 128])]; + tensor q_133_cast_fp16 = reshape(shape = var_5174, x = linear_201_cast_fp16)[name = string("q_133_cast_fp16")]; + tensor encoder_layers_22_self_attn_linear_k_weight_to_fp16 = const()[name = string("encoder_layers_22_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(484024128)))]; + tensor encoder_layers_22_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_22_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(486121344)))]; + tensor linear_202_cast_fp16 = linear(bias = encoder_layers_22_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_22_self_attn_linear_k_weight_to_fp16, x = input_1183_cast_fp16)[name = string("linear_202_cast_fp16")]; + tensor var_5179 = const()[name = string("op_5179"), val = tensor([1, -1, 8, 128])]; + tensor k_89_cast_fp16 = reshape(shape = var_5179, x = linear_202_cast_fp16)[name = string("k_89_cast_fp16")]; + tensor encoder_layers_22_self_attn_linear_v_weight_to_fp16 = const()[name = string("encoder_layers_22_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(486123456)))]; + tensor encoder_layers_22_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_22_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488220672)))]; + tensor linear_203_cast_fp16 = linear(bias = encoder_layers_22_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_22_self_attn_linear_v_weight_to_fp16, x = input_1183_cast_fp16)[name = string("linear_203_cast_fp16")]; + tensor var_5184 = const()[name = string("op_5184"), val = tensor([1, -1, 8, 128])]; + tensor v_45_cast_fp16 = reshape(shape = var_5184, x = linear_203_cast_fp16)[name = string("v_45_cast_fp16")]; + tensor value_53_perm_0 = const()[name = string("value_53_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_22_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_22_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488222784)))]; + tensor var_5197_cast_fp16 = add(x = q_133_cast_fp16, y = encoder_layers_22_self_attn_pos_bias_u_to_fp16)[name = string("op_5197_cast_fp16")]; + tensor encoder_layers_22_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_22_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488224896)))]; + tensor var_5199_cast_fp16 = add(x = q_133_cast_fp16, y = encoder_layers_22_self_attn_pos_bias_v_to_fp16)[name = string("op_5199_cast_fp16")]; + tensor q_with_bias_v_45_perm_0 = const()[name = string("q_with_bias_v_45_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_579_transpose_x_0 = const()[name = string("x_579_transpose_x_0"), val = bool(false)]; + bool x_579_transpose_y_0 = const()[name = string("x_579_transpose_y_0"), val = bool(false)]; + tensor op_5201_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488227008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488340736))))[name = string("op_5201_to_fp16_quantized")]; + tensor q_with_bias_v_45_cast_fp16 = transpose(perm = q_with_bias_v_45_perm_0, x = var_5199_cast_fp16)[name = string("transpose_164")]; + tensor x_579_cast_fp16 = matmul(transpose_x = x_579_transpose_x_0, transpose_y = x_579_transpose_y_0, x = q_with_bias_v_45_cast_fp16, y = op_5201_to_fp16_quantized)[name = string("x_579_cast_fp16")]; + tensor x_581_pad_0 = const()[name = string("x_581_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_581_mode_0 = const()[name = string("x_581_mode_0"), val = string("constant")]; + fp16 const_365_to_fp16 = const()[name = string("const_365_to_fp16"), val = fp16(0x0p+0)]; + tensor x_581_cast_fp16 = pad(constant_val = const_365_to_fp16, mode = x_581_mode_0, pad = x_581_pad_0, x = x_579_cast_fp16)[name = string("x_581_cast_fp16")]; + tensor var_5209 = const()[name = string("op_5209"), val = tensor([1, 8, -1, 14])]; + tensor x_583_cast_fp16 = reshape(shape = var_5209, x = x_581_cast_fp16)[name = string("x_583_cast_fp16")]; + tensor var_5213_begin_0 = const()[name = string("op_5213_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_5213_end_0 = const()[name = string("op_5213_end_0"), val = tensor([1, 8, 112, 14])]; + tensor var_5213_end_mask_0 = const()[name = string("op_5213_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_5213_cast_fp16 = slice_by_index(begin = var_5213_begin_0, end = var_5213_end_0, end_mask = var_5213_end_mask_0, x = x_583_cast_fp16)[name = string("op_5213_cast_fp16")]; + tensor var_5214 = const()[name = string("op_5214"), val = tensor([1, 8, 14, 111])]; + tensor matrix_bd_89_cast_fp16 = reshape(shape = var_5214, x = var_5213_cast_fp16)[name = string("matrix_bd_89_cast_fp16")]; + bool matrix_ac_45_transpose_x_0 = const()[name = string("matrix_ac_45_transpose_x_0"), val = bool(false)]; + bool matrix_ac_45_transpose_y_0 = const()[name = string("matrix_ac_45_transpose_y_0"), val = bool(false)]; + tensor transpose_140_perm_0 = const()[name = string("transpose_140_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_141_perm_0 = const()[name = string("transpose_141_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_141 = transpose(perm = transpose_141_perm_0, x = k_89_cast_fp16)[name = string("transpose_162")]; + tensor transpose_140 = transpose(perm = transpose_140_perm_0, x = var_5197_cast_fp16)[name = string("transpose_163")]; + tensor matrix_ac_45_cast_fp16 = matmul(transpose_x = matrix_ac_45_transpose_x_0, transpose_y = matrix_ac_45_transpose_y_0, x = transpose_140, y = transpose_141)[name = string("matrix_ac_45_cast_fp16")]; + tensor matrix_bd_91_begin_0 = const()[name = string("matrix_bd_91_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_91_end_0 = const()[name = string("matrix_bd_91_end_0"), val = tensor([1, 8, 14, 56])]; + tensor matrix_bd_91_end_mask_0 = const()[name = string("matrix_bd_91_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_91_cast_fp16 = slice_by_index(begin = matrix_bd_91_begin_0, end = matrix_bd_91_end_0, end_mask = matrix_bd_91_end_mask_0, x = matrix_bd_89_cast_fp16)[name = string("matrix_bd_91_cast_fp16")]; + tensor var_5223_cast_fp16 = add(x = matrix_ac_45_cast_fp16, y = matrix_bd_91_cast_fp16)[name = string("op_5223_cast_fp16")]; + fp16 _inversed_scores_89_y_0_to_fp16 = const()[name = string("_inversed_scores_89_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_89_cast_fp16 = mul(x = var_5223_cast_fp16, y = _inversed_scores_89_y_0_to_fp16)[name = string("_inversed_scores_89_cast_fp16")]; + tensor scores_91_cast_fp16 = select(a = var_44_to_fp16, b = _inversed_scores_89_cast_fp16, cond = mask_11)[name = string("scores_91_cast_fp16")]; + tensor var_5229_cast_fp16 = softmax(axis = var_58, x = scores_91_cast_fp16)[name = string("op_5229_cast_fp16")]; + tensor input_1185_cast_fp16 = select(a = var_43_to_fp16, b = var_5229_cast_fp16, cond = mask_11)[name = string("input_1185_cast_fp16")]; + bool x_585_transpose_x_0 = const()[name = string("x_585_transpose_x_0"), val = bool(false)]; + bool x_585_transpose_y_0 = const()[name = string("x_585_transpose_y_0"), val = bool(false)]; + tensor value_53_cast_fp16 = transpose(perm = value_53_perm_0, x = v_45_cast_fp16)[name = string("transpose_161")]; + tensor x_585_cast_fp16 = matmul(transpose_x = x_585_transpose_x_0, transpose_y = x_585_transpose_y_0, x = input_1185_cast_fp16, y = value_53_cast_fp16)[name = string("x_585_cast_fp16")]; + tensor var_5233_perm_0 = const()[name = string("op_5233_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_5234 = const()[name = string("op_5234"), val = tensor([1, -1, 1024])]; + tensor var_5233_cast_fp16 = transpose(perm = var_5233_perm_0, x = x_585_cast_fp16)[name = string("transpose_160")]; + tensor input_1187_cast_fp16 = reshape(shape = var_5234, x = var_5233_cast_fp16)[name = string("input_1187_cast_fp16")]; + tensor encoder_layers_22_self_attn_linear_out_weight_to_fp16 = const()[name = string("encoder_layers_22_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488341056)))]; + tensor encoder_layers_22_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_22_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(490438272)))]; + tensor linear_205_cast_fp16 = linear(bias = encoder_layers_22_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_22_self_attn_linear_out_weight_to_fp16, x = input_1187_cast_fp16)[name = string("linear_205_cast_fp16")]; + tensor input_1191_cast_fp16 = add(x = input_1181_cast_fp16, y = linear_205_cast_fp16)[name = string("input_1191_cast_fp16")]; + tensor x_589_axes_0 = const()[name = string("x_589_axes_0"), val = tensor([-1])]; + tensor encoder_layers_22_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_22_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(490440384)))]; + tensor encoder_layers_22_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_22_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(490442496)))]; + tensor x_589_cast_fp16 = layer_norm(axes = x_589_axes_0, beta = encoder_layers_22_norm_conv_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_22_norm_conv_weight_to_fp16, x = input_1191_cast_fp16)[name = string("x_589_cast_fp16")]; + tensor input_1193_perm_0 = const()[name = string("input_1193_perm_0"), val = tensor([0, 2, 1])]; + string input_1195_pad_type_0 = const()[name = string("input_1195_pad_type_0"), val = string("valid")]; + tensor input_1195_strides_0 = const()[name = string("input_1195_strides_0"), val = tensor([1])]; + tensor input_1195_pad_0 = const()[name = string("input_1195_pad_0"), val = tensor([0, 0])]; + tensor input_1195_dilations_0 = const()[name = string("input_1195_dilations_0"), val = tensor([1])]; + int32 input_1195_groups_0 = const()[name = string("input_1195_groups_0"), val = int32(1)]; + tensor encoder_layers_22_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(490444608))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(492541824))))[name = string("encoder_layers_22_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_1193_cast_fp16 = transpose(perm = input_1193_perm_0, x = x_589_cast_fp16)[name = string("transpose_159")]; + tensor input_1195_cast_fp16 = conv(dilations = input_1195_dilations_0, groups = input_1195_groups_0, pad = input_1195_pad_0, pad_type = input_1195_pad_type_0, strides = input_1195_strides_0, weight = encoder_layers_22_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1193_cast_fp16)[name = string("input_1195_cast_fp16")]; + int32 x_591_split_num_splits_0 = const()[name = string("x_591_split_num_splits_0"), val = int32(2)]; + int32 x_591_split_axis_0 = const()[name = string("x_591_split_axis_0"), val = int32(1)]; + tensor x_591_split_cast_fp16_0, tensor x_591_split_cast_fp16_1 = split(axis = x_591_split_axis_0, num_splits = x_591_split_num_splits_0, x = input_1195_cast_fp16)[name = string("x_591_split_cast_fp16")]; + tensor x_591_split_1_sigmoid_cast_fp16 = sigmoid(x = x_591_split_cast_fp16_1)[name = string("x_591_split_1_sigmoid_cast_fp16")]; + tensor x_591_cast_fp16 = mul(x = x_591_split_cast_fp16_0, y = x_591_split_1_sigmoid_cast_fp16)[name = string("x_591_cast_fp16")]; + tensor input_1197_cast_fp16 = select(a = var_43_to_fp16, b = x_591_cast_fp16, cond = var_574)[name = string("input_1197_cast_fp16")]; + bool new_x_91_interleave_0 = const()[name = string("new_x_91_interleave_0"), val = bool(false)]; + tensor new_x_91_cast_fp16 = concat(axis = var_58, interleave = new_x_91_interleave_0, values = (cache_91_cast_fp16, input_1197_cast_fp16))[name = string("new_x_91_cast_fp16")]; + tensor var_5273_begin_0 = const()[name = string("op_5273_begin_0"), val = tensor([0, 0, 14])]; + tensor var_5273_end_0 = const()[name = string("op_5273_end_0"), val = tensor([1, 1024, 22])]; + tensor var_5273_end_mask_0 = const()[name = string("op_5273_end_mask_0"), val = tensor([true, true, true])]; + tensor var_5273_cast_fp16 = slice_by_index(begin = var_5273_begin_0, end = var_5273_end_0, end_mask = var_5273_end_mask_0, x = new_x_91_cast_fp16)[name = string("op_5273_cast_fp16")]; + string x_593_pad_type_0 = const()[name = string("x_593_pad_type_0"), val = string("valid")]; + int32 x_593_groups_0 = const()[name = string("x_593_groups_0"), val = int32(1024)]; + tensor x_593_strides_0 = const()[name = string("x_593_strides_0"), val = tensor([1])]; + tensor x_593_pad_0 = const()[name = string("x_593_pad_0"), val = tensor([0, 0])]; + tensor x_593_dilations_0 = const()[name = string("x_593_dilations_0"), val = tensor([1])]; + tensor encoder_layers_22_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(492545984))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(492555264))))[name = string("encoder_layers_22_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_593_cast_fp16 = conv(dilations = x_593_dilations_0, groups = x_593_groups_0, pad = x_593_pad_0, pad_type = x_593_pad_type_0, strides = x_593_strides_0, weight = encoder_layers_22_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_91_cast_fp16)[name = string("x_593_cast_fp16")]; + tensor input_1199_perm_0 = const()[name = string("input_1199_perm_0"), val = tensor([0, 2, 1])]; + tensor x_595_axes_0 = const()[name = string("x_595_axes_0"), val = tensor([-1])]; + tensor encoder_layers_22_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_22_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(492557376)))]; + tensor encoder_layers_22_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_22_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(492559488)))]; + tensor input_1199_cast_fp16 = transpose(perm = input_1199_perm_0, x = x_593_cast_fp16)[name = string("transpose_158")]; + tensor x_595_cast_fp16 = layer_norm(axes = x_595_axes_0, beta = encoder_layers_22_conv_batch_norm_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_22_conv_batch_norm_weight_to_fp16, x = input_1199_cast_fp16)[name = string("x_595_cast_fp16")]; + tensor input_1201_perm_0 = const()[name = string("input_1201_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1201_cast_fp16 = transpose(perm = input_1201_perm_0, x = x_595_cast_fp16)[name = string("transpose_157")]; + tensor input_1203_cast_fp16 = silu(x = input_1201_cast_fp16)[name = string("input_1203_cast_fp16")]; + string x_597_pad_type_0 = const()[name = string("x_597_pad_type_0"), val = string("valid")]; + tensor x_597_strides_0 = const()[name = string("x_597_strides_0"), val = tensor([1])]; + tensor x_597_pad_0 = const()[name = string("x_597_pad_0"), val = tensor([0, 0])]; + tensor x_597_dilations_0 = const()[name = string("x_597_dilations_0"), val = tensor([1])]; + int32 x_597_groups_0 = const()[name = string("x_597_groups_0"), val = int32(1)]; + tensor encoder_layers_22_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(492561600))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(493610240))))[name = string("encoder_layers_22_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_597_cast_fp16 = conv(dilations = x_597_dilations_0, groups = x_597_groups_0, pad = x_597_pad_0, pad_type = x_597_pad_type_0, strides = x_597_strides_0, weight = encoder_layers_22_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1203_cast_fp16)[name = string("x_597_cast_fp16")]; + tensor input_1205_perm_0 = const()[name = string("input_1205_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1205_cast_fp16 = transpose(perm = input_1205_perm_0, x = x_597_cast_fp16)[name = string("transpose_156")]; + tensor input_1207_cast_fp16 = add(x = input_1191_cast_fp16, y = input_1205_cast_fp16)[name = string("input_1207_cast_fp16")]; + tensor input_1209_axes_0 = const()[name = string("input_1209_axes_0"), val = tensor([-1])]; + tensor encoder_layers_22_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_22_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(493612352)))]; + tensor encoder_layers_22_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_22_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(493614464)))]; + tensor input_1209_cast_fp16 = layer_norm(axes = input_1209_axes_0, beta = encoder_layers_22_norm_feed_forward2_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_22_norm_feed_forward2_weight_to_fp16, x = input_1207_cast_fp16)[name = string("input_1209_cast_fp16")]; + tensor encoder_layers_22_feed_forward2_linear1_weight_to_fp16 = const()[name = string("encoder_layers_22_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(493616576)))]; + tensor encoder_layers_22_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_22_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(502005248)))]; + tensor linear_206_cast_fp16 = linear(bias = encoder_layers_22_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_22_feed_forward2_linear1_weight_to_fp16, x = input_1209_cast_fp16)[name = string("linear_206_cast_fp16")]; + tensor input_1213_cast_fp16 = silu(x = linear_206_cast_fp16)[name = string("input_1213_cast_fp16")]; + tensor encoder_layers_22_feed_forward2_linear2_weight_to_fp16 = const()[name = string("encoder_layers_22_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(502013504)))]; + tensor encoder_layers_22_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_22_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(510402176)))]; + tensor linear_207_cast_fp16 = linear(bias = encoder_layers_22_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_22_feed_forward2_linear2_weight_to_fp16, x = input_1213_cast_fp16)[name = string("linear_207_cast_fp16")]; + fp16 var_5316_to_fp16 = const()[name = string("op_5316_to_fp16"), val = fp16(0x1p-1)]; + tensor var_5317_cast_fp16 = mul(x = linear_207_cast_fp16, y = var_5316_to_fp16)[name = string("op_5317_cast_fp16")]; + tensor input_1219_cast_fp16 = add(x = input_1207_cast_fp16, y = var_5317_cast_fp16)[name = string("input_1219_cast_fp16")]; + tensor input_1221_axes_0 = const()[name = string("input_1221_axes_0"), val = tensor([-1])]; + tensor encoder_layers_22_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_22_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(510404288)))]; + tensor encoder_layers_22_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_22_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(510406400)))]; + tensor input_1221_cast_fp16 = layer_norm(axes = input_1221_axes_0, beta = encoder_layers_22_norm_out_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_22_norm_out_weight_to_fp16, x = input_1219_cast_fp16)[name = string("input_1221_cast_fp16")]; + tensor cache_93_begin_0 = const()[name = string("cache_93_begin_0"), val = tensor([23, 0, 0, 0])]; + tensor cache_93_end_0 = const()[name = string("cache_93_end_0"), val = tensor([24, 1, 42, 1024])]; + tensor cache_93_end_mask_0 = const()[name = string("cache_93_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_93_squeeze_mask_0 = const()[name = string("cache_93_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_93_cast_fp16 = slice_by_index(begin = cache_93_begin_0, end = cache_93_end_0, end_mask = cache_93_end_mask_0, squeeze_mask = cache_93_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_93_cast_fp16")]; + tensor cache_begin_0 = const()[name = string("cache_begin_0"), val = tensor([23, 0, 0, 0])]; + tensor cache_end_0 = const()[name = string("cache_end_0"), val = tensor([24, 1, 1024, 8])]; + tensor cache_end_mask_0 = const()[name = string("cache_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_squeeze_mask_0 = const()[name = string("cache_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_cast_fp16 = slice_by_index(begin = cache_begin_0, end = cache_end_0, end_mask = cache_end_mask_0, squeeze_mask = cache_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_cast_fp16")]; + tensor input_1223_axes_0 = const()[name = string("input_1223_axes_0"), val = tensor([-1])]; + tensor encoder_layers_23_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_23_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(510408512)))]; + tensor encoder_layers_23_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_23_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(510410624)))]; + tensor input_1223_cast_fp16 = layer_norm(axes = input_1223_axes_0, beta = encoder_layers_23_norm_feed_forward1_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_23_norm_feed_forward1_weight_to_fp16, x = input_1221_cast_fp16)[name = string("input_1223_cast_fp16")]; + tensor encoder_layers_23_feed_forward1_linear1_weight_to_fp16 = const()[name = string("encoder_layers_23_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(510412736)))]; + tensor encoder_layers_23_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_23_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(518801408)))]; + tensor linear_208_cast_fp16 = linear(bias = encoder_layers_23_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_23_feed_forward1_linear1_weight_to_fp16, x = input_1223_cast_fp16)[name = string("linear_208_cast_fp16")]; + tensor input_1227_cast_fp16 = silu(x = linear_208_cast_fp16)[name = string("input_1227_cast_fp16")]; + tensor encoder_layers_23_feed_forward1_linear2_weight_to_fp16 = const()[name = string("encoder_layers_23_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(518809664)))]; + tensor encoder_layers_23_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_23_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(527198336)))]; + tensor linear_209_cast_fp16 = linear(bias = encoder_layers_23_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_23_feed_forward1_linear2_weight_to_fp16, x = input_1227_cast_fp16)[name = string("linear_209_cast_fp16")]; + fp16 var_5353_to_fp16 = const()[name = string("op_5353_to_fp16"), val = fp16(0x1p-1)]; + tensor var_5354_cast_fp16 = mul(x = linear_209_cast_fp16, y = var_5353_to_fp16)[name = string("op_5354_cast_fp16")]; + tensor input_1233_cast_fp16 = add(x = input_1221_cast_fp16, y = var_5354_cast_fp16)[name = string("input_1233_cast_fp16")]; + tensor key_axes_0 = const()[name = string("key_axes_0"), val = tensor([-1])]; + tensor encoder_layers_23_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_23_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(527200448)))]; + tensor encoder_layers_23_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_23_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(527202560)))]; + tensor key_cast_fp16 = layer_norm(axes = key_axes_0, beta = encoder_layers_23_norm_self_att_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_23_norm_self_att_weight_to_fp16, x = input_1233_cast_fp16)[name = string("key_cast_fp16")]; + bool input_1235_interleave_0 = const()[name = string("input_1235_interleave_0"), val = bool(false)]; + tensor input_1235_cast_fp16 = concat(axis = var_67, interleave = input_1235_interleave_0, values = (cache_93_cast_fp16, key_cast_fp16))[name = string("input_1235_cast_fp16")]; + tensor var_5376_begin_0 = const()[name = string("op_5376_begin_0"), val = tensor([0, 14, 0])]; + tensor var_5376_end_0 = const()[name = string("op_5376_end_0"), val = tensor([1, 42, 1024])]; + tensor var_5376_end_mask_0 = const()[name = string("op_5376_end_mask_0"), val = tensor([true, true, true])]; + tensor var_5376_cast_fp16 = slice_by_index(begin = var_5376_begin_0, end = var_5376_end_0, end_mask = var_5376_end_mask_0, x = cache_93_cast_fp16)[name = string("op_5376_cast_fp16")]; + bool cache_last_channel_cur_interleave_0 = const()[name = string("cache_last_channel_cur_interleave_0"), val = bool(false)]; + tensor cache_last_channel_cur_cast_fp16 = concat(axis = var_67, interleave = cache_last_channel_cur_interleave_0, values = (var_5376_cast_fp16, key_cast_fp16))[name = string("cache_last_channel_cur_cast_fp16")]; + tensor encoder_layers_23_self_attn_linear_q_weight_to_fp16 = const()[name = string("encoder_layers_23_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(527204672)))]; + tensor encoder_layers_23_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_23_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(529301888)))]; + tensor linear_210_cast_fp16 = linear(bias = encoder_layers_23_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_23_self_attn_linear_q_weight_to_fp16, x = key_cast_fp16)[name = string("linear_210_cast_fp16")]; + tensor var_5387 = const()[name = string("op_5387"), val = tensor([1, -1, 8, 128])]; + tensor q_139_cast_fp16 = reshape(shape = var_5387, x = linear_210_cast_fp16)[name = string("q_139_cast_fp16")]; + tensor encoder_layers_23_self_attn_linear_k_weight_to_fp16 = const()[name = string("encoder_layers_23_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(529304000)))]; + tensor encoder_layers_23_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_23_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531401216)))]; + tensor linear_211_cast_fp16 = linear(bias = encoder_layers_23_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_23_self_attn_linear_k_weight_to_fp16, x = input_1235_cast_fp16)[name = string("linear_211_cast_fp16")]; + tensor var_5392 = const()[name = string("op_5392"), val = tensor([1, -1, 8, 128])]; + tensor k_93_cast_fp16 = reshape(shape = var_5392, x = linear_211_cast_fp16)[name = string("k_93_cast_fp16")]; + tensor encoder_layers_23_self_attn_linear_v_weight_to_fp16 = const()[name = string("encoder_layers_23_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531403328)))]; + tensor encoder_layers_23_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_23_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(533500544)))]; + tensor linear_212_cast_fp16 = linear(bias = encoder_layers_23_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_23_self_attn_linear_v_weight_to_fp16, x = input_1235_cast_fp16)[name = string("linear_212_cast_fp16")]; + tensor var_5397 = const()[name = string("op_5397"), val = tensor([1, -1, 8, 128])]; + tensor v_cast_fp16 = reshape(shape = var_5397, x = linear_212_cast_fp16)[name = string("v_cast_fp16")]; + tensor value_perm_0 = const()[name = string("value_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_23_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_23_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(533502656)))]; + tensor var_5410_cast_fp16 = add(x = q_139_cast_fp16, y = encoder_layers_23_self_attn_pos_bias_u_to_fp16)[name = string("op_5410_cast_fp16")]; + tensor encoder_layers_23_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_23_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(533504768)))]; + tensor var_5412_cast_fp16 = add(x = q_139_cast_fp16, y = encoder_layers_23_self_attn_pos_bias_v_to_fp16)[name = string("op_5412_cast_fp16")]; + tensor q_with_bias_v_perm_0 = const()[name = string("q_with_bias_v_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_605_transpose_x_0 = const()[name = string("x_605_transpose_x_0"), val = bool(false)]; + bool x_605_transpose_y_0 = const()[name = string("x_605_transpose_y_0"), val = bool(false)]; + tensor op_5414_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(533506880))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(533620608))))[name = string("op_5414_to_fp16_quantized")]; + tensor q_with_bias_v_cast_fp16 = transpose(perm = q_with_bias_v_perm_0, x = var_5412_cast_fp16)[name = string("transpose_155")]; + tensor x_605_cast_fp16 = matmul(transpose_x = x_605_transpose_x_0, transpose_y = x_605_transpose_y_0, x = q_with_bias_v_cast_fp16, y = op_5414_to_fp16_quantized)[name = string("x_605_cast_fp16")]; + tensor x_607_pad_0 = const()[name = string("x_607_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_607_mode_0 = const()[name = string("x_607_mode_0"), val = string("constant")]; + fp16 const_378_to_fp16 = const()[name = string("const_378_to_fp16"), val = fp16(0x0p+0)]; + tensor x_607_cast_fp16 = pad(constant_val = const_378_to_fp16, mode = x_607_mode_0, pad = x_607_pad_0, x = x_605_cast_fp16)[name = string("x_607_cast_fp16")]; + tensor var_5422 = const()[name = string("op_5422"), val = tensor([1, 8, -1, 14])]; + tensor x_609_cast_fp16 = reshape(shape = var_5422, x = x_607_cast_fp16)[name = string("x_609_cast_fp16")]; + tensor var_5426_begin_0 = const()[name = string("op_5426_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_5426_end_0 = const()[name = string("op_5426_end_0"), val = tensor([1, 8, 112, 14])]; + tensor var_5426_end_mask_0 = const()[name = string("op_5426_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_5426_cast_fp16 = slice_by_index(begin = var_5426_begin_0, end = var_5426_end_0, end_mask = var_5426_end_mask_0, x = x_609_cast_fp16)[name = string("op_5426_cast_fp16")]; + tensor var_5427 = const()[name = string("op_5427"), val = tensor([1, 8, 14, 111])]; + tensor matrix_bd_93_cast_fp16 = reshape(shape = var_5427, x = var_5426_cast_fp16)[name = string("matrix_bd_93_cast_fp16")]; + bool matrix_ac_transpose_x_0 = const()[name = string("matrix_ac_transpose_x_0"), val = bool(false)]; + bool matrix_ac_transpose_y_0 = const()[name = string("matrix_ac_transpose_y_0"), val = bool(false)]; + tensor transpose_142_perm_0 = const()[name = string("transpose_142_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_143_perm_0 = const()[name = string("transpose_143_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_143 = transpose(perm = transpose_143_perm_0, x = k_93_cast_fp16)[name = string("transpose_153")]; + tensor transpose_142 = transpose(perm = transpose_142_perm_0, x = var_5410_cast_fp16)[name = string("transpose_154")]; + tensor matrix_ac_cast_fp16 = matmul(transpose_x = matrix_ac_transpose_x_0, transpose_y = matrix_ac_transpose_y_0, x = transpose_142, y = transpose_143)[name = string("matrix_ac_cast_fp16")]; + tensor matrix_bd_begin_0 = const()[name = string("matrix_bd_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_end_0 = const()[name = string("matrix_bd_end_0"), val = tensor([1, 8, 14, 56])]; + tensor matrix_bd_end_mask_0 = const()[name = string("matrix_bd_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_cast_fp16 = slice_by_index(begin = matrix_bd_begin_0, end = matrix_bd_end_0, end_mask = matrix_bd_end_mask_0, x = matrix_bd_93_cast_fp16)[name = string("matrix_bd_cast_fp16")]; + tensor var_5436_cast_fp16 = add(x = matrix_ac_cast_fp16, y = matrix_bd_cast_fp16)[name = string("op_5436_cast_fp16")]; + fp16 _inversed_scores_93_y_0_to_fp16 = const()[name = string("_inversed_scores_93_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_93_cast_fp16 = mul(x = var_5436_cast_fp16, y = _inversed_scores_93_y_0_to_fp16)[name = string("_inversed_scores_93_cast_fp16")]; + tensor scores_cast_fp16 = select(a = var_44_to_fp16, b = _inversed_scores_93_cast_fp16, cond = mask_11)[name = string("scores_cast_fp16")]; + tensor var_5442_cast_fp16 = softmax(axis = var_58, x = scores_cast_fp16)[name = string("op_5442_cast_fp16")]; + tensor input_1237_cast_fp16 = select(a = var_43_to_fp16, b = var_5442_cast_fp16, cond = mask_11)[name = string("input_1237_cast_fp16")]; + bool x_611_transpose_x_0 = const()[name = string("x_611_transpose_x_0"), val = bool(false)]; + bool x_611_transpose_y_0 = const()[name = string("x_611_transpose_y_0"), val = bool(false)]; + tensor value_cast_fp16 = transpose(perm = value_perm_0, x = v_cast_fp16)[name = string("transpose_152")]; + tensor x_611_cast_fp16 = matmul(transpose_x = x_611_transpose_x_0, transpose_y = x_611_transpose_y_0, x = input_1237_cast_fp16, y = value_cast_fp16)[name = string("x_611_cast_fp16")]; + tensor var_5446_perm_0 = const()[name = string("op_5446_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_5447 = const()[name = string("op_5447"), val = tensor([1, -1, 1024])]; + tensor var_5446_cast_fp16 = transpose(perm = var_5446_perm_0, x = x_611_cast_fp16)[name = string("transpose_151")]; + tensor input_1239_cast_fp16 = reshape(shape = var_5447, x = var_5446_cast_fp16)[name = string("input_1239_cast_fp16")]; + tensor encoder_layers_23_self_attn_linear_out_weight_to_fp16 = const()[name = string("encoder_layers_23_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(533620928)))]; + tensor encoder_layers_23_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_23_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(535718144)))]; + tensor linear_214_cast_fp16 = linear(bias = encoder_layers_23_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_23_self_attn_linear_out_weight_to_fp16, x = input_1239_cast_fp16)[name = string("linear_214_cast_fp16")]; + tensor input_1243_cast_fp16 = add(x = input_1233_cast_fp16, y = linear_214_cast_fp16)[name = string("input_1243_cast_fp16")]; + tensor x_615_axes_0 = const()[name = string("x_615_axes_0"), val = tensor([-1])]; + tensor encoder_layers_23_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_23_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(535720256)))]; + tensor encoder_layers_23_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_23_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(535722368)))]; + tensor x_615_cast_fp16 = layer_norm(axes = x_615_axes_0, beta = encoder_layers_23_norm_conv_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_23_norm_conv_weight_to_fp16, x = input_1243_cast_fp16)[name = string("x_615_cast_fp16")]; + tensor input_1245_perm_0 = const()[name = string("input_1245_perm_0"), val = tensor([0, 2, 1])]; + string input_1247_pad_type_0 = const()[name = string("input_1247_pad_type_0"), val = string("valid")]; + tensor input_1247_strides_0 = const()[name = string("input_1247_strides_0"), val = tensor([1])]; + tensor input_1247_pad_0 = const()[name = string("input_1247_pad_0"), val = tensor([0, 0])]; + tensor input_1247_dilations_0 = const()[name = string("input_1247_dilations_0"), val = tensor([1])]; + int32 input_1247_groups_0 = const()[name = string("input_1247_groups_0"), val = int32(1)]; + tensor encoder_layers_23_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(535724480))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(537821696))))[name = string("encoder_layers_23_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_1245_cast_fp16 = transpose(perm = input_1245_perm_0, x = x_615_cast_fp16)[name = string("transpose_150")]; + tensor input_1247_cast_fp16 = conv(dilations = input_1247_dilations_0, groups = input_1247_groups_0, pad = input_1247_pad_0, pad_type = input_1247_pad_type_0, strides = input_1247_strides_0, weight = encoder_layers_23_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1245_cast_fp16)[name = string("input_1247_cast_fp16")]; + int32 x_617_split_num_splits_0 = const()[name = string("x_617_split_num_splits_0"), val = int32(2)]; + int32 x_617_split_axis_0 = const()[name = string("x_617_split_axis_0"), val = int32(1)]; + tensor x_617_split_cast_fp16_0, tensor x_617_split_cast_fp16_1 = split(axis = x_617_split_axis_0, num_splits = x_617_split_num_splits_0, x = input_1247_cast_fp16)[name = string("x_617_split_cast_fp16")]; + tensor x_617_split_1_sigmoid_cast_fp16 = sigmoid(x = x_617_split_cast_fp16_1)[name = string("x_617_split_1_sigmoid_cast_fp16")]; + tensor x_617_cast_fp16 = mul(x = x_617_split_cast_fp16_0, y = x_617_split_1_sigmoid_cast_fp16)[name = string("x_617_cast_fp16")]; + tensor input_1249_cast_fp16 = select(a = var_43_to_fp16, b = x_617_cast_fp16, cond = var_574)[name = string("input_1249_cast_fp16")]; + bool new_x_interleave_0 = const()[name = string("new_x_interleave_0"), val = bool(false)]; + tensor new_x_cast_fp16 = concat(axis = var_58, interleave = new_x_interleave_0, values = (cache_cast_fp16, input_1249_cast_fp16))[name = string("new_x_cast_fp16")]; + tensor cache_last_time_cur_begin_0 = const()[name = string("cache_last_time_cur_begin_0"), val = tensor([0, 0, 14])]; + tensor cache_last_time_cur_end_0 = const()[name = string("cache_last_time_cur_end_0"), val = tensor([1, 1024, 22])]; + tensor cache_last_time_cur_end_mask_0 = const()[name = string("cache_last_time_cur_end_mask_0"), val = tensor([true, true, true])]; + tensor cache_last_time_cur_cast_fp16 = slice_by_index(begin = cache_last_time_cur_begin_0, end = cache_last_time_cur_end_0, end_mask = cache_last_time_cur_end_mask_0, x = new_x_cast_fp16)[name = string("cache_last_time_cur_cast_fp16")]; + string x_619_pad_type_0 = const()[name = string("x_619_pad_type_0"), val = string("valid")]; + int32 x_619_groups_0 = const()[name = string("x_619_groups_0"), val = int32(1024)]; + tensor x_619_strides_0 = const()[name = string("x_619_strides_0"), val = tensor([1])]; + tensor x_619_pad_0 = const()[name = string("x_619_pad_0"), val = tensor([0, 0])]; + tensor x_619_dilations_0 = const()[name = string("x_619_dilations_0"), val = tensor([1])]; + tensor encoder_layers_23_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(537825856))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(537835136))))[name = string("encoder_layers_23_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_619_cast_fp16 = conv(dilations = x_619_dilations_0, groups = x_619_groups_0, pad = x_619_pad_0, pad_type = x_619_pad_type_0, strides = x_619_strides_0, weight = encoder_layers_23_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_cast_fp16)[name = string("x_619_cast_fp16")]; + tensor input_1251_perm_0 = const()[name = string("input_1251_perm_0"), val = tensor([0, 2, 1])]; + tensor x_621_axes_0 = const()[name = string("x_621_axes_0"), val = tensor([-1])]; + tensor encoder_layers_23_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_23_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(537837248)))]; + tensor encoder_layers_23_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_23_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(537839360)))]; + tensor input_1251_cast_fp16 = transpose(perm = input_1251_perm_0, x = x_619_cast_fp16)[name = string("transpose_149")]; + tensor x_621_cast_fp16 = layer_norm(axes = x_621_axes_0, beta = encoder_layers_23_conv_batch_norm_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_23_conv_batch_norm_weight_to_fp16, x = input_1251_cast_fp16)[name = string("x_621_cast_fp16")]; + tensor input_1253_perm_0 = const()[name = string("input_1253_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1253_cast_fp16 = transpose(perm = input_1253_perm_0, x = x_621_cast_fp16)[name = string("transpose_148")]; + tensor input_1255_cast_fp16 = silu(x = input_1253_cast_fp16)[name = string("input_1255_cast_fp16")]; + string x_623_pad_type_0 = const()[name = string("x_623_pad_type_0"), val = string("valid")]; + tensor x_623_strides_0 = const()[name = string("x_623_strides_0"), val = tensor([1])]; + tensor x_623_pad_0 = const()[name = string("x_623_pad_0"), val = tensor([0, 0])]; + tensor x_623_dilations_0 = const()[name = string("x_623_dilations_0"), val = tensor([1])]; + int32 x_623_groups_0 = const()[name = string("x_623_groups_0"), val = int32(1)]; + tensor encoder_layers_23_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(537841472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(538890112))))[name = string("encoder_layers_23_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_623_cast_fp16 = conv(dilations = x_623_dilations_0, groups = x_623_groups_0, pad = x_623_pad_0, pad_type = x_623_pad_type_0, strides = x_623_strides_0, weight = encoder_layers_23_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1255_cast_fp16)[name = string("x_623_cast_fp16")]; + tensor input_1257_perm_0 = const()[name = string("input_1257_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1257_cast_fp16 = transpose(perm = input_1257_perm_0, x = x_623_cast_fp16)[name = string("transpose_147")]; + tensor input_1259_cast_fp16 = add(x = input_1243_cast_fp16, y = input_1257_cast_fp16)[name = string("input_1259_cast_fp16")]; + tensor input_1261_axes_0 = const()[name = string("input_1261_axes_0"), val = tensor([-1])]; + tensor encoder_layers_23_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_23_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(538892224)))]; + tensor encoder_layers_23_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_23_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(538894336)))]; + tensor input_1261_cast_fp16 = layer_norm(axes = input_1261_axes_0, beta = encoder_layers_23_norm_feed_forward2_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_23_norm_feed_forward2_weight_to_fp16, x = input_1259_cast_fp16)[name = string("input_1261_cast_fp16")]; + tensor encoder_layers_23_feed_forward2_linear1_weight_to_fp16 = const()[name = string("encoder_layers_23_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(538896448)))]; + tensor encoder_layers_23_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_23_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(547285120)))]; + tensor linear_215_cast_fp16 = linear(bias = encoder_layers_23_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_23_feed_forward2_linear1_weight_to_fp16, x = input_1261_cast_fp16)[name = string("linear_215_cast_fp16")]; + tensor input_1265_cast_fp16 = silu(x = linear_215_cast_fp16)[name = string("input_1265_cast_fp16")]; + tensor encoder_layers_23_feed_forward2_linear2_weight_to_fp16 = const()[name = string("encoder_layers_23_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(547293376)))]; + tensor encoder_layers_23_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_23_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(555682048)))]; + tensor linear_216_cast_fp16 = linear(bias = encoder_layers_23_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_23_feed_forward2_linear2_weight_to_fp16, x = input_1265_cast_fp16)[name = string("linear_216_cast_fp16")]; + fp16 var_5529_to_fp16 = const()[name = string("op_5529_to_fp16"), val = fp16(0x1p-1)]; + tensor var_5530_cast_fp16 = mul(x = linear_216_cast_fp16, y = var_5529_to_fp16)[name = string("op_5530_cast_fp16")]; + tensor input_1271_cast_fp16 = add(x = input_1259_cast_fp16, y = var_5530_cast_fp16)[name = string("input_1271_cast_fp16")]; + tensor audio_signal_axes_0 = const()[name = string("audio_signal_axes_0"), val = tensor([-1])]; + tensor encoder_layers_23_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_23_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(555684160)))]; + tensor encoder_layers_23_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_23_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(555686272)))]; + tensor audio_signal_cast_fp16 = layer_norm(axes = audio_signal_axes_0, beta = encoder_layers_23_norm_out_bias_to_fp16, epsilon = var_41_to_fp16, gamma = encoder_layers_23_norm_out_weight_to_fp16, x = input_1271_cast_fp16)[name = string("audio_signal_cast_fp16")]; + int32 obj_5_axis_0 = const()[name = string("obj_5_axis_0"), val = int32(0)]; + tensor obj_5_cast_fp16 = stack(axis = obj_5_axis_0, values = (var_483_cast_fp16, var_696_cast_fp16, var_909_cast_fp16, var_1122_cast_fp16, var_1335_cast_fp16, var_1548_cast_fp16, var_1761_cast_fp16, var_1974_cast_fp16, var_2187_cast_fp16, var_2400_cast_fp16, var_2613_cast_fp16, var_2826_cast_fp16, var_3039_cast_fp16, var_3252_cast_fp16, var_3465_cast_fp16, var_3678_cast_fp16, var_3891_cast_fp16, var_4104_cast_fp16, var_4317_cast_fp16, var_4530_cast_fp16, var_4743_cast_fp16, var_4956_cast_fp16, var_5169_cast_fp16, cache_last_channel_cur_cast_fp16))[name = string("obj_5_cast_fp16")]; + int32 obj_7_axis_0 = const()[name = string("obj_7_axis_0"), val = int32(0)]; + tensor obj_7_cast_fp16 = stack(axis = obj_7_axis_0, values = (var_587_cast_fp16, var_800_cast_fp16, var_1013_cast_fp16, var_1226_cast_fp16, var_1439_cast_fp16, var_1652_cast_fp16, var_1865_cast_fp16, var_2078_cast_fp16, var_2291_cast_fp16, var_2504_cast_fp16, var_2717_cast_fp16, var_2930_cast_fp16, var_3143_cast_fp16, var_3356_cast_fp16, var_3569_cast_fp16, var_3782_cast_fp16, var_3995_cast_fp16, var_4208_cast_fp16, var_4421_cast_fp16, var_4634_cast_fp16, var_4847_cast_fp16, var_5060_cast_fp16, var_5273_cast_fp16, cache_last_time_cur_cast_fp16))[name = string("obj_7_cast_fp16")]; + tensor var_5546 = add(x = cache_len, y = max_audio_length_1)[name = string("op_5546")]; + string var_5546_promoted_to_fp16_dtype_0 = const()[name = string("op_5546_promoted_to_fp16_dtype_0"), val = string("fp16")]; + fp16 const_384_to_fp16 = const()[name = string("const_384_to_fp16"), val = fp16(-inf)]; + fp16 var_48_promoted_to_fp16 = const()[name = string("op_48_promoted_to_fp16"), val = fp16(0x1.5p+5)]; + tensor var_5546_to_fp16 = cast(dtype = var_5546_promoted_to_fp16_dtype_0, x = var_5546)[name = string("cast_6")]; + tensor clip_1_cast_fp16 = clip(alpha = const_384_to_fp16, beta = var_48_promoted_to_fp16, x = var_5546_to_fp16)[name = string("clip_1_cast_fp16")]; + int32 var_5588_one_hot_vector_size_0 = const()[name = string("op_5588_one_hot_vector_size_0"), val = int32(128)]; + int32 var_5588_axis_0 = const()[name = string("op_5588_axis_0"), val = int32(-1)]; + int32 var_5588_on_value_0 = const()[name = string("op_5588_on_value_0"), val = int32(1)]; + int32 var_5588_off_value_0 = const()[name = string("op_5588_off_value_0"), val = int32(0)]; + tensor var_5588 = one_hot(axis = var_5588_axis_0, indices = prompt_id, off_value = var_5588_off_value_0, on_value = var_5588_on_value_0, one_hot_vector_size = var_5588_one_hot_vector_size_0)[name = string("op_5588")]; + tensor var_5591_axes_0 = const()[name = string("op_5591_axes_0"), val = tensor([1])]; + string cast_245_to_fp16_dtype_0 = const()[name = string("cast_245_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_5588_to_fp16 = cast(dtype = cast_245_to_fp16_dtype_0, x = var_5588)[name = string("cast_5")]; + tensor var_5591_cast_fp16 = expand_dims(axes = var_5591_axes_0, x = var_5588_to_fp16)[name = string("op_5591_cast_fp16")]; + tensor one_hot_reps_0 = const()[name = string("one_hot_reps_0"), val = tensor([1, 14, 1])]; + tensor one_hot_cast_fp16 = tile(reps = one_hot_reps_0, x = var_5591_cast_fp16)[name = string("one_hot_cast_fp16")]; + int32 var_5600 = const()[name = string("op_5600"), val = int32(-1)]; + bool input_1273_interleave_0 = const()[name = string("input_1273_interleave_0"), val = bool(false)]; + tensor input_1273_cast_fp16 = concat(axis = var_5600, interleave = input_1273_interleave_0, values = (audio_signal_cast_fp16, one_hot_cast_fp16))[name = string("input_1273_cast_fp16")]; + tensor prompt_kernel_0_weight_to_fp16 = const()[name = string("prompt_kernel_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(555688384)))]; + tensor prompt_kernel_0_bias_to_fp16 = const()[name = string("prompt_kernel_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(560407040)))]; + tensor linear_217_cast_fp16 = linear(bias = prompt_kernel_0_bias_to_fp16, weight = prompt_kernel_0_weight_to_fp16, x = input_1273_cast_fp16)[name = string("linear_217_cast_fp16")]; + tensor input_cast_fp16 = relu(x = linear_217_cast_fp16)[name = string("input_cast_fp16")]; + tensor prompt_kernel_2_weight_to_fp16 = const()[name = string("prompt_kernel_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(560411200)))]; + tensor prompt_kernel_2_bias_to_fp16 = const()[name = string("prompt_kernel_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(564605568)))]; + tensor linear_218_cast_fp16 = linear(bias = prompt_kernel_2_bias_to_fp16, weight = prompt_kernel_2_weight_to_fp16, x = input_cast_fp16)[name = string("linear_218_cast_fp16")]; + tensor var_5613_perm_0 = const()[name = string("op_5613_perm_0"), val = tensor([0, 2, 1])]; + string var_5613_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_5613_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + string cast_246_dtype_0 = const()[name = string("cast_246_dtype_0"), val = string("int32")]; + tensor var_5621_perm_0 = const()[name = string("op_5621_perm_0"), val = tensor([1, 0, 2, 3])]; + string var_5621_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_5621_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor var_5624_perm_0 = const()[name = string("op_5624_perm_0"), val = tensor([1, 0, 2, 3])]; + string var_5624_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_5624_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + string cast_247_dtype_0 = const()[name = string("cast_247_dtype_0"), val = string("int32")]; + tensor cache_len_out = cast(dtype = cast_247_dtype_0, x = clip_1_cast_fp16)[name = string("cast_0")]; + tensor var_5624_cast_fp16 = transpose(perm = var_5624_perm_0, x = obj_7_cast_fp16)[name = string("transpose_144")]; + tensor cache_time_out = cast(dtype = var_5624_cast_fp16_to_fp32_dtype_0, x = var_5624_cast_fp16)[name = string("cast_1")]; + tensor var_5621_cast_fp16 = transpose(perm = var_5621_perm_0, x = obj_5_cast_fp16)[name = string("transpose_145")]; + tensor cache_channel_out = cast(dtype = var_5621_cast_fp16_to_fp32_dtype_0, x = var_5621_cast_fp16)[name = string("cast_2")]; + tensor encoded_length = cast(dtype = cast_246_dtype_0, x = clip_0_cast_fp16)[name = string("cast_3")]; + tensor var_5613_cast_fp16 = transpose(perm = var_5613_perm_0, x = linear_218_cast_fp16)[name = string("transpose_146")]; + tensor encoded = cast(dtype = var_5613_cast_fp16_to_fp32_dtype_0, x = var_5613_cast_fp16)[name = string("cast_4")]; + } -> (encoded, encoded_length, cache_channel_out, cache_time_out, cache_len_out); +} \ No newline at end of file diff --git a/multilingual/1120ms/encoder.mlmodelc/weights/weight.bin b/multilingual/1120ms/encoder.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..75baa186cbe9d8ebd13f24efa19b659fbe668357 --- /dev/null +++ b/multilingual/1120ms/encoder.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5f23e8a17303bd5838e6ad4a402d26552f06a3097834d9fc4bf8e11973e63aa7 +size 564607680 diff --git a/multilingual/1120ms/encoder.mlpackage/Data/com.apple.CoreML/model.mlmodel b/multilingual/1120ms/encoder.mlpackage/Data/com.apple.CoreML/model.mlmodel new file mode 100644 index 0000000000000000000000000000000000000000..78c3b0392296a1b794a1c100c3971938466237ae --- /dev/null +++ b/multilingual/1120ms/encoder.mlpackage/Data/com.apple.CoreML/model.mlmodel @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:66717fbcd7c596e73f7da91a275ba5d7f9f808ed141cc3f565f03f0565882926 +size 800661 diff --git a/multilingual/1120ms/encoder.mlpackage/Data/com.apple.CoreML/weights/weight.bin 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"3DFEC7C7-7F84-47C7-AA9B-03492E557363": { + "author": "com.apple.CoreML", + "description": "CoreML Model Specification", + "name": "model.mlmodel", + "path": "com.apple.CoreML/model.mlmodel" + } + }, + "rootModelIdentifier": "3DFEC7C7-7F84-47C7-AA9B-03492E557363" +} diff --git a/multilingual/1120ms/joint.mlmodelc/analytics/coremldata.bin b/multilingual/1120ms/joint.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..85344aefdca7ccb7cbe67e9a7beaa7894ff29250 --- /dev/null +++ b/multilingual/1120ms/joint.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a285566495ca331c28bd65cb8cd6869402e41b7e1acaca31afd91458a2070130 +size 243 diff --git a/multilingual/1120ms/joint.mlmodelc/coremldata.bin b/multilingual/1120ms/joint.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..a3a7df8b45babdc266afb4e770e305c0746798e2 --- /dev/null +++ b/multilingual/1120ms/joint.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:38ad7913a24486cc3178df1a42d6a8233bcd54c5b42f59bc419ff9101bd19135 +size 341 diff --git a/multilingual/1120ms/joint.mlmodelc/model.mil b/multilingual/1120ms/joint.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..ed1622830370095ef3dc9ffc07f8ed95de1105d7 --- /dev/null +++ b/multilingual/1120ms/joint.mlmodelc/model.mil @@ -0,0 +1,31 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.5.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] +{ + func main(tensor decoder, tensor encoder) { + tensor input_1_perm_0 = const()[name = string("input_1_perm_0"), val = tensor([0, 2, 1])]; + string encoder_to_fp16_dtype_0 = const()[name = string("encoder_to_fp16_dtype_0"), val = string("fp16")]; + tensor input_3_perm_0 = const()[name = string("input_3_perm_0"), val = tensor([0, 2, 1])]; + string decoder_to_fp16_dtype_0 = const()[name = string("decoder_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_enc_weight_to_fp16 = const()[name = string("module_enc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor module_enc_bias_to_fp16 = const()[name = string("module_enc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1310848)))]; + tensor encoder_to_fp16 = cast(dtype = encoder_to_fp16_dtype_0, x = encoder)[name = string("cast_2")]; + tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = encoder_to_fp16)[name = string("transpose_1")]; + tensor linear_0_cast_fp16 = linear(bias = module_enc_bias_to_fp16, weight = module_enc_weight_to_fp16, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor module_pred_weight_to_fp16 = const()[name = string("module_pred_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1312192)))]; + tensor module_pred_bias_to_fp16 = const()[name = string("module_pred_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2131456)))]; + tensor decoder_to_fp16 = cast(dtype = decoder_to_fp16_dtype_0, x = decoder)[name = string("cast_1")]; + tensor input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = decoder_to_fp16)[name = string("transpose_0")]; + tensor linear_1_cast_fp16 = linear(bias = module_pred_bias_to_fp16, weight = module_pred_weight_to_fp16, x = input_3_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_23_axes_0 = const()[name = string("op_23_axes_0"), val = tensor([2])]; + tensor var_23_cast_fp16 = expand_dims(axes = var_23_axes_0, x = linear_0_cast_fp16)[name = string("op_23_cast_fp16")]; + tensor var_25_axes_0 = const()[name = string("op_25_axes_0"), val = tensor([1])]; + tensor var_25_cast_fp16 = expand_dims(axes = var_25_axes_0, x = linear_1_cast_fp16)[name = string("op_25_cast_fp16")]; + tensor input_5_cast_fp16 = add(x = var_23_cast_fp16, y = var_25_cast_fp16)[name = string("input_5_cast_fp16")]; + tensor input_7_cast_fp16 = relu(x = input_5_cast_fp16)[name = string("input_7_cast_fp16")]; + tensor module_joint_net_2_weight_to_fp16 = const()[name = string("module_joint_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2132800)))]; + tensor module_joint_net_2_bias_to_fp16 = const()[name = string("module_joint_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18885504)))]; + tensor linear_2_cast_fp16 = linear(bias = module_joint_net_2_bias_to_fp16, weight = module_joint_net_2_weight_to_fp16, x = input_7_cast_fp16)[name = string("linear_2_cast_fp16")]; + string linear_2_cast_fp16_to_fp32_dtype_0 = const()[name = string("linear_2_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor logits = cast(dtype = linear_2_cast_fp16_to_fp32_dtype_0, x = linear_2_cast_fp16)[name = string("cast_0")]; + } -> (logits); +} \ No newline at end of file diff --git a/multilingual/1120ms/joint.mlmodelc/weights/weight.bin b/multilingual/1120ms/joint.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..0d899ae2b3a3c9be8967b683e91cd8ca7252c8ec --- /dev/null +++ b/multilingual/1120ms/joint.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f4c8ae93e304a187ebfa0b88c812b70e79b625a549727922e7f63d61c1c7b6dd +size 18911744 diff --git a/multilingual/1120ms/joint.mlpackage/Data/com.apple.CoreML/model.mlmodel b/multilingual/1120ms/joint.mlpackage/Data/com.apple.CoreML/model.mlmodel new file mode 100644 index 0000000000000000000000000000000000000000..22c812b223b151bb52a58d7d5ab722254c83e9fd --- /dev/null +++ b/multilingual/1120ms/joint.mlpackage/Data/com.apple.CoreML/model.mlmodel @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8897a4790a6c88fde37843c50b4c45d5ea24f24ea5811fe7555b54fcdde8a5c0 +size 4486 diff --git a/multilingual/1120ms/joint.mlpackage/Data/com.apple.CoreML/weights/weight.bin b/multilingual/1120ms/joint.mlpackage/Data/com.apple.CoreML/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..0d899ae2b3a3c9be8967b683e91cd8ca7252c8ec --- /dev/null +++ b/multilingual/1120ms/joint.mlpackage/Data/com.apple.CoreML/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f4c8ae93e304a187ebfa0b88c812b70e79b625a549727922e7f63d61c1c7b6dd +size 18911744 diff --git a/multilingual/1120ms/joint.mlpackage/Manifest.json b/multilingual/1120ms/joint.mlpackage/Manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..f97f8d4fd73506ae49bbd5bc163cd2079627721d --- /dev/null +++ b/multilingual/1120ms/joint.mlpackage/Manifest.json @@ -0,0 +1,18 @@ +{ + "fileFormatVersion": "1.0.0", + "itemInfoEntries": { + "3F33ED2D-8E5C-4BBD-B243-7D003571E7E2": { + "author": "com.apple.CoreML", + "description": "CoreML Model Specification", + "name": "model.mlmodel", + "path": "com.apple.CoreML/model.mlmodel" + }, + "7D35F675-3334-491B-8264-00E768D11202": { + "author": "com.apple.CoreML", + "description": "CoreML Model Weights", + "name": "weights", + "path": "com.apple.CoreML/weights" + } + }, + "rootModelIdentifier": "3F33ED2D-8E5C-4BBD-B243-7D003571E7E2" +} diff --git a/multilingual/1120ms/joint_noencproj_batched.mlmodelc/analytics/coremldata.bin b/multilingual/1120ms/joint_noencproj_batched.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..e018ec9de1fd95cbb225a25d41f7166cc2650ccd --- /dev/null +++ b/multilingual/1120ms/joint_noencproj_batched.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e5f4a9b0771be9af64fd93db7ceb42dbd305920b1260fe2219f1b046e84841cd +size 243 diff --git a/multilingual/1120ms/joint_noencproj_batched.mlmodelc/coremldata.bin b/multilingual/1120ms/joint_noencproj_batched.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..26d077246e6f610576835d6040df735a7222e4a5 --- /dev/null +++ b/multilingual/1120ms/joint_noencproj_batched.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fc07c4c2de2b13127f406ee70373b2c178702a03755bdc7a2bd57e623b5e65c5 +size 406 diff --git a/multilingual/1120ms/joint_noencproj_batched.mlmodelc/model.mil b/multilingual/1120ms/joint_noencproj_batched.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..89b37c4bfd82ec0f8905ac19299fbe7a5f1d7e73 --- /dev/null +++ b/multilingual/1120ms/joint_noencproj_batched.mlmodelc/model.mil @@ -0,0 +1,26 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.10.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] +{ + func main(tensor decoder, tensor encoder_proj) { + tensor input_1_perm_0 = const()[name = string("input_1_perm_0"), val = tensor([0, 2, 1])]; + string decoder_to_fp16_dtype_0 = const()[name = string("decoder_to_fp16_dtype_0"), val = string("fp16")]; + tensor joint_module_pred_weight_to_fp16 = const()[name = string("joint_module_pred_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor joint_module_pred_bias_to_fp16 = const()[name = string("joint_module_pred_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(819328)))]; + tensor decoder_to_fp16 = cast(dtype = decoder_to_fp16_dtype_0, x = decoder)[name = string("cast_2")]; + tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = decoder_to_fp16)[name = string("transpose_0")]; + tensor linear_0_cast_fp16 = linear(bias = joint_module_pred_bias_to_fp16, weight = joint_module_pred_weight_to_fp16, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_15_axes_0 = const()[name = string("op_15_axes_0"), val = tensor([2])]; + string encoder_proj_to_fp16_dtype_0 = const()[name = string("encoder_proj_to_fp16_dtype_0"), val = string("fp16")]; + tensor encoder_proj_to_fp16 = cast(dtype = encoder_proj_to_fp16_dtype_0, x = encoder_proj)[name = string("cast_1")]; + tensor var_15_cast_fp16 = expand_dims(axes = var_15_axes_0, x = encoder_proj_to_fp16)[name = string("op_15_cast_fp16")]; + tensor var_17_axes_0 = const()[name = string("op_17_axes_0"), val = tensor([1])]; + tensor var_17_cast_fp16 = expand_dims(axes = var_17_axes_0, x = linear_0_cast_fp16)[name = string("op_17_cast_fp16")]; + tensor input_3_cast_fp16 = add(x = var_15_cast_fp16, y = var_17_cast_fp16)[name = string("input_3_cast_fp16")]; + tensor input_5_cast_fp16 = relu(x = input_3_cast_fp16)[name = string("input_5_cast_fp16")]; + tensor joint_module_joint_net_2_weight_to_fp16 = const()[name = string("joint_module_joint_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(820672)))]; + tensor joint_module_joint_net_2_bias_to_fp16 = const()[name = string("joint_module_joint_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17573376)))]; + tensor linear_1_cast_fp16 = linear(bias = joint_module_joint_net_2_bias_to_fp16, weight = joint_module_joint_net_2_weight_to_fp16, x = input_5_cast_fp16)[name = string("linear_1_cast_fp16")]; + string linear_1_cast_fp16_to_fp32_dtype_0 = const()[name = string("linear_1_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor logits = cast(dtype = linear_1_cast_fp16_to_fp32_dtype_0, x = linear_1_cast_fp16)[name = string("cast_0")]; + } -> (logits); +} \ No newline at end of file diff --git a/multilingual/1120ms/joint_noencproj_batched.mlmodelc/weights/weight.bin b/multilingual/1120ms/joint_noencproj_batched.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..defecb9c76ab612924f900c8d498e0e5ff52cc43 --- /dev/null +++ b/multilingual/1120ms/joint_noencproj_batched.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8d6b104e9d6990c07d6cd41bafe27cae8d39cfe037ec701584c47af1094daeeb +size 17599616 diff --git a/multilingual/1120ms/joint_noencproj_batched.mlpackage/Data/com.apple.CoreML/model.mlmodel b/multilingual/1120ms/joint_noencproj_batched.mlpackage/Data/com.apple.CoreML/model.mlmodel new file mode 100644 index 0000000000000000000000000000000000000000..a0b5eefd8a70260c93d94ed4e787b2ab3501d27f --- /dev/null +++ b/multilingual/1120ms/joint_noencproj_batched.mlpackage/Data/com.apple.CoreML/model.mlmodel @@ -0,0 +1,3 @@ +version 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b/multilingual/1120ms/joint_noencproj_batched.mlpackage/Manifest.json @@ -0,0 +1,18 @@ +{ + "fileFormatVersion": "1.0.0", + "itemInfoEntries": { + "55B094CA-55E5-480A-8B14-30A24DC3EEF0": { + "author": "com.apple.CoreML", + "description": "CoreML Model Weights", + "name": "weights", + "path": "com.apple.CoreML/weights" + }, + "CA02FD13-87CE-4425-9B49-DE8265EC1B54": { + "author": "com.apple.CoreML", + "description": "CoreML Model Specification", + "name": "model.mlmodel", + "path": "com.apple.CoreML/model.mlmodel" + } + }, + "rootModelIdentifier": "CA02FD13-87CE-4425-9B49-DE8265EC1B54" +} diff --git a/multilingual/1120ms/metadata.json b/multilingual/1120ms/metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..f5e53a70872ef1298f09ff14bc4d2e9e3e4ed687 --- /dev/null +++ b/multilingual/1120ms/metadata.json @@ -0,0 +1,196 @@ +{ + "model": "nvidia/nemotron-asr-streaming-multilingual-0.6b", + "model_class": "nemo.collections.asr.models.rnnt_bpe_models_prompt.EncDecRNNTBPEModelWithPrompt", + "sample_rate": 16000, + "mel_features": 128, + "chunk_mel_frames": 112, + "pre_encode_cache": 9, + "total_mel_frames": 121, + "att_context_size": [ + 42, + 13 + ], + "vocab_size": 13087, + "blank_idx": 13087, + "cache_channel_shape": [ + 1, + 24, + 42, + 1024 + ], + "cache_time_shape": [ + 1, + 24, + 1024, + 8 + ], + "decoder_hidden": 640, + "decoder_layers": 2, + "encoder_dim": 1024, + "num_prompts": 128, + "prompt_dictionary": { + "af-ZA": 54, + "am-ET": 49, + "ar": 7, + "ar-AR": 7, + "auto": 101, + "ay-BO": 81, + "az-AZ": 66, + "bg": 30, + "bg-BG": 30, + "bn-IN": 36, + "cs": 22, + "cs-CZ": 22, + "da": 25, + "da-DK": 25, + "de": 9, + "de-DE": 9, + "el": 21, + "el-GR": 21, + "en": 0, + "en-GB": 1, + "en-US": 0, + "enGB": 1, + "es": 3, + "es-ES": 2, + "es-US": 3, + "esES": 2, + "et": 60, + "et-EE": 60, + "fa-IR": 38, + "fi": 26, + "fi-FI": 26, + "fr": 8, + "fr-CA": 100, + "fr-FR": 8, + "gn-PY": 82, + "gu-IN": 42, + "ha-NG": 50, + "haw-US": 97, + "he-IL": 64, + "hi": 6, + "hi-HI": 6, + "hi-IN": 6, + "hr": 29, + "hr-HR": 29, + "hu": 23, + "hu-HU": 23, + "hy-AM": 68, + "id-ID": 34, + "ig-NG": 53, + "it": 15, + "it-IT": 15, + "ja-JA": 10, + "ja-JP": 10, + "ka-GE": 67, + "km-KH": 47, + "kn-IN": 43, + "ko": 14, + "ko-KO": 14, + "ko-KR": 14, + "ku-TR": 65, + "ky-KG": 71, + "ln-CD": 58, + "lt": 31, + "lt-LT": 31, + "lv": 61, + "lv-LV": 61, + "mi-NZ": 96, + "ml-IN": 44, + "mr-IN": 41, + "ms-MY": 35, + "mt-MT": 102, + "nah-MX": 83, + "nb": 103, + "nb-NO": 103, + "ne-NP": 46, + "nl": 16, + "nl-NL": 16, + "nn": 104, + "nn-NO": 104, + "no": 27, + "no-NO": 27, + "ny-MW": 57, + "or-KE": 59, + "pl": 17, + "pl-PL": 17, + "pt": 13, + "pt-BR": 12, + "pt-PT": 13, + "qu-PE": 80, + "ro": 20, + "ro-RO": 20, + "ru": 11, + "ru-RU": 11, + "rw-RW": 55, + "si-LK": 45, + "sk": 28, + "sk-SK": 28, + "sl": 62, + "sl-SI": 62, + "sm-WS": 98, + "so-SO": 56, + "sv": 24, + "sv-SE": 24, + "sw-KE": 48, + "ta-IN": 39, + "te-IN": 40, + "tg-TJ": 70, + "th-TH": 32, + "to-TO": 99, + "tr": 18, + "tr-TR": 18, + "uk": 19, + "uk-UA": 19, + "ur-PK": 37, + "uz-UZ": 69, + "vi-VN": 33, + "yo-NG": 52, + "zh-CN": 4, + "zh-TW": 5, + "zh-ZH": 4, + "zu-ZA": 51 + }, + "default_prompt_id": 101, + "lang_tag_token_ids": [ + 1, + 256, + 397, + 518, + 673, + 814, + 907, + 993, + 1125, + 1232, + 1279, + 1383, + 1455, + 1603, + 1724, + 1841, + 1929, + 2021, + 2124, + 2205, + 2322, + 2440, + 2529, + 2809, + 2947, + 2986, + 3051, + 3064, + 3134, + 3247, + 3446, + 7489, + 9532, + 9544, + 9596, + 9695, + 9815, + 9847, + 12944 + ] +} \ No newline at end of file diff --git a/multilingual/1120ms/preprocessor.mlmodelc/analytics/coremldata.bin b/multilingual/1120ms/preprocessor.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..ad2f75126e610a1d2b9c57b0159359bd06a40490 --- /dev/null +++ b/multilingual/1120ms/preprocessor.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:63c74dcaace5d0cef0b6bcd65225e8985e605517fa97f95b13218d02735b6a42 +size 243 diff --git a/multilingual/1120ms/preprocessor.mlmodelc/coremldata.bin b/multilingual/1120ms/preprocessor.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..0944be2be6b80940760d1f5f5f0f11ac817288bb --- /dev/null +++ b/multilingual/1120ms/preprocessor.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f7b11e08aba46d1845d8ad3f247717e0f6fae35b21d71d52e44a69ea73587bfe +size 371 diff --git a/multilingual/1120ms/preprocessor.mlmodelc/model.mil b/multilingual/1120ms/preprocessor.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..0b8261362f9cbf465b530a0d2d0ee9a2b2f462cd --- /dev/null +++ b/multilingual/1120ms/preprocessor.mlmodelc/model.mil @@ -0,0 +1,122 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.5.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] +{ + func main(tensor audio, tensor audio_length) [FlexibleShapeInformation = tuple>>, tuple, ?>>>>((("DefaultShapes", {{"audio", [1, 1]}}), ("RangeDims", {{"audio", [[1, 1], [1, 480000]]}})))] { + int32 var_9 = const()[name = string("op_9"), val = int32(1)]; + int32 var_10 = const()[name = string("op_10"), val = int32(160)]; + int32 var_12 = const()[name = string("op_12"), val = int32(0)]; + int32 var_33 = const()[name = string("op_33"), val = int32(512)]; + tensor var_34 = add(x = audio_length, y = var_33)[name = string("op_34")]; + int32 var_35 = const()[name = string("op_35"), val = int32(512)]; + tensor var_36 = sub(x = var_34, y = var_35)[name = string("op_36")]; + tensor floor_div_0 = floor_div(x = var_36, y = var_10)[name = string("floor_div_0")]; + tensor var_39 = equal(x = audio_length, y = var_12)[name = string("op_39")]; + tensor var_40 = const()[name = string("op_40"), val = tensor([0])]; + tensor mel_length = select(a = var_40, b = floor_div_0, cond = var_39)[name = string("seq_len")]; + string audio_to_fp16_dtype_0 = const()[name = string("audio_to_fp16_dtype_0"), val = string("fp16")]; + tensor audio_to_fp16 = cast(dtype = audio_to_fp16_dtype_0, x = audio)[name = string("cast_14")]; + tensor var_42_shape_cast_fp16 = shape(x = audio_to_fp16)[name = string("op_42_shape_cast_fp16")]; + int32 gather_0_axis_0 = const()[name = string("gather_0_axis_0"), val = int32(0)]; + int32 gather_0_batch_dims_0 = const()[name = string("gather_0_batch_dims_0"), val = int32(0)]; + bool gather_0_validate_indices_0 = const()[name = string("gather_0_validate_indices_0"), val = bool(false)]; + string var_42_shape_cast_fp16_to_int16_dtype_0 = const()[name = string("op_42_shape_cast_fp16_to_int16_dtype_0"), val = string("int16")]; + uint16 select_0_to_uint16 = const()[name = string("select_0_to_uint16"), val = uint16(1)]; + tensor var_42_shape_cast_fp16_to_int16 = cast(dtype = var_42_shape_cast_fp16_to_int16_dtype_0, x = var_42_shape_cast_fp16)[name = string("cast_13")]; + int16 gather_0_cast_uint16 = gather(axis = gather_0_axis_0, batch_dims = gather_0_batch_dims_0, indices = select_0_to_uint16, validate_indices = gather_0_validate_indices_0, x = var_42_shape_cast_fp16_to_int16)[name = string("gather_0_cast_uint16")]; + string gather_0_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_0_cast_uint16_to_int32_dtype_0"), val = string("int32")]; + int32 const_0 = const()[name = string("const_0"), val = int32(0)]; + int32 const_1 = const()[name = string("const_1"), val = int32(1)]; + int32 gather_0_cast_uint16_to_int32 = cast(dtype = gather_0_cast_uint16_to_int32_dtype_0, x = gather_0_cast_uint16)[name = string("cast_12")]; + tensor var_43 = range_1d(end = gather_0_cast_uint16_to_int32, start = const_0, step = const_1)[name = string("op_43")]; + tensor var_44_axes_0 = const()[name = string("op_44_axes_0"), val = tensor([0])]; + tensor var_44 = expand_dims(axes = var_44_axes_0, x = var_43)[name = string("op_44")]; + tensor var_45_axes_0 = const()[name = string("op_45_axes_0"), val = tensor([1])]; + tensor var_45 = expand_dims(axes = var_45_axes_0, x = audio_length)[name = string("op_45")]; + tensor timemask = less(x = var_44, y = var_45)[name = string("timemask")]; + tensor var_48_begin_0 = const()[name = string("op_48_begin_0"), val = tensor([0, 0])]; + tensor var_48_end_0 = const()[name = string("op_48_end_0"), val = tensor([1, 1])]; + tensor var_48_end_mask_0 = const()[name = string("op_48_end_mask_0"), val = tensor([true, false])]; + tensor var_48_squeeze_mask_0 = const()[name = string("op_48_squeeze_mask_0"), val = tensor([false, true])]; + tensor var_48_cast_fp16 = slice_by_index(begin = var_48_begin_0, end = var_48_end_0, end_mask = var_48_end_mask_0, squeeze_mask = var_48_squeeze_mask_0, x = audio_to_fp16)[name = string("op_48_cast_fp16")]; + tensor var_49_axes_0 = const()[name = string("op_49_axes_0"), val = tensor([1])]; + tensor var_49_cast_fp16 = expand_dims(axes = var_49_axes_0, x = var_48_cast_fp16)[name = string("op_49_cast_fp16")]; + tensor var_51_begin_0 = const()[name = string("op_51_begin_0"), val = tensor([0, 1])]; + tensor var_51_end_0 = const()[name = string("op_51_end_0"), val = tensor([1, 0])]; + tensor var_51_end_mask_0 = const()[name = string("op_51_end_mask_0"), val = tensor([true, true])]; + tensor var_51_cast_fp16 = slice_by_index(begin = var_51_begin_0, end = var_51_end_0, end_mask = var_51_end_mask_0, x = audio_to_fp16)[name = string("op_51_cast_fp16")]; + tensor var_53_begin_0 = const()[name = string("op_53_begin_0"), val = tensor([0, 0])]; + tensor var_53_end_0 = const()[name = string("op_53_end_0"), val = tensor([1, -1])]; + tensor var_53_end_mask_0 = const()[name = string("op_53_end_mask_0"), val = tensor([true, false])]; + tensor var_53_cast_fp16 = slice_by_index(begin = var_53_begin_0, end = var_53_end_0, end_mask = var_53_end_mask_0, x = audio_to_fp16)[name = string("op_53_cast_fp16")]; + fp16 var_54_to_fp16 = const()[name = string("op_54_to_fp16"), val = fp16(0x1.f0cp-1)]; + tensor var_55_cast_fp16 = mul(x = var_53_cast_fp16, y = var_54_to_fp16)[name = string("op_55_cast_fp16")]; + tensor var_56_cast_fp16 = sub(x = var_51_cast_fp16, y = var_55_cast_fp16)[name = string("op_56_cast_fp16")]; + bool x_3_interleave_0 = const()[name = string("x_3_interleave_0"), val = bool(false)]; + tensor x_3_cast_fp16 = concat(axis = var_9, interleave = x_3_interleave_0, values = (var_49_cast_fp16, var_56_cast_fp16))[name = string("x_3_cast_fp16")]; + tensor var_59 = logical_not(x = timemask)[name = string("op_59")]; + fp16 var_16_to_fp16 = const()[name = string("op_16_to_fp16"), val = fp16(0x0p+0)]; + tensor input_1_cast_fp16 = select(a = var_16_to_fp16, b = x_3_cast_fp16, cond = var_59)[name = string("input_1_cast_fp16")]; + tensor concat_1x = const()[name = string("concat_1x"), val = tensor([1, 1, -1])]; + tensor input_3_cast_fp16 = reshape(shape = concat_1x, x = input_1_cast_fp16)[name = string("input_3_cast_fp16")]; + tensor input_5_pad_0 = const()[name = string("input_5_pad_0"), val = tensor([0, 0, 0, 0, 256, 256])]; + string input_5_mode_0 = const()[name = string("input_5_mode_0"), val = string("constant")]; + fp16 const_3_to_fp16 = const()[name = string("const_3_to_fp16"), val = fp16(0x0p+0)]; + tensor input_5_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = input_5_mode_0, pad = input_5_pad_0, x = input_3_cast_fp16)[name = string("input_5_cast_fp16")]; + tensor concat_2x = const()[name = string("concat_2x"), val = tensor([1, -1])]; + tensor input_cast_fp16 = reshape(shape = concat_2x, x = input_5_cast_fp16)[name = string("input_cast_fp16")]; + tensor expand_dims_3 = const()[name = string("expand_dims_3"), val = tensor([160])]; + tensor expand_dims_4_axes_0 = const()[name = string("expand_dims_4_axes_0"), val = tensor([1])]; + tensor expand_dims_4_cast_fp16 = expand_dims(axes = expand_dims_4_axes_0, x = input_cast_fp16)[name = string("expand_dims_4_cast_fp16")]; + string conv_0_pad_type_0 = const()[name = string("conv_0_pad_type_0"), val = string("valid")]; + tensor conv_0_pad_0 = const()[name = string("conv_0_pad_0"), val = tensor([0, 0])]; + tensor conv_0_dilations_0 = const()[name = string("conv_0_dilations_0"), val = tensor([1])]; + int32 conv_0_groups_0 = const()[name = string("conv_0_groups_0"), val = int32(1)]; + tensor expand_dims_1_to_fp16 = const()[name = string("expand_dims_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor conv_0_cast_fp16 = conv(dilations = conv_0_dilations_0, groups = conv_0_groups_0, pad = conv_0_pad_0, pad_type = conv_0_pad_type_0, strides = expand_dims_3, weight = expand_dims_1_to_fp16, x = expand_dims_4_cast_fp16)[name = string("conv_0_cast_fp16")]; + string conv_1_pad_type_0 = const()[name = string("conv_1_pad_type_0"), val = string("valid")]; + tensor conv_1_pad_0 = const()[name = string("conv_1_pad_0"), val = tensor([0, 0])]; + tensor conv_1_dilations_0 = const()[name = string("conv_1_dilations_0"), val = tensor([1])]; + int32 conv_1_groups_0 = const()[name = string("conv_1_groups_0"), val = int32(1)]; + tensor expand_dims_2_to_fp16 = const()[name = string("expand_dims_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263296)))]; + tensor conv_1_cast_fp16 = conv(dilations = conv_1_dilations_0, groups = conv_1_groups_0, pad = conv_1_pad_0, pad_type = conv_1_pad_type_0, strides = expand_dims_3, weight = expand_dims_2_to_fp16, x = expand_dims_4_cast_fp16)[name = string("conv_1_cast_fp16")]; + int32 stack_0_axis_0 = const()[name = string("stack_0_axis_0"), val = int32(-1)]; + tensor stack_0_cast_fp16 = stack(axis = stack_0_axis_0, values = (conv_0_cast_fp16, conv_1_cast_fp16))[name = string("stack_0_cast_fp16")]; + fp16 var_19_promoted_to_fp16 = const()[name = string("op_19_promoted_to_fp16"), val = fp16(0x1p+1)]; + tensor var_74_cast_fp16 = pow(x = stack_0_cast_fp16, y = var_19_promoted_to_fp16)[name = string("op_74_cast_fp16")]; + tensor var_76_axes_0 = const()[name = string("op_76_axes_0"), val = tensor([-1])]; + bool var_76_keep_dims_0 = const()[name = string("op_76_keep_dims_0"), val = bool(false)]; + tensor var_76_cast_fp16 = reduce_sum(axes = var_76_axes_0, keep_dims = var_76_keep_dims_0, x = var_74_cast_fp16)[name = string("op_76_cast_fp16")]; + tensor x_11_cast_fp16 = identity(x = var_76_cast_fp16)[name = string("x_11_cast_fp16")]; + bool x_13_transpose_x_0 = const()[name = string("x_13_transpose_x_0"), val = bool(false)]; + bool x_13_transpose_y_0 = const()[name = string("x_13_transpose_y_0"), val = bool(false)]; + tensor const_4_to_fp16 = const()[name = string("const_4_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(526528)))]; + tensor x_13_cast_fp16 = matmul(transpose_x = x_13_transpose_x_0, transpose_y = x_13_transpose_y_0, x = const_4_to_fp16, y = x_11_cast_fp16)[name = string("x_13_cast_fp16")]; + fp16 var_83_to_fp16 = const()[name = string("op_83_to_fp16"), val = fp16(0x1p-24)]; + tensor var_84_cast_fp16 = add(x = x_13_cast_fp16, y = var_83_to_fp16)[name = string("op_84_cast_fp16")]; + fp32 x_epsilon_0 = const()[name = string("x_epsilon_0"), val = fp32(0x1p-149)]; + tensor x_cast_fp16 = log(epsilon = x_epsilon_0, x = var_84_cast_fp16)[name = string("x_cast_fp16")]; + tensor var_86_shape_cast_fp16 = shape(x = x_cast_fp16)[name = string("op_86_shape_cast_fp16")]; + int32 gather_5_axis_0 = const()[name = string("gather_5_axis_0"), val = int32(0)]; + int32 gather_5_batch_dims_0 = const()[name = string("gather_5_batch_dims_0"), val = int32(0)]; + bool gather_5_validate_indices_0 = const()[name = string("gather_5_validate_indices_0"), val = bool(false)]; + string var_86_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_86_shape_cast_fp16_to_uint16_dtype_0"), val = string("uint16")]; + uint16 select_5_to_uint16 = const()[name = string("select_5_to_uint16"), val = uint16(2)]; + tensor var_86_shape_cast_fp16_to_uint16 = cast(dtype = var_86_shape_cast_fp16_to_uint16_dtype_0, x = var_86_shape_cast_fp16)[name = string("cast_11")]; + uint16 gather_5_cast_uint16 = gather(axis = gather_5_axis_0, batch_dims = gather_5_batch_dims_0, indices = select_5_to_uint16, validate_indices = gather_5_validate_indices_0, x = var_86_shape_cast_fp16_to_uint16)[name = string("gather_5_cast_uint16")]; + string gather_5_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_5_cast_uint16_to_int32_dtype_0"), val = string("int32")]; + int32 const_5 = const()[name = string("const_5"), val = int32(0)]; + int32 const_6 = const()[name = string("const_6"), val = int32(1)]; + int32 gather_5_cast_uint16_to_int32 = cast(dtype = gather_5_cast_uint16_to_int32_dtype_0, x = gather_5_cast_uint16)[name = string("cast_10")]; + tensor mask_1 = range_1d(end = gather_5_cast_uint16_to_int32, start = const_5, step = const_6)[name = string("mask_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([0])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = mask_1)[name = string("expand_dims_0")]; + tensor var_91_axes_0 = const()[name = string("op_91_axes_0"), val = tensor([1])]; + tensor var_91 = expand_dims(axes = var_91_axes_0, x = mel_length)[name = string("op_91")]; + tensor mask = greater_equal(x = expand_dims_0, y = var_91)[name = string("mask")]; + tensor var_93_axes_0 = const()[name = string("op_93_axes_0"), val = tensor([1])]; + tensor var_93 = expand_dims(axes = var_93_axes_0, x = mask)[name = string("op_93")]; + tensor processed_signal_cast_fp16 = select(a = var_16_to_fp16, b = x_cast_fp16, cond = var_93)[name = string("processed_signal_cast_fp16")]; + string processed_signal_cast_fp16_to_fp32_dtype_0 = const()[name = string("processed_signal_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor mel = cast(dtype = processed_signal_cast_fp16_to_fp32_dtype_0, x = processed_signal_cast_fp16)[name = string("cast_9")]; + } -> (mel, mel_length); +} \ No newline at end of file diff --git a/multilingual/1120ms/preprocessor.mlmodelc/weights/weight.bin b/multilingual/1120ms/preprocessor.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..86dd375f6649d262d58c9cf8b89006ceab216411 --- /dev/null +++ b/multilingual/1120ms/preprocessor.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:297514e2b211d14b0e53cb97193d679bb89ead98d28e578f3f1d049ddbcc36b3 +size 592384 diff --git a/multilingual/1120ms/preprocessor.mlpackage/Data/com.apple.CoreML/model.mlmodel b/multilingual/1120ms/preprocessor.mlpackage/Data/com.apple.CoreML/model.mlmodel new file mode 100644 index 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b/multilingual/1120ms/preprocessor.mlpackage/Manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..f37d623ccae92b31ab7e0394862e22e42ab33ed9 --- /dev/null +++ b/multilingual/1120ms/preprocessor.mlpackage/Manifest.json @@ -0,0 +1,18 @@ +{ + "fileFormatVersion": "1.0.0", + "itemInfoEntries": { + "3E6D2C42-B6EA-47F8-9EF3-C237CF3E03ED": { + "author": "com.apple.CoreML", + "description": "CoreML Model Weights", + "name": "weights", + "path": "com.apple.CoreML/weights" + }, + "B931B309-6180-4936-9202-560DF3279ED9": { + "author": "com.apple.CoreML", + "description": "CoreML Model Specification", + "name": "model.mlmodel", + "path": "com.apple.CoreML/model.mlmodel" + } + }, + "rootModelIdentifier": "B931B309-6180-4936-9202-560DF3279ED9" +} diff --git a/multilingual/1120ms/tokenizer.json b/multilingual/1120ms/tokenizer.json new file mode 100644 index 0000000000000000000000000000000000000000..5c9c31a266fd62950553b9d5fef65598813f55e0 --- /dev/null +++ b/multilingual/1120ms/tokenizer.json @@ -0,0 +1,13089 @@ +{ + "0": "", + "1": "", + "2": "\u2581", + "3": "\u0438", + "4": ".", + "5": "\u0435", + "6": ",", + "7": "\u0430", + "8": "\u0441", + "9": "\u043e", + "10": "\u043d", + "11": "\u0442", + "12": "\u0442\u0430", + "13": "\u044f", + "14": "\u043a", + "15": "\u2581\u043d\u0430", + "16": "\u043b", + "17": "\u0443", + "18": "\u0437", + "19": "\u0440", + "20": "\u0442\u043e", + "21": "\u043d\u0430", + "22": "\u2581\u0434\u0430", + "23": "\u0432\u0430", + "24": "\u0440\u0430", + "25": "\u0434", + "26": "e", + "27": "\u043a\u0430", + "28": "\u2581\u0437\u0430", + "29": "\u043d\u043e", + "30": "\u043c", + "31": "\u043d\u0438", + "32": "\u044a", + "33": "\u043c\u0435", + "34": "t", + "35": "\u0441\u0442", + "36": "\u043f", + "37": "\u2581\u043f\u043e", + "38": "a", + "39": "\u043d\u0435", + "40": "s", + "41": "\u2581\u0441\u0435", + "42": "\u0440\u0435", + "43": "\u0432", + "44": "\u043a\u043e", + "45": "\u2581\u0432", + "46": "o", + "47": "i", + "48": "\u2581\u0441", + "49": "\u0433", + "50": "\u0442\u0435", + "51": "\u043b\u0438", + "52": "\u0447\u0435", + "53": "\u0431", + "54": "\u2581\u0438", + "55": "\u0442\u0438", + "56": "\u0436", + "57": "\u2581\u043e\u0442", + "58": "\u0447", + "59": "r", + "60": "\u0438\u0442\u0435", + "61": "\u043c\u0430", + "62": "\u0445", + "63": "\u0432\u0435", + "64": "\u0432\u0438", + "65": "\u0440\u0438", + "66": "l", + "67": "\u0448", + "68": "u", + "69": "\u0432\u043e", + "70": "\u2581\u0435", + "71": "d", + "72": "\u0434\u0438", + "73": "c", + "74": "\u0433\u0430", + "75": "\u043c\u0438", + "76": "\u043b\u0430", + "77": "\u043f\u043e", + "78": "\u2581\u0441\u044a", + "79": "\u0439", + "80": "\u0449\u0435", + "81": "\u043b\u0435", + "82": "\u0440\u043e", + "83": "\u0434\u0435", + "84": "\u0433\u043e", + "85": "h", + "86": "m", + "87": "\u0446\u0438", + "88": "\u0449", + "89": "\u2581\u043f\u0440\u043e", + "90": "\u2581\u0438\u0437", + "91": "\u0434\u0430", + "92": "\u043c\u043e", + "93": "\u0441\u0438", + "94": "\u0418", + "95": "\u2581\u0442\u043e\u0432\u0430", + "96": "\u043d\u0438\u044f", + "97": "p", + "98": "\u043d\u0438\u0442\u0435", + "99": "\u0435\u043d\u0438\u0435", + "100": "\u043b\u043e", + "101": "\u041d", + "102": "\u0444", + "103": "\u2581\u0434\u043e", + "104": "\u041f", + "105": "\u2581\u043f\u0440\u0435\u0434", + "106": "\u2581\u043f\u0440\u0438", + "107": "\u043f\u0440\u0430\u0432", + "108": "\u0421", + "109": "\u2581\u0441\u0430", + "110": "\u2581\u0440\u0430\u0437", + "111": "\u0449\u043e", + "112": "\u2581\u043e\u0431", + "113": "n", + "114": "\u2581\u0438\u043c\u0430", + "115": "\u043f\u0430", + "116": "\u0441\u0442\u0430", + "117": "\u0412", + "118": "\u2581\u0442\u0440\u044f\u0431\u0432\u0430", + "119": "g", + "120": "\u043d\u043e\u0441\u0442", + "121": "f", + "122": "\u0431\u0438", + "123": "\u0447\u0430", + "124": "en", + "125": "in", + "126": "\u0446\u0438\u044f", + "127": "\u0432\u044a\u0440", + "128": "on", + "129": "y", + "130": "er", + "131": "an", + "132": "\u0422", + "133": "w", + "134": "\u0432\u0430\u043d\u0435", + "135": "\u041a", + "136": "\u2581\u043a\u043e\u0438\u0442\u043e", + "137": "\u2581\u043c\u043d\u043e\u0433\u043e", + "138": "\u2581\u043f\u0440\u0435", + "139": "0", + "140": "b", + "141": "v", + "142": "\u0434\u044a\u0440\u0436\u0430", + "143": "\u0446\u0435", + "144": "\u0410", + "145": "\u0436\u0434\u0430", + "146": "\u2581\u0411\u043b\u0430\u0433\u043e\u0434\u0430\u0440\u044f", + "147": "2", + "148": "\u041c", + "149": "1", + "150": "\u2581the", + "151": "\u041e", + "152": "\u2581\u043c\u043e\u0436\u0435", + "153": "\u0414", + "154": "\u0441\u043b\u0435\u0434", + "155": "\u0433\u043e\u0432\u043e\u0440", + "156": "\u0440\u0430\u0431\u043e\u0442", + "157": "\u2581\u0432\u044a\u043f\u0440\u043e\u0441", + "158": "\u0434\u043e\u0431", + "159": "\u0417", + "160": "\u0446", + "161": "k", + "162": "\u2581\u0422\u043e\u0432\u0430", + "163": "?", + "164": "\u2581\u0431\u044a\u0434\u0435", + "165": "\u2581\u043a\u043e\u043c\u0438\u0441\u0438\u044f", + "166": "\u0415", + "167": "\u2581\u0442\u043e\u0437\u0438", + "168": "\u2581\u0442\u0435\u0437\u0438", + "169": "\u2581\u0433\u043e\u0441\u043f\u043e\u0434\u0438\u043d", + "170": "\u2581\u0432\u044a\u0437", + "171": "\u2581\u0415\u0432\u0440\u043e\u043f\u0435\u0439\u0441\u043a\u0438\u044f", + "172": "\u0411", + "173": "\u0413", + "174": "\u0420", + "175": "\u044e", + "176": "3", + "177": "5", + "178": "q", + "179": "I", + "180": "\u00e9", + "181": "4", + "182": "z", + "183": "A", + "184": "6", + "185": "j", + "186": "E", + "187": "7", + "188": "\u0429", + "189": "T", + "190": "8", + "191": "9", + "192": "\u041b", + "193": "S", + "194": "\u0424", + "195": "x", + "196": "C", + "197": "\u0425", + "198": "\u044c", + "199": "M", + "200": "P", + "201": "\u0423", + "202": "D", + "203": "B", + "204": "\u0427", + "205": "U", + "206": "W", + "207": "\u0428", + "208": "\u0426", + "209": "N", + "210": "O", + "211": "G", + "212": "\u00e0", + "213": "F", + "214": "L", + "215": "\u00e8", + "216": "V", + "217": "R", + "218": "\u0627", + "219": "\u042e", + "220": "\u00f3", + "221": "\u042f", + "222": "H", + "223": "\u03b1", + "224": "\u00fc", + "225": "\u00e4", + "226": "\u0644", + "227": "\u0416", + "228": "J", + "229": "\u00ed", + "230": "\u03c4", + "231": "\u03b9", + "232": "\u00e1", + "233": "\u03bf", + "234": "K", + "235": "\u03b5", + "236": "\u064a", + "237": "\u00ea", + "238": "Y", + "239": "\u03bd", + "240": "\u0646", + "241": "\u00f6", + "242": "\u0645", + "243": "\u00e7", + "244": "\u03c1", + "245": "\u0419", + "246": "\u0648", + "247": "\u03c3", + "248": "\u03c0", + "249": "\u0119", + "250": "\u03c5", + "251": "\u062a", + "252": "\u03b7", + "253": "\u0631", + "254": "\u03bc", + "255": "\u03ba", + "256": "", + "257": "st", + "258": "ch", + "259": "n\u00ed", + "260": "\u2581s", + "261": "le", + "262": "li", + "263": "\u2581po", + "264": "\u2581v", + "265": "\u017e", + "266": "\u010d", + "267": "\u2581to", + "268": "no", + "269": "to", + "270": "\u2581z", + "271": "me", + "272": "\u2581se", + "273": "\u2581a", + "274": "te", + "275": "\u2581je", + "276": "ho", + "277": "\u2581pro", + "278": "\u016f", + "279": "n\u011b", + "280": "ro", + "281": "\u2581na", + "282": "ce", + "283": "\u2581o", + "284": "la", + "285": "\u0161", + "286": "\u2581ne", + "287": "ni", + "288": "ra", + "289": "ti", + "290": "lo", + "291": "ko", + "292": "\u2581\u017ee", + "293": "n\u00e1", + "294": "po", + "295": "je", + "296": "\u011b", + "297": "de", + "298": "na", + "299": "mi", + "300": "\u2581do", + "301": "ci", + "302": "\u2581k", + "303": "ku", + "304": "\u0159e", + "305": "\u2581by", + "306": "ve", + "307": "\u2581za", + "308": "m\u011b", + "309": "\u2581A", + "310": "\u00fd", + "311": "re", + "312": "v\u00e1", + "313": "ou", + "314": "vo", + "315": "n\u00e9", + "316": "va", + "317": "\u017ee", + "318": "mo", + "319": "v\u011b", + "320": "j\u00ed", + "321": "t\u011b", + "322": "v\u00fd", + "323": "\u2581tak", + "324": "ze", + "325": "\u0159\u00ed", + "326": "ne", + "327": "\u0161e", + "328": "\u2581vy", + "329": "ka", + "330": "ji", + "331": "ky", + "332": "r\u00e1", + "333": "ovat", + "334": "\u2581ob", + "335": "c\u00ed", + "336": "\u2581jak", + "337": "\u2581p\u0159e", + "338": "ny", + "339": "v\u00ed", + "340": "n\u00fd", + "341": "vi", + "342": "\u2581in", + "343": "pr\u00e1v", + "344": "\u00fa", + "345": "\u2581co", + "346": "\u2581tak\u00e9", + "347": "ent", + "348": "\u2581pan", + "349": "\u2581D\u011bkuji", + "350": "\u2581kter\u00e9", + "351": "\u0159i", + "352": "\u2581aby", + "353": "\u2581p\u0159\u00ed", + "354": "\u2581p\u0159i", + "355": "prav", + "356": "\u0159", + "357": "vrop", + "358": "\u2581bude", + "359": "\u2581roz", + "360": "\u2581jsou", + "361": "ov\u00e9", + "362": "\u2581jsme", + "363": "sk\u00e9", + "364": "ov\u00e1n\u00ed", + "365": "\u2581tady", + "366": "sk\u00fd", + "367": "d\u011bl", + "368": "\u2581mus\u00ed", + "369": "Z", + "370": "klad", + "371": "\u2581tedy", + "372": "dob", + "373": "\u2581To", + "374": "\u00e1ln\u00ed", + "375": "\u2581Je", + "376": "\u2581st\u00e1t", + "377": "\u0148", + "378": "oval", + "379": "\u2581proto\u017ee", + "380": "\u2581jsem", + "381": "\u2581kter\u00fd", + "382": "p\u0159edsed", + "383": "\u2581b\u00fdt", + "384": "\u010f", + "385": "\u010c", + "386": "\u0165", + "387": "\u0160", + "388": "\u0158", + "389": "\u017d", + "390": "\u0103", + "391": "\u0142", + "392": "\u017c", + "393": "\u0105", + "394": "X", + "395": "\u00da", + "396": "\u015b", + "397": "", + "398": "\u2581for", + "399": "\u2581det", + "400": "\u2581at", + "401": "\u00e6", + "402": "et", + "403": "\u2581og", + "404": "\u2581vi", + "405": "al", + "406": "\u2581de", + "407": "\u2581der", + "408": "\u2581til", + "409": "or", + "410": "\u2581er", + "411": "om", + "412": "\u00e5", + "413": "\u00f8", + "414": "and", + "415": "\u2581har", + "416": "at", + "417": "\u2581f", + "418": "\u2581i", + "419": "\u2581s\u00e5", + "420": "\u2581af", + "421": "ge", + "422": "ar", + "423": "is", + "424": "ing", + "425": "\u2581med", + "426": "\u2581p\u00e5", + "427": "\u2581be", + "428": "un", + "429": "lig", + "430": "\u2581ikke", + "431": "\u2581man", + "432": "ig", + "433": "\u2581som", + "434": "\u00f8r", + "435": "\u2581Og", + "436": "el", + "437": "ag", + "438": "\u2581skal", + "439": "erne", + "440": "\u2581Det", + "441": "\u2581den", + "442": "ste", + "443": "ning", + "444": "\u2581jeg", + "445": "id", + "446": "\u2581kan", + "447": "\u2581ogs\u00e5", + "448": "\u2581vil", + "449": "ske", + "450": "iv", + "451": "\u2581ud", + "452": "\u2581her", + "453": "ion", + "454": "am", + "455": "ur", + "456": "for", + "457": "\u2581pr", + "458": "else", + "459": "\u2581sig", + "460": "\u2581men", + "461": "\u2581ind", + "462": "\u2581jo", + "463": "ende", + "464": "\u2581v\u00e6re", + "465": "\u2581Vi", + "466": "ation", + "467": "\u2581m\u00e5", + "468": "mme", + "469": "ighed", + "470": "tage", + "471": "\u2581op", + "472": "\u2581Jeg", + "473": "\u2581hvor", + "474": "\u2581ved", + "475": "\u2581f\u00e5", + "476": "\u2581fra", + "477": "\u2581over", + "478": "\u2581have", + "479": "kke", + "480": "\u2581meget", + "481": "\u2581S\u00e5", + "482": "\u2581Tak", + "483": "\u2581noget", + "484": "\u2581alle", + "485": "brug", + "486": "\u2581komme", + "487": "\u2581Men", + "488": "\u2581var", + "489": "hold", + "490": "arbejde", + "491": "\u2581eller", + "492": "\u2581vores", + "493": "\u2581frem", + "494": "\u2581alts\u00e5", + "495": "\u2581vigtig", + "496": "v\u00e6r", + "497": "\u2581EU", + "498": "\u2581g\u00f8re", + "499": "\u2581nogle", + "500": "skab", + "501": "\u2581sp\u00f8rgsm\u00e5l", + "502": "\u2581kunne", + "503": "\u2581kommissionen", + "504": "\u2581hvis", + "505": "\u00d8", + "506": "\u00c6", + "507": "\u03c2", + "508": "\u03bb", + "509": "\u03af", + "510": "\u03cc", + "511": "\u0131", + "512": "\u03ad", + "513": "\u03ac", + "514": "\u03c9", + "515": "\u03b3", + "516": "\u03b4", + "517": "\u03ae", + "518": "", + "519": "\u2581die", + "520": "\u2581und", + "521": "\u2581das", + "522": "sch", + "523": "\u2581ist", + "524": "\u2581ich", + "525": "\u2581ein", + "526": "\u2581ge", + "527": "ung", + "528": "it", + "529": "\u2581wir", + "530": "\u2581zu", + "531": "\u2581so", + "532": "\u2581da", + "533": "\u2581S", + "534": "\u2581auch", + "535": "gen", + "536": "\u2581nicht", + "537": "\u2581W", + "538": "\u2581B", + "539": "\u2581E", + "540": "\u2581F", + "541": "ll", + "542": "\u2581es", + "543": "\u2581K", + "544": "ie", + "545": "au", + "546": "\u2581P", + "547": "ich", + "548": "\u2581eine", + "549": "lich", + "550": "ck", + "551": "ten", + "552": "mal", + "553": "ein", + "554": "\u2581T", + "555": "\u2581dann", + "556": "\u2581Und", + "557": "\u2581mit", + "558": "\u2581auf", + "559": "hr", + "560": "ter", + "561": "tz", + "562": "\u2581dass", + "563": "\u2581G", + "564": "ben", + "565": "um", + "566": "us", + "567": "cht", + "568": "il", + "569": "\u2581Das", + "570": "\u2581diese", + "571": "\u2581noch", + "572": "\u2581jetzt", + "573": "ut", + "574": "\u2581ver", + "575": "kt", + "576": "\u2581Ich", + "577": "\u2581hier", + "578": "\u2581hat", + "579": "\u2581haben", + "580": "\u2581von", + "581": "ri", + "582": "ach", + "583": "ol", + "584": "\u2581Da", + "585": "\u2581als", + "586": "sp", + "587": "\u2581f\u00fcr", + "588": "ell", + "589": "\u2581sich", + "590": "\u2581was", + "591": "\u2581ja", + "592": "uch", + "593": "\u2581kann", + "594": "\u2581sind", + "595": "wi", + "596": "\u2581aus", + "597": "rei", + "598": "\u2581wie", + "599": "\u2581Ge", + "600": "und", + "601": "\u2581St", + "602": "isch", + "603": "\u2581sie", + "604": "\u2581Ja", + "605": "\u2581du", + "606": "\u2581war", + "607": "\u2581im", + "608": "\u2581dem", + "609": "\u2581aber", + "610": "\u2581oder", + "611": "\u00df", + "612": "\u2581Sch", + "613": "\u2581uns", + "614": "\u2581habe", + "615": "\u2581wenn", + "616": "\u2581wo", + "617": "\u2581bei", + "618": "\u2581ihr", + "619": "\u2581Ma", + "620": "zu", + "621": "\u2581schon", + "622": "\u2581De", + "623": "\u2581Sie", + "624": "\u2581\u00fcber", + "625": "\u2581vor", + "626": "\u2581Die", + "627": "\u2581ganz", + "628": "iert", + "629": "\u2581Le", + "630": "\u2581viel", + "631": "\u2581In", + "632": "\u2581Also", + "633": "\u2581Ver", + "634": "\u2581sehr", + "635": "\u2581Re", + "636": "halt", + "637": "\u2581einfach", + "638": "\u2581werden", + "639": "\u2581sein", + "640": "\u2581Wir", + "641": "\u2581nur", + "642": "\u2581immer", + "643": "ieren", + "644": "\u2581muss", + "645": "\u2581wieder", + "646": "\u2581mir", + "647": "\u2581gut", + "648": "\u2581mehr", + "649": "\u2581Mi", + "650": "\u2581nach", + "651": "\u2581Ha", + "652": "\u2581weil", + "653": "\u2581Aber", + "654": "kommen", + "655": "\u2581gibt", + "656": "\u2581meine", + "657": "\u2581andere", + "658": "\u2581k\u00f6nnen", + "659": "\u2581machen", + "660": "\u2581nat\u00fcrlich", + "661": "\u2581bisschen", + "662": "\u2581durch", + "663": "sehen", + "664": "\u2581weiter", + "665": "\u2581keine", + "666": "\u2581sagen", + "667": "\u2581wirklich", + "668": "\u2581eigentlich", + "669": "\u2581jede", + "670": "schaft", + "671": "\u2581glaube", + "672": "\u00dc", + "673": "", + "674": "\u03c7", + "675": "\u03c4\u03b1", + "676": "\u2581\u03bd\u03b1", + "677": "\u03b5\u03b9", + "678": "\u2581\u03ba\u03b1\u03b9", + "679": "\u03bc\u03b1", + "680": "\u03b2", + "681": "\u03c3\u03b7", + "682": "\u03c4\u03b5", + "683": "\u03ce", + "684": "\u03b8", + "685": "\u03c6", + "686": "\u03c0\u03bf", + "687": "\u03cd", + "688": "\u2581\u03c4\u03bf", + "689": "\u03af\u03b1", + "690": "\u03c4\u03b9", + "691": "\u03b1\u03bd", + "692": "\u03bf\u03c5", + "693": "\u03c1\u03b1", + "694": "\u2581\u03b3\u03b9\u03b1", + "695": "\u03b5\u03af", + "696": "\u03c4\u03b7", + "697": "\u03be", + "698": "\u03ba\u03b1", + "699": "\u2581\u03c4\u03b7\u03bd", + "700": "\u2581\u03c4\u03b7", + "701": "\u03bc\u03b5", + "702": "\u03c4\u03bf", + "703": "\u03bf\u03cd", + "704": "\u2581\u03c4\u03bf\u03c5", + "705": "\u2581\u03c0\u03c1\u03bf", + "706": "\u2581\u03bc\u03b5", + "707": "\u03b6", + "708": "\u2581\u03b8\u03b1", + "709": "\u2581\u03b5\u03af\u03bd\u03b1\u03b9", + "710": "\u03c1\u03bf", + "711": "\u03c9\u03bd", + "712": "\u03bc\u03ad", + "713": "\u2581\u03c0\u03bf\u03c5", + "714": "\u03b9\u03b1", + "715": "\u03bd\u03bf", + "716": "\u03b9\u03ba\u03ae", + "717": "\u03ce\u03bd", + "718": "\u03c1\u03b9", + "719": "\u03b8\u03b5", + "720": "\u0395", + "721": "\u03c1\u03af", + "722": "\u2581\u03cc\u03c4\u03b9", + "723": "\u03bf\u03c5\u03bc\u03b5", + "724": "\u2581\u03b1\u03c0\u03cc", + "725": "\u03bb\u03bf", + "726": "\u03c1\u03ac", + "727": "\u03b9\u03bf", + "728": "\u2581\u03c4\u03c9\u03bd", + "729": "\u03b5\u03c5", + "730": "\u03bb\u03b7", + "731": "\u03bf\u03c5\u03bd", + "732": "\u0391", + "733": "\u2581\u03c3\u03b5", + "734": "\u03a0", + "735": "\u2581\u03c3\u03c5\u03bd", + "736": "\u03c6\u03bf\u03c1", + "737": "\u2581\u03b4\u03b5\u03bd", + "738": "\u03a3", + "739": "\u2581\u03c3\u03c4\u03bf", + "740": "\u2581\u03b4\u03b9", + "741": "\u03c4\u03ac", + "742": "\u2581\u03b1\u03c5\u03c4\u03cc", + "743": "\u2581\u03b4\u03b9\u03b1", + "744": "\u03b9\u03c3\u03c4", + "745": "\u2581\u03c0\u03bf\u03bb\u03cd", + "746": "\u2581\u03c0\u03c1\u03ad\u03c0\u03b5\u03b9", + "747": "\u2581\u03c3\u03c4\u03b7\u03bd", + "748": "\u03c3\u03bf\u03c5\u03bc\u03b5", + "749": "\u03b9\u03ba\u03ac", + "750": "\u03a4", + "751": "\u2581\u03b5\u03c0", + "752": "\u039a", + "753": "\u03c8", + "754": "\u2581\u03b1\u03c0\u03bf", + "755": "\u2581\u03bf\u03b9", + "756": "\u03b5\u03c4\u03b1\u03b9", + "757": "\u2581\u03b5\u03c0\u03b9", + "758": "\u2581\u03a5\u03c0\u03cc\u03c4\u03b9\u03c4\u03bb\u03bf\u03b9", + "759": "\u2581AUTHORWAVE", + "760": "\u03bf\u03cd\u03bc\u03b5", + "761": "\u03b9\u03ba\u03cc", + "762": "\u2581\u039a\u03b1\u03b9", + "763": "\u03c0\u03c1\u03cc", + "764": "\u2581\u0395\u03c5\u03c7\u03b1\u03c1\u03b9\u03c3\u03c4\u03ce", + "765": "\u2581\u03bc\u03b9\u03b1", + "766": "\u2581\u03ad\u03bd\u03b1", + "767": "\u2581\u03c3\u03c5\u03bc", + "768": "\u039c", + "769": "\u2581\u03c0\u03b5\u03c1\u03b9", + "770": "\u2581\u03b1\u03c5\u03c4\u03ae", + "771": "\u03ae\u03c3\u03b5\u03b9", + "772": "\u039f", + "773": "\u03b9\u03ba\u03ad", + "774": "\u2581\u03ba\u03b1\u03c4\u03ac", + "775": "\u0393", + "776": "\u0398", + "777": "\u2581\u0395\u03c5\u03c1\u03c9\u03c0\u03b1\u03ca\u03ba\u03ae", + "778": "\u2581\u03ad\u03c7\u03bf\u03c5\u03bc\u03b5", + "779": "\u2581\u03b1\u03bb\u03bb\u03ac", + "780": "\u03b5\u03c1\u03b3", + "781": "\u0397", + "782": "\u2581\u03b8\u03ad\u03bc\u03b1", + "783": "\u03bf\u03bb\u03bf\u03b3", + "784": "\u03cc\u03c4\u03b7\u03c4\u03b1", + "785": "\u2581\u03ad\u03c7\u03b5\u03b9", + "786": "\u03c0\u03bf\u03bb\u03b9\u03c4", + "787": "\u0394", + "788": "\u2581\u03bb\u03bf\u03b9\u03c0\u03cc\u03bd", + "789": "\u03bf\u03bd\u03c4\u03b1\u03b9", + "790": "\u039d", + "791": "\u03c6\u03ad\u03c1", + "792": "\u2581\u0395\u03c0\u03b9\u03c4\u03c1\u03bf\u03c0\u03ae", + "793": "\u2581\u03b1\u03c5\u03c4\u03ac", + "794": "\u2581\u0388\u03bd\u03c9\u03c3\u03b7", + "795": "\u03a5", + "796": "\u03ca", + "797": "\u2581\u0394\u03b5\u03bd", + "798": "\u2581\u03ad\u03c7\u03bf\u03c5\u03bd", + "799": "\u2581\u03c5\u03c0\u03ac\u03c1\u03c7\u03b5\u03b9", + "800": "\u0392", + "801": "\u0399", + "802": "\u039b", + "803": "\u03a6", + "804": "\u03a1", + "805": "\u03a7", + "806": "\u039e", + "807": "\u03a9", + "808": "\u0396", + "809": "\u03a8", + "810": "\u0389", + "811": "\u0386", + "812": "\u038c", + "813": "\u0388", + "814": "", + "815": "ma", + "816": "ta", + "817": "se", + "818": "da", + "819": "si", + "820": "\u2581on", + "821": "\u00f5", + "822": "ks", + "823": "ga", + "824": "\u2581et", + "825": "\u2581ka", + "826": "he", + "827": "mu", + "828": "tu", + "829": "ha", + "830": "ja", + "831": "gi", + "832": "\u2581selle", + "833": "\u2581ole", + "834": "nd", + "835": "oo", + "836": "gu", + "837": "ju", + "838": "est", + "839": "\u2581ei", + "840": "\u2581pa", + "841": "nud", + "842": "\u2581v\u00e4ga", + "843": "\u2581see", + "844": "tud", + "845": "\u2581pea", + "846": "nda", + "847": "\u00e4r", + "848": "\u2581Euroopa", + "849": "\u2581kui", + "850": "vad", + "851": "ke", + "852": "sta", + "853": "sed", + "854": "\u2581v\u00f5i", + "855": "di", + "856": "\u2581saa", + "857": "mise", + "858": "\u2581siis", + "859": "\u2581su", + "860": "ide", + "861": "pool", + "862": "val", + "863": "tus", + "864": "\u2581seda", + "865": "\u2581Me", + "866": "\u2581vastu", + "867": "\u2581j\u00e4", + "868": "\u2581tule", + "869": "selt", + "870": "ment", + "871": "\u2581kes", + "872": "ndus", + "873": "\u2581t\u00f6\u00f6", + "874": "\u2581k\u00f5ik", + "875": "dus", + "876": "\u2581m\u00f5", + "877": "eeri", + "878": "\u2581meie", + "879": "\u2581meil", + "880": "\u2581ning", + "881": "v\u00f5t", + "882": "\u2581mida", + "883": "\u2581arv", + "884": "\u2581See", + "885": "takse", + "886": "\u2581vaja", + "887": "\u2581osa", + "888": "\u00f5igus", + "889": "\u2581nende", + "890": "\u2581n\u00fc\u00fcd", + "891": "\u2581aasta", + "892": "tsiooni", + "893": "\u2581inim", + "894": "\u2581need", + "895": "tsus", + "896": "riigi", + "897": "\u2581t\u00e4h", + "898": "\u2581Liidu", + "899": "\u2581v\u00e4lja", + "900": "\u00c4", + "901": "\u00d5", + "902": "\u00e3", + "903": "Q", + "904": "\u0107", + "905": "\u0639", + "906": "\u00f1", + "907": "", + "908": "t\u00e4", + "909": "ssa", + "910": "lla", + "911": "\u2581ett\u00e4", + "912": "ksi", + "913": "ty", + "914": "ki", + "915": "v\u00e4", + "916": "pa", + "917": "lle", + "918": "lu", + "919": "tta", + "920": "st\u00e4", + "921": "isi", + "922": "ise", + "923": "ll\u00e4", + "924": "kin", + "925": "n\u00e4", + "926": "\u00e4\u00e4n", + "927": "kse", + "928": "tte", + "929": "j\u00e4", + "930": "tt\u00e4", + "931": "ss\u00e4", + "932": "ista", + "933": "inen", + "934": "k\u00e4", + "935": "llis", + "936": "t\u00f6", + "937": "\u2581my\u00f6s", + "938": "vu", + "939": "taan", + "940": "\u2581t\u00e4m\u00e4", + "941": "\u2581voi", + "942": "utta", + "943": "iden", + "944": "nyt", + "945": "\u2581niin", + "946": "\u2581Kiitos", + "947": "\u2581ovat", + "948": "h\u00e4n", + "949": "suu", + "950": "\u2581toimi", + "951": "aika", + "952": "\u2581T\u00e4m\u00e4", + "953": "\u2581p\u00e4\u00e4", + "954": "\u2581mutta", + "955": "\u2581k\u00e4y", + "956": "\u2581t\u00e4ss\u00e4", + "957": "\u2581asia", + "958": "\u2581T\u00e4", + "959": "\u2581jotka", + "960": "\u2581ty\u00f6", + "961": "neet", + "962": "\u2581t\u00e4ytyy", + "963": "\u2581sitten", + "964": "\u2581Euroopan", + "965": "\u2581puolesta", + "966": "\u2581halua", + "967": "\u2581siit\u00e4", + "968": "\u2581komissio", + "969": "\u2581hyv\u00e4", + "970": "\u2581hyvin", + "971": "\u2581puhu", + "972": "\u2581meid\u00e4n", + "973": "\u2581vastaan", + "974": "\u2581t\u00e4rke\u00e4", + "975": "\u2581kaikki", + "976": "\u2581Kiitoksia", + "977": "\u2581viel\u00e4", + "978": "\u2581muut", + "979": "\u2581paljon", + "980": "mahdollis", + "981": "parlament", + "982": "\u2581pit\u00e4isi", + "983": "\u2581hyv\u00e4ksy", + "984": "\u2581puheenjohtaja", + "985": "\u2581liitty", + "986": "\u0101", + "987": "\u10d0", + "988": "\u10d8", + "989": "\u012b", + "990": "\u0113", + "991": "\u00eb", + "992": "\u10d4", + "993": "", + "994": "\u2581est", + "995": "\u2581c", + "996": "\u2581d", + "997": "\u2581la", + "998": "\u2581p", + "999": "\u2581que", + "1000": "\u2581en", + "1001": "\u2581le", + "1002": "\u2581\u00e0", + "1003": "es", + "1004": "\u2581l", + "1005": "\u2581un", + "1006": "\u2581pas", + "1007": "\u2581les", + "1008": "\u2581qui", + "1009": "\u2581il", + "1010": "\u2581vous", + "1011": "\u2581des", + "1012": "\u2581ce", + "1013": "\u2581qu", + "1014": "\u2581pour", + "1015": "\u2581n", + "1016": "\u2581par", + "1017": "\u2581\u00e7a", + "1018": "\u2581une", + "1019": "\u2581b", + "1020": "ant", + "1021": "\u2581j", + "1022": "ais", + "1023": "ez", + "1024": "\u2581dans", + "1025": "\u2581va", + "1026": "\u2581C", + "1027": "tre", + "1028": "ir", + "1029": "elle", + "1030": "eur", + "1031": "\u2581sur", + "1032": "\u2581re", + "1033": "\u2581con", + "1034": "\u2581ma", + "1035": "\u2581Et", + "1036": "\u2581au", + "1037": "ement", + "1038": "tion", + "1039": "t\u00e9", + "1040": "\u2581tout", + "1041": "mp", + "1042": "ique", + "1043": "\u2581plus", + "1044": "eux", + "1045": "\u2581d\u00e9", + "1046": "\u2581fait", + "1047": "qu", + "1048": "\u2581ai", + "1049": "\u2581comme", + "1050": "ens", + "1051": "ac", + "1052": "\u2581l\u00e0", + "1053": "\u2581si", + "1054": "ait", + "1055": "che", + "1056": "\u2581mais", + "1057": "que", + "1058": "ul", + "1059": "\u2581avec", + "1060": "\u2581bien", + "1061": "\u2581tu", + "1062": "age", + "1063": "\u2581mon", + "1064": "\u2581Donc", + "1065": "end", + "1066": "\u2581faire", + "1067": "\u2581\u00eatre", + "1068": "ver", + "1069": "\u2581peu", + "1070": "\u2581m\u00eame", + "1071": "tra", + "1072": "cha", + "1073": "\u2581nous", + "1074": "\u2581donc", + "1075": "\u2581sont", + "1076": "\u2581moi", + "1077": "ille", + "1078": "ff", + "1079": "\u2581ex", + "1080": "ien", + "1081": "\u2581Il", + "1082": "\u2581tr\u00e8s", + "1083": "\u2581cette", + "1084": "im", + "1085": "it\u00e9", + "1086": "\u2581dire", + "1087": "\u2581peut", + "1088": "ance", + "1089": "aire", + "1090": "m\u00e9", + "1091": "\u2581app", + "1092": "\u2581aussi", + "1093": "\u2581petit", + "1094": "aux", + "1095": "\u2581parce", + "1096": "onne", + "1097": "mb", + "1098": "man", + "1099": "\u2581On", + "1100": "\u2581quand", + "1101": "\u2581autre", + "1102": "\u00f4", + "1103": "\u2581chose", + "1104": "\u2581puis", + "1105": "\u2581\u00e9tait", + "1106": "ndre", + "1107": "port", + "1108": "\u2581vraiment", + "1109": "ence", + "1110": "\u2581Mais", + "1111": "\u00ee", + "1112": "\u2581avoir", + "1113": "form", + "1114": "\u2581faut", + "1115": "\u2581Alors", + "1116": "ign", + "1117": "\u2581o\u00f9", + "1118": "pr\u00e8s", + "1119": "\u2581beaucoup", + "1120": "ture", + "1121": "\u00fb", + "1122": "\u00c7", + "1123": "\u00e2", + "1124": "\u00f9", + "1125": "", + "1126": "sz", + "1127": "\u2581az", + "1128": "\u2581hogy", + "1129": "\u0151", + "1130": "\u00e1s", + "1131": "ok", + "1132": "gy", + "1133": "ek", + "1134": "\u00e1l", + "1135": "\u00e9s", + "1136": "em", + "1137": "\u00e1r", + "1138": "\u2581meg", + "1139": "\u2581\u00e9s", + "1140": "\u2581is", + "1141": "\u2581ez", + "1142": "\u2581egy", + "1143": "os", + "1144": "ak", + "1145": "ban", + "1146": "nak", + "1147": "\u00edt", + "1148": "ik", + "1149": "unk", + "1150": "\u2581nem", + "1151": "oz", + "1152": "\u00fcl", + "1153": "\u00e1n", + "1154": "\u00e1t", + "1155": "cs", + "1156": "\u00e9l", + "1157": "\u00e9r", + "1158": "nek", + "1159": "\u2581mi", + "1160": "szer", + "1161": "bb", + "1162": "\u2581K\u00f6sz\u00f6n\u00f6m", + "1163": "s\u00e9g", + "1164": "\u2581kell", + "1165": "\u00e9n", + "1166": "hat", + "1167": "\u2581ha", + "1168": "s\u00e1g", + "1169": "\u2581sz\u00e9pen", + "1170": "\u00e9rt", + "1171": "\u00e9k", + "1172": "ott", + "1173": "\u00f6n", + "1174": "\u00e9p", + "1175": "el\u0151", + "1176": "\u00fcnk", + "1177": "\u2581van", + "1178": "\u2581ki", + "1179": "\u2581fel", + "1180": "\u00e9ny", + "1181": "v\u00e9", + "1182": "leg", + "1183": "eket", + "1184": "\u2581Az", + "1185": "juk", + "1186": "\u2581k\u00f6z", + "1187": "\u0171", + "1188": "\u2581nagyon", + "1189": "\u2581tud", + "1190": "\u2581jelen", + "1191": "\u2581amely", + "1192": "\u2581lehet", + "1193": "\u2581ami", + "1194": "\u2581k\u00e9rd\u00e9s", + "1195": "\u2581ellen", + "1196": "tart", + "1197": "r\u0151l", + "1198": "\u00c9", + "1199": "orsz\u00e1g", + "1200": "rend", + "1201": "r\u00f3l", + "1202": "\u2581vagy", + "1203": "\u2581fontos", + "1204": "\u2581Eur\u00f3pai", + "1205": "\u2581akkor", + "1206": "\u2581jog", + "1207": "\u2581fog", + "1208": "fogad", + "1209": "kapcsol", + "1210": "\u2581r\u00e9sz", + "1211": "\u00e1ci\u00f3", + "1212": "\u2581volt", + "1213": "\u2581eln\u00f6k", + "1214": "\u2581bizotts\u00e1g", + "1215": "\u2581gondol", + "1216": "\u2581olyan", + "1217": "\u2581illetve", + "1218": "\u2581tag\u00e1llam", + "1219": "\u2581pedig", + "1220": "\u2581Teh\u00e1t", + "1221": "\u2581eur\u00f3pai", + "1222": "\u2581sz\u00fcks\u00e9g", + "1223": "szavaz", + "1224": "\u2581teh\u00e1t", + "1225": "k\u00f6vetkez", + "1226": "\u2581\u00f6ssze", + "1227": "\u2581biztos", + "1228": "\u00d6", + "1229": "\u00c1", + "1230": "\u00cd", + "1231": "\u0150", + "1232": "", + "1233": "\u2581u", + "1234": "\u2581bi", + "1235": "\u2581sa", + "1236": "\u0107e", + "1237": "\u2581od", + "1238": "ru", + "1239": "\u2581iz", + "1240": "go", + "1241": "nje", + "1242": "sti", + "1243": "\u0111", + "1244": "\u2581pri", + "1245": "ima", + "1246": "nu", + "1247": "\u2581pre", + "1248": "\u2581Hvala", + "1249": "lje", + "1250": "\u2581\u0161to", + "1251": "\u010di", + "1252": "nja", + "1253": "zi", + "1254": "vr", + "1255": "\u0107i", + "1256": "\u010de", + "1257": "ca", + "1258": "\u2581koji", + "1259": "ba", + "1260": "\u2581raz", + "1261": "\u05d9", + "1262": "\u05d5", + "1263": "\u05d4", + "1264": "\u062f", + "1265": "\u05dc", + "1266": "\u0629", + "1267": "\u0628", + "1268": "\u0647", + "1269": "\u0623", + "1270": "\u05d0", + "1271": "\u0633", + "1272": "\u0643", + "1273": "\u05ea", + "1274": "\u05e8", + "1275": "\u021b", + "1276": "\u05de", + "1277": "\u0642", + "1278": "\u05e9", + "1279": "", + "1280": "\u2581di", + "1281": "\u2581e", + "1282": "\u2581che", + "1283": "\u2581\u00e8", + "1284": "co", + "1285": "\u2581per", + "1286": "\u2581al", + "1287": "\u2581non", + "1288": "do", + "1289": "gli", + "1290": "so", + "1291": "amo", + "1292": "sa", + "1293": "ndo", + "1294": "\u2581una", + "1295": "fi", + "1296": "pi", + "1297": "nti", + "1298": "tto", + "1299": "tro", + "1300": "\u2581fa", + "1301": "chi", + "1302": "\u2581qua", + "1303": "zione", + "1304": "bi", + "1305": "\u2581del", + "1306": "mente", + "1307": "pe", + "1308": "ssi", + "1309": "\u2581ri", + "1310": "\u2581sono", + "1311": "\u2581me", + "1312": "\u2581questo", + "1313": "nte", + "1314": "tti", + "1315": "t\u00e0", + "1316": "\u2581nel", + "1317": "\u2581anche", + "1318": "sso", + "1319": "\u2581perch\u00e9", + "1320": "\u2581pi\u00f9", + "1321": "nta", + "1322": "\u2581come", + "1323": "cu", + "1324": "\u2581quindi", + "1325": "ggi", + "1326": "nza", + "1327": "sto", + "1328": "\u2581ho", + "1329": "\u00f2", + "1330": "\u2581della", + "1331": "gra", + "1332": "\u2581fare", + "1333": "spe", + "1334": "cco", + "1335": "nde", + "1336": "mento", + "1337": "fe", + "1338": "gio", + "1339": "pu", + "1340": "\u2581questa", + "1341": "\u2581tra", + "1342": "zza", + "1343": "sci", + "1344": "\u2581ba", + "1345": "\u2581dei", + "1346": "\u2581poi", + "1347": "sco", + "1348": "stra", + "1349": "\u2581quel", + "1350": "qui", + "1351": "\u2581delle", + "1352": "\u2581cosa", + "1353": "\u2581molto", + "1354": "sse", + "1355": "zioni", + "1356": "\u2581vol", + "1357": "\u2581inter", + "1358": "sce", + "1359": "\u2581fatto", + "1360": "\u2581com", + "1361": "\u2581quello", + "1362": "\u2581essere", + "1363": "\u2581due", + "1364": "\u2581abbiamo", + "1365": "\u2581comp", + "1366": "\u2581tutti", + "1367": "\u00ec", + "1368": "\u2581prima", + "1369": "\u2581parte", + "1370": "\u2581cos\u00ec", + "1371": "\u2581sempre", + "1372": "\u2581tutto", + "1373": "\u2581video", + "1374": "\u2581maglia", + "1375": "\u2581imp", + "1376": "\u2581cui", + "1377": "\u2581dove", + "1378": "\u2581col", + "1379": "\u2581Quindi", + "1380": "sione", + "1381": "rebbe", + "1382": "scri", + "1383": "", + "1384": "\u0117", + "1385": "ai", + "1386": "\u0173", + "1387": "\u2581ir", + "1388": "as", + "1389": "\u012f", + "1390": "\u2581kad", + "1391": "\u0117s", + "1392": "\u2581tai", + "1393": "\u016b", + "1394": "t\u0173", + "1395": "\u2581yra", + "1396": "i\u0173", + "1397": "uo", + "1398": "\u2581ko", + "1399": "\u2581i\u0161", + "1400": "tin", + "1401": "\u2581vis", + "1402": "\u010dia", + "1403": "\u2581kuri", + "1404": "d\u0117", + "1405": "ly", + "1406": "gal", + "1407": "\u2581\u0161i", + "1408": "iau", + "1409": "jo", + "1410": "tar", + "1411": "yb", + "1412": "\u2581Ir", + "1413": "\u2581tik", + "1414": "ijos", + "1415": "sak", + "1416": "\u2581turi", + "1417": "oje", + "1418": "\u2581Tai", + "1419": "j\u0173", + "1420": "\u2581apie", + "1421": "\u2581nu", + "1422": "\u2581mes", + "1423": "\u2581u\u017e", + "1424": "i\u0161k", + "1425": "\u2581gali", + "1426": "\u2581d\u0117l", + "1427": "\u2581labai", + "1428": "imas", + "1429": "klaus", + "1430": "laik", + "1431": "\u2581Europos", + "1432": "\u2581a\u0161", + "1433": "veik", + "1434": "\u2581b\u016bt\u0173", + "1435": "darb", + "1436": "\u2581kaip", + "1437": "\u2581teis", + "1438": "\u2581daug", + "1439": "\u2581tikrai", + "1440": "\u2581pra", + "1441": "reik", + "1442": "\u2581buvo", + "1443": "tur\u0117", + "1444": "\u2581valstyb", + "1445": "\u2581reikia", + "1446": "\u2581b\u016bti", + "1447": "\u2581A\u0161", + "1448": "\u2581m\u016bs\u0173", + "1449": "\u2581j\u016bs", + "1450": "vyk", + "1451": "\u2581A\u010di\u016b", + "1452": "cija", + "1453": "\u012e", + "1454": "\u0146", + "1455": "", + "1456": "\u2581no", + "1457": "j\u0101", + "1458": "iem", + "1459": "t\u0101", + "1460": "\u0101k", + "1461": "\u2581ar", + "1462": "\u0101m", + "1463": "\u2581pie", + "1464": "ies", + "1465": "ot", + "1466": "k\u0101", + "1467": "\u013c", + "1468": "tr", + "1469": "\u2581t\u0101", + "1470": "\u012bt", + "1471": "n\u0101", + "1472": "\u2581uz", + "1473": "\u2581tas", + "1474": "\u0113t", + "1475": "dz", + "1476": "\u2581ar\u012b", + "1477": "\u2581vien", + "1478": "\u2581jau", + "1479": "\u2581k\u0101", + "1480": "\u2581ie", + "1481": "gad", + "1482": "\u2581kur", + "1483": "\u2581kas", + "1484": "\u2581Un", + "1485": "\u2581m\u0113s", + "1486": "iet", + "1487": "d\u0101", + "1488": "\u012bg", + "1489": "\u2581Ta", + "1490": "\u2581k\u0101d", + "1491": "kaut", + "1492": "\u0113m", + "1493": "\u2581lie", + "1494": "umu", + "1495": "ties", + "1496": "dar", + "1497": "l\u0113", + "1498": "\u2581vai", + "1499": "\u2581bija", + "1500": "\u2581mums", + "1501": "\u2581tad", + "1502": "\u2581bet", + "1503": "\u012bba", + "1504": "\u2581ga", + "1505": "\u2581Latvijas", + "1506": "ija", + "1507": "kr", + "1508": "v\u0113", + "1509": "sim", + "1510": "\u2581\u0161o", + "1511": "dien", + "1512": "gan", + "1513": "\u012bgi", + "1514": "\u2581ap", + "1515": "\u0123", + "1516": "\u2581b\u016bt", + "1517": "dom\u0101", + "1518": "\u2581tev", + "1519": "m\u0113r", + "1520": "\u2581daudz", + "1521": "\u2581aiz", + "1522": "\u2581T\u0101", + "1523": "\u2581t\u0101d", + "1524": "\u2581tur", + "1525": "\u2581mon\u0113t", + "1526": "\u2581v\u0113l", + "1527": "\u2581laik", + "1528": "\u2581cilv\u0113", + "1529": "\u2581nav", + "1530": "\u2581lab", + "1531": "\u2581\u013coti", + "1532": "aug", + "1533": "\u2581l\u012bdz", + "1534": "\u2581lai", + "1535": "\u0161ana", + "1536": "\u2581Nu", + "1537": "\u2581vi\u0146a", + "1538": "\u2581savu", + "1539": "\u2581cit", + "1540": "teik", + "1541": "\u2581darb", + "1542": "\u2581Ne", + "1543": "zin", + "1544": "\u2581pirm", + "1545": "\u2581Latvi", + "1546": "\u2581tie\u0161", + "1547": "\u2581vi\u0146i", + "1548": "\u0113ja", + "1549": "dz\u012bvo", + "1550": "\u2581vi\u0146\u0161", + "1551": "\u2581pils\u0113", + "1552": "in\u0101t", + "1553": "\u2581vi\u0146u", + "1554": "\u2581tagad", + "1555": "k\u0101rt", + "1556": "\u2581pats", + "1557": "\u2581vair\u0101k", + "1558": "reiz", + "1559": "\u2581tikai", + "1560": "sakta", + "1561": "\u2581bij", + "1562": "\u2581Vi\u0146", + "1563": "\u2581sev", + "1564": "\u2581m\u0101j", + "1565": "v\u0113rt", + "1566": "\u258120", + "1567": "\u2581ce\u013c", + "1568": "tiek", + "1569": "iski", + "1570": "\u2581dz\u012bv", + "1571": "\u2581k\u0101p\u0113c", + "1572": "\u2581Bet", + "1573": "\u2581p\u0113c", + "1574": "\u2581noz\u012bm\u0113", + "1575": "niek", + "1576": "\u012bb\u0101", + "1577": "\u2581pal\u012bdz", + "1578": "\u2581protams", + "1579": "\u2581stils", + "1580": "\u2581vajadz", + "1581": "\u2581att\u012bst\u012b", + "1582": "\u2581svar\u012bg", + "1583": "\u2581sievie", + "1584": "\u2581grib", + "1585": "\u2581da\u017e\u0101d", + "1586": "\u2581valst", + "1587": "\u2581banka", + "1588": "\u2581iesp\u0113ja", + "1589": "\u2581bez", + "1590": "pr\u0101t", + "1591": "v\u0113rt\u012bb", + "1592": "\u2581person", + "1593": "pasaules", + "1594": "\u2581varb\u016bt", + "1595": "\u2581vienk\u0101r\u0161i", + "1596": "\u2581nauda", + "1597": "mekl\u0113", + "1598": "brauc", + "1599": "\u2581nevar", + "1600": "\u0101cijas", + "1601": "sp\u0113j", + "1602": "\u0137", + "1603": "", + "1604": "\u2581een", + "1605": "\u2581het", + "1606": "\u2581dat", + "1607": "\u2581we", + "1608": "\u2581ik", + "1609": "ij", + "1610": "\u2581En", + "1611": "\u2581te", + "1612": "\u2581ook", + "1613": "\u2581niet", + "1614": "\u2581dan", + "1615": "\u2581zo", + "1616": "\u2581voor", + "1617": "\u2581met", + "1618": "\u2581aan", + "1619": "\u2581zijn", + "1620": "\u2581Ik", + "1621": "\u2581wel", + "1622": "\u2581wat", + "1623": "aar", + "1624": "\u2581ze", + "1625": "ken", + "1626": "\u2581heb", + "1627": "der", + "1628": "ui", + "1629": "den", + "1630": "\u2581daar", + "1631": "\u2581maar", + "1632": "op", + "1633": "\u2581heel", + "1634": "\u2581nog", + "1635": "\u2581Dus", + "1636": "oor", + "1637": "\u2581hebben", + "1638": "\u2581uit", + "1639": "\u2581of", + "1640": "ven", + "1641": "\u2581Maar", + "1642": "\u2581Dat", + "1643": "\u2581gaan", + "1644": "elijk", + "1645": "\u2581naar", + "1646": "\u2581moet", + "1647": "acht", + "1648": "\u2581waar", + "1649": "\u2581dus", + "1650": "\u2581ben", + "1651": "\u2581goed", + "1652": "\u2581Het", + "1653": "\u2581even", + "1654": "ond", + "1655": "eld", + "1656": "\u2581dit", + "1657": "\u2581wil", + "1658": "rij", + "1659": "\u2581echt", + "1660": "\u2581doen", + "1661": "\u2581gewoon", + "1662": "lijk", + "1663": "tijd", + "1664": "\u2581meer", + "1665": "\u2581mijn", + "1666": "\u2581We", + "1667": "\u2581gaat", + "1668": "werk", + "1669": "\u2581hoe", + "1670": "uw", + "1671": "\u2581eigenlijk", + "1672": "\u2581deze", + "1673": "zelf", + "1674": "vol", + "1675": "\u2581veel", + "1676": "atie", + "1677": "\u2581kunnen", + "1678": "\u2581door", + "1679": "llen", + "1680": "\u2581mee", + "1681": "\u2581onder", + "1682": "\u2581toe", + "1683": "\u2581zit", + "1684": "\u2581mensen", + "1685": "\u2581hij", + "1686": "\u2581denk", + "1687": "\u2581zie", + "1688": "\u2581heeft", + "1689": "\u2581kl", + "1690": "nnen", + "1691": "\u2581zien", + "1692": "komen", + "1693": "\u2581natuurlijk", + "1694": "heid", + "1695": "\u2581Dan", + "1696": "\u2581vind", + "1697": "\u2581wordt", + "1698": "\u2581iets", + "1699": "\u2581maken", + "1700": "\u2581doe", + "1701": "\u2581Wat", + "1702": "\u2581wij", + "1703": "\u2581beetje", + "1704": "\u2581worden", + "1705": "\u2581Want", + "1706": "\u2581twee", + "1707": "\u2581hem", + "1708": "\u2581had", + "1709": "\u2581jullie", + "1710": "\u2581Als", + "1711": "\u2581kijken", + "1712": "\u2581toch", + "1713": "\u2581tot", + "1714": "nieuw", + "1715": "lang", + "1716": "\u2581Nou", + "1717": "\u2581krijg", + "1718": "houd", + "1719": "\u2581hele", + "1720": "\u2581allemaal", + "1721": "\u2581want", + "1722": "\u2581zeggen", + "1723": "\u2581leuk", + "1724": "", + "1725": "nie", + "1726": "\u2581w", + "1727": "cz", + "1728": "wa", + "1729": "\u2581si\u0119", + "1730": "\u2581jest", + "1731": "my", + "1732": "\u0142a", + "1733": "cie", + "1734": "czy", + "1735": "\u2581nie", + "1736": "wie", + "1737": "\u2581wy", + "1738": "nia", + "1739": "wo", + "1740": "rze", + "1741": "\u0142o", + "1742": "\u2581\u017ce", + "1743": "dzi", + "1744": "ej", + "1745": "\u00f3w", + "1746": "dzie", + "1747": "\u2581prze", + "1748": "\u015bci", + "1749": "by", + "1750": "za", + "1751": "dy", + "1752": "ry", + "1753": "\u0144", + "1754": "j\u0105", + "1755": "we", + "1756": "cze", + "1757": "owa", + "1758": "ego", + "1759": "\u017ce", + "1760": "cy", + "1761": "rzy", + "1762": "mie", + "1763": "\u2581przy", + "1764": "\u0142y", + "1765": "rz", + "1766": "szy", + "1767": "sze", + "1768": "\u015b\u0107", + "1769": "wia", + "1770": "zy", + "1771": "\u017cy", + "1772": "\u2581tutaj", + "1773": "j\u0119", + "1774": "pie", + "1775": "nych", + "1776": "\u2581tym", + "1777": "\u2581mo\u017ce", + "1778": "cji", + "1779": "\u2581pod", + "1780": "\u2581ale", + "1781": "\u2581tego", + "1782": "owy", + "1783": "uje", + "1784": "\u2581bo", + "1785": "\u2581by\u0142", + "1786": "n\u0105", + "1787": "bie", + "1788": "sy", + "1789": "\u2581te\u017c", + "1790": "\u2581bardzo", + "1791": "\u2581s\u0105", + "1792": "\u2581b\u0119dzie", + "1793": "\u2581Po", + "1794": "ski", + "1795": "\u2581kt\u00f3re", + "1796": "\u017a", + "1797": "\u2581ju\u017c", + "1798": "\u2581dla", + "1799": "\u0142em", + "1800": "nego", + "1801": "\u2581Nie", + "1802": "\u2581No", + "1803": "\u2581praw", + "1804": "cja", + "1805": "\u2581ten", + "1806": "\u2581takie", + "1807": "owa\u0107", + "1808": "\u2581kt\u00f3ry", + "1809": "\u2581w\u0142a\u015bnie", + "1810": "\u2581jeszcze", + "1811": "\u2581tam", + "1812": "\u2581\u017ceby", + "1813": "\u2581by\u0107", + "1814": "\u2581wi\u0119c", + "1815": "\u2581czyli", + "1816": "\u2581sobie", + "1817": "\u2581sam", + "1818": "\u2581tylko", + "1819": "\u2581tej", + "1820": "\u2581spraw", + "1821": "\u2581Na", + "1822": "\u2581m\u00f3wi", + "1823": "\u2581osob", + "1824": "\u2581czas", + "1825": "\u2581prac", + "1826": "\u2581Czy", + "1827": "\u2581prostu", + "1828": "\u2581teraz", + "1829": "st\u0119p", + "1830": "\u2581Was", + "1831": "\u2581my\u015bl", + "1832": "\u2581powiedz", + "1833": "\u2581zrobi", + "1834": "li\u015bmy", + "1835": "\u2581jakie\u015b", + "1836": "aj\u0105c", + "1837": "\u2581widz", + "1838": "\u2581kart", + "1839": "\u2581musi", + "1840": "\u2581pyta", + "1841": "", + "1842": "pt", + "1843": "PT", + "1844": "<", + "1845": ">", + "1846": "-", + "1847": "\u2581\u00e9", + "1848": "\u2581n\u00e3o", + "1849": "\u2581eu", + "1850": "\u2581um", + "1851": "\u2581voc\u00ea", + "1852": "\u2581para", + "1853": "\u00e3o", + "1854": "\u2581aqui", + "1855": "\u2581uma", + "1856": "\u00e7\u00e3o", + "1857": "\u2581ca", + "1858": "\u2581pe", + "1859": "\u2581tem", + "1860": "\u2581em", + "1861": "\u2581gente", + "1862": "\u2581O", + "1863": "\u2581ele", + "1864": "pre", + "1865": "ria", + "1866": "\u2581fo", + "1867": "mos", + "1868": "nho", + "1869": "\u2581Ent\u00e3o", + "1870": "bo", + "1871": "io", + "1872": "nha", + "1873": "\u2581isso", + "1874": "\u2581por", + "1875": "\u2581muito", + "1876": "nto", + "1877": "\u2581Eu", + "1878": "\u2581est\u00e1", + "1879": "idade", + "1880": "\u2581a\u00ed", + "1881": "be", + "1882": "\u2581esse", + "1883": "\u2581pode", + "1884": "\u2581como", + "1885": "ente", + "1886": "\u2581tamb\u00e9m", + "1887": "\u2581essa", + "1888": "lha", + "1889": "\u2581j\u00e1", + "1890": "\u2581mas", + "1891": "\u2581pessoa", + "1892": "qua", + "1893": "\u2581n\u00e9", + "1894": "\u2581fazer", + "1895": "\u2581t\u00e1", + "1896": "lho", + "1897": "\u2581l\u00e1", + "1898": "fica", + "1899": "\u2581vou", + "1900": "\u2581porque", + "1901": "\u2581Se", + "1902": "\u2581fala", + "1903": "\u2581coisa", + "1904": "\u2581N\u00e3o", + "1905": "...", + "1906": "\u2581s\u00f3", + "1907": "\u2581n\u00f3s", + "1908": "\u00e7o", + "1909": "\u2581Por", + "1910": "\u2581assim", + "1911": "\u2581Co", + "1912": "iza", + "1913": "\u2581bem", + "1914": "\u2581todo", + "1915": "eira", + "1916": "\u2581sua", + "1917": "\u00eancia", + "1918": "\u00e7\u00f5es", + "1919": "\u2581Voc\u00ea", + "1920": "\u2581tudo", + "1921": "\u2581agora", + "1922": "eiro", + "1923": "\u00e1rio", + "1924": "\u2581at\u00e9", + "1925": "\u2581mesmo", + "1926": "\u2581vamos", + "1927": "\u2581quando", + "1928": "ciona", + "1929": "", + "1930": "\u2581\u00een", + "1931": "\u021bi", + "1932": "\u2581s\u0103", + "1933": "\u2581\u0219i", + "1934": "\u2581cu", + "1935": "\u2581c\u0103", + "1936": "\u2581care", + "1937": "\u2581mai", + "1938": "r\u0103", + "1939": "sc", + "1940": "c\u0103", + "1941": "\u2581am", + "1942": "are", + "1943": "\u2581din", + "1944": "\u2581fi", + "1945": "\u2581este", + "1946": "t\u0103", + "1947": "\u2581pentru", + "1948": "rea", + "1949": "\u0219ti", + "1950": "\u0219", + "1951": "ele", + "1952": "du", + "1953": "\u2581M", + "1954": "\u2581fac", + "1955": "\u00e2n", + "1956": "\u2581sunt", + "1957": "\u2581I", + "1958": "\u2581acest", + "1959": "ului", + "1960": "lor", + "1961": "\u2581mult", + "1962": "\u0219i", + "1963": "\u2581mo", + "1964": "\u2581fost", + "1965": "per", + "1966": "\u2581foarte", + "1967": "\u2581\u0218i", + "1968": "\u2581m\u0103", + "1969": "s\u0103", + "1970": "cur", + "1971": "tor", + "1972": "\u2581cum", + "1973": "inte", + "1974": "at\u0103", + "1975": "\u0219te", + "1976": "\u2581dac\u0103", + "1977": "\u00e2nd", + "1978": "\u2581subliniere", + "1979": "\u2581dar", + "1980": "\u2581sau", + "1981": "tat", + "1982": "ori", + "1983": "\u2581v\u0103", + "1984": "\u2581asta", + "1985": "n\u0103", + "1986": "\u2581prim", + "1987": "\u2581a\u0219a", + "1988": "eaz\u0103", + "1989": "\u2581\u00eentr", + "1990": "\u2581spun", + "1991": "\u2581lui", + "1992": "\u2581sub", + "1993": "itate", + "1994": "\u2581aici", + "1995": "\u2581bine", + "1996": "\u2581c\u00e2nd", + "1997": "\u2581prin", + "1998": "\u2581alt", + "1999": "\u2581nici", + "2000": "stru", + "2001": "\u2581c\u00e2t", + "2002": "\u2581vede", + "2003": "fer", + "2004": "\u2581dup\u0103", + "2005": "\u2581ju", + "2006": "\u2581despre", + "2007": "\u2581timp", + "2008": "\u2581acum", + "2009": "\u2581poate", + "2010": "\u2581spus", + "2011": "\u2581lucru", + "2012": "\u2581f\u0103cut", + "2013": "p\u0103r", + "2014": "\u2581urm\u0103", + "2015": "\u2581atunci", + "2016": "\u2581fr", + "2017": "\u2581chiar", + "2018": "\u2581\u00eencep", + "2019": "\u0218", + "2020": "\u00ce", + "2021": "", + "2022": "\u2581\u043d\u0435", + "2023": "\u044b", + "2024": "\u0442\u044c", + "2025": "\u2581\u044d\u0442\u043e", + "2026": "\u0436\u0435", + "2027": "\u2581\u0447\u0442\u043e", + "2028": "\u2581\u0442\u043e", + "2029": "\u043b\u044c", + "2030": "\u2581\u043e", + "2031": "\u2581\u0443", + "2032": "\u0430\u0442\u044c", + "2033": "\u2581\u0442\u0430\u043a", + "2034": "\u2581\u043a\u0430\u043a", + "2035": "\u043a\u0438", + "2036": "\u0441\u044f", + "2037": "\u0435\u043c", + "2038": "\u2581\u0432\u044b", + "2039": "\u2581\u0431\u044b", + "2040": "\u2581\u0432\u0441\u0435", + "2041": "\u0440\u0443", + "2042": "\u0431\u043e", + "2043": "\u2581\u0418", + "2044": "\u2581\u0432\u043e\u0442", + "2045": "\u043a\u0443", + "2046": "\u2581\u0412", + "2047": "\u0447\u0438", + "2048": "\u043e\u0439", + "2049": "\u043c\u0443", + "2050": "\u2581\u0441\u043e", + "2051": "\u0442\u044b", + "2052": "\u043d\u0443", + "2053": "\u0441\u044c", + "2054": "\u2581\u0435\u0441\u0442\u044c", + "2055": "\u0442\u0443", + "2056": "\u043d\u044b", + "2057": "\u0448\u0435", + "2058": "\u2581\u043c\u044b", + "2059": "\u0434\u0443", + "2060": "\u0438\u0442\u044c", + "2061": "\u044d", + "2062": "\u0434\u0435\u043b", + "2063": "\u043b\u044f", + "2064": "\u043c\u0435\u043d", + "2065": "\u0436\u0438", + "2066": "\u0441\u0442\u043e", + "2067": "\u0445\u043e", + "2068": "\u0441\u0442\u0432", + "2069": "\u0432\u044b", + "2070": "\u0432\u0435\u0440", + "2071": "\u0437\u043d\u0430", + "2072": "\u0441\u0442\u0438", + "2073": "\u0448\u0438", + "2074": "\u0435\u0442\u0441\u044f", + "2075": "\u0443\u044e", + "2076": "\u0440\u044b", + "2077": "\u0445\u043e\u0434", + "2078": "\u0430\u0435\u0442", + "2079": "\u043d\u044b\u0439", + "2080": "\u043f\u0435\u0440", + "2081": "\u2581\u041f\u043e", + "2082": "\u043b\u0443\u0447", + "2083": "\u043d\u044b\u0435", + "2084": "\u0442\u043e\u0440", + "2085": "\u2581\u0442\u0430\u043c", + "2086": "\u2581\u0431\u0443\u0434\u0435\u0442", + "2087": "\u2581\u0441\u0430\u043c", + "2088": "\u2581\u0434\u043b\u044f", + "2089": "\u2581\u043e\u0447\u0435\u043d\u044c", + "2090": "\u0435\u043d\u0438\u044f", + "2091": "\u0430\u044e\u0442", + "2092": "\u2581\u041d\u0443", + "2093": "\u2581\u042d\u0442\u043e", + "2094": "\u2581\u0414\u0430", + "2095": "\u2581\u043c\u0435\u043d\u044f", + "2096": "\u2581\u0435\u0441\u043b\u0438", + "2097": "\u2581\u0422\u043e", + "2098": "\u0435\u043d\u044c", + "2099": "\u043d\u044b\u0445", + "2100": "\u2581\u0435\u0449\u0435", + "2101": "\u2581\u0432\u0430\u043c", + "2102": "\u2581\u043f\u0435\u0440\u0435", + "2103": "\u2581\u0437\u0434\u0435\u0441\u044c", + "2104": "\u2581\u043f\u0440\u043e\u0441\u0442\u043e", + "2105": "\u2581\u0412\u043e\u0442", + "2106": "\u2581\u041d\u043e", + "2107": "\u2581\u0447\u0442\u043e\u0431\u044b", + "2108": "\u0441\u043c\u043e\u0442\u0440", + "2109": "\u2581\u0441\u0435\u0439\u0447\u0430\u0441", + "2110": "\u2581\u043c\u043e\u0436\u0435\u0442", + "2111": "\u2581\u044d\u0442\u0438", + "2112": "\u0430\u043b\u044c\u043d\u043e", + "2113": "\u0434\u043e\u043b", + "2114": "\u2581\u041d\u0430", + "2115": "\u2581\u0422\u0430\u043a", + "2116": "\u2581\u043a\u043e\u0433\u0434\u0430", + "2117": "\u0451", + "2118": "\u0430\u0439\u0442\u0435", + "2119": "\u043f\u0438\u0441", + "2120": "\u0442\u0435\u043b\u044c\u043d\u043e", + "2121": "\u0435\u0448\u044c", + "2122": "\u2581\u0434\u0440\u0443\u0433", + "2123": "\u042d", + "2124": "", + "2125": "ov", + "2126": "\u013e", + "2127": "sk", + "2128": "\u2581aj", + "2129": "ob", + "2130": "t\u00e1", + "2131": "a\u0165", + "2132": "\u2581bol", + "2133": "\u2581s\u00fa", + "2134": "\u2581ako", + "2135": "\u017ei", + "2136": "\u2581sme", + "2137": "\u2581V", + "2138": "ali", + "2139": "\u2581alebo", + "2140": "\u2581\u010do", + "2141": "i\u0165", + "2142": "\u2581m\u00e1", + "2143": "\u00fdch", + "2144": "\u2581z\u00e1", + "2145": "\u2581tie", + "2146": "\u2581nejak", + "2147": "\u2581v\u00fd", + "2148": "\u010das", + "2149": "nov", + "2150": "rov", + "2151": "\u2581ktor\u00e9", + "2152": "aj\u00fa", + "2153": "ova\u0165", + "2154": "\u2581ke\u010f", + "2155": "\u2581str", + "2156": "\u2581\u0161kol", + "2157": "n\u00fa", + "2158": "\u2581ktor", + "2159": "\u2581vlastne", + "2160": "\u2581pr\u00ed", + "2161": "nej", + "2162": "\u2581ve\u013emi", + "2163": "\u0161ie", + "2164": "rob", + "2165": "\u2581tr", + "2166": "n\u00fdch", + "2167": "enie", + "2168": "\u2581spo", + "2169": "\u2581rok", + "2170": "osti", + "2171": "\u2581t\u00fdm", + "2172": "\u2581m\u00f4\u017ee", + "2173": "\u2581ktor\u00fd", + "2174": "os\u0165", + "2175": "\u2581projekt", + "2176": "\u2581kon", + "2177": "\u2581vzdel\u00e1va", + "2178": "\u2581Tak\u017ee", + "2179": "\u2581e\u0161te", + "2180": "\u2581t\u00fdch", + "2181": "\u2581mal", + "2182": "\u2581cel", + "2183": "\u2581potom", + "2184": "\u2581svoj", + "2185": "enia", + "2186": "\u00e1lne", + "2187": "ie\u0165", + "2188": "\u2581teda", + "2189": "jedn", + "2190": "sled", + "2191": "\u2581mo\u017eno", + "2192": "\u2581v\u00e1m", + "2193": "chod", + "2194": "uj\u00fa", + "2195": "tvor", + "2196": "\u2581druh", + "2197": "\u2581Slovensk", + "2198": "h\u013ead", + "2199": "stup", + "2200": "\u2581\u013eud\u00ed", + "2201": "\u2581napr\u00edklad", + "2202": "\u2581ve\u013ek", + "2203": "\u2581nie\u010do", + "2204": "\u010e", + "2205": "", + "2206": "sl", + "2207": "lj", + "2208": "kot", + "2209": "ih", + "2210": "\u2581svet", + "2211": "\u2581ta", + "2212": "\u2581tako", + "2213": "\u2581kar", + "2214": "\u2581nek", + "2215": "jih", + "2216": "udi", + "2217": "\u2581vse", + "2218": "\u2581drug", + "2219": "\u2581ima", + "2220": "kaj", + "2221": "\u2581smo", + "2222": "del", + "2223": "\u2581sem", + "2224": "\u2581lahko", + "2225": "\u2581samo", + "2226": "\u2581ve\u010d", + "2227": "nih", + "2228": "\u2581dr\u017eav", + "2229": "\u2581zelo", + "2230": "\u2581zdaj", + "2231": "\u2581razum", + "2232": "\u2581\u0161e", + "2233": "\u2581tega", + "2234": "\u2581ljudi", + "2235": "\u2581pred", + "2236": "\u2581sta", + "2237": "nost", + "2238": "\u2581ampak", + "2239": "\u2581novinar", + "2240": "\u2581naprej", + "2241": "\u2581mora", + "2242": "\u2581Vs", + "2243": "krat", + "2244": "\u2581Ampak", + "2245": "\u2581vedno", + "2246": "\u2581velik", + "2247": "\u2581kako", + "2248": "\u2581najbolj", + "2249": "ziroma", + "2250": "\u2581vsi", + "2251": "\u2581nekaj", + "2252": "\u2581kater", + "2253": "\u2581res", + "2254": "\u2581tukaj", + "2255": "\u2581dogaja", + "2256": "\u2581svoje", + "2257": "\u2581let", + "2258": "daj", + "2259": "\u2581pripri\u010da", + "2260": "\u2581\u010dlovek", + "2261": "\u2581ho\u010de", + "2262": "\u2581vojn", + "2263": "\u2581Pre", + "2264": "\u2581dobr", + "2265": "ljan", + "2266": "\u2581moj", + "2267": "\u2581dejansko", + "2268": "\u2581ljudje", + "2269": "\u2581mediji", + "2270": "\u2581prot", + "2271": "\u2581narav", + "2272": "bilo", + "2273": "\u2581Afrik", + "2274": "\u2581vzhod", + "2275": "\u2581\u010dlove\u0161tva", + "2276": "\u2581kriz", + "2277": "\u2581pogled", + "2278": "\u2581medije", + "2279": "poved", + "2280": "\u2581za\u010del", + "2281": "\u2581ve\u010din", + "2282": "imajo", + "2283": "\u2581Ljudje", + "2284": "\u2581dru\u017eb", + "2285": "\u2581govorim", + "2286": "\u2581informacij", + "2287": "\u2581kultur", + "2288": "\u2581bli\u017enj", + "2289": "\u2581podobno", + "2290": "\u2581njihov", + "2291": "\u2581konc", + "2292": "\u2581pisa", + "2293": "\u2581zaveda", + "2294": "\u2581vsak", + "2295": "\u017eivel", + "2296": "\u2581funkcionira", + "2297": "\u2581internet", + "2298": "\u2581islamsk", + "2299": "\u2581film", + "2300": "\u2581otroci", + "2301": "\u2581prihaja", + "2302": "\u2581politi\u010dn", + "2303": "\u2581popoln", + "2304": "\u2581Velik", + "2305": "\u2581druga\u010den", + "2306": "\u2581recimo", + "2307": "\u2581resnic", + "2308": "solutno", + "2309": "\u2581Bli\u017en", + "2310": "\u2581Evropsk", + "2311": "\u2581muslimani", + "2312": "\u2581nadzoruje", + "2313": "\u2581socialne", + "2314": "\u2581zgodovin", + "2315": "\u2581\u010dlove\u0161k", + "2316": "\u2581\u017eivljenj", + "2317": "\u2581prijatelj", + "2318": "\u2581vendar", + "2319": "\u2581ljudem", + "2320": "\u2581\u0161tevil", + "2321": "\u2581Sirij", + "2322": "", + "2323": "\u2581att", + "2324": "\u2581och", + "2325": "\u2581\u00e4r", + "2326": "\u2581f\u00f6r", + "2327": "\u2581h\u00e4r", + "2328": "\u2581jag", + "2329": "\u00e4n", + "2330": "\u2581till", + "2331": "\u2581h", + "2332": "\u2581inte", + "2333": "\u2581Och", + "2334": "\u2581av", + "2335": "\u2581om", + "2336": "\u2581ska", + "2337": "\u2581ut", + "2338": "\u2581ett", + "2339": "all", + "2340": "\u2581ocks\u00e5", + "2341": "\u2581Jag", + "2342": "era", + "2343": "pp", + "2344": "\u2581upp", + "2345": "\u2581d\u00e5", + "2346": "\u2581d\u00e4r", + "2347": "\u2581lite", + "2348": "\u00e5r", + "2349": "sam", + "2350": "isk", + "2351": "het", + "2352": "f\u00f6r", + "2353": "\u2581kommer", + "2354": "\u2581vill", + "2355": "\u00f6r", + "2356": "erna", + "2357": "ande", + "2358": "s\u00e4tt", + "2359": "\u2581finns", + "2360": "\u2581n\u00e4r", + "2361": "\u2581vara", + "2362": "ade", + "2363": "s\u00f6k", + "2364": "\u2581hur", + "2365": "\u2581vad", + "2366": "bil", + "2367": "\u2581g\u00f6ra", + "2368": "\u2581f\u00e5r", + "2369": "verk", + "2370": "\u2581mycket", + "2371": "\u2581v\u00e4l", + "2372": "kom", + "2373": "\u2581g\u00f6r", + "2374": "\u2581ni", + "2375": "\u2581bara", + "2376": "\u2581fr\u00e5n", + "2377": "st\u00e4ll", + "2378": "\u2581v\u00e4ldigt", + "2379": "\u2581min", + "2380": "\u2581olika", + "2381": "\u2581alla", + "2382": "lev", + "2383": "\u2581fram", + "2384": "\u2581kanske", + "2385": "\u2581v\u00e5r", + "2386": "\u2581tid", + "2387": "skap", + "2388": "h\u00e5ll", + "2389": "\u2581F\u00f6r", + "2390": "\u2581g\u00e5r", + "2391": "\u2581blir", + "2392": "\u2581under", + "2393": "\u2581l\u00e4r", + "2394": "\u2581ny", + "2395": "\u2581D\u00e5", + "2396": "\u2581b\u00f6rja", + "2397": "r\u00e4tt", + "2398": "\u2581\u00f6ver", + "2399": "\u2581oss", + "2400": "\u2581exempel", + "2401": "\u2581skulle", + "2402": "g\u00e5ng", + "2403": "\u2581kunna", + "2404": "\u2581andra", + "2405": "\u2581n\u00e5gon", + "2406": "\u2581jobba", + "2407": "land", + "2408": "\u2581n\u00e5got", + "2409": "\u2581beh\u00f6ver", + "2410": "\u2581s\u00e4ga", + "2411": "klar", + "2412": "\u2581m\u00e5nga", + "2413": "\u2581skriv", + "2414": "\u2581anv\u00e4nda", + "2415": "\u2581sj\u00e4lv", + "2416": "\u2581samma", + "2417": "l\u00e4gg", + "2418": "\u2581m\u00e5ste", + "2419": "\u2581efter", + "2420": "text", + "2421": "\u2581prata", + "2422": "\u2581klicka", + "2423": "\u2581hitta", + "2424": "\u2581tror", + "2425": "\u2581n\u00e5gonting", + "2426": "fr\u00e5ga", + "2427": "\u2581titta", + "2428": "\u2581tycker", + "2429": "\u2581ganska", + "2430": "\u2581j\u00e4tte", + "2431": "\u2581Vad", + "2432": "\u2581genom", + "2433": "\u2581\u00e4ven", + "2434": "\u2581t\u00e4nker", + "2435": "arbete", + "2436": "\u2581faktiskt", + "2437": "person", + "2438": "\u2581komma", + "2439": "bygg", + "2440": "", + "2441": "\u0456", + "2442": "\u043d\u0456", + "2443": "\u0454", + "2444": "\u0457", + "2445": "\u2581\u0437", + "2446": "\u2581\u0449\u043e", + "2447": "\u0432\u0456", + "2448": "\u0440\u0456", + "2449": "\u0446\u0456", + "2450": "\u2581\u0456", + "2451": "\u043b\u0456", + "2452": "\u043c\u0456", + "2453": "\u0431\u0443", + "2454": "\u0434\u0456", + "2455": "\u043e\u0433\u043e", + "2456": "\u2581\u0432\u0438", + "2457": "\u2581\u0446\u0435", + "2458": "\u0435\u0440", + "2459": "\u0441\u0456", + "2460": "\u2581\u044f\u043a", + "2461": "\u043e\u043c\u0443", + "2462": "\u2581\u0432\u0456\u0434", + "2463": "\u0434\u043e", + "2464": "\u0442\u0456", + "2465": "\u0456\u043d", + "2466": "\u0431\u0430", + "2467": "\u2581\u0406", + "2468": "\u043f\u0456", + "2469": "\u043f\u0435", + "2470": "\u0435\u043d\u043d\u044f", + "2471": "\u043d\u044c", + "2472": "\u043a\u0456", + "2473": "\u0437\u0430", + "2474": "\u043d\u0438\u0445", + "2475": "\u2581\u043f\u0456\u0434", + "2476": "\u043d\u0438\u0439", + "2477": "\u0440\u0430\u0437", + "2478": "\u043d\u044f", + "2479": "\u043b\u044e", + "2480": "\u043f\u0438", + "2481": "\u0441\u043e", + "2482": "\u0431\u0456", + "2483": "\u0442\u044c\u0441\u044f", + "2484": "\u2581\u0440\u043e\u0437", + "2485": "\u0441\u0442\u0456", + "2486": "\u2581\u044f\u043a\u0456", + "2487": "\u0443\u0432\u0430\u0442\u0438", + "2488": "\u2581\u043d\u0430\u0441", + "2489": "\u0430\u043d\u043d\u044f", + "2490": "\u043d\u043e\u0433\u043e", + "2491": "\u2581\u0432\u043e\u043d\u0438", + "2492": "\u0430\u044e\u0442\u044c", + "2493": "\u2581\u0434\u0443\u0436\u0435", + "2494": "\u2581\u0417\u0430", + "2495": "\u043b\u0443", + "2496": "\u043a\u0456\u0432", + "2497": "\u2581\u041c\u0438", + "2498": "\u2581\u0442\u043e\u043c\u0443", + "2499": "\u2581\u0431\u0443\u0434\u0435", + "2500": "\u2581\u0432\u0436\u0435", + "2501": "\u2581\u0426\u0435", + "2502": "\u0446\u044c", + "2503": "\u2581\u0447\u0430\u0441", + "2504": "\u0456\u0441\u0442\u044c", + "2505": "\u0446\u044f", + "2506": "\u2581\u0431\u0443\u043b\u043e", + "2507": "\u2581\u0430\u043b\u0435", + "2508": "\u0431\u0456\u043b\u044c\u0448", + "2509": "\u043f\u0440\u0430\u0446", + "2510": "\u0442\u0440\u0438\u043c", + "2511": "\u0430\u0454\u043c\u043e", + "2512": "\u0430\u0454\u0442\u044c\u0441\u044f", + "2513": "\u2581\u0442\u0443\u0442", + "2514": "\u0443\u044e\u0442\u044c", + "2515": "\u0430\u0446\u0456\u0457", + "2516": "\u2581\u044f\u043a\u0438\u0439", + "2517": "\u043c\u0435\u043d\u0442", + "2518": "\u2581\u043b\u044e\u0434\u0438", + "2519": "\u0443\u0432\u0430\u043d\u043d\u044f", + "2520": "\u2581\u044f\u043a\u0449\u043e", + "2521": "\u0444\u043e\u0440", + "2522": "\u2581\u0431\u0435\u0437", + "2523": "\u0443\u043a\u0440\u0430\u0457\u043d", + "2524": "\u0443\u0432\u0430\u043b\u0438", + "2525": "\u0440\u043e\u0437\u0443\u043c\u0456", + "2526": "\u0404", + "2527": "\u0407", + "2528": "\u0406", + "2529": "", + "2530": "\u0641", + "2531": "\u062d", + "2532": "\u0650", + "2533": "\u064f", + "2534": "\u062c", + "2535": "\u2581\u0627\u0644", + "2536": "\u0635", + "2537": "\u2581\u0648", + "2538": "\u0652", + "2539": "\u0637", + "2540": "\u0634", + "2541": "\u064e", + "2542": "\u062e", + "2543": "\u0632", + "2544": "\u0627\u0646", + "2545": "\u2581\u0623", + "2546": "\u0636", + "2547": "\u0627\u0644", + "2548": "\u2581\u0628", + "2549": "\u2581\u0627\u0644\u0645", + "2550": "\u2581\u0641\u064a", + "2551": "\u2581\u0645\u0646", + "2552": "\u0649", + "2553": "\u0627\u062a", + "2554": "\u064e\u0651", + "2555": "\u064a\u0646", + "2556": "\u0647\u0627", + "2557": "\u064a\u0629", + "2558": "\u062b", + "2559": "\u063a", + "2560": "\u2581\u0645", + "2561": "\u0627\u0631", + "2562": "\u2581\u0648\u064e", + "2563": "\u0644\u0627", + "2564": "\u0630", + "2565": "\u0648\u0644", + "2566": "\u0626", + "2567": "\u064e\u0627", + "2568": "\u0627\u0645", + "2569": "\u0648\u0646", + "2570": "\u0648\u0627", + "2571": "\u2581\u0639\u0644\u0649", + "2572": "\u2581\u0648\u0627\u0644", + "2573": "\u2581\u0627\u0644\u0652", + "2574": "\u0646\u0627", + "2575": "\u2581\u0627\u0644\u0623", + "2576": "\u0645\u0627", + "2577": "\u064a\u0631", + "2578": "\u0644\u0650", + "2579": "\u0638", + "2580": "\u0627\u0621", + "2581": "\u0644\u064e", + "2582": "\u0648\u0631", + "2583": "\u2581\u0627\u0644\u062a", + "2584": "\u2581\u0623\u0646", + "2585": "\u0627\u0628", + "2586": "\u0645\u064e", + "2587": "\u0643\u064e", + "2588": "\u062a\u064e", + "2589": "\u0651", + "2590": "\u0647\u0645", + "2591": "\u0639\u064e", + "2592": "\u0627\u062f", + "2593": "\u2581\u0625", + "2594": "\u0646\u0652", + "2595": "\u2581\u0623\u064e", + "2596": "\u0627\u0633", + "2597": "\u2581\u0627\u0644\u0633", + "2598": "\u064f\u0648", + "2599": "\u0628\u0650", + "2600": "\u2581\u0627\u0644\u0639", + "2601": "\u0622", + "2602": "\u064a\u0647", + "2603": "\u0650\u0651", + "2604": "\u2581\u0644\u0644", + "2605": "\u064d", + "2606": "\u2581\u0627\u0644\u062d", + "2607": "\u0646\u064e", + "2608": "\u064a\u0627", + "2609": "\u2581\u0641\u0649", + "2610": "\u2581\u0628\u0627\u0644", + "2611": "\u0648\u0645", + "2612": "\u2581\u0639\u0646", + "2613": "\u2581\u0627\u0644\u0646", + "2614": "\u0645\u064e\u0627", + "2615": "\u064a\u062f", + "2616": "\u2581\u0645\u0627", + "2617": "\u0627\u0639", + "2618": "\u064e\u064a\u0652", + "2619": "\u0627\u064b", + "2620": "\u064b\u0627", + "2621": "\u2581\u0645\u0639", + "2622": "\u0633\u062a", + "2623": "\u0645\u064f", + "2624": "\u2581\u0627\u0644\u0634", + "2625": "\u0634\u0631", + "2626": "\u2581\u0643\u0627\u0646", + "2627": "\u0625", + "2628": "\u0625\u0650", + "2629": "\u0627\u0641", + "2630": "\u0627\u062d", + "2631": "\u064c", + "2632": "\u062d\u064e", + "2633": "\u0630\u0627", + "2634": "\u2581\u0641\u0650\u064a", + "2635": "\u0631\u0628", + "2636": "\u2581\u064a\u0639\u0646\u064a", + "2637": "\u2581\u064a\u064e", + "2638": "\u064e\u0629\u0650", + "2639": "\u2581\u0627\u0644\u0642", + "2640": "\u0642\u064e", + "2641": "\u0646\u064e\u0627", + "2642": "\u064f\u0651", + "2643": "\u2581\u0627\u0644\u062c", + "2644": "\u0633\u064e", + "2645": "\u2581\u0627\u0644\u0628", + "2646": "\u2581\u0627\u0644\u062f", + "2647": "\u0645\u0652", + "2648": "\u0645\u0631", + "2649": "\u064f\u0648\u0646\u064e", + "2650": "\u0624", + "2651": "\u2581\u0627\u0644\u0627", + "2652": "\u0645\u0650", + "2653": "\u064e\u0648\u0652", + "2654": "\u0628\u0631", + "2655": "\u2581\u0628\u064a", + "2656": "\u2581\u0627\u0644\u0631", + "2657": "\u0628\u064e", + "2658": "\u0647\u064e\u0627", + "2659": "\u0647\u064f", + "2660": "\u0648\u0642", + "2661": "\u0627\u062c", + "2662": "\u2581\u0641\u064e", + "2663": "\u2581\u0622\u0647", + "2664": "\u2581\u0627\u0644\u0641", + "2665": "\u0652\u062a\u064e", + "2666": "\u2581\u0643\u0644", + "2667": "\u2581\u0627\u0644\u0635", + "2668": "\u2581\u0625\u0644\u0649", + "2669": "\u2581\u0647\u0648", + "2670": "\u2581\u0645\u0650\u0646\u0652", + "2671": "\u0648\u062f", + "2672": "\u0648\u0628", + "2673": "\u2581\u0648\u0623", + "2674": "\u062e\u0644", + "2675": "\u0631\u064e", + "2676": "\u062d\u062f", + "2677": "\u064a\u0645", + "2678": "\u2581\u0627\u0644\u0625", + "2679": "\u062f\u064e", + "2680": "\u0641\u064e", + "2681": "\u0647\u064f\u0645\u0652", + "2682": "\u0646\u0650", + "2683": "\u062c\u064e", + "2684": "\u064f\u0648\u0627", + "2685": "\u0641\u0631", + "2686": "\u064e\u0639\u0652", + "2687": "\u2581\u0623\u0648", + "2688": "\u2581\u0625\u0646", + "2689": "\u0648\u0633", + "2690": "\u0644\u064e\u0627", + "2691": "\u062c\u0645", + "2692": "\u0650\u064a\u0646\u064e", + "2693": "\u064e\u0651\u0627", + "2694": "\u0648\u0641", + "2695": "\u0648\u062c", + "2696": "\u2581\u0627\u0644\u062e", + "2697": "\u0639\u0645\u0644", + "2698": "\u2581\u0644\u0645", + "2699": "\u064e\u0627\u062a\u0650", + "2700": "\u2581\u0647\u0630\u0627", + "2701": "\u2581\u0623\u064e\u0646\u0652", + "2702": "\u2581\u0645\u0634", + "2703": "\u2581\u0628\u0639\u062f", + "2704": "\u2581\u0627\u0644\u0652\u0645\u064f", + "2705": "\u2581\u0627\u0644\u0637", + "2706": "\u0650\u0647\u0650", + "2707": "\u2581\u0627\u0644\u0644\u0649", + "2708": "\u0621", + "2709": "\u2581\u0627\u0644\u0644\u064a", + "2710": "\u2581\u0639\u064e\u0644\u064e\u0649", + "2711": "\u0652\u062a\u0650", + "2712": "\u2581\u0627\u0644\u0652\u0623\u064e", + "2713": "\u0630\u064e\u0627", + "2714": "\u064e\u0631\u064e", + "2715": "\u2581\u0623\u0646\u0627", + "2716": "\u0643\u064f\u0645\u0652", + "2717": "\u2581\u0627\u0644\u0652\u0645\u064e", + "2718": "\u2581\u0625\u0650\u0646\u064e\u0651", + "2719": "\u0652\u0631\u064e", + "2720": "\u2581\u0647\u0630\u0647", + "2721": "\u064e\u0644\u064e", + "2722": "\u064e\u0631\u0652", + "2723": "\u2581\u0627\u0633\u062a", + "2724": "\u2581\u0645\u0635\u0631", + "2725": "\u0650\u064a\u064e", + "2726": "\u0652\u0631\u0650", + "2727": "\u064e\u062d\u0652", + "2728": "\u0631\u0650\u064a", + "2729": "\u064e\u062f\u0652", + "2730": "\u2581\u0645\u0650\u0646\u064e", + "2731": "\u2581\u0648\u064e\u0644\u064e", + "2732": "\u2581\u0648\u064e\u0627\u0644\u0652", + "2733": "\u2581\u0643\u0645\u0627", + "2734": "\u0628\u0642\u0649", + "2735": "\u062f\u0650\u064a", + "2736": "\u2581\u0627\u0644\u0644\u0647", + "2737": "\u2581\u0627\u0644\u064e\u0651\u0630\u0650\u064a", + "2738": "\u2581\u0627\u0644\u0630\u064a", + "2739": "\u0639\u0631\u0641", + "2740": "\u2581\u0627\u0644\u0652\u0639\u064e", + "2741": "\u064e\u0647\u064f", + "2742": "\u0634\u0639\u0631", + "2743": "\u2581\u0644\u0643\u0646", + "2744": "\u0639\u0644\u0645", + "2745": "\u064e\u0629\u064f", + "2746": "\u064b", + "2747": "\u2581\u0646\u0641\u0633", + "2748": "\u0650\u064a\u064e\u0651\u0629\u0650", + "2749": "\u064e\u062a\u0652", + "2750": "\u2581\u0648\u064e\u0623\u064e", + "2751": "\u064e\u0629\u064d", + "2752": "\u0645\u062b\u0644", + "2753": "\u2581\u063a\u064a\u0631", + "2754": "\u0627\u0626\u064a", + "2755": "\u2581\u0625\u0650\u0644\u064e\u0649", + "2756": "\u2581\u0648\u0627\u062d\u062f", + "2757": "\u2581\u0623\u064e\u0646\u064e\u0651", + "2758": "\u2581\u0647\u064e\u0630\u064e\u0627", + "2759": "\u2581\u0630\u0644\u0643", + "2760": "\u064e\u0629\u064e", + "2761": "\u2581\u062d\u062a\u0649", + "2762": "\u2581\u0647\u064e\u0644\u0652", + "2763": "\u061f", + "2764": "\u060c", + "2765": "vy", + "2766": "\u2581byl", + "2767": "\u0147", + "2768": "\u0164", + "2769": "\u00d3", + "2770": "\u00e6r", + "2771": "\u2581blev", + "2772": "ft", + "2773": "lige", + "2774": "ved", + "2775": "'", + "2776": "\u00c5", + "2777": "\u2581H", + "2778": "\u2581D", + "2779": "aus", + "2780": "\u2581N", + "2781": "\u2581Be", + "2782": "mm", + "2783": "ab", + "2784": "\u2581Er", + "2785": "ssen", + "2786": "hl", + "2787": "hn", + "2788": "ischen", + "2789": "\u2581wurde", + "2790": "rie", + "2791": "lei", + "2792": "\u2581An", + "2793": "\u2581Ein", + "2794": "etz", + "2795": "rau", + "2796": "ische", + "2797": "\u00e4h", + "2798": "\u2581mein", + "2799": "\u2581So", + "2800": "\u2581hatte", + "2801": "\u2581unter", + "2802": "\u2581Zu", + "2803": "\u2581ihn", + "2804": "\u2581Jahr", + "2805": "\u2581zwei", + "2806": "keit", + "2807": "\u2581ihm", + "2808": "\u2581Aus", + "2809": "", + "2810": "\u2581you", + "2811": "\u2581that", + "2812": "\u2581and", + "2813": "\u2581can", + "2814": "\u2581it", + "2815": "\u2581your", + "2816": "ed", + "2817": "\u2581Okay", + "2818": "\u2581just", + "2819": "ay", + "2820": "\u2581Yeah", + "2821": "\u2581with", + "2822": "th", + "2823": "\u2581Thank", + "2824": "\u2581thank", + "2825": "\u2581help", + "2826": "\u2581please", + "2827": "\u2581one", + "2828": "\u2581there", + "2829": "ic", + "2830": "\u2581much", + "2831": "\u2581what", + "2832": "\u2581my", + "2833": "hi", + "2834": "\u2581will", + "2835": "\u2581would", + "2836": "\u2581if", + "2837": "\u2581two", + "2838": "\u2581this", + "2839": "\u2581he", + "2840": "\u2581go", + "2841": "\u2581all", + "2842": "\u2581Oh", + "2843": "\u2581like", + "2844": "\u2581very", + "2845": "\u2581The", + "2846": "\u2581today", + "2847": "\u2581not", + "2848": "\u2581yeah", + "2849": "\u2581take", + "2850": "ight", + "2851": "ex", + "2852": "\u2581Ok", + "2853": "\u2581seven", + "2854": "\u2581number", + "2855": "\u2581know", + "2856": "\u2581about", + "2857": "\u2581four", + "2858": "\u2581okay", + "2859": "\u2581name", + "2860": "\u2581And", + "2861": "\u2581five", + "2862": "\u2581How", + "2863": "\u2581account", + "2864": "\u2581any", + "2865": "\u2581three", + "2866": "\u2581could", + "2867": "\u2581up", + "2868": "\u2581get", + "2869": "\u2581phone", + "2870": "\u2581great", + "2871": "\u2581six", + "2872": "\u2581eight", + "2873": "\u2581now", + "2874": "\u2581nine", + "2875": "\u2581That", + "2876": "\u2581address", + "2877": "\u2581look", + "2878": "\u2581call", + "2879": "ill", + "2880": "\u2581You", + "2881": "\u2581but", + "2882": "\u2581got", + "2883": "\u2581don", + "2884": "\u2581email", + "2885": "\u2581calling", + "2886": "\u2581problem", + "2887": "\u2581right", + "2888": "\u2581good", + "2889": "\u2581well", + "2890": "\u2581out", + "2891": "\u2581What", + "2892": "\u2581how", + "2893": "\u2581really", + "2894": "\u2581anything", + "2895": "\u2581actually", + "2896": "\u2581from", + "2897": "\u2581think", + "2898": "\u2581time", + "2899": "\u2581some", + "2900": "\u2581ask", + "2901": "\u2581else", + "2902": "other", + "2903": "\u2581fine", + "2904": "able", + "2905": "\u2581Good", + "2906": "\u2581when", + "2907": "\u2581full", + "2908": "\u2581confirm", + "2909": "\u2581give", + "2910": "\u2581more", + "2911": "ever", + "2912": "\u2581month", + "2913": "\u2581information", + "2914": "\u2581sure", + "2915": "\u2581survey", + "2916": "\u2581sorry", + "2917": "\u2581send", + "2918": "\u2581through", + "2919": "\u2581check", + "2920": "\u2581long", + "2921": "\u2581birth", + "2922": "\u2581should", + "2923": "\u2581twenty", + "2924": "\u2581make", + "2925": "\u2581zero", + "2926": "ful", + "2927": "\u2581store", + "2928": "\u2581policy", + "2929": "\u2581back", + "2930": "\u2581again", + "2931": "\u2581first", + "2932": "\u2581Could", + "2933": "\u2581work", + "2934": "\u2581afternoon", + "2935": "\u2581after", + "2936": "\u2581insurance", + "2937": "\u2581customer", + "2938": "\u2581payment", + "2939": "\u2581question", + "2940": "\u2581receive", + "2941": "\u2581possible", + "2942": "\u2581moment", + "2943": "\u2581system", + "2944": "\u2581change", + "2945": "\u2581hundred", + "2946": "\u2581nineteen", + "2947": "", + "2948": "\u2581.", + "2949": "\u2581,", + "2950": "\u2581st", + "2951": "\u2581are", + "2952": "ow", + "2953": "ive", + "2954": "ate", + "2955": "ad", + "2956": "ect", + "2957": "\u2581they", + "2958": "\u2581as", + "2959": "ng", + "2960": "ity", + "2961": "ther", + "2962": "act", + "2963": "ist", + "2964": "\u2581our", + "2965": "\u2581sp", + "2966": "ally", + "2967": "\u2581his", + "2968": "\u2581But", + "2969": "\u2581has", + "2970": "\u2581also", + "2971": "\u2581which", + "2972": "\u2581He", + "2973": "\u2581uh", + "2974": "day", + "2975": "\u2581people", + "2976": "\u2581who", + "2977": "\u2581thing", + "2978": "\u2581because", + "2979": "\u2581other", + "2980": "ough", + "2981": "\u2581part", + "2982": "\u2581say", + "2983": "\u2581year", + "2984": "side", + "2985": "\"", + "2986": "", + "2987": "\u2581y", + "2988": "\u2581el", + "2989": "ci\u00f3n", + "2990": "\u2581Es", + "2991": "res", + "2992": "\u2581los", + "2993": "\u2581La", + "2994": "dos", + "2995": "\u00eda", + "2996": "\u2581El", + "2997": "\u2581las", + "2998": "\u2581m\u00e1s", + "2999": "men", + "3000": "\u00f1o", + "3001": "\u2581esta", + "3002": "idad", + "3003": "par", + "3004": "\u00bf", + "3005": "r\u00eda", + "3006": "\u2581fue", + "3007": "rio", + "3008": "enta", + "3009": "\u00f3n", + "3010": "cho", + "3011": "ciones", + "3012": "ble", + "3013": "\u2581Ca", + "3014": "\u2581muy", + "3015": "\u2581tambi\u00e9n", + "3016": "\u2581tiene", + "3017": "\u00f1a", + "3018": "\u2581Su", + "3019": "\u2581pero", + "3020": "\u2581son", + "3021": "encia", + "3022": "si\u00f3n", + "3023": "\u2581hay", + "3024": "\u2581puede", + "3025": "ncia", + "3026": "\u2581mucho", + "3027": "\u2581Si", + "3028": "\u2581pues", + "3029": "miento", + "3030": "\u2581Con", + "3031": "ones", + "3032": "ecto", + "3033": "iendo", + "3034": "\u2581d\u00eda", + "3035": "\u2581sobre", + "3036": "\u2581primer", + "3037": "\u2581qu\u00e9", + "3038": "\u2581gusta", + "3039": "\u2581San", + "3040": "\u2581hacer", + "3041": "cional", + "3042": "\u2581verdad", + "3043": "\u2581persona", + "3044": "\u2581pasa", + "3045": "\u2581mejor", + "3046": "qu\u00ed", + "3047": "\u2581Fue", + "3048": "\u2581Com", + "3049": "\u2581ciudad", + "3050": "\u00d1", + "3051": "", + "3052": "cia", + "3053": "\u2581lo", + "3054": "\u2581Y", + "3055": "ron", + "3056": "les", + "3057": "\u2581mu", + "3058": "cio", + "3059": "\u2581yo", + "3060": "bu", + "3061": "\u2581s\u00ed", + "3062": "\u2581Pero", + "3063": "\u2581as\u00ed", + "3064": "", + "3065": "r\u00e9", + "3066": "\u00e9e", + "3067": "\u2581Les", + "3068": "nt", + "3069": "our", + "3070": "\u2581Ce", + "3071": "com", + "3072": "\u2581Elle", + "3073": "\u2581Cet", + "3074": "ux", + "3075": "ale", + "3076": "ier", + "3077": "ction", + "3078": "\u2581cha", + "3079": "\u2581pr\u00e9", + "3080": "\u2581deux", + "3081": "if", + "3082": "l\u00e9", + "3083": "\u00e8re", + "3084": "i\u00e8re", + "3085": "iste", + "3086": "\u2581parti", + "3087": "\u2581\u00e9t\u00e9", + "3088": "cette", + "3089": "avec", + "3090": "\u2581tou", + "3091": "jour", + "3092": "app", + "3093": "cul", + "3094": "\u2581\u00e9gale", + "3095": "aine", + "3096": "gue", + "3097": "\u2581tr\u00e8", + "3098": "\u2581nombre", + "3099": "\u2581\u00e9tai", + "3100": "tout", + "3101": "\u2581grand", + "3102": "\u2581commun", + "3103": "Une", + "3104": "\u0153", + "3105": "\u00ef", + "3106": "\u00c0", + "3107": "\u0152", + "3108": "\u00c8", + "3109": "\u014d", + "3110": "\u00ff", + "3111": "\u014c", + "3112": "\u00d4", + "3113": "\u00ca", + "3114": "\u00c2", + "3115": "\u2581m", + "3116": "av", + "3117": "ouv", + "3118": "\u00eat", + "3119": "ois", + "3120": "pri", + "3121": "voir", + "3122": "sion", + "3123": "ix", + "3124": "ang", + "3125": "\u00e9tait", + "3126": "ard", + "3127": "aient", + "3128": "\u0106", + "3129": "\u0130", + "3130": "\u00d9", + "3131": "\u00db", + "3132": "\u00cb", + "3133": "\u00cf", + "3134": "", + "3135": "\u05d1", + "3136": "\u05e2", + "3137": "\u05e7", + "3138": "\u05d7", + "3139": "\u05db", + "3140": "\u05d3", + "3141": "\u05e0", + "3142": "\u2581\u05d1", + "3143": "\u05d9\u05dd", + "3144": "\u05d2", + "3145": "\u2581\u05de", + "3146": "\u05e1", + "3147": "\u05dd", + "3148": "\u05d5\u05ea", + "3149": "\u05e6", + "3150": "\u05e4", + "3151": "\u05d8", + "3152": "\u05d5\u05e8", + "3153": "\u05d6", + "3154": "\u2581\u05dc", + "3155": "\u05e0\u05d9", + "3156": "\u2581\u05e9\u05dc", + "3157": "\u2581\u05d4\u05de", + "3158": "\u05df", + "3159": "\u05da", + "3160": "\u05de\u05d5", + "3161": "\u05d1\u05d9", + "3162": "\u05e0\u05d5", + "3163": "\u05d5\u05dc", + "3164": "\u05dc\u05d9", + "3165": "\u05d1\u05e8", + "3166": "\u05d3\u05d9", + "3167": "\u2581\u05d0\u05ea", + "3168": "\u2581\u05e2\u05dc", + "3169": "\u05e9\u05d9", + "3170": "\u05de\u05e9", + "3171": "\u05d5\u05df", + "3172": "\u05d9\u05e8", + "3173": "\u05e0\u05d4", + "3174": "\u05d9\u05ea", + "3175": "\u05e4\u05e8", + "3176": "\u05e3", + "3177": "\u05db\u05dc", + "3178": "\u2581\u05d4\u05d5\u05d0", + "3179": "\u05de\u05d9", + "3180": "\u05d5\u05d1", + "3181": "\u05e8\u05d5", + "3182": "\u2581\u05d1\u05de", + "3183": "\u05e4\u05d9", + "3184": "\u05d0\u05d9", + "3185": "\u2581\u05d4\u05e9", + "3186": "\u05d7\u05d9", + "3187": "\u05d7\u05d5", + "3188": "\u05dc\u05d5", + "3189": "\u05d1\u05e2", + "3190": "\u2581\u05d4\u05d0", + "3191": "\u05e7\u05e8", + "3192": "\u2581\u05dc\u05d0", + "3193": "\u05e0\u05d9\u05dd", + "3194": "\u05e1\u05d9", + "3195": "\u05e8\u05d9", + "3196": "\u2581\u05dc\u05d4", + "3197": "\u05e9\u05e8", + "3198": "\u05d5\u05d3", + "3199": "\u05d9\u05df", + "3200": "\u05d5\u05e4", + "3201": "\u05d0\u05dc", + "3202": "\u2581\u05d4\u05d7", + "3203": "\u05d3\u05e8", + "3204": "\u05e0\u05d5\u05ea", + "3205": "\u2581\u05d4\u05e2", + "3206": "\u05e8\u05d9\u05dd", + "3207": "\u05e4\u05d5", + "3208": "\u05e6\u05d9", + "3209": "\u2581\u05dc\u05de", + "3210": "\u05d0\u05e8", + "3211": "\u05d0\u05d5\u05ea", + "3212": "\u05d8\u05d9", + "3213": "\u2581\u05d4\u05e1", + "3214": "\u05d9\u05d5\u05ea", + "3215": "\u05db\u05d9", + "3216": "\u05e5", + "3217": "\u2581\u05d0\u05d5", + "3218": "\u2581\u05d5\u05d4", + "3219": "\u2581\u05d6\u05d4", + "3220": "\u2581\u05d4\u05d9\u05d0", + "3221": "\u2581\u05d4\u05e6", + "3222": "\u05de\u05e8", + "3223": "\u05e4\u05e2", + "3224": "\u2581\u05d4\u05e4", + "3225": "\u05db\u05df", + "3226": "\u2581\u05d4\u05d9\u05d4", + "3227": "\u05d8\u05e8", + "3228": "\u05d6\u05e8", + "3229": "\u2581\u05e9\u05e0", + "3230": "\u05d0\u05d7\u05e8", + "3231": "\u2581\u05e8\u05d1", + "3232": "\u2581\u05d6\u05d5", + "3233": "\u2581\u05d4\u05e8", + "3234": "\u05de\u05d9\u05dd", + "3235": "\u2581\u05d5\u05de", + "3236": "\u05e8\u05d0\u05e9", + "3237": "\u2581\u05dc\u05d0\u05d7\u05e8", + "3238": "\u05d7\u05dc\u05e7", + "3239": "\u05de\u05df", + "3240": "\u2581\u05d4\u05d9\u05d5", + "3241": "\u05de\u05e1\u05e4\u05e8", + "3242": "\u2581\u05d9\u05d5\u05ea\u05e8", + "3243": "\u05d0\u05d7\u05d3", + "3244": "\u2581\u05d4\u05d9\u05d9\u05ea", + "3245": "\u05e2\u05e6\u05de", + "3246": "\u05de\u05e7\u05d5\u05dd", + "3247": "", + "3248": "\u093e", + "3249": "\u0930", + "3250": "\u0928", + "3251": "\u0915", + "3252": "\u0938", + "3253": "\u0964", + "3254": "\u093f", + "3255": "\u092e", + "3256": "\u0940", + "3257": "\u0932", + "3258": "\u0947", + "3259": "\u092a", + "3260": "\u2581\u0939\u0948", + "3261": "\u094d", + "3262": "\u0939", + "3263": "\u091c", + "3264": "\u0935", + "3265": "\u0924", + "3266": "\u0902", + "3267": "\u091f", + "3268": "\u094b", + "3269": "\u0941", + "3270": "\u0917", + "3271": "\u2581\u0915\u0947", + "3272": "\u2581\u092c", + "3273": "\u2581\u092e\u0947\u0902", + "3274": "\u0936", + "3275": "\u0928\u0947", + "3276": "\u0942", + "3277": "\u092f", + "3278": "\u0928\u093e", + "3279": "\u0924\u093e", + "3280": "\u0926", + "3281": "\u091a", + "3282": "\u2581\u0906", + "3283": "\u092c", + "3284": "\u2581\u0915\u0930", + "3285": "\u2581\u0915\u0940", + "3286": "\u2581\u0905", + "3287": "\u0930\u094d", + "3288": "\u2581\u0939\u094b", + "3289": "\u2581\u0914\u0930", + "3290": "\u090f", + "3291": "\u0916", + "3292": "\u2581\u0924\u094b", + "3293": "\u2581\u0939\u0948\u0902", + "3294": "\u2581\u0938\u0947", + "3295": "\u2581\u0915\u093e", + "3296": "\u094b\u0902", + "3297": "\u2581\u0915\u094b", + "3298": "\u2581\u0915\u093f", + "3299": "\u0924\u0947", + "3300": "\u092b", + "3301": "\u0927", + "3302": "\u0930\u093e", + "3303": "\u0935\u093e", + "3304": "\u2581\u091c\u093e", + "3305": "\u0921", + "3306": "\u0948", + "3307": "\u2581\u0928\u0939\u0940\u0902", + "3308": "\u0909", + "3309": "\u094d\u092f", + "3310": "\u0908", + "3311": "\u2581\u092d\u0940", + "3312": "\u0915\u093e", + "3313": "\u2581\u0926", + "3314": "\u0921\u093c", + "3315": "\u0915\u0947", + "3316": "\u0930\u0940", + "3317": "\u0924\u0940", + "3318": "\u0907", + "3319": "\u2581\u090f\u0915", + "3320": "\u094d\u0930", + "3321": "\u2581\u0907\u0938", + "3322": "\u2581\u092a\u094d\u0930", + "3323": "\u2581\u0909\u0938", + "3324": "\u092f\u093e", + "3325": "\u2581\u092a\u0930", + "3326": "\u092e\u093e", + "3327": "\u092d", + "3328": "\u0947\u0902", + "3329": "\u0932\u0947", + "3330": "\u2581\u0935\u094b", + "3331": "\u0932\u093e", + "3332": "\u094c", + "3333": "\u0938\u0947", + "3334": "\u2581\u0939\u092e", + "3335": "\u2581\u091c\u094b", + "3336": "\u0915\u094d", + "3337": "\u0917\u093e", + "3338": "\u0923", + "3339": "\u2581\u0935\u093f", + "3340": "\u0939\u093e", + "3341": "\u0928\u0940", + "3342": "\u2581\u0906\u092a", + "3343": "\u093f\u092f\u093e", + "3344": "\u2581\u092e\u0948\u0902", + "3345": "\u0902\u0917", + "3346": "\u0938\u094d", + "3347": "\u2581\u0939\u0940", + "3348": "\u0925", + "3349": "\u0930\u0947", + "3350": "\u2581\u092a\u093e", + "3351": "\u093f\u0924", + "3352": "\u0949", + "3353": "\u092d\u093e", + "3354": "\u0938\u0940", + "3355": "\u0901", + "3356": "\u2581\u092f\u0947", + "3357": "\u0915\u094d\u0937", + "3358": "\u091b", + "3359": "\u2581\u0925\u093e", + "3360": "\u0924\u093f", + "3361": "\u2581\u0932\u093f\u090f", + "3362": "\u2581\u0926\u0947", + "3363": "\u0932\u0940", + "3364": "\u2581\u0915\u094d\u092f\u093e", + "3365": "\u2581\u0938\u0902", + "3366": "\u0937", + "3367": "\u2581\u092f\u0939", + "3368": "\u2581\u0939\u093e\u0901", + "3369": "\u0920", + "3370": "\u0924\u094d\u0930", + "3371": "\u0902\u0926", + "3372": "\u0918", + "3373": "\u2581\u092c\u0939\u0941\u0924", + "3374": "\u2581\u0938\u092e", + "3375": "\u094d\u092f\u093e", + "3376": "\u2581\u0932\u0917", + "3377": "\u2581\u0926\u094b", + "3378": "\u093c", + "3379": "\u2581\u0926\u0947\u0916", + "3380": "\u0913", + "3381": "\u0926\u093e", + "3382": "\u2581\u0928\u093f", + "3383": "\u0902\u0921", + "3384": "\u0926\u0940", + "3385": "\u2581\u0930\u0939\u0947", + "3386": "\u2581\u0932\u094b\u0917", + "3387": "\u2581\u092c\u093e\u0924", + "3388": "\u2581\u0915\u0941\u091b", + "3389": "\u093e\u0907", + "3390": "\u2581\u0905\u091a\u094d\u091b\u093e", + "3391": "\u2581\u0938\u0941", + "3392": "\u2581\u0938\u093e\u0925", + "3393": "\u2581\u0915\u0939\u093e", + "3394": "\u2581\u0915\u093f\u092f\u093e", + "3395": "\u0938\u094d\u091f", + "3396": "\u2581\u0938\u092c", + "3397": "\u0922\u093c", + "3398": "\u2581\u0930\u0939\u093e", + "3399": "\u2581\u0917\u092f\u093e", + "3400": "\u2581\u092b\u093f\u0930", + "3401": "\u2581\u092a\u0947", + "3402": "\u2581\u0905\u092c", + "3403": "\u0938\u094d\u0925", + "3404": "\u2581\u091c\u0940", + "3405": "\u2581\u091a\u0932", + "3406": "\u2581\u092c\u093e\u0930", + "3407": "\u2581\u0925\u0947", + "3408": "\u0938\u094d\u0924", + "3409": "\u2581\u0925\u0940", + "3410": "\u2581\u092e\u093f\u0932", + "3411": "\u2581\u0915\u094b\u0908", + "3412": "\u0943", + "3413": "\u2581\u092e\u0924\u0932\u092c", + "3414": "\u093f\u092f\u094b\u0902", + "3415": "\u2581\u0939\u0942\u0901", + "3416": "\u2581\u0905\u092d\u0940", + "3417": "\u0947\u0902\u0917\u0947", + "3418": "\u2581\u092c\u094b\u0932", + "3419": "\u091d", + "3420": "\u2581\u0930\u0939\u0940", + "3421": "\u091a\u093e\u0930", + "3422": "\u2581\u0905\u092a\u0928\u0947", + "3423": "\u2581\u092c\u093e\u0926", + "3424": "\u2581\u0932\u0947\u0915\u093f\u0928", + "3425": "\u0924\u094d\u0924", + "3426": "\u0910", + "3427": "\u2581\u092e\u0941\u091d\u0947", + "3428": "\u2581\u092e\u0947\u0930\u0947", + "3429": "\u0911", + "3430": "!", + "3431": "\u0906", + "3432": "\u090a", + "3433": "\u0922", + "3434": "\u091e", + "3435": "\u0905", + "3436": "\u0903", + "3437": "\u0914", + "3438": "\u090b", + "3439": "\u0945", + "3440": "\u0919", + "3441": "\u090d", + "3442": "\u0950", + "3443": "\u0960", + "3444": "\u0931", + "3445": "\u00cc", + "3446": "", + "3447": "\u2581\u3044", + "3448": "\u2581\u3002", + "3449": "\u2581\u3001", + "3450": "\u2581\u306e", + "3451": "\u2581\u3046", + "3452": "\u2581\u3093", + "3453": "\u2581\u306a", + "3454": "\u2581\u304b", + "3455": "\u2581\u3067", + "3456": "\u2581\u3063", + "3457": "\u2581\u3066", + "3458": "\u2581\u3042", + "3459": "\u2581\u305f", + "3460": "\u2581\u3068", + "3461": "\u2581\u3059", + "3462": "\u2581\u308b", + "3463": "\u2581\u306f", + "3464": "\u2581\u306b", + "3465": "\u2581\u3057", + "3466": "\u2581\u305d", + "3467": "\u2581\u3082", + "3468": "\u2581\u30fc", + "3469": "\u2581\u307e", + "3470": "\u2581\u304c", + "3471": "\u2581\u306d", + "3472": "\u2581\u3089", + "3473": "\u2581\u308c", + "3474": "\u2581\u3060", + "3475": "\u2581\u30f3", + "3476": "\u2581\u3053", + "3477": "\u2581\u3088", + "3478": "\u2581\u308a", + "3479": "\u2581\u3092", + "3480": "\u4ee5", + "3481": "\u4ed5", + "3482": "\u2581\u53cb", + "3483": "\u2581\u6771", + "3484": "\u2581\u9055", + "3485": "\u2581\u6587", + "3486": "\u2581\u30a1", + "3487": "\u2581\u30ce", + "3488": "\u2581\u6210", + "3489": "\u2581\u660e", + "3490": "\u2581\u4e16", + "3491": "\u2581\u5f37", + "3492": "\u2581\u66f2", + "3493": "\u2581\u8868", + "3494": "\u2581\u6708", + "3495": "\u2581\u60c5", + "3496": "\u2581\u6d3b", + "3497": "\u2581\u753a", + "3498": "\u2581\u4ed8", + "3499": "\u2581\u3075", + "3500": "\u2581\u3072", + "3501": "\u2581\u8cb7", + "3502": "\u2581\u9023", + "3503": "\u2581\u3080", + "3504": "\u2581\u533a", + "3505": "\u2581\u30da", + "3506": "\u2581\u78ba", + "3507": "\u2581\u6d41", + "3508": "\u2581\u671f", + "3509": "\u2581\u6d77", + "3510": "\u2581\u8a2d", + "3511": "\u2581\u8a9e", + "3512": "\u2581\u66f8", + "3513": "\u2581\u6599", + "3514": "\u2581\u8981", + "3515": "\u2581\u79d1", + "3516": "\u2581\u80b2", + "3517": "\u2581\u30b4", + "3518": "\u2581\u5b89", + "3519": "\u2581\u516d", + "3520": "\u2581\u6709", + "3521": "\u2581\u30b2", + "3522": "\u2581\u539f", + "3523": "\u2581\u80fd", + "3524": "\u2581\u58f2", + "3525": "\u2581\u611b", + "3526": "\u2581\u4eac", + "3527": "\u2581\u5236", + "3528": "\u2581\u30b6", + "3529": "\u2581\u826f", + "3530": "\u2581\u30ae", + "3531": "\u2581\u30e4", + "3532": "\u2581\u4e03", + "3533": "\u2581\u7121", + "3534": "\u2581\u8003", + "3535": "\u2581\u7279", + "3536": "\u2581\u767e", + "3537": "\u2581\u5c11", + "3538": "\u2581\u53c2", + "3539": "\u2581\u7537", + "3540": "\u2581\u4fdd", + "3541": "\u2581\u5712", + "3542": "\u666e", + "3543": "\u2581\u4ed6", + "3544": "\u2581\u30a9", + "3545": "\u2581\u6b21", + "3546": "\u2581\u512a", + "3547": "\u2581\u304e", + "3548": "\u2581\u8abf", + "3549": "\u2581\u6f14", + "3550": "\u2581\u53e3", + "3551": "\u2581\u98a8", + "3552": "\u2581\u9001", + "3553": "\u2581\u904b", + "3554": "\u2581\u99c5", + "3555": "\u2581\u5c40", + "3556": "\u53d7", + "3557": "\u2581\u7f6e", + "3558": "\u2581\u90fd", + "3559": "\u2581\u4fe1", + "3560": "\u2581\u7f8e", + "3561": "\u2581\u89aa", + "3562": "\u2581\u3005", + "3563": "\u2581\u96f6", + "3564": "\u2581\u5143", + "3565": "\u2581\u59cb", + "3566": "\u2581\u9078", + "3567": "\u2581\u5de5", + "3568": "\u2581\u754c", + "3569": "\u2581\u8eab", + "3570": "\u2581\u5e83", + "3571": "\u2581\u5411", + "3572": "\u2581\u7d44", + "3573": "\u2581\u5728", + "3574": "\u2581\u5354", + "3575": "\u2581\u6c34", + "3576": "\u2581\u5dde", + "3577": "\u2581\u4f11", + "3578": "\u2581\u548c", + "3579": "\u2581\u653e", + "3580": "\u2581\u69d8", + "3581": "\u2581\u7d42", + "3582": "\u2581\u969b", + "3583": "\u2581\u52a0", + "3584": "\u2581\u5357", + "3585": "\u2581\u5207", + "3586": "\u2581\u4e0d", + "3587": "\u2581\u50d5", + "3588": "\u2581\u4f8b", + "3589": "\u2581\u65e9", + "3590": "\u2581\u65cf", + "3591": "\u2581\u3047", + "3592": "\u2581\u7d4c", + "3593": "\u2581\u4f9b", + "3594": "\u2581\u5f62", + "3595": "\u2581\u767d", + "3596": "\u2581\u6728", + "3597": "\u2581\u7b49", + "3598": "\u2581\u5929", + "3599": "\u2581\u5229", + "3600": "\u2581\u73fe", + "3601": "\u2581\u5fdc", + "3602": "\u2581\u9928", + "3603": "\u2581\u5404", + "3604": "\u2581\u70b9", + "3605": "\u2581\u52d9", + "3606": "\u2581\u30f4", + "3607": "\u2581\u771f", + "3608": "\u2581\u6307", + "3609": "\u2581\u671d", + "3610": "\u2581\u97f3", + "3611": "\u2581\u4e88", + "3612": "\u2581\u5e73", + "3613": "\u2581\u984c", + "3614": "\u2581\u4f4f", + "3615": "\u2581\u5186", + "3616": "\u2581\u4f1d", + "3617": "\u2581\u56e3", + "3618": "\u2581\u6751", + "3619": "\u2581\u76f8", + "3620": "\u2581\u8853", + "3621": "\u2581\u5e30", + "3622": "\u2581\u53e4", + "3623": "\u2581\u8ab0", + "3624": "\u2581\u53ef", + "3625": "\u2581\u592b", + "3626": "\u2581\u5f7c", + "3627": "\u2581\u533b", + "3628": "\u2581\u7a7a", + "3629": "\u2581\u8cde", + "3630": "\u2581\u653f", + "3631": "\u2581\u4ea4", + "3632": "\u2581\u6c11", + "3633": "\u2581\u6c7a", + "3634": "\u5c02", + "3635": "\u2581\u7523", + "3636": "\u2581\u9650", + "3637": "\u2581\u795e", + "3638": "\u2581\u57fa", + "3639": "\u2581\u60aa", + "3640": "\u2581\u9662", + "3641": "\u2581\u7cfb", + "3642": "\u2581\u5f15", + "3643": "\u2581\u65c5", + "3644": "\u2581\u6280", + "3645": "\u2581\u53f0", + "3646": "\u2581\u52dd", + "3647": "\u2581\u3086", + "3648": "\u2581\u57df", + "3649": "\u2581\u518d", + "3650": "\u2581\u554f", + "3651": "\u2581\u8272", + "3652": "\u2581\u30d2", + "3653": "\u4f01", + "3654": "\u767b", + "3655": "\u7dcf", + "3656": "\u6539", + "3657": "\u63a2", + "3658": "\u7570", + "3659": "\u5b8c", + "3660": "\u4f0a", + "3661": "\u9811", + "3662": "\u6df1", + "3663": "\u7af6", + "3664": "\u63a8", + "3665": "\u8fb2", + "3666": "\u6628", + "3667": "\u639b", + "3668": "\u5bc4", + "3669": "\u4ed9", + "3670": "\u5371", + "3671": "\u8f9e", + "3672": "\u6f2b", + "3673": "\u57fc", + "3674": "\u8a73", + "3675": "\u5e7c", + "3676": "\u6271", + "3677": "\u4f59", + "3678": "\u63cf", + "3679": "\u63a1", + "3680": "\u88ab", + "3681": "\u30f6", + "3682": "\u4ff3", + "3683": "\u6803", + "3684": "\u56fa", + "3685": "\u526f", + "3686": "\u6df7", + "3687": "\u6551", + "3688": "\u7518", + "3689": "\u4e92", + "3690": "\u9589", + "3691": "\u75b2", + "3692": "\u4e9c", + "3693": "\u501f", + "3694": "\u690d", + "3695": "\u8cac", + "3696": "\u4eee", + "3697": "\u8da3", + "3698": "\u8f9b", + "3699": "\u8131", + "3700": "\u6050", + "3701": "\u6ce3", + "3702": "\u5951", + "3703": "\u60b2", + "3704": "\u96a0", + "3705": "\u662d", + "3706": "\u9ebb", + "3707": "\u54f2", + "3708": "\u5ba3", + "3709": "\u6c96", + "3710": "\u60a9", + "3711": "\u6d6e", + "3712": "\u8af8", + "3713": "\u5de8", + "3714": "\u5348", + "3715": "\u5360", + "3716": "\u8ddd", + "3717": "\u7e4b", + "3718": "\u6e0b", + "3719": "\u5fd9", + "3720": "\u6c5a", + "3721": "\u5ef6", + "3722": "\u5192", + "3723": "\u8a2a", + "3724": "\u6cbf", + "3725": "\u552f", + "3726": "\u6279", + "3727": "\u90f5", + "3728": "\u4f9d", + "3729": "\u63da", + "3730": "\u52c7", + "3731": "\u8a95", + "3732": "\u67d4", + "3733": "\u50be", + "3734": "\u5bc2", + "3735": "\u8a89", + "3736": "\u61f8", + "3737": "\u9ec4", + "3738": "\u90a6", + "3739": "\u81e8", + "3740": "\u5b09", + "3741": "\u7dba", + "3742": "\u5d29", + "3743": "\u8cfc", + "3744": "\u6d45", + "3745": "\u7e70", + "3746": "\u7dad", + "3747": "\u55ab", + "3748": "\u7a3c", + "3749": "\u71c3", + "3750": "\u65e2", + "3751": "\u8e0f", + "3752": "\u55a7", + "3753": "\u61a7", + "3754": "\u795d", + "3755": "\u6f01", + "3756": "\u8352", + "3757": "\u7dca", + "3758": "\u7372", + "3759": "\u98fe", + "3760": "\u70ad", + "3761": "\u642d", + "3762": "\u52aa", + "3763": "\u72d9", + "3764": "\u8a34", + "3765": "\u5bc5", + "3766": "\u9867", + "3767": "\u6311", + "3768": "\u61d0", + "3769": "\u72ed", + "3770": "\u96f0", + "3771": "\u62db", + "3772": "\u5857", + "3773": "\u6392", + "3774": "\u963f", + "3775": "\u596a", + "3776": "\u96c7", + "3777": "\u57cb", + "3778": "\u5c65", + "3779": "\u4fb5", + "3780": "\u61b2", + "3781": "\u8a72", + "3782": "\u786c", + "3783": "\u8caf", + "3784": "\u80f8", + "3785": "\u983b", + "3786": "\u52e7", + "3787": "\u9b45", + "3788": "\u5fe0", + "3789": "\u8328", + "3790": "\u6291", + "3791": "\u9a5a", + "3792": "\u75e9", + "3793": "\u5996", + "3794": "\u63c3", + "3795": "\u885d", + "3796": "\u54c0", + "3797": "\u829d", + "3798": "\u504f", + "3799": "\u5f27", + "3800": "\u4ef0", + "3801": "\u6f70", + "3802": "\u6dbc", + "3803": "\u8ae6", + "3804": "\u98fd", + "3805": "\u598a", + "3806": "\u633f", + "3807": "\u8010", + "3808": "\u8ce2", + "3809": "\u902e", + "3810": "\u62ab", + "3811": "\u6d69", + "3812": "\u900f", + "3813": "\u6328", + "3814": "\u4fc3", + "3815": "\u667a", + "3816": "\u507d", + "3817": "\u62d3", + "3818": "\u63a7", + "3819": "\u64a4", + "3820": "\u6f5c", + "3821": "\u6817", + "3822": "\u5553", + "3823": "\u7fa8", + "3824": "\u8d08", + "3825": "\u52b1", + "3826": "\u4f3a", + "3827": "\u5410", + "3828": "\u5faa", + "3829": "\u9700", + "3830": "\u6442", + "3831": "\u6dfb", + "3832": "\u7ffb", + "3833": "\u7761", + "3834": "\u5b64", + "3835": "\u7b20", + "3836": "\u6606", + "3837": "\u583a", + "3838": "\u6c88", + "3839": "\u4fd7", + "3840": "\u51fd", + "3841": "\u5302", + "3842": "\u906d", + "3843": "\u6eb6", + "3844": "\u52f2", + "3845": "\u7f70", + "3846": "\u8a87", + "3847": "\u659c", + "3848": "\u935b", + "3849": "\u8cb0", + "3850": "\u7de9", + "3851": "\u62bd", + "3852": "\u7652", + "3853": "\u53e9", + "3854": "\u4f46", + "3855": "\u683d", + "3856": "\u8cbf", + "3857": "\u8107", + "3858": "\u5036", + "3859": "\u9022", + "3860": "\u5949", + "3861": "\u662f", + "3862": "\u8912", + "3863": "\u9271", + "3864": "\u8cbc", + "3865": "\u4f73", + "3866": "\u75be", + "3867": "\u5e61", + "3868": "\u67b6", + "3869": "\u546a", + "3870": "\u4e32", + "3871": "\u5e7e", + "3872": "\u6c99", + "3873": "\u62d2", + "3874": "\u8105", + "3875": "\u8b21", + "3876": "\u631f", + "3877": "\u62cd", + "3878": "\u938c", + "3879": "\u80c3", + "3880": "\u99b4", + "3881": "\u9077", + "3882": "\u5197", + "3883": "\u7b51", + "3884": "\u6f2c", + "3885": "\u6068", + "3886": "\u6bb4", + "3887": "\u66c7", + "3888": "\u7fcc", + "3889": "\u8ecc", + "3890": "\u5378", + "3891": "\u6b53", + "3892": "\u6de1", + "3893": "\u6f0f", + "3894": "\u8986", + "3895": "\u72e9", + "3896": "\u755c", + "3897": "\u84b8", + "3898": "\u854e", + "3899": "\u6cf0", + "3900": "\u7d1b", + "3901": "\u7d5e", + "3902": "\u8a50", + "3903": "\u6905", + "3904": "\u6052", + "3905": "\u5132", + "3906": "\u64ec", + "3907": "\u53d4", + "3908": "\u53ec", + "3909": "\u5e7d", + "3910": "\u80ba", + "3911": "\u7b87", + "3912": "\u80a5", + "3913": "\u758e", + "3914": "\u9676", + "3915": "\u65e8", + "3916": "\u90b8", + "3917": "\u5449", + "3918": "\u51c6", + "3919": "\u8df3", + "3920": "\u757f", + "3921": "\u5ef7", + "3922": "\u920d", + "3923": "\u6e9c", + "3924": "\u6170", + "3925": "\u72a0", + "3926": "\u7e4a", + "3927": "\u82b3", + "3928": "\u7272", + "3929": "\u773a", + "3930": "\u90ca", + "3931": "\u618e", + "3932": "\u514b", + "3933": "\u731b", + "3934": "\u63aa", + "3935": "\u9a19", + "3936": "\u6d78", + "3937": "\u6148", + "3938": "\u52a3", + "3939": "\u93ae", + "3940": "\u8650", + "3941": "\u8e74", + "3942": "\u82d7", + "3943": "\u9665", + "3944": "\u5f90", + "3945": "\u62ed", + "3946": "\u58cc", + "3947": "\u614c", + "3948": "\u6349", + "3949": "\u819c", + "3950": "\u508d", + "3951": "\u565b", + "3952": "\u819a", + "3953": "\u6f20", + "3954": "\u606d", + "3955": "\u81a8", + "3956": "\u6a3d", + "3957": "\u820c", + "3958": "\u611a", + "3959": "\u7881", + "3960": "\u82a6", + "3961": "\u5eca", + "3962": "\u5674", + "3963": "\u7f8a", + "3964": "\u85ab", + "3965": "\u7be0", + "3966": "\u59a5", + "3967": "\u78ef", + "3968": "\u6851", + "3969": "\u7092", + "3970": "\u62d8", + "3971": "\u690e", + "3972": "\u7c98", + "3973": "\u5208", + "3974": "\u8061", + "3975": "\u537f", + "3976": "\u80e1", + "3977": "\u5d07", + "3978": "\u84b2", + "3979": "\u5270", + "3980": "\u745e", + "3981": "\u6e13", + "3982": "\u8ced", + "3983": "\u6e67", + "3984": "\u70f9", + "3985": "\u51dd", + "3986": "\u7d3a", + "3987": "\u9038", + "3988": "\u7261", + "3989": "\u58a8", + "3990": "\u840c", + "3991": "\u622f", + "3992": "\u8429", + "3993": "\u79e9", + "3994": "\u6367", + "3995": "\u69fb", + "3996": "\u8154", + "3997": "\u8776", + "3998": "\u8d05", + "3999": "\u7a4f", + "4000": "\u6562", + "4001": "\u64c1", + "4002": "\u8d74", + "4003": "\u78d0", + "4004": "\u58ee", + "4005": "\u8a93", + "4006": "\u62b9", + "4007": "\u6ea2", + "4008": "\u53f1", + "4009": "\u53f6", + "4010": "\u59a8", + "4011": "\u6cb8", + "4012": "\u7d33", + "4013": "\u963b", + "4014": "\u5984", + "4015": "\u6590", + "4016": "\u5983", + "4017": "\u5de7", + "4018": "\u540a", + "4019": "\u60da", + "4020": "\u8236", + "4021": "\u52ff", + "4022": "\u61c7", + "4023": "\u7525", + "4024": "\u60dc", + "4025": "\u7b39", + "4026": "\u6b86", + "4027": "\u6fe1", + "4028": "\u60e3", + "4029": "\u6020", + "4030": "\u6dc0", + "4031": "\u5265", + "4032": "\u66d6", + "4033": "\u6b64", + "4034": "\u85dd", + "4035": "\u8fb0", + "4036": "\u632b", + "4037": "\u66ab", + "4038": "\u6155", + "4039": "\u78a7", + "4040": "\u5634", + "4041": "\u3062", + "4042": "\u6d2a", + "4043": "\u865c", + "4044": "\u9065", + "4045": "\u92ed", + "4046": "\u5a2f", + "4047": "\u814e", + "4048": "\u871c", + "4049": "\u8a02", + "4050": "\u74e6", + "4051": "\u5944", + "4052": "\u64ad", + "4053": "\u75d5", + "4054": "\u7db4", + "4055": "\u7a40", + "4056": "\u9699", + "4057": "\u5384", + "4058": "\u5448", + "4059": "\u66f0", + "4060": "\u5d16", + "4061": "\u64e6", + "4062": "\u70cf", + "4063": "\u62c9", + "4064": "\u8861", + "4065": "\u6731", + "4066": "\u5606", + "4067": "\u8339", + "4068": "\u5cef", + "4069": "\u6ff1", + "4070": "\u84bc", + "4071": "\u30f1", + "4072": "\u6d12", + "4073": "\u85a9", + "4074": "\u8acf", + "4075": "\u55c5", + "4076": "\u689d", + "4077": "\u8096", + "4078": "\u785d", + "4079": "\u8a63", + "4080": "\u8cd1", + "4081": "\u67a2", + "4082": "\u6e9d", + "4083": "\u7a00", + "4084": "\u6a58", + "4085": "\u7766", + "4086": "\u9673", + "4087": "\u91e7", + "4088": "\u91b8", + "4089": "\u55aa", + "4090": "\u67af", + "4091": "\u6881", + "4092": "\u86cd", + "4093": "\u7ce7", + "4094": "\u90ed", + "4095": "\u7058", + "4096": "\u723d", + "4097": "\u7c97", + "4098": "\u8702", + "4099": "\u636e", + "4100": "\u5112", + "4101": "\u80a1", + "4102": "\u978d", + "4103": "\u61f2", + "4104": "\u5b54", + "4105": "\u6f06", + "4106": "\u8499", + "4107": "\u693f", + "4108": "\u7345", + "4109": "\u73c8", + "4110": "\u7554", + "4111": "\u9a28", + "4112": "\u675c", + "4113": "\u7984", + "4114": "\u52c3", + "4115": "\u9ac4", + "4116": "\u5f0a", + "4117": "\u77ef", + "4118": "\u9df2", + "4119": "\u58ec", + "4120": "\u6666", + "4121": "\u6e15", + "4122": "\u85cd", + "4123": "\u533f", + "4124": "\u582a", + "4125": "\u7aaa", + "4126": "\u5289", + "4127": "\u6182", + "4128": "\u5091", + "4129": "\u63b4", + "4130": "\u540e", + "4131": "\u916a", + "4132": "\u5176", + "4133": "\u82eb", + "4134": "\u30c5", + "4135": "\u63c9", + "4136": "\u73a9", + "4137": "\u80f4", + "4138": "\u8910", + "4139": "\u8afe", + "4140": "\u5598", + "4141": "\u559a", + "4142": "\u8594", + "4143": "\u8cc4", + "4144": "\u7fe0", + "4145": "\u5023", + "4146": "\u576a", + "4147": "\u6109", + "4148": "\u6276", + "4149": "\u670b", + "4150": "\u5351", + "4151": "\u66fe", + "4152": "\u786b", + "4153": "\u51a8", + "4154": "\u5b78", + "4155": "\u6c7d", + "4156": "\u837b", + "4157": "\u8461", + "4158": "\u6eba", + "4159": "\u8fbf", + "4160": "\u4e91", + "4161": "\u5fcc", + "4162": "\u7815", + "4163": "\u6734", + "4164": "\u6a8e", + "4165": "\u9320", + "4166": "\u5e63", + "4167": "\u80af", + "4168": "\u81b5", + "4169": "\u52c5", + "4170": "\u65bc", + "4171": "\u7947", + "4172": "\u8304", + "4173": "\u6591", + "4174": "\u50c5", + "4175": "\u8a60", + "4176": "\u96bc", + "4177": "\u98e2", + "4178": "\u7a3d", + "4179": "\u5dba", + "4180": "\u6df5", + "4181": "\u8b83", + "4182": "\u7aae", + "4183": "\u7be4", + "4184": "\u97fb", + "4185": "\u6897", + "4186": "\u72f8", + "4187": "\u69cd", + "4188": "\u8b17", + "4189": "\u8ab9", + "4190": "\u9010", + "4191": "\u53d9", + "4192": "\u5420", + "4193": "\u725f", + "4194": "\u9838", + "4195": "\u52fe", + "4196": "\u717d", + "4197": "\u7460", + "4198": "\u4fb6", + "4199": "\u68b6", + "4200": "\u8997", + "4201": "\u95a4", + "4202": "\u51a5", + "4203": "\u5dfe", + "4204": "\u5f04", + "4205": "\u83e9", + "4206": "\u8526", + "4207": "\u99a8", + "4208": "\u6fc1", + "4209": "\u714e", + "4210": "\u8218", + "4211": "\u6876", + "4212": "\u79e6", + "4213": "\u9061", + "4214": "\u5806", + "4215": "\u6afb", + "4216": "\u6e07", + "4217": "\u77ad", + "4218": "\u81c6", + "4219": "\u4fe3", + "4220": "\u7169", + "4221": "\u54b3", + "4222": "\u5506", + "4223": "\u60f9", + "4224": "\u6775", + "4225": "\u7c9f", + "4226": "\u9091", + "4227": "\u553e", + "4228": "\u6756", + "4229": "\u6960", + "4230": "\u6b6a", + "4231": "\u711a", + "4232": "\u8fb1", + "4233": "\u559d", + "4234": "\u6e58", + "4235": "\u76f2", + "4236": "\u8b39", + "4237": "\u8e2a", + "4238": "\u965b", + "4239": "\u589f", + "4240": "\u64b0", + "4241": "\u6ccc", + "4242": "\u6f15", + "4243": "\u8a6b", + "4244": "\u771e", + "4245": "\u90c1", + "4246": "\u6e1a", + "4247": "\u8210", + "4248": "\u8235", + "4249": "\u8e8a", + "4250": "\u58f9", + "4251": "\u5c6f", + "4252": "\u7435", + "4253": "\u7436", + "4254": "\u7a92", + "4255": "\u82af", + "4256": "\u8e87", + "4257": "\u4e1e", + "4258": "\u7262", + "4259": "\u8305", + "4260": "\u5f57", + "4261": "\u699b", + "4262": "\u7b95", + "4263": "\u82ad", + "4264": "\u918d", + "4265": "\u9190", + "4266": "\u9945", + "4267": "\u5815", + "4268": "\u5deb", + "4269": "\u6a9c", + "4270": "\u914c", + "4271": "\u96eb", + "4272": "\u6b3e", + "4273": "\u9d3b", + "4274": "\u4f10", + "4275": "\u7901", + "4276": "\u7a83", + "4277": "\u8389", + "4278": "\u929a", + "4279": "\u6191", + "4280": "\u639f", + "4281": "\u6492", + "4282": "\u6a0b", + "4283": "\u7336", + "4284": "\u868a", + "4285": "\u88fe", + "4286": "\u96cc", + "4287": "\u6216", + "4288": "\u643e", + "4289": "\u6cc4", + "4290": "\u7109", + "4291": "\u7940", + "4292": "\u7b8b", + "4293": "\u919c", + "4294": "\u9d5c", + "4295": "\u51f1", + "4296": "\u5c16", + "4297": "\u6c23", + "4298": "\u75d2", + "4299": "\u830e", + "4300": "\u745b", + "4301": "\u602f", + "4302": "\u698e", + "4303": "\u6feb", + "4304": "\u7099", + "4305": "\u97ad", + "4306": "\u9b4f", + "4307": "\u4f5b", + "4308": "\u51b6", + "4309": "\u55dc", + "4310": "\u5750", + "4311": "\u6144", + "4312": "\u61c9", + "4313": "\u6c50", + "4314": "\u73c2", + "4315": "\u8fc5", + "4316": "\u62f7", + "4317": "\u9019", + "4318": "\u5ae1", + "4319": "\u60bc", + "4320": "\u637b", + "4321": "\u6a3a", + "4322": "\u85c1", + "4323": "\u932c", + "4324": "\u50ad", + "4325": "\u5243", + "4326": "\u5d4c", + "4327": "\u727d", + "4328": "\u937c", + "4329": "\u4fae", + "4330": "\u5f59", + "4331": "\u6bec", + "4332": "\u4ea8", + "4333": "\u4f86", + "4334": "\u5b8d", + "4335": "\u8a1b", + "4336": "\u9ab8", + "4337": "\u4ec7", + "4338": "\u5df4", + "4339": "\u6c3e", + "4340": "\u71e6", + "4341": "\u783a", + "4342": "\u79df", + "4343": "\u8549", + "4344": "\u5614", + "4345": "\u6703", + "4346": "\u67da", + "4347": "\u69cc", + "4348": "\u83ab", + "4349": "\u88d4", + "4350": "\u91d8", + "4351": "\u51a4", + "4352": "\u51b4", + "4353": "\u64ab", + "4354": "\u8d0b", + "4355": "\u30f5", + "4356": "\u4e9b", + "4357": "\u4f43", + "4358": "\u72d0", + "4359": "\u56a2", + "4360": "\u92f3", + "4361": "\u5dbd", + "4362": "\u9f4b", + "4363": "\u51f9", + "4364": "\u54fa", + "4365": "\u57f4", + "4366": "\u65fa", + "4367": "\u86cb", + "4368": "\u8cdc", + "4369": "\u4f0d", + "4370": "\u545f", + "4371": "\u5937", + "4372": "\u5dbc", + "4373": "\u6c4e", + "4374": "\u9739", + "4375": "\u5875", + "4376": "\u6101", + "4377": "\u8106", + "4378": "\u97ee", + "4379": "\u540f", + "4380": "\u5957", + "4381": "\u5993", + "4382": "\u68b1", + "4383": "\u6d1b", + "4384": "\u6f31", + "4385": "\u725d", + "4386": "\u798d", + "4387": "\u7d21", + "4388": "\u810a", + "4389": "\u8cd3", + "4390": "\u586b", + "4391": "\u673d", + "4392": "\u6a2b", + "4393": "\u8299", + "4394": "\u84c9", + "4395": "\u9310", + "4396": "\u5835", + "4397": "\u5f14", + "4398": "\u633d", + "4399": "\u6955", + "4400": "\u6c72", + "4401": "\u5294", + "4402": "\u5eb8", + "4403": "\u694a", + "4404": "\u7826", + "4405": "\u9c57", + "4406": "\u61a4", + "4407": "\u634f", + "4408": "\u6d29", + "4409": "\u723e", + "4410": "\u750d", + "4411": "\u817f", + "4412": "\u9c52", + "4413": "\u685f", + "4414": "\u6a7f", + "4415": "\u82db", + "4416": "\u982c", + "4417": "\u55da", + "4418": "\u5751", + "4419": "\u5b75", + "4420": "\u5e87", + "4421": "\u68a2", + "4422": "\u6b05", + "4423": "\u7560", + "4424": "\u7a7f", + "4425": "\u8513", + "4426": "\u8d99", + "4427": "\u927e", + "4428": "\u4f51", + "4429": "\u5dcc", + "4430": "\u5f77", + "4431": "\u65a7", + "4432": "\u68d8", + "4433": "\u6dd8", + "4434": "\u7b94", + "4435": "\u7d2f", + "4436": "\u8729", + "4437": "\u908a", + "4438": "\u9ebf", + "4439": "\u5713", + "4440": "\u66a2", + "4441": "\u69ae", + "4442": "\u6b89", + "4443": "\u6d8c", + "4444": "\u7aea", + "4445": "\u8aee", + "4446": "\u96db", + "4447": "\u9bf5", + "4448": "\u4e14", + "4449": "\u5347", + "4450": "\u5954", + "4451": "\u5ce8", + "4452": "\u7149", + "4453": "\u7791", + "4454": "\u8276", + "4455": "\u840e", + "4456": "\u8568", + "4457": "\u85aa", + "4458": "\u8f0c", + "4459": "\u5dfd", + "4460": "\u66f3", + "4461": "\u6cab", + "4462": "\u82a5", + "4463": "\u8511", + "4464": "\u93a7", + "4465": "\u9f20", + "4466": "\u51ea", + "4467": "\u5c51", + "4468": "\u5d14", + "4469": "\u5d6f", + "4470": "\u6a59", + "4471": "\u6e38", + "4472": "\u7a1c", + "4473": "\u8072", + "4474": "\u511a", + "4475": "\u695a", + "4476": "\u8006", + "4477": "\u82b9", + "4478": "\u83d6", + "4479": "\u88f3", + "4480": "\u9017", + "4481": "\u905c", + "4482": "\u9640", + "4483": "\u4ff8", + "4484": "\u5a29", + "4485": "\u5cd9", + "4486": "\u6190", + "4487": "\u6241", + "4488": "\u626e", + "4489": "\u6faa", + "4490": "\u7729", + "4491": "\u7f75", + "4492": "\u8036", + "4493": "\u8058", + "4494": "\u9c3b", + "4495": "\u309d", + "4496": "\u56c3", + "4497": "\u5f7f", + "4498": "\u6167", + "4499": "\u66dd", + "4500": "\u6fe0", + "4501": "\u8309", + "4502": "\u976d", + "4503": "\u9daf", + "4504": "\u9e92", + "4505": "\u30f0", + "4506": "\u4e5e", + "4507": "\u50b2", + "4508": "\u54e8", + "4509": "\u5f6c", + "4510": "\u73c0", + "4511": "\u79e4", + "4512": "\u84ec", + "4513": "\u8ebe", + "4514": "\u9075", + "4515": "\u51f8", + "4516": "\u53a9", + "4517": "\u6168", + "4518": "\u698a", + "4519": "\u6c8c", + "4520": "\u75b1", + "4521": "\u8fe6", + "4522": "\u53e1", + "4523": "\u543b", + "4524": "\u5b2c", + "4525": "\u5d69", + "4526": "\u660f", + "4527": "\u8171", + "4528": "\u8888", + "4529": "\u9c39", + "4530": "\u57e0", + "4531": "\u5be1", + "4532": "\u5cfb", + "4533": "\u5df7", + "4534": "\u62d7", + "4535": "\u62d9", + "4536": "\u63c4", + "4537": "\u63f6", + "4538": "\u65a1", + "4539": "\u6962", + "4540": "\u6dcb", + "4541": "\u722c", + "4542": "\u7425", + "4543": "\u805a", + "4544": "\u80da", + "4545": "\u81a0", + "4546": "\u8292", + "4547": "\u8703", + "4548": "\u87ba", + "4549": "\u9910", + "4550": "\u9dfa", + "4551": "\u51e0", + "4552": "\u52ab", + "4553": "\u5321", + "4554": "\u63d6", + "4555": "\u6b3d", + "4556": "\u7422", + "4557": "\u7825", + "4558": "\u877f", + "4559": "\u8adc", + "4560": "\u8ae7", + "4561": "\u8dbe", + "4562": "\u50d1", + "4563": "\u5a9a", + "4564": "\u5b5f", + "4565": "\u5b95", + "4566": "\u5bd3", + "4567": "\u5f8a", + "4568": "\u5f98", + "4569": "\u6357", + "4570": "\u66d9", + "4571": "\u7e82", + "4572": "\u7fc1", + "4573": "\u81bf", + "4574": "\u85ea", + "4575": "\u8a0a", + "4576": "\u8fc2", + "4577": "\u932b", + "4578": "\u4fef", + "4579": "\u5a3c", + "4580": "\u689f", + "4581": "\u6e3e", + "4582": "\u6ffe", + "4583": "\u79bf", + "4584": "\u7ce0", + "4585": "\u8180", + "4586": "\u82c5", + "4587": "\u8877", + "4588": "\u8c79", + "4589": "\u9798", + "4590": "\u9eb9", + "4591": "\u9ece", + "4592": "\u6abb", + "4593": "\u6e25", + "4594": "\u9149", + "4595": "\u97a0", + "4596": "\u567a", + "4597": "\u60f0", + "4598": "\u646f", + "4599": "\u65db", + "4600": "\u6bc0", + "4601": "\u6d38", + "4602": "\u6dd1", + "4603": "\u71fb", + "4604": "\u77b0", + "4605": "\u7ac8", + "4606": "\u7cfe", + "4607": "\u86d9", + "4608": "\u8e44", + "4609": "\u502d", + "4610": "\u536f", + "4611": "\u56c1", + "4612": "\u5830", + "4613": "\u6652", + "4614": "\u6a13", + "4615": "\u72db", + "4616": "\u84fc", + "4617": "\u86db", + "4618": "\u8718", + "4619": "\u8b33", + "4620": "\u52be", + "4621": "\u5403", + "4622": "\u5484", + "4623": "\u5631", + "4624": "\u6070", + "4625": "\u60b6", + "4626": "\u69c7", + "4627": "\u7325", + "4628": "\u7396", + "4629": "\u792b", + "4630": "\u7977", + "4631": "\u7ad9", + "4632": "\u7ae3", + "4633": "\u7d68", + "4634": "\u7e1e", + "4635": "\u966a", + "4636": "\u4e58", + "4637": "\u53e2", + "4638": "\u5c39", + "4639": "\u61be", + "4640": "\u62ee", + "4641": "\u633a", + "4642": "\u6582", + "4643": "\u6714", + "4644": "\u701e", + "4645": "\u7587", + "4646": "\u77a5", + "4647": "\u7a63", + "4648": "\u7f79", + "4649": "\u8aeb", + "4650": "\u9013", + "4651": "\u96f9", + "4652": "\u981a", + "4653": "\u4f3d", + "4654": "\u5eff", + "4655": "\u60df", + "4656": "\u63bb", + "4657": "\u6523", + "4658": "\u6bb2", + "4659": "\u6c5d", + "4660": "\u6d59", + "4661": "\u806f", + "4662": "\u8a54", + "4663": "\u96bb", + "4664": "\u9801", + "4665": "\u9913", + "4666": "\u50b3", + "4667": "\u51b2", + "4668": "\u65a5", + "4669": "\u7e3d", + "4670": "\u8151", + "4671": "\u92f8", + "4672": "\u9695", + "4673": "\u9812", + "4674": "\u9837", + "4675": "\u4ec0", + "4676": "\u54ed", + "4677": "\u5718", + "4678": "\u5851", + "4679": "\u59e6", + "4680": "\u5bf5", + "4681": "\u615f", + "4682": "\u6b12", + "4683": "\u7953", + "4684": "\u79bd", + "4685": "\u7c50", + "4686": "\u8695", + "4687": "\u8ce6", + "4688": "\u8f62", + "4689": "\u912d", + "4690": "\u92d2", + "4691": "\u985b", + "4692": "\u9c48", + "4693": "\u4e11", + "4694": "\u5b30", + "4695": "\u5ba6", + "4696": "\u5be6", + "4697": "\u5c4d", + "4698": "\u67e9", + "4699": "\u6d9b", + "4700": "\u7473", + "4701": "\u75bc", + "4702": "\u7aa9", + "4703": "\u7dfb", + "4704": "\u811b", + "4705": "\u936c", + "4706": "\u4eab", + "4707": "\u53ad", + "4708": "\u54bd", + "4709": "\u5632", + "4710": "\u6a05", + "4711": "\u71ed", + "4712": "\u75d9", + "4713": "\u7624", + "4714": "\u7e23", + "4715": "\u808b", + "4716": "\u809b", + "4717": "\u8654", + "4718": "\u895f", + "4719": "\u9583", + "4720": "\u9b6f", + "4721": "\u55a9", + "4722": "\u55fd", + "4723": "\u56a5", + "4724": "\u58d5", + "4725": "\u601c", + "4726": "\u634c", + "4727": "\u7b4f", + "4728": "\u7baa", + "4729": "\u7e6d", + "4730": "\u85cf", + "4731": "\u86fe", + "4732": "\u8a03", + "4733": "\u8caa", + "4734": "\u98af", + "4735": "\u531d", + "4736": "\u5480", + "4737": "\u548e", + "4738": "\u56bc", + "4739": "\u5c53", + "4740": "\u5e9a", + "4741": "\u6115", + "4742": "\u6ef8", + "4743": "\u707c", + "4744": "\u7b25", + "4745": "\u8700", + "4746": "\u8a36", + "4747": "\u8a85", + "4748": "\u8d14", + "4749": "\u91ac", + "4750": "\u9c10", + "4751": "\u4fc4", + "4752": "\u5026", + "4753": "\u5039", + "4754": "\u5239", + "4755": "\u5699", + "4756": "\u5859", + "4757": "\u685d", + "4758": "\u6adb", + "4759": "\u7119", + "4760": "\u76e7", + "4761": "\u7ac4", + "4762": "\u7d18", + "4763": "\u7d62", + "4764": "\u83f0", + "4765": "\u8466", + "4766": "\u849c", + "4767": "\u8541", + "4768": "\u8599", + "4769": "\u8606", + "4770": "\u8b01", + "4771": "\u8fa3", + "4772": "\u9761", + "4773": "\u99d5", + "4774": "\u9d0e", + "4775": "\u4ec4", + "4776": "\u4f98", + "4777": "\u5016", + "4778": "\u5080", + "4779": "\u50fb", + "4780": "\u5121", + "4781": "\u524b", + "4782": "\u5f45", + "4783": "\u6802", + "4784": "\u6854", + "4785": "\u68b5", + "4786": "\u6ef2", + "4787": "\u6fb3", + "4788": "\u6fe4", + "4789": "\u7368", + "4790": "\u7577", + "4791": "\u75d4", + "4792": "\u7626", + "4793": "\u7960", + "4794": "\u79b0", + "4795": "\u81a3", + "4796": "\u834f", + "4797": "\u8944", + "4798": "\u8a25", + "4799": "\u8de8", + "4800": "\u8e93", + "4801": "\u90b1", + "4802": "\u9264", + "4803": "\u93d1", + "4804": "\u95ca", + "4805": "\u96c9", + "4806": "\u9d6c", + "4807": "\u53db", + "4808": "\u543c", + "4809": "\u59d0", + "4810": "\u5f4c", + "4811": "\u66fc", + "4812": "\u6c83", + "4813": "\u6f23", + "4814": "\u6f38", + "4815": "\u700b", + "4816": "\u721b", + "4817": "\u7690", + "4818": "\u7c3e", + "4819": "\u7fe1", + "4820": "\u82d3", + "4821": "\u839e", + "4822": "\u84d1", + "4823": "\u857e", + "4824": "\u874b", + "4825": "\u8766", + "4826": "\u892a", + "4827": "\u9119", + "4828": "\u914b", + "4829": "\u92e4", + "4830": "\u937e", + "4831": "\u9435", + "4832": "\u5191", + "4833": "\u557c", + "4834": "\u5617", + "4835": "\u5c4f", + "4836": "\u65af", + "4837": "\u6900", + "4838": "\u6e20", + "4839": "\u71be", + "4840": "\u7280", + "4841": "\u76ba", + "4842": "\u7768", + "4843": "\u78cb", + "4844": "\u7b67", + "4845": "\u7cca", + "4846": "\u837c", + "4847": "\u83b1", + "4848": "\u8fa8", + "4849": "\u901e", + "4850": "\u9081", + "4851": "\u936e", + "4852": "\u968b", + "4853": "\u9786", + "4854": "\u978b", + "4855": "\u4e56", + "4856": "\u55df", + "4857": "\u5700", + "4858": "\u5fd6", + "4859": "\u60e0", + "4860": "\u61ba", + "4861": "\u6518", + "4862": "\u6727", + "4863": "\u675e", + "4864": "\u69d9", + "4865": "\u6b98", + "4866": "\u6deb", + "4867": "\u7015", + "4868": "\u70b8", + "4869": "\u71d0", + "4870": "\u7b50", + "4871": "\u7ff3", + "4872": "\u813e", + "4873": "\u81c0", + "4874": "\u8b49", + "4875": "\u9318", + "4876": "\u9d2c", + "4877": "\u308e", + "4878": "\u4e8e", + "4879": "\u5055", + "4880": "\u54ac", + "4881": "\u5516", + "4882": "\u555c", + "4883": "\u5703", + "4884": "\u58fd", + "4885": "\u59da", + "4886": "\u59e5", + "4887": "\u5a49", + "4888": "\u5b0c", + "4889": "\u5b55", + "4890": "\u5c60", + "4891": "\u5cb1", + "4892": "\u5ed3", + "4893": "\u61ab", + "4894": "\u621f", + "4895": "\u6309", + "4896": "\u637a", + "4897": "\u6853", + "4898": "\u6939", + "4899": "\u6977", + "4900": "\u6ac2", + "4901": "\u704c", + "4902": "\u71d7", + "4903": "\u7526", + "4904": "\u788d", + "4905": "\u795f", + "4906": "\u79ae", + "4907": "\u7a79", + "4908": "\u7b4d", + "4909": "\u7c17", + "4910": "\u814b", + "4911": "\u832b", + "4912": "\u8494", + "4913": "\u8afa", + "4914": "\u8cb6", + "4915": "\u9059", + "4916": "\u9211", + "4917": "\u9328", + "4918": "\u9771", + "4919": "\u98c4", + "4920": "\u9af7", + "4921": "\u9d60", + "4922": "\u9f0e", + "4923": "\u4ea6", + "4924": "\u4f47", + "4925": "\u5072", + "4926": "\u526a", + "4927": "\u5271", + "4928": "\u57d2", + "4929": "\u59f6", + "4930": "\u5c0d", + "4931": "\u5e47", + "4932": "\u5fbd", + "4933": "\u606b", + "4934": "\u652b", + "4935": "\u6b78", + "4936": "\u72e1", + "4937": "\u77bc", + "4938": "\u786f", + "4939": "\u7afa", + "4940": "\u7b0f", + "4941": "\u7bdd", + "4942": "\u7c00", + "4943": "\u7c7e", + "4944": "\u7f6b", + "4945": "\u807e", + "4946": "\u8139", + "4947": "\u8521", + "4948": "\u8557", + "4949": "\u876e", + "4950": "\u8cfd", + "4951": "\u8d16", + "4952": "\u8fad", + "4953": "\u92ea", + "4954": "\u9b93", + "4955": "\u9c2f", + "4956": "\u9c3a", + "4957": "\u4e24", + "4958": "\u4e4e", + "4959": "\u5118", + "4960": "\u530d", + "4961": "\u5310", + "4962": "\u5686", + "4963": "\u5f1b", + "4964": "\u5fa8", + "4965": "\u60e1", + "4966": "\u619a", + "4967": "\u6698", + "4968": "\u68c9", + "4969": "\u6a02", + "4970": "\u6bb7", + "4971": "\u6beb", + "4972": "\u6c40", + "4973": "\u70d9", + "4974": "\u72c4", + "4975": "\u73ea", + "4976": "\u7433", + "4977": "\u74e3", + "4978": "\u7b8f", + "4979": "\u7e5a", + "4980": "\u8207", + "4981": "\u822b", + "4982": "\u8237", + "4983": "\u8317", + "4984": "\u849f", + "4985": "\u84bb", + "4986": "\u86ed", + "4987": "\u88a2", + "4988": "\u8956", + "4989": "\u8966", + "4990": "\u8cf4", + "4991": "\u8d04", + "4992": "\u8e59", + "4993": "\u8f4d", + "4994": "\u8f9f", + "4995": "\u8faf", + "4996": "\u9182", + "4997": "\u9187", + "4998": "\u947d", + "4999": "\u9846", + "5000": "\u9870", + "5001": "\u9c2d", + "5002": "\u51f0", + "5003": "\u5475", + "5004": "\u566a", + "5005": "\u5bf6", + "5006": "\u61fa", + "5007": "\u6372", + "5008": "\u63a0", + "5009": "\u69b4", + "5010": "\u71df", + "5011": "\u7370", + "5012": "\u754f", + "5013": "\u755d", + "5014": "\u7566", + "5015": "\u76c8", + "5016": "\u7827", + "5017": "\u7a62", + "5018": "\u7d06", + "5019": "\u7fc6", + "5020": "\u803d", + "5021": "\u8205", + "5022": "\u8569", + "5023": "\u86f8", + "5024": "\u8882", + "5025": "\u893b", + "5026": "\u8eaf", + "5027": "\u8fed", + "5028": "\u9005", + "5029": "\u9082", + "5030": "\u9089", + "5031": "\u920e", + "5032": "\u929b", + "5033": "\u95dc", + "5034": "\u9e1e", + "5035": "\u9f67", + "5036": "\u4ea5", + "5037": "\u52f8", + "5038": "\u543d", + "5039": "\u54a5", + "5040": "\u5967", + "5041": "\u598d", + "5042": "\u5a62", + "5043": "\u5c24", + "5044": "\u5c41", + "5045": "\u6134", + "5046": "\u65b7", + "5047": "\u65f1", + "5048": "\u6688", + "5049": "\u67b7", + "5050": "\u67d8", + "5051": "\u6ac3", + "5052": "\u6adf", + "5053": "\u6bd8", + "5054": "\u6c6a", + "5055": "\u6f74", + "5056": "\u6fb1", + "5057": "\u7164", + "5058": "\u7194", + "5059": "\u7576", + "5060": "\u777e", + "5061": "\u7893", + "5062": "\u7a84", + "5063": "\u7bc1", + "5064": "\u7c2a", + "5065": "\u7e79", + "5066": "\u7ff9", + "5067": "\u8000", + "5068": "\u8387", + "5069": "\u83f4", + "5070": "\u8602", + "5071": "\u8737", + "5072": "\u8904", + "5073": "\u890c", + "5074": "\u8b2c", + "5075": "\u8ce3", + "5076": "\u8eb0", + "5077": "\u8ecb", + "5078": "\u903c", + "5079": "\u93ac", + "5080": "\u975c", + "5081": "\u9b43", + "5082": "\u9b9f", + "5083": "\u9cf6", + "5084": "\u9f5f", + "5085": "\u9f6c", + "5086": "\u301c", + "5087": "\u30ee", + "5088": "\u4e9f", + "5089": "\u4ec6", + "5090": "\u51cb", + "5091": "\u54a4", + "5092": "\u5544", + "5093": "\u57dc", + "5094": "\u5a11", + "5095": "\u5a36", + "5096": "\u6089", + "5097": "\u620a", + "5098": "\u620e", + "5099": "\u64bc", + "5100": "\u64f2", + "5101": "\u6578", + "5102": "\u6726", + "5103": "\u687f", + "5104": "\u6a1f", + "5105": "\u6aae", + "5106": "\u6c81", + "5107": "\u6d63", + "5108": "\u6d9c", + "5109": "\u6ed3", + "5110": "\u703e", + "5111": "\u71e7", + "5112": "\u7232", + "5113": "\u733e", + "5114": "\u7464", + "5115": "\u7469", + "5116": "\u766c", + "5117": "\u776b", + "5118": "\u77ee", + "5119": "\u788c", + "5120": "\u7a1f", + "5121": "\u7a4e", + "5122": "\u7be5", + "5123": "\u7bf3", + "5124": "\u7cb9", + "5125": "\u7dec", + "5126": "\u7f77", + "5127": "\u7f9e", + "5128": "\u8216", + "5129": "\u847a", + "5130": "\u8acd", + "5131": "\u8af7", + "5132": "\u8b04", + "5133": "\u8da8", + "5134": "\u8e4a", + "5135": "\u8e81", + "5136": "\u8f3b", + "5137": "\u900d", + "5138": "\u970d", + "5139": "\u9b06", + "5140": "\u9baa", + "5141": "\u9ef4", + "5142": "\u4f7b", + "5143": "\u5167", + "5144": "\u51c9", + "5145": "\u525d", + "5146": "\u52d2", + "5147": "\u5396", + "5148": "\u53b6", + "5149": "\u5538", + "5150": "\u5556", + "5151": "\u5885", + "5152": "\u592d", + "5153": "\u5ba5", + "5154": "\u5be2", + "5155": "\u5df2", + "5156": "\u608d", + "5157": "\u62c7", + "5158": "\u6350", + "5159": "\u6426", + "5160": "\u649a", + "5161": "\u64a5", + "5162": "\u64d4", + "5163": "\u652a", + "5164": "\u665d", + "5165": "\u6753", + "5166": "\u6763", + "5167": "\u6787", + "5168": "\u6867", + "5169": "\u6930", + "5170": "\u6a47", + "5171": "\u6b23", + "5172": "\u6cd7", + "5173": "\u6db8", + "5174": "\u6df9", + "5175": "\u6e2d", + "5176": "\u6eff", + "5177": "\u6f58", + "5178": "\u6fd4", + "5179": "\u6fd8", + "5180": "\u6fdf", + "5181": "\u70ac", + "5182": "\u7147", + "5183": "\u71a8", + "5184": "\u71f5", + "5185": "\u72fd", + "5186": "\u73bb", + "5187": "\u763b", + "5188": "\u7647", + "5189": "\u779e", + "5190": "\u7895", + "5191": "\u79a7", + "5192": "\u79be", + "5193": "\u79c9", + "5194": "\u7d72", + "5195": "\u7d89", + "5196": "\u7e0b", + "5197": "\u7e37", + "5198": "\u7e6b", + "5199": "\u81fa", + "5200": "\u8271", + "5201": "\u856a", + "5202": "\u867b", + "5203": "\u8778", + "5204": "\u89ba", + "5205": "\u8a1d", + "5206": "\u8abc", + "5207": "\u8b6f", + "5208": "\u8f15", + "5209": "\u9438", + "5210": "\u958f", + "5211": "\u9a5b", + "5212": "\u9ad9", + "5213": "\u9b18", + "5214": "\u9b4d", + "5215": "\u9b4e", + "5216": "\u9bf0", + "5217": "\u9bf1", + "5218": "\u9d61", + "5219": "\u9e1a", + "5220": "\u9edb", + "5221": "\u9f3e", + "5222": "\u4e9e", + "5223": "\u4f83", + "5224": "\u4fad", + "5225": "\u4fce", + "5226": "\u5011", + "5227": "\u52de", + "5228": "\u5319", + "5229": "\u541e", + "5230": "\u54b8", + "5231": "\u54c8", + "5232": "\u564e", + "5233": "\u5664", + "5234": "\u56d3", + "5235": "\u58de", + "5236": "\u5abd", + "5237": "\u5ff8", + "5238": "\u5ffd", + "5239": "\u6029", + "5240": "\u604d", + "5241": "\u6063", + "5242": "\u60c7", + "5243": "\u61ae", + "5244": "\u622a", + "5245": "\u6258", + "5246": "\u64bb", + "5247": "\u6572", + "5248": "\u658c", + "5249": "\u660a", + "5250": "\u6919", + "5251": "\u69ce", + "5252": "\u6d8e", + "5253": "\u6dee", + "5254": "\u6dfa", + "5255": "\u6e5b", + "5256": "\u6eaf", + "5257": "\u6f09", + "5258": "\u6f6f", + "5259": "\u6fb9", + "5260": "\u7114", + "5261": "\u711c", + "5262": "\u7156", + "5263": "\u71d4", + "5264": "\u7337", + "5265": "\u736a", + "5266": "\u73ca", + "5267": "\u743f", + "5268": "\u745a", + "5269": "\u751c", + "5270": "\u752b", + "5271": "\u7564", + "5272": "\u7586", + "5273": "\u766a", + "5274": "\u76ea", + "5275": "\u77a0", + "5276": "\u783f", + "5277": "\u7957", + "5278": "\u798a", + "5279": "\u7aba", + "5280": "\u7b08", + "5281": "\u7b19", + "5282": "\u7bad", + "5283": "\u7c38", + "5284": "\u80e4", + "5285": "\u81cd", + "5286": "\u821b", + "5287": "\u827e", + "5288": "\u8318", + "5289": "\u83aa", + "5290": "\u8403", + "5291": "\u8431", + "5292": "\u848b", + "5293": "\u8597", + "5294": "\u85f9", + "5295": "\u86ce", + "5296": "\u86ef", + "5297": "\u8815", + "5298": "\u88b1", + "5299": "\u8977", + "5300": "\u89af", + "5301": "\u89c0", + "5302": "\u8a48", + "5303": "\u8aa6", + "5304": "\u8acc", + "5305": "\u8ae4", + "5306": "\u8b7d", + "5307": "\u8c50", + "5308": "\u8cce", + "5309": "\u8ce4", + "5310": "\u8d6d", + "5311": "\u8dcb", + "5312": "\u8e42", + "5313": "\u8e99", + "5314": "\u8f46", + "5315": "\u8f64", + "5316": "\u9041", + "5317": "\u9248", + "5318": "\u9249", + "5319": "\u932e", + "5320": "\u96d9", + "5321": "\u98ee", + "5322": "\u991e", + "5323": "\u9952", + "5324": "\u9957", + "5325": "\u99c8", + "5326": "\u99dd", + "5327": "\u9a57", + "5328": "\u9d44", + "5329": "\u9dd7", + "5330": "\u9eb4", + "5331": "\u9ed1", + "5332": "\ud857\udc4b", + "5333": "\u4e15", + "5334": "\u4e2a", + "5335": "\u4e99", + "5336": "\u4eb0", + "5337": "\u4efd", + "5338": "\u5047", + "5339": "\u50d6", + "5340": "\u50ed", + "5341": "\u524c", + "5342": "\u528d", + "5343": "\u52bf", + "5344": "\u5377", + "5345": "\u53c3", + "5346": "\u548b", + "5347": "\u54ab", + "5348": "\u54ea", + "5349": "\u5583", + "5350": "\u55ae", + "5351": "\u56b4", + "5352": "\u56c2", + "5353": "\u56d1", + "5354": "\u57b3", + "5355": "\u5852", + "5356": "\u58d8", + "5357": "\u5919", + "5358": "\u5934", + "5359": "\u5987", + "5360": "\u59b2", + "5361": "\u59c6", + "5362": "\u5ae3", + "5363": "\u5be5", + "5364": "\u5bf9", + "5365": "\u5c07", + "5366": "\u5c08", + "5367": "\u5d5c", + "5368": "\u5e08", + "5369": "\u5e1a", + "5370": "\u5e36", + "5371": "\u5e96", + "5372": "\u5eec", + "5373": "\u5f61", + "5374": "\u5f9e", + "5375": "\u5fb7", + "5376": "\u60fb", + "5377": "\u613f", + "5378": "\u6147", + "5379": "\u618a", + "5380": "\u61c3", + "5381": "\u61ff", + "5382": "\u6208", + "5383": "\u6230", + "5384": "\u6237", + "5385": "\u6289", + "5386": "\u62c2", + "5387": "\u62cc", + "5388": "\u62d4", + "5389": "\u6369", + "5390": "\u63ac", + "5391": "\u6451", + "5392": "\u6493", + "5393": "\u64b9", + "5394": "\u652c", + "5395": "\u6656", + "5396": "\u678c", + "5397": "\u6837", + "5398": "\u68b3", + "5399": "\u69ff", + "5400": "\u6a31", + "5401": "\u6a84", + "5402": "\u6aa2", + "5403": "\u6aaa", + "5404": "\u6aac", + "5405": "\u6ab8", + "5406": "\u6ae8", + "5407": "\u6b1d", + "5408": "\u6c9b", + "5409": "\u6cbd", + "5410": "\u6d35", + "5411": "\u6da6", + "5412": "\u6e8c", + "5413": "\u6ec9", + "5414": "\u6eef", + "5415": "\u6efe", + "5416": "\u6f11", + "5417": "\u6f32", + "5418": "\u6f6d", + "5419": "\u7165", + "5420": "\u71fc", + "5421": "\u7252", + "5422": "\u72f7", + "5423": "\u7463", + "5424": "\u7511", + "5425": "\u758b", + "5426": "\u75cd", + "5427": "\u75f0", + "5428": "\u7672", + "5429": "\u767c", + "5430": "\u76c2", + "5431": "\u775b", + "5432": "\u77dc", + "5433": "\u77e9", + "5434": "\u787c", + "5435": "\u78a9", + "5436": "\u7941", + "5437": "\u798e", + "5438": "\u79b9", + "5439": "\u7b1e", + "5440": "\u7b45", + "5441": "\u7b86", + "5442": "\u7c11", + "5443": "\u7cae", + "5444": "\u7d45", + "5445": "\u7d7d", + "5446": "\u7d93", + "5447": "\u7da0", + "5448": "\u7dac", + "5449": "\u7db8", + "5450": "\u7dd8", + "5451": "\u7e12", + "5452": "\u7e61", + "5453": "\u7e69", + "5454": "\u7e6a", + "5455": "\u7e8c", + "5456": "\u7eb8", + "5457": "\u7ec8", + "5458": "\u804a", + "5459": "\u8070", + "5460": "\u8085", + "5461": "\u80c4", + "5462": "\u820d", + "5463": "\u8229", + "5464": "\u8258", + "5465": "\u8278", + "5466": "\u83eb", + "5467": "\u8514", + "5468": "\u851a", + "5469": "\u860a", + "5470": "\u863f", + "5471": "\u86de", + "5472": "\u870a", + "5473": "\u8753", + "5474": "\u8755", + "5475": "\u87c4", + "5476": "\u87e0", + "5477": "\u884d", + "5478": "\u88dd", + "5479": "\u89bd", + "5480": "\u89bf", + "5481": "\u8a3b", + "5482": "\u8ac4", + "5483": "\u8b74", + "5484": "\u8b80", + "5485": "\u8b93", + "5486": "\u8bf7", + "5487": "\u8c6c", + "5488": "\u8c98", + "5489": "\u8d39", + "5490": "\u8d6b", + "5491": "\u8de3", + "5492": "\u8e89", + "5493": "\u8efe", + "5494": "\u8f49", + "5495": "\u8ff8", + "5496": "\u8ff9", + "5497": "\u914a", + "5498": "\u9169", + "5499": "\u91aa", + "5500": "\u923f", + "5501": "\u929c", + "5502": "\u934d", + "5503": "\u943a", + "5504": "\u945a", + "5505": "\u94bf", + "5506": "\u95bb", + "5507": "\u95ee", + "5508": "\u965e", + "5509": "\u96dc", + "5510": "\u9706", + "5511": "\u9730", + "5512": "\u97cb", + "5513": "\u985a", + "5514": "\u9986", + "5515": "\u99c1", + "5516": "\u99f1", + "5517": "\u9a55", + "5518": "\u9b51", + "5519": "\u93b9", + "5520": "\u6248", + "5521": "\u9e7c", + "5522": "\u9c24", + "5523": "\u8757", + "5524": "\u6777", + "5525": "\u66c9", + "5526": "\u9c67", + "5527": "\u9c47", + "5528": "\u9214", + "5529": "\u6eaa", + "5530": "\u65a4", + "5531": "\u734f", + "5532": "\u6670", + "5533": "\u76d2", + "5534": "\u5e5f", + "5535": "\u8f5f", + "5536": "\u8ad2", + "5537": "\u7b92", + "5538": "\u75e3", + "5539": "\u9ea9", + "5540": "\u699c", + "5541": "\u9b92", + "5542": "\u5398", + "5543": "\u8cc2", + "5544": "\u84a1", + "5545": "\u85af", + "5546": "\u6a80", + "5547": "\u8e35", + "5548": "\u5366", + "5549": "\u7962", + "5550": "\u60b8", + "5551": "\u7b48", + "5552": "\u76c3", + "5553": "\u67a1", + "5554": "\u87a2", + "5555": "\u9b41", + "5556": "\u7fb9", + "5557": "\u6bef", + "5558": "\u7bed", + "5559": "\u7621", + "5560": "\u5653", + "5561": "\u535c", + "5562": "\u7d2c", + "5563": "\u58f7", + "5564": "\u55e3", + "5565": "\u80f1", + "5566": "\u96c1", + "5567": "\u6634", + "5568": "\u6602", + "5569": "\u647a", + "5570": "\u8b02", + "5571": "\u818f", + "5572": "\u7d9c", + "5573": "\u87fb", + "5574": "\u81e5", + "5575": "\u9bab", + "5576": "\u6ad3", + "5577": "\u88df", + "5578": "\u59be", + "5579": "\u74dc", + "5580": "\u9eb5", + "5581": "\u87f2", + "5582": "\u9e78", + "5583": "\u515c", + "5584": "\u7e8f", + "5585": "\u9306", + "5586": "\u88b4", + "5587": "\u74e2", + "5588": "\u4e19", + "5589": "\u7aff", + "5590": "\u5962", + "5591": "\u852d", + "5592": "\u67ca", + "5593": "\u55ac", + "5594": "\u9921", + "5595": "\u8fc4", + "5596": "\u676d", + "5597": "\u7c95", + "5598": "\u64e2", + "5599": "\u9784", + "5600": "\u8e5f", + "5601": "\u7e55", + "5602": "\u8087", + "5603": "\u9742", + "5604": "\u907d", + "5605": "\u57c3", + "5606": "\u6813", + "5607": "\u751a", + "5608": "\u714c", + "5609": "\u67f5", + "5610": "\u51cc", + "5611": "\u853d", + "5612": "\u71c8", + "5613": "\u9949", + "5614": "\u91c7", + "5615": "\u8463", + "5616": "\u696f", + "5617": "\u57a2", + "5618": "\u6e26", + "5619": "\u6bc5", + "5620": "\u6028", + "5621": "\u5687", + "5622": "\u9e9f", + "5623": "\u67d1", + "5624": "\u6689", + "5625": "\u7dcb", + "5626": "\u75e2", + "5627": "\u6893", + "5628": "\u6e4a", + "5629": "\u901d", + "5630": "\u7aaf", + "5631": "\u5740", + "5632": "\u7e4d", + "5633": "\u63c6", + "5634": "\u60e7", + "5635": "\u5df3", + "5636": "\u58fa", + "5637": "\u7483", + "5638": "\u80b4", + "5639": "\u8098", + "5640": "\u9b8e", + "5641": "\u8a6e", + "5642": "\u514e", + "5643": "\u9aed", + "5644": "\u8471", + "5645": "\u5840", + "5646": "\u53ea", + "5647": "\u7ca5", + "5648": "\u8a23", + "5649": "\u6284", + "5650": "\u5f10", + "5651": "\u5446", + "5652": "\u8338", + "5653": "\u5ec9", + "5654": "\u7078", + "5655": "\u681e", + "5656": "\u5e25", + "5657": "\u82fa", + "5658": "\u6953", + "5659": "\u724c", + "5660": "\u7d79", + "5661": "\u68af", + "5662": "\u6234", + "5663": "\u4e98", + "5664": "\u5bb5", + "5665": "\u8b5a", + "5666": "\u5efb", + "5667": "\u9bdb", + "5668": "\u99b3", + "5669": "\u51e7", + "5670": "\u7a14", + "5671": "\u7f60", + "5672": "\u9192", + "5673": "\u75b9", + "5674": "\u7dbb", + "5675": "\u589c", + "5676": "\u9262", + "5677": "\u72d7", + "5678": "\u6912", + "5679": "\u4ed4", + "5680": "\u7cde", + "5681": "\u8d66", + "5682": "\u8404", + "5683": "\u82d4", + "5684": "\u7027", + "5685": "\u8823", + "5686": "\u59d1", + "5687": "\u8017", + "5688": "\u51db", + "5689": "\u98f4", + "5690": "\u68fa", + "5691": "\u60a6", + "5692": "\u9bad", + "5693": "\u87f9", + "5694": "\u7709", + "5695": "\u6816", + "5696": "\u9bc9", + "5697": "\u8587", + "5698": "\u541f", + "5699": "\u9591", + "5700": "\u86ee", + "5701": "\u85fb", + "5702": "\u7a9f", + "5703": "\u8c8c", + "5704": "\u5a7f", + "5705": "\u817a", + "5706": "\u75fa", + "5707": "\u9688", + "5708": "\u81fc", + "5709": "\u7d10", + "5710": "\u7dbf", + "5711": "\u69fd", + "5712": "\u9be8", + "5713": "\u7409", + "5714": "\u53c9", + "5715": "\u4ff5", + "5716": "\u7259", + "5717": "\u831c", + "5718": "\u7432", + "5719": "\u5e16", + "5720": "\u906e", + "5721": "\u6ef4", + "5722": "\u932f", + "5723": "\u907c", + "5724": "\u9bd6", + "5725": "\u59dc", + "5726": "\u8749", + "5727": "\u9813", + "5728": "\u7897", + "5729": "\u732a", + "5730": "\u9a30", + "5731": "\u5b9b", + "5732": "\u914e", + "5733": "\u71d5", + "5734": "\u9cf3", + "5735": "\u5ac9", + "5736": "\u5766", + "5737": "\u6c70", + "5738": "\u9d28", + "5739": "\u8f3f", + "5740": "\u984e", + "5741": "\u8aed", + "5742": "\u760d", + "5743": "\u6841", + "5744": "\u842c", + "5745": "\u904d", + "5746": "\u67d0", + "5747": "\u9756", + "5748": "\u58f1", + "5749": "\u971e", + "5750": "\u865a", + "5751": "\u5e06", + "5752": "\u7a6b", + "5753": "\u81b3", + "5754": "\u9ba8", + "5755": "\u6681", + "5756": "\u62d0", + "5757": "\u5b8b", + "5758": "\u51e1", + "5759": "\u6ce1", + "5760": "\u5451", + "5761": "\u9ce9", + "5762": "\u55b0", + "5763": "\u56da", + "5764": "\u59ea", + "5765": "\u584a", + "5766": "\u59ac", + "5767": "\u7d17", + "5768": "\u74f6", + "5769": "\u5c3a", + "5770": "\u77db", + "5771": "\u5ee3", + "5772": "\u9e93", + "5773": "\u84cb", + "5774": "\u6f02", + "5775": "\u6643", + "5776": "\u5f84", + "5777": "\u5146", + "5778": "\u67ff", + "5779": "\u4fa0", + "5780": "\u9b31", + "5781": "\u5bf8", + "5782": "\u638c", + "5783": "\u5b9c", + "5784": "\u8ce0", + "5785": "\u6f84", + "5786": "\u674f", + "5787": "\u59fb", + "5788": "\u53a8", + "5789": "\u95a5", + "5790": "\u68f2", + "5791": "\u4faf", + "5792": "\u731f", + "5793": "\u674e", + "5794": "\u7985", + "5795": "\u8b19", + "5796": "\u86c7", + "5797": "\u80c6", + "5798": "\u30c2", + "5799": "\u6627", + "5800": "\u971c", + "5801": "\u845b", + "5802": "\u65ac", + "5803": "\u7c60", + "5804": "\u66f9", + "5805": "\u60e8", + "5806": "\u7e2b", + "5807": "\u7070", + "5808": "\u6842", + "5809": "\u8fbb", + "5810": "\u864e", + "5811": "\u7c92", + "5812": "\u7b1b", + "5813": "\u5507", + "5814": "\u9175", + "5815": "\u80ce", + "5816": "\u722a", + "5817": "\u73e0", + "5818": "\u76fe", + "5819": "\u6bbb", + "5820": "\u9418", + "5821": "\u925b", + "5822": "\u9685", + "5823": "\u821f", + "5824": "\u9285", + "5825": "\u570b", + "5826": "\u9326", + "5827": "\u70c8", + "5828": "\u9df9", + "5829": "\u92fc", + "5830": "\u6795", + "5831": "\u5824", + "5832": "\u8a1f", + "5833": "\u51f6", + "5834": "\u673a", + "5835": "\u5eb6", + "5836": "\u5c3c", + "5837": "\u5589", + "5838": "\u6850", + "5839": "\u819d", + "5840": "\u58c7", + "5841": "\u84c4", + "5842": "\u82bd", + "5843": "\u8607", + "5844": "\u7bb8", + "5845": "\u5ce0", + "5846": "\u8c9e", + "5847": "\u7089", + "5848": "\u5ce1", + "5849": "\u7d46", + "5850": "\u6ecb", + "5851": "\u8896", + "5852": "\u74a7", + "5853": "\u5609", + "5854": "\u7f36", + "5855": "\u8679", + "5856": "\u88f8", + "5857": "\u8015", + "5858": "\u60a0", + "5859": "\u8475", + "5860": "\u642c", + "5861": "\u664b", + "5862": "\u5f26", + "5863": "\u990c", + "5864": "\u8247", + "5865": "\u4eae", + "5866": "\u816b", + "5867": "\u72fc", + "5868": "\u697c", + "5869": "\u9905", + "5870": "\u723a", + "5871": "\u53c8", + "5872": "\u4f8d", + "5873": "\u68df", + "5874": "\u596e", + "5875": "\u50e7", + "5876": "\u84ee", + "5877": "\u828b", + "5878": "\u7573", + "5879": "\u5bb4", + "5880": "\u99ff", + "5881": "\u916c", + "5882": "\u68da", + "5883": "\u5256", + "5884": "\u8cca", + "5885": "\u8870", + "5886": "\u5841", + "5887": "\u8b5c", + "5888": "\u65cb", + "5889": "\u8b90", + "5890": "\u80aa", + "5891": "\u8178", + "5892": "\u83f1", + "5893": "\u95b2", + "5894": "\u7b52", + "5895": "\u54c9", + "5896": "\u9675", + "5897": "\u5a46", + "5898": "\u6b04", + "5899": "\u9855", + "5900": "\u9042", + "5901": "\u7e1b", + "5902": "\u8ef8", + "5903": "\u585e", + "5904": "\u5b8f", + "5905": "\u7def", + "5906": "\u7434", + "5907": "\u5bb0", + "5908": "\u91dc", + "5909": "\u862d", + "5910": "\u9298", + "5911": "\u6f64", + "5912": "\u66a6", + "5913": "\u4f0e", + "5914": "\u75f4", + "5915": "\u73b2", + "5916": "\u75ab", + "5917": "\u660c", + "5918": "\u73ed", + "5919": "\u5f80", + "5920": "\u5203", + "5921": "\u6f54", + "5922": "\u6d32", + "5923": "\u5982", + "5924": "\u5cac", + "5925": "\u7950", + "5926": "\u67cf", + "5927": "\u518a", + "5928": "\u96c0", + "5929": "\u88c2", + "5930": "\u53eb", + "5931": "\u7f85", + "5932": "\u7c8b", + "5933": "\u67f1", + "5934": "\u7948", + "5935": "\u6566", + "5936": "\u30f2", + "5937": "\u5c09", + "5938": "\u3045", + "5939": "\u7db1", + "5940": "\u4e4f", + "5941": "\u6b3a", + "5942": "\u66fd", + "5943": "\u6df3", + "5944": "\u7fd4", + "5945": "\u628a", + "5946": "\u6b96", + "5947": "\u6daf", + "5948": "\u6212", + "5949": "\u5a92", + "5950": "\u7b26", + "5951": "\u9162", + "5952": "\u9177", + "5953": "\u8c9d", + "5954": "\u5cf0", + "5955": "\u5bdb", + "5956": "\u96b7", + "5957": "\u733f", + "5958": "\u764c", + "5959": "\u7dbe", + "5960": "\u6ce5", + "5961": "\u7c9b", + "5962": "\u6249", + "5963": "\u5a20", + "5964": "\u8f14", + "5965": "\u76bf", + "5966": "\u9f13", + "5967": "\u719f", + "5968": "\u6717", + "5969": "\u99d2", + "5970": "\u92ad", + "5971": "\u82d1", + "5972": "\u9396", + "5973": "\u809d", + "5974": "\u5782", + "5975": "\u5104", + "5976": "\u78c1", + "5977": "\u6d1e", + "5978": "\u95c7", + "5979": "\u8987", + "5980": "\u51a0", + "5981": "\u58b3", + "5982": "\u4e3c", + "5983": "\u5be7", + "5984": "\u77b3", + "5985": "\u7656", + "5986": "\u525b", + "5987": "\u83ca", + "5988": "\u5b22", + "5989": "\u9047", + "5990": "\u80a2", + "5991": "\u654f", + "5992": "\u5c0b", + "5993": "\u72c2", + "5994": "\u67f4", + "5995": "\u5f6b", + "5996": "\u5805", + "5997": "\u679d", + "5998": "\u7d2b", + "5999": "\u62fe", + "6000": "\u5d8b", + "6001": "\u9084", + "6002": "\u7e26", + "6003": "\u80de", + "6004": "\u6069", + "6005": "\u3043", + "6006": "\u91c8", + "6007": "\u5c3b", + "6008": "\u5eb5", + "6009": "\u5a01", + "6010": "\u5c1a", + "6011": "\u62f3", + "6012": "\u64b2", + "6013": "\u5320", + "6014": "\u6676", + "6015": "\u61a9", + "6016": "\u7965", + "6017": "\u7832", + "6018": "\u7126", + "6019": "\u5c3f", + "6020": "\u9b42", + "6021": "\u7a42", + "6022": "\u8f44", + "6023": "\u62dd", + "6024": "\u91a4", + "6025": "\u658e", + "6026": "\u621a", + "6027": "\u5de3", + "6028": "\u572d", + "6029": "\u9727", + "6030": "\u6f6e", + "6031": "\u57f9", + "6032": "\u5f81", + "6033": "\u5f25", + "6034": "\u5b5d", + "6035": "\u8150", + "6036": "\u8ca2", + "6037": "\u6ca1", + "6038": "\u68cb", + "6039": "\u5f70", + "6040": "\u5e3d", + "6041": "\u83cc", + "6042": "\u7891", + "6043": "\u6597", + "6044": "\u63fa", + "6045": "\u7cf8", + "6046": "\u9d8f", + "6047": "\u9f3b", + "6048": "\u7235", + "6049": "\u85a6", + "6050": "\u808c", + "6051": "\u5c48", + "6052": "\u7d0b", + "6053": "\u67a0", + "6054": "\u57a3", + "6055": "\u65ec", + "6056": "\u614e", + "6057": "\u968f", + "6058": "\u8ed2", + "6059": "\u4e59", + "6060": "\u7384", + "6061": "\u5200", + "6062": "\u8df5", + "6063": "\u4f0f", + "6064": "\u5642", + "6065": "\u5e84", + "6066": "\u78e8", + "6067": "\u9694", + "6068": "\u9686", + "6069": "\u7a74", + "6070": "\u76c6", + "6071": "\u8ca7", + "6072": "\u9375", + "6073": "\u5cb3", + "6074": "\u616e", + "6075": "\u5374", + "6076": "\u62f6", + "6077": "\u5f13", + "6078": "\u5373", + "6079": "\u59d3", + "6080": "\u6398", + "6081": "\u6d6a", + "6082": "\u8b72", + "6083": "\u6589", + "6084": "\u9a0e", + "6085": "\u5968", + "6086": "\u8b00", + "6087": "\u5854", + "6088": "\u6ed1", + "6089": "\u5098", + "6090": "\u96f7", + "6091": "\u4fca", + "6092": "\u8edf", + "6093": "\u8f1d", + "6094": "\u6458", + "6095": "\u6176", + "6096": "\u6c57", + "6097": "\u6fa4", + "6098": "\u7bc7", + "6099": "\u8b0e", + "6100": "\u5e7b", + "6101": "\u9903", + "6102": "\u5339", + "6103": "\u543e", + "6104": "\u93e1", + "6105": "\u68d2", + "6106": "\u6d99", + "6107": "\u8cc3", + "6108": "\u8302", + "6109": "\u609f", + "6110": "\u5504", + "6111": "\u846c", + "6112": "\u6469", + "6113": "\u7c3f", + "6114": "\u6e09", + "6115": "\u511f", + "6116": "\u50da", + "6117": "\u65ed", + "6118": "\u8102", + "6119": "\u5e8f", + "6120": "\u8cab", + "6121": "\u3049", + "6122": "\u8aa4", + "6123": "\u7ffc", + "6124": "\u5bee", + "6125": "\u6edd", + "6126": "\u6c37", + "6127": "\u91dd", + "6128": "\u96c5", + "6129": "\u502b", + "6130": "\u85e9", + "6131": "\u5237", + "6132": "\u9663", + "6133": "\u7551", + "6134": "\u5a9b", + "6135": "\u5c3d", + "6136": "\u8cdb", + "6137": "\u50b5", + "6138": "\u8aa0", + "6139": "\u716e", + "6140": "\u6843", + "6141": "\u52d8", + "6142": "\u7a32", + "6143": "\u68c4", + "6144": "\u8170", + "6145": "\u83c5", + "6146": "\u7344", + "6147": "\u67f3", + "6148": "\u7ca7", + "6149": "\u5f18", + "6150": "\u9db4", + "6151": "\u80a9", + "6152": "\u9283", + "6153": "\u6c41", + "6154": "\u706f", + "6155": "\u773c", + "6156": "\u7db2", + "6157": "\u5c01", + "6158": "\u564c", + "6159": "\u7a3f", + "6160": "\u9644", + "6161": "\u676f", + "6162": "\u6094", + "6163": "\u9ea6", + "6164": "\u6e7f", + "6165": "\u9774", + "6166": "\u307a", + "6167": "\u6b8a", + "6168": "\u62b5", + "6169": "\u790e", + "6170": "\u8c5a", + "6171": "\u9ed9", + "6172": "\u7de0", + "6173": "\u9154", + "6174": "\u4e43", + "6175": "\u71e5", + "6176": "\u934b", + "6177": "\u8c6a", + "6178": "\u8a0e", + "6179": "\u6fc3", + "6180": "\u7d05", + "6181": "\u7968", + "6182": "\u708e", + "6183": "\u76ae", + "6184": "\u7e2e", + "6185": "\u5fb9", + "6186": "\u6749", + "6187": "\u8f03", + "6188": "\u7a1a", + "6189": "\u5800", + "6190": "\u5e33", + "6191": "\u5fcd", + "6192": "\u4f2f", + "6193": "\u8a17", + "6194": "\u77e2", + "6195": "\u8a69", + "6196": "\u8feb", + "6197": "\u5076", + "6198": "\u6838", + "6199": "\u5100", + "6200": "\u53cc", + "6201": "\u5230", + "6202": "\u524a", + "6203": "\u6db2", + "6204": "\u99c6", + "6205": "\u4e80", + "6206": "\u8972", + "6207": "\u8846", + "6208": "\u5510", + "6209": "\u7cd6", + "6210": "\u5de1", + "6211": "\u7e41", + "6212": "\u81ed", + "6213": "\u708a", + "6214": "\u9670", + "6215": "\u8155", + "6216": "\u6d44", + "6217": "\u5629", + "6218": "\u54b2", + "6219": "\u76d7", + "6220": "\u8108", + "6221": "\u6ede", + "6222": "\u7267", + "6223": "\u574a", + "6224": "\u5305", + "6225": "\u81f3", + "6226": "\u679a", + "6227": "\u5049", + "6228": "\u81f4", + "6229": "\u8a13", + "6230": "\u8ca8", + "6231": "\u8033", + "6232": "\u6f22", + "6233": "\u65d7", + "6234": "\u5df1", + "6235": "\u6247", + "6236": "\u6885", + "6237": "\u63e1", + "6238": "\u6b27", + "6239": "\u8584", + "6240": "\u6065", + "6241": "\u732e", + "6242": "\u9810", + "6243": "\u4ec1", + "6244": "\u9f8d", + "6245": "\u8a70", + "6246": "\u73cd", + "6247": "\u5f69", + "6248": "\u5feb", + "6249": "\u6cbc", + "6250": "\u6bd2", + "6251": "\u4e39", + "6252": "\u53e5", + "6253": "\u9234", + "6254": "\u91e3", + "6255": "\u7e01", + "6256": "\u5fae", + "6257": "\u5999", + "6258": "\u62ec", + "6259": "\u6669", + "6260": "\u7c89", + "6261": "\u9eba", + "6262": "\u5353", + "6263": "\u570f", + "6264": "\u517c", + "6265": "\u6b20", + "6266": "\u8e0a", + "6267": "\u8133", + "6268": "\u7adc", + "6269": "\u9cf4", + "6270": "\u5f66", + "6271": "\u5ac1", + "6272": "\u9a12", + "6273": "\u9aea", + "6274": "\u8acb", + "6275": "\u5375", + "6276": "\u640d", + "6277": "\u8377", + "6278": "\u68a8", + "6279": "\u5531", + "6280": "\u5d50", + "6281": "\u5e4c", + "6282": "\u4f34", + "6283": "\u624d", + "6284": "\u961c", + "6285": "\u81d3", + "6286": "\u7363", + "6287": "\u7bb1", + "6288": "\u7956", + "6289": "\u7532", + "6290": "\u6d74", + "6291": "\u5c0a", + "6292": "\u907f", + "6293": "\u6607", + "6294": "\u718a", + "6295": "\u58c1", + "6296": "\u4e18", + "6297": "\u6790", + "6298": "\u5b6b", + "6299": "\u5e72", + "6300": "\u7687", + "6301": "\u539a", + "6302": "\u4ead", + "6303": "\u970a", + "6304": "\u7b46", + "6305": "\u627f", + "6306": "\u5747", + "6307": "\u878d", + "6308": "\u5f8b", + "6309": "\u7dd1", + "6310": "\u5426", + "6311": "\u9b3c", + "6312": "\u587e", + "6313": "\u811a", + "6314": "\u9808", + "6315": "\u90aa", + "6316": "\u888b", + "6317": "\u6e56", + "6318": "\u4e73", + "6319": "\u88d5", + "6320": "\u63ee", + "6321": "\u51cd", + "6322": "\u6ec5", + "6323": "\u4e7e", + "6324": "\u3074", + "6325": "\u7fbd", + "6326": "\u6162", + "6327": "\u5019", + "6328": "\u62e1", + "6329": "\u6fef", + "6330": "\u8cb8", + "6331": "\u7802", + "6332": "\u656c", + "6333": "\u6982", + "6334": "\u5e81", + "6335": "\u7159", + "6336": "\u57f7", + "6337": "\u5e95", + "6338": "\u88c1", + "6339": "\u558b", + "6340": "\u9e97", + "6341": "\u9732", + "6342": "\u96f2", + "6343": "\u9aa8", + "6344": "\u500d", + "6345": "\u6bbf", + "6346": "\u5947", + "6347": "\u6613", + "6348": "\u5c64", + "6349": "\u6577", + "6350": "\u5e55", + "6351": "\u6bdb", + "6352": "\u7206", + "6353": "\u6687", + "6354": "\u68b0", + "6355": "\u8cb4", + "6356": "\u96a3", + "6357": "\u8f38", + "6358": "\u3077", + "6359": "\u67c4", + "6360": "\u59eb", + "6361": "\u7bc4", + "6362": "\u63b2", + "6363": "\u5618", + "6364": "\u585a", + "6365": "\u5264", + "6366": "\u5145", + "6367": "\u4f75", + "6368": "\u5974", + "6369": "\u675f", + "6370": "\u5893", + "6371": "\u702c", + "6372": "\u520a", + "6373": "\u8863", + "6374": "\u629e", + "6375": "\u7e04", + "6376": "\u77ac", + "6377": "\u5c04", + "6378": "\u83d3", + "6379": "\u52df", + "6380": "\u4e71", + "6381": "\u8fce", + "6382": "\u62b1", + "6383": "\u6c38", + "6384": "\u7af9", + "6385": "\u9178", + "6386": "\u523a", + "6387": "\u95a3", + "6388": "\u90f7", + "6389": "\u4e5f", + "6390": "\u61b6", + "6391": "\u5263", + "6392": "\u529f", + "6393": "\u9e7f", + "6394": "\u725b", + "6395": "\u79d8", + "6396": "\u4ecf", + "6397": "\u96c4", + "6398": "\u866b", + "6399": "\u5584", + "6400": "\u5c4a", + "6401": "\u8266", + "6402": "\u7247", + "6403": "\u8907", + "6404": "\u70ba", + "6405": "\u6cf3", + "6406": "\u5b9d", + "6407": "\u6fc0", + "6408": "\u5e79", + "6409": "\u81e3", + "6410": "\u4e4b", + "6411": "\u6691", + "6412": "\u6d66", + "6413": "\u770b", + "6414": "\u7591", + "6415": "\u8a98", + "6416": "\u66b4", + "6417": "\u8056", + "6418": "\u6368", + "6419": "\u677f", + "6420": "\u685c", + "6421": "\u7834", + "6422": "\u9769", + "6423": "\u5e0c", + "6424": "\u5e45", + "6425": "\u5442", + "6426": "\u6298", + "6427": "\u8a3a", + "6428": "\u4f38", + "6429": "\u60d1", + "6430": "\u6e2c", + "6431": "\u99d0", + "6432": "\u7a93", + "6433": "\u7d00", + "6434": "\u820e", + "6435": "\u7f72", + "6436": "\u60a3", + "6437": "\u5cb8", + "6438": "\u7e3e", + "6439": "\u6e7e", + "6440": "\u5c90", + "6441": "\u6a39", + "6442": "\u7d0d", + "6443": "\u79c0", + "6444": "\u514d", + "6445": "\u8b1d", + "6446": "\u6c60", + "6447": "\u7981", + "6448": "\u80cc", + "6449": "\u8e8d", + "6450": "\u8074", + "6451": "\u6297", + "6452": "\u8c46", + "6453": "\u7a0e", + "6454": "\u594f", + "6455": "\u8349", + "6456": "\u5f3e", + "6457": "\u6075", + "6458": "\u8001", + "6459": "\u793c", + "6460": "\u89d2", + "6461": "\u7ae5", + "6462": "\u5be9", + "6463": "\u88cf", + "6464": "\u5439", + "6465": "\u7720", + "6466": "\u6b6f", + "6467": "\u62e0", + "6468": "\u5bd2", + "6469": "\u6163", + "6470": "\u89e6", + "6471": "\u98fc", + "6472": "\u8358", + "6473": "\u7fa4", + "6474": "\u8ff7", + "6475": "\u6cca", + "6476": "\u5b97", + "6477": "\u65e6", + "6478": "\u50b7", + "6479": "\u984d", + "6480": "\u5869", + "6481": "\u5238", + "6482": "\u5e8a", + "6483": "\u9759", + "6484": "\u7559", + "6485": "\u8457", + "6486": "\u6cb9", + "6487": "\u8a8c", + "6488": "\u7f6a", + "6489": "\u7d14", + "6490": "\u8179", + "6491": "\u5075", + "6492": "\u5247", + "6493": "\u58ca", + "6494": "\u672d", + "6495": "\u8f2a", + "6496": "\u6383", + "6497": "\u707d", + "6498": "\u95d8", + "6499": "\u5f31", + "6500": "\u523b", + "6501": "\u822a", + "6502": "\u7b54", + "6503": "\u6804", + "6504": "\u59ff", + "6505": "\u4ea1", + "6506": "\u7e54", + "6507": "\u6557", + "6508": "\u7ae0", + "6509": "\u5438", + "6510": "\u4ee4", + "6511": "\u9bae", + "6512": "\u88dc", + "6513": "\u5915", + "6514": "\u635c", + "6515": "\u6012", + "6516": "\u6a21", + "6517": "\u76ca", + "6518": "\u559c", + "6519": "\u83ef", + "6520": "\u7d75", + "6521": "\u7533", + "6522": "\u76e4", + "6523": "\u8efd", + "6524": "\u7a4d", + "6525": "\u6a19", + "6526": "\u968e", + "6527": "\u7701", + "6528": "\u5bc6", + "6529": "\u9805", + "6530": "\u732b", + "6531": "\u5f93", + "6532": "\u975e", + "6533": "\u5e1d", + "6534": "\u5b63", + "6535": "\u6355", + "6536": "\u515a", + "6537": "\u6211", + "6538": "\u5727", + "6539": "\u9999", + "6540": "\u7b4b", + "6541": "\u8f29", + "6542": "\u7c4d", + "6543": "\u4e01", + "6544": "\u62bc", + "6545": "\u5c3e", + "6546": "\u97d3", + "6547": "\u64cd", + "6548": "\u6697", + "6549": "\u75c7", + "6550": "\u6563", + "6551": "\u7a81", + "6552": "\u9069", + "6553": "\u96d1", + "6554": "\u8de1", + "6555": "\u53b3", + "6556": "\u4e86", + "6557": "\u9ce5", + "6558": "\u9003", + "6559": "\u8b1b", + "6560": "\u6674", + "6561": "\u5fb4", + "6562": "\u5211", + "6563": "\u99c4", + "6564": "\u5009", + "6565": "\u56f0", + "6566": "\u77ed", + "6567": "\u5a66", + "6568": "\u9063", + "6569": "\u7565", + "6570": "\u9f62", + "6571": "\u9707", + "6572": "\u6575", + "6573": "\u8535", + "6574": "\u535a", + "6575": "\u8840", + "6576": "\u6e80", + "6577": "\u5fd7", + "6578": "\u8217", + "6579": "\u5b99", + "6580": "\u90e1", + "6581": "\u90a3", + "6582": "\u5bff", + "6583": "\u907a", + "6584": "\u79cb", + "6585": "\u6975", + "6586": "\u91cc", + "6587": "\u5ec3", + "6588": "\u56e0", + "6589": "\u5178", + "6590": "\u67d3", + "6591": "\u5f92", + "6592": "\u5dfb", + "6593": "\u9802", + "6594": "\u5742", + "6595": "\u8d85", + "6596": "\u6cb3", + "6597": "\u76db", + "6598": "\u72ac", + "6599": "\u8c4a", + "6600": "\u7aef", + "6601": "\u7d39", + "6602": "\u9996", + "6603": "\u6e6f", + "6604": "\u967d", + "6605": "\u7cbe", + "6606": "\u7949", + "6607": "\u6b73", + "6608": "\u7df4", + "6609": "\u6c5f", + "6610": "\u602a", + "6611": "\u5370", + "6612": "\u7b97", + "6613": "\u7d19", + "6614": "\u6255", + "6615": "\u6c42", + "6616": "\u969c", + "6617": "\u7c21", + "6618": "\u5fa1", + "6619": "\u9014", + "6620": "\u5275", + "6621": "\u8cc0", + "6622": "\u8239", + "6623": "\u5802", + "6624": "\u83dc", + "6625": "\u30a5", + "6626": "\u52e4", + "6627": "\u75db", + "6628": "\u4e26", + "6629": "\u666f", + "6630": "\u96ea", + "6631": "\u7bc0", + "6632": "\u9451", + "6633": "\u6d5c", + "6634": "\u663c", + "6635": "\u6e05", + "6636": "\u629c", + "6637": "\u52e2", + "6638": "\u66ae", + "6639": "\u9280", + "6640": "\u76df", + "6641": "\u9b5a", + "6642": "\u7387", + "6643": "\u6d0b", + "6644": "\u5bfa", + "6645": "\u5f01", + "6646": "\u7686", + "6647": "\u5fb3", + "6648": "\u8336", + "6649": "\u7b11", + "6650": "\u6e21", + "6651": "\u5948", + "6652": "\u9806", + "6653": "\u6cc1", + "6654": "\u8ac7", + "6655": "\u821e", + "6656": "\u6848", + "6657": "\u5ca9", + "6658": "\u8ca0", + "6659": "\u65e7", + "6660": "\u8ca1", + "6661": "\u8a31", + "6662": "\u6545", + "6663": "\u51ac", + "6664": "\u6a2a", + "6665": "\u5965", + "6666": "\u8a33", + "6667": "\u6bd4", + "6668": "\u56f2", + "6669": "\u505c", + "6670": "\u7bc9", + "6671": "\u6ce2", + "6672": "\u59b9", + "6673": "\u6797", + "6674": "\u6696", + "6675": "\u7d22", + "6676": "\u8d64", + "6677": "\u7d66", + "6678": "\u672b", + "6679": "\u50ac", + "6680": "\u6b66", + "6681": "\u6d17", + "6682": "\u9045", + "6683": "\u8ff0", + "6684": "\u9ed2", + "6685": "\u72af", + "6686": "\u5de6", + "6687": "\u6e90", + "6688": "\u9b54", + "6689": "\u7d30", + "6690": "\u4e45", + "6691": "\u4e0e", + "6692": "\u6e1b", + "6693": "\u7d1a", + "6694": "\u8cbb", + "6695": "\u8d8a", + "6696": "\u5dee", + "6697": "\u59bb", + "6698": "\u9818", + "6699": "\u885b", + "6700": "\u4e38", + "6701": "\u7d61", + "6702": "\u968a", + "6703": "\u85ac", + "6704": "\u6c0f", + "6705": "\u671b", + "6706": "\u4f3c", + "6707": "\u5c31", + "6708": "\u53f3", + "6709": "\u6761", + "6710": "\u5e03", + "6711": "\u51e6", + "6712": "\u8c37", + "6713": "\u7b56", + "6714": "\u52b9", + "6715": "\u5fd8", + "6716": "\u71b1", + "6717": "\u5fa9", + "6718": "\u59c9", + "6719": "\u30cc", + "6720": "\u632f", + "6721": "\u8ab2", + "6722": "\u898f", + "6723": "\u5012", + "6724": "\u6e2f", + "6725": "\u6ce8", + "6726": "\u68ee", + "6727": "\u9632", + "6728": "\u7d99", + "6729": "\u9000", + "6730": "\u6839", + "6731": "\u706b", + "6732": "\u66ff", + "6733": "\u9678", + "6734": "\u53bb", + "6735": "\u8996", + "6736": "\u6574", + "6737": "\u6e96", + "6738": "\u5ead", + "6739": "\u30be", + "6740": "\u72ec", + "6741": "\u6483", + "6742": "\u5150", + "6743": "\u6a4b", + "6744": "\u307d", + "6745": "\u63db", + "6746": "\u5ff5", + "6747": "\u8b58", + "6748": "\u306c", + "6749": "\u6253", + "6750": "\u6d25", + "6751": "\u96e8", + "6752": "\u5e78", + "6753": "\u542b", + "6754": "\u796d", + "6755": "\u97ff", + "6756": "\u52b4", + "6757": "\u51c4", + "6758": "\u5c06", + "6759": "\u5b98", + "6760": "\u82e6", + "6761": "\u8ffd", + "6762": "\u9060", + "6763": "\u672a", + "6764": "\u8ca9", + "6765": "\u5a18", + "6766": "\u8857", + "6767": "\u66dc", + "6768": "\u7a0b", + "6769": "\u63d0", + "6770": "\u7389", + "6771": "\u5224", + "6772": "\u79fb", + "6773": "\u653b", + "6774": "\u4f4e", + "6775": "\u88c5", + "6776": "\u65ad", + "6777": "\u53ca", + "6778": "\u8a3c", + "6779": "\u8c61", + "6780": "\u5b88", + "6781": "\u9752", + "6782": "\u5bcc", + "6783": "\u623b", + "6784": "\u8a5e", + "6785": "\u5409", + "6786": "\u6295", + "6787": "\u6b74", + "6788": "\u6ca2", + "6789": "\u8f09", + "6790": "\u5177", + "6791": "\u5eab", + "6792": "\u9664", + "6793": "\u74b0", + "6794": "\u5c55", + "6795": "\u5352", + "6796": "\u4e89", + "6797": "\u5931", + "6798": "\u623f", + "6799": "\u6625", + "6800": "\u6319", + "6801": "\u6f5f", + "6802": "\u8fd4", + "6803": "\u99ac", + "6804": "\u6b32", + "6805": "\u6750", + "6806": "\u6238", + "6807": "\u56f3", + "6808": "\u5bdd", + "6809": "\u990a", + "6810": "\u713c", + "6811": "\u5c0e", + "6812": "\u5922", + "6813": "\u7c73", + "6814": "\u51b7", + "6815": "\u606f", + "6816": "\u5175", + "6817": "\u5e2d", + "6818": "\u6e08", + "6819": "\u5287", + "6820": "\u63f4", + "6821": "\u98ef", + "6822": "\u592e", + "6823": "\u967a", + "6824": "\u670d", + "6825": "\u614b", + "6826": "\u8d70", + "6827": "\u8a55", + "6828": "\u5c45", + "6829": "\u6a29", + "6830": "\u8ad6", + "6831": "\u5f1f", + "6832": "\u3085", + "6833": "\u5883", + "6834": "\u5bdf", + "6835": "\u6388", + "6836": "\u983c", + "6837": "\u6d3e", + "6838": "\u64ae", + "6839": "\u7d20", + "6840": "\u4fee", + "6841": "\u7b2c", + "6842": "\u8cea", + "6843": "\u544a", + "6844": "\u8208", + "6845": "\u79d2", + "6846": "\u5b87", + "6847": "\u8089", + "6848": "\u5144", + "6849": "\u50cf", + "6850": "\u79f0", + "6851": "\u5024", + "6852": "\u982d", + "6853": "\u9031", + "6854": "\u7763", + "6855": "\u6d88", + "6856": "\u5b85", + "6857": "\u82b8", + "6858": "\u9854", + "6859": "\u8aad", + "6860": "\u4ef2", + "6861": "\u904a", + "6862": "\u8a66", + "6863": "\u901f", + "6864": "\u9152", + "6865": "\u5bbf", + "6866": "\u96e2", + "6867": "\u677e", + "6868": "\u5897", + "6869": "\u6bba", + "6870": "\u9244", + "6871": "\u53f8", + "6872": "\u5bb3", + "6873": "\u5272", + "6874": "\u77f3", + "6875": "\u590f", + "6876": "\u7248", + "6877": "\u4f50", + "6878": "\u52a9", + "6879": "\u82f1", + "6880": "\u53f7", + "6881": "\u60f3", + "6882": "\u7ba1", + "6883": "\u6025", + "6884": "\u9803", + "6885": "\u3065", + "6886": "\u82e5", + "6887": "\u604b", + "6888": "\u9020", + "6889": "\u53f2", + "6890": "\u6cc9", + "6891": "\u91cf", + "6892": "\u88fd", + "6893": "\u5e9c", + "6894": "\u8db3", + "6895": "\u6016", + "6896": "\u738b", + "6897": "\u59d4", + "6898": "\u4e21", + "6899": "\u8fba", + "6900": "\u6b8b", + "6901": "\u9006", + "6902": "\u5099", + "6903": "\u8ecd", + "6904": "\u8b66", + "6905": "\u67fb", + "6906": "\u5217", + "6907": "\u7de8", + "6908": "\u6bb5", + "6909": "\u53cd", + "6910": "\u30bc", + "6911": "\u643a", + "6912": "\u6b69", + "6913": "\u682a", + "6914": "\u5668", + "6915": "\u5ea7", + "6916": "\u98db", + "6917": "\u4e08", + "6918": "\u82b1", + "6919": "\u4fa1", + "6920": "\u76e3", + "6921": "\u5d0e", + "6922": "\u85e4", + "6923": "\u30d8", + "6924": "\u5468", + "6925": "\u6bce", + "6926": "\u7d71", + "6927": "\u53ce", + "6928": "\u843d", + "6929": "\u661f", + "6930": "\u964d", + "6931": "\u62c5", + "6932": "\u5074", + "6933": "\u7642", + "6934": "\u5e2b", + "6935": "\u5199", + "6936": "\u985e", + "6937": "\u547d", + "6938": "\u4ecb", + "6939": "\u9858", + "6940": "\u8b77", + "6941": "\u57ce", + "6942": "\u6b7b", + "6943": "\u679c", + "6944": "\u962a", + "6945": "\u4efb", + "6946": "\u66f4", + "6947": "\u5e38", + "6948": "\u4fbf", + "6949": "\u305c", + "6950": "\u691c", + "6951": "\u904e", + "6952": "\u8cc7", + "6953": "\u50cd", + "6954": "\u8a8d", + "6955": "\u822c", + "6956": "\u793a", + "6957": "\u5ba2", + "6958": "\u7fd2", + "6959": "\u7a76", + "6960": "\u534a", + "6961": "\u9332", + "6962": "\u5b57", + "6963": "\u6614", + "6964": "\u5eb7", + "6965": "\u90ce", + "6966": "\u5f71", + "6967": "\u899a", + "6968": "\u578b", + "6969": "\u58f0", + "6970": "\u4ef6", + "6971": "\u7fa9", + "6972": "\u65bd", + "6973": "\u798f", + "6974": "\u5bb9", + "6975": "\u8def", + "6976": "\u547c", + "6977": "\u5f79", + "6978": "\u5358", + "6979": "\u4e95", + "6980": "\u72b6", + "6981": "\u5efa", + "6982": "\u7531", + "6983": "\u5c5e", + "6984": "\u52c9", + "6985": "\u571f", + "6986": "\u8449", + "6987": "\u8d77", + "6988": "\u89a7", + "6989": "\u914d", + "6990": "\u5f35", + "6991": "\u63a5", + "6992": "\u8fbc", + "6993": "\u5f85", + "6994": "\u5ba4", + "6995": "\u75c5", + "6996": "\u5e2f", + "6997": "\u5acc", + "6998": "\u5a5a", + "6999": "\u5149", + "7000": "\u500b", + "7001": "\u8077", + "7002": "\u55b6", + "7003": "\u307c", + "7004": "\u7814", + "7005": "\u8a08", + "7006": "\u76f4", + "7007": "\u96e3", + "7008": "\u305e", + "7009": "\u7d76", + "7010": "\u30e8", + "7011": "\u7167", + "7012": "\u897f", + "7013": "\u7d04", + "7014": "\u5b58", + "7015": "\u9a13", + "7016": "\u6cbb", + "7017": "\u7236", + "7018": "\u89e3", + "7019": "\u5ca1", + "7020": "\u8ee2", + "7021": "\u5546", + "7022": "\u9032", + "7023": "\u4fc2", + "7024": "\u8aac", + "7025": "\u89b3", + "7026": "\u7403", + "7027": "\u4e57", + "7028": "\u5bae", + "7029": "\u652f", + "7030": "\u5f97", + "7031": "\u541b", + "7032": "\u8b70", + "7033": "\u5065", + "7034": "\u9580", + "7035": "\u6b62", + "7036": "\u91cd", + "7037": "\u6e29", + "7038": "\u7dd2", + "7039": "\u7740", + "7040": "\u98f2", + "7041": "\u6bcd", + "7042": "\u58eb", + "7043": "\u3056", + "7044": "\u96c6", + "7045": "\u4e07", + "7046": "\u592a", + "7047": "\u7d9a", + "7048": "\u7dda", + "7049": "\u7a2e", + "7050": "\u683c", + "7051": "\u4f4d", + "7052": "\u30e6", + "7053": "\u6b4c", + "7054": "\u591c", + "7055": "\u5171", + "7056": "\u6b63", + "7057": "\u5fc5", + "7058": "\u30d2", + "7059": "\u8272", + "7060": "\u554f", + "7061": "\u518d", + "7062": "\u57df", + "7063": "\u3086", + "7064": "\u52dd", + "7065": "\u53f0", + "7066": "\u6280", + "7067": "\u65c5", + "7068": "\u5f15", + "7069": "\u7cfb", + "7070": "\u9662", + "7071": "\u60aa", + "7072": "\u57fa", + "7073": "\u795e", + "7074": "\u9650", + "7075": "\u7523", + "7076": "\u6c7a", + "7077": "\u6c11", + "7078": "\u4ea4", + "7079": "\u653f", + "7080": "\u8cde", + "7081": "\u7a7a", + "7082": "\u533b", + "7083": "\u5f7c", + "7084": "\u592b", + "7085": "\u53ef", + "7086": "\u8ab0", + "7087": "\u53e4", + "7088": "\u5e30", + "7089": "\u8853", + "7090": "\u76f8", + "7091": "\u6751", + "7092": "\u56e3", + "7093": "\u4f1d", + "7094": "\u5186", + "7095": "\u4f4f", + "7096": "\u984c", + "7097": "\u5e73", + "7098": "\u4e88", + "7099": "\u97f3", + "7100": "\u671d", + "7101": "\u6307", + "7102": "\u771f", + "7103": "\u30f4", + "7104": "\u52d9", + "7105": "\u70b9", + "7106": "\u5404", + "7107": "\u9928", + "7108": "\u5fdc", + "7109": "\u73fe", + "7110": "\u5229", + "7111": "\u5929", + "7112": "\u7b49", + "7113": "\u6728", + "7114": "\u767d", + "7115": "\u5f62", + "7116": "\u4f9b", + "7117": "\u7d4c", + "7118": "\u3047", + "7119": "\u65cf", + "7120": "\u65e9", + "7121": "\u4f8b", + "7122": "\u50d5", + "7123": "\u4e0d", + "7124": "\u5207", + "7125": "\u5357", + "7126": "\u52a0", + "7127": "\u969b", + "7128": "\u7d42", + "7129": "\u69d8", + "7130": "\u653e", + "7131": "\u548c", + "7132": "\u4f11", + "7133": "\u5dde", + "7134": "\u6c34", + "7135": "\u5354", + "7136": "\u5728", + "7137": "\u7d44", + "7138": "\u5411", + "7139": "\u5e83", + "7140": "\u8eab", + "7141": "\u754c", + "7142": "\u5de5", + "7143": "\u9078", + "7144": "\u59cb", + "7145": "\u5143", + "7146": "\u96f6", + "7147": "\u3005", + "7148": "\u89aa", + "7149": "\u7f8e", + "7150": "\u4fe1", + "7151": "\u90fd", + "7152": "\u7f6e", + "7153": "\u5c40", + "7154": "\u99c5", + "7155": "\u904b", + "7156": "\u9001", + "7157": "\u98a8", + "7158": "\u53e3", + "7159": "\u6f14", + "7160": "\u8abf", + "7161": "\u304e", + "7162": "\u512a", + "7163": "\u6b21", + "7164": "\u30a9", + "7165": "\u4ed6", + "7166": "\u5712", + "7167": "\u4fdd", + "7168": "\u7537", + "7169": "\u53c2", + "7170": "\u5c11", + "7171": "\u767e", + "7172": "\u7279", + "7173": "\u8003", + "7174": "\u7121", + "7175": "\u4e03", + "7176": "\u30e4", + "7177": "\u30ae", + "7178": "\u826f", + "7179": "\u30b6", + "7180": "\u5236", + "7181": "\u4eac", + "7182": "\u611b", + "7183": "\u58f2", + "7184": "\u80fd", + "7185": "\u539f", + "7186": "\u30b2", + "7187": "\u6709", + "7188": "\u516d", + "7189": "\u5b89", + "7190": "\u30b4", + "7191": "\u80b2", + "7192": "\u79d1", + "7193": "\u8981", + "7194": "\u6599", + "7195": "\u66f8", + "7196": "\u8a9e", + "7197": "\u8a2d", + "7198": "\u6d77", + "7199": "\u671f", + "7200": "\u6d41", + "7201": "\u78ba", + "7202": "\u30da", + "7203": "\u533a", + "7204": "\u3080", + "7205": "\u9023", + "7206": "\u8cb7", + "7207": "\u3072", + "7208": "\u3075", + "7209": "\u4ed8", + "7210": "\u753a", + "7211": "\u6d3b", + "7212": "\u60c5", + "7213": "\u6708", + "7214": "\u8868", + "7215": "\u66f2", + "7216": "\u5f37", + "7217": "\u4e16", + "7218": "\u660e", + "7219": "\u6210", + "7220": "\u30ce", + "7221": "\u30a1", + "7222": "\u6587", + "7223": "\u9055", + "7224": "\u6771", + "7225": "\u53cb", + "7226": "\u610f", + "7227": "\u529b", + "7228": "\u5f0f", + "7229": "\u6cd5", + "7230": "\u5831", + "7231": "\u54e1", + "7232": "\u5fc3", + "7233": "\u5c4b", + "7234": "\u54c1", + "7235": "\u5317", + "7236": "\u5148", + "7237": "\u5cf6", + "7238": "\u5473", + "7239": "\u5ddd", + "7240": "\u958b", + "7241": "\u5343", + "7242": "\u95a2", + "7243": "\u516b", + "7244": "\u96fb", + "7245": "\u7136", + "7246": "\u5ea6", + "7247": "\u4ffa", + "7248": "\u9054", + "7249": "\u9762", + "7250": "\u4e5d", + "7251": "\u6570", + "7252": "\u53d6", + "7253": "\u697d", + "7254": "\u91d1", + "7255": "\u6027", + "7256": "\u91ce", + "7257": "\u5225", + "7258": "\u6226", + "7259": "\u516c", + "7260": "\u6a5f", + "7261": "\u9053", + "7262": "\u76ee", + "7263": "\u8a18", + "7264": "\u3073", + "7265": "\u767a", + "7266": "\u5bfe", + "7267": "\u7acb", + "7268": "\u521d", + "7269": "\u5316", + "7270": "\u30bd", + "7271": "\u56db", + "7272": "\u30ef", + "7273": "\u7530", + "7274": "\u6301", + "7275": "\u30ac", + "7276": "\u8eca", + "7277": "\u756a", + "7278": "\u30d4", + "7279": "\u805e", + "7280": "\u56de", + "7281": "\u3041", + "7282": "\u3076", + "7283": "\u30d9", + "7284": "\u4e94", + "7285": "\u3052", + "7286": "\u5b9f", + "7287": "\u30dc", + "7288": "\u5e97", + "7289": "\u5c0f", + "7290": "\u5b9a", + "7291": "\u30e2", + "7292": "\u9577", + "7293": "\u65b0", + "7294": "\u30cf", + "7295": "\u30b1", + "7296": "\u5916", + "7297": "\u30dd", + "7298": "\u8fd1", + "7299": "\u6240", + "7300": "\u3078", + "7301": "\u770c", + "7302": "\u540c", + "7303": "\u30cd", + "7304": "\u5185", + "7305": "\u5973", + "7306": "\u30db", + "7307": "\u4f53", + "7308": "\u597d", + "7309": "\u30c4", + "7310": "\u30bb", + "7311": "\u77e5", + "7312": "\u5c71", + "7313": "\u6765", + "7314": "\u30a7", + "7315": "\u4f7f", + "7316": "\u30e7", + "7317": "\u30ba", + "7318": "\u4e3b", + "7319": "\u52d5", + "7320": "\u7406", + "7321": "\u7269", + "7322": "\u6620", + "7323": "\u8005", + "7324": "\u3050", + "7325": "\u7684", + "7326": "\u4ee3", + "7327": "\u5909", + "7328": "\u6559", + "7329": "\u793e", + "7330": "\u7528", + "7331": "\u8a71", + "7332": "\u540d", + "7333": "\u69cb", + "7334": "\u9ad8", + "7335": "\u6700", + "7336": "\u305a", + "7337": "\u30df", + "7338": "\u6821", + "7339": "\u30c0", + "7340": "\u98df", + "7341": "\u5f8c", + "7342": "\u624b", + "7343": "\u4e09", + "7344": "\u901a", + "7345": "\u611f", + "7346": "\u5408", + "7347": "\u591a", + "7348": "\u696d", + "7349": "\u5165", + "7350": "\u30a8", + "7351": "\u5834", + "7352": "\u3079", + "7353": "\u4e0a", + "7354": "\u5bb6", + "7355": "\u79c1", + "7356": "\u5e74", + "7357": "\u9593", + "7358": "\u753b", + "7359": "\u524d", + "7360": "\u4e0b", + "7361": "\u30e3", + "7362": "\u5730", + "7363": "\u4e8c", + "7364": "\u30a6", + "7365": "\u30ca", + "7366": "\u30d3", + "7367": "\u81ea", + "7368": "\u5168", + "7369": "\u30d1", + "7370": "\u7d50", + "7371": "\u30d6", + "7372": "\u30e5", + "7373": "\u5e02", + "7374": "\u30b5", + "7375": "\u6c17", + "7376": "\u65b9", + "7377": "\u30c7", + "7378": "\u5341", + "7379": "\u30ad", + "7380": "\u5f53", + "7381": "\u56fd", + "7382": "\u4f5c", + "7383": "\u30a3", + "7384": "\u90e8", + "7385": "\u30aa", + "7386": "\u30cb", + "7387": "\u30c1", + "7388": "\u30e0", + "7389": "\u30b0", + "7390": "\u30e1", + "7391": "\u3054", + "7392": "\u5b50", + "7393": "\u3070", + "7394": "\u751f", + "7395": "\u307b", + "7396": "\u3071", + "7397": "\u305b", + "7398": "\u4f55", + "7399": "\u51fa", + "7400": "\u8a00", + "7401": "\u4eca", + "7402": "\u30d0", + "7403": "\u4e8b", + "7404": "\u4e2d", + "7405": "\u30d7", + "7406": "\u6642", + "7407": "\u30b3", + "7408": "\u898b", + "7409": "\u30c6", + "7410": "\u4f1a", + "7411": "\u30de", + "7412": "\u30ab", + "7413": "\u601d", + "7414": "\u30ed", + "7415": "\u30b8", + "7416": "\u30d5", + "7417": "\u30b7", + "7418": "\u3081", + "7419": "\u30ec", + "7420": "\u30c9", + "7421": "\u5206", + "7422": "\u3087", + "7423": "\u308d", + "7424": "\u5b66", + "7425": "\u884c", + "7426": "\u30bf", + "7427": "\u5927", + "7428": "\u3064", + "7429": "\u672c", + "7430": "\u65e5", + "7431": "\u308f", + "7432": "\u4e00", + "7433": "\u30af", + "7434": "\u307f", + "7435": "\u30ea", + "7436": "\u30a2", + "7437": "\u30c3", + "7438": "\u4eba", + "7439": "\u30e9", + "7440": "\uff1f", + "7441": "\u304a", + "7442": "\u3058", + "7443": "\u30a4", + "7444": "\u30eb", + "7445": "\u30c8", + "7446": "\u3083", + "7447": "\u304d", + "7448": "\u3055", + "7449": "\u3061", + "7450": "\u3084", + "7451": "\u30b9", + "7452": "\u3069", + "7453": "\u3051", + "7454": "\u304f", + "7455": "\u3048", + "7456": "\u3092", + "7457": "\u308a", + "7458": "\u3088", + "7459": "\u3053", + "7460": "\u30f3", + "7461": "\u3060", + "7462": "\u308c", + "7463": "\u3089", + "7464": "\u306d", + "7465": "\u304c", + "7466": "\u307e", + "7467": "\u30fc", + "7468": "\u3082", + "7469": "\u305d", + "7470": "\u3057", + "7471": "\u306b", + "7472": "\u306f", + "7473": "\u308b", + "7474": "\u3059", + "7475": "\u3068", + "7476": "\u305f", + "7477": "\u3042", + "7478": "\u3066", + "7479": "\u3063", + "7480": "\u3067", + "7481": "\u304b", + "7482": "\u306a", + "7483": "\u3093", + "7484": "\u3046", + "7485": "\u306e", + "7486": "\u3001", + "7487": "\u3002", + "7488": "\u3044", + "7489": "", + "7490": "\uc774", + "7491": "\uac00", + "7492": "\uc744", + "7493": "\ub294", + "7494": "\uc5d0", + "7495": "\ub3c4", + "7496": "\uace0", + "7497": "\uc758", + "7498": "\uc9c0", + "7499": "\ub97c", + "7500": "\u2581\uadf8", + "7501": "\ub2e4", + "7502": "\uc740", + "7503": "\uae30", + "7504": "\ud55c", + "7505": "\uc5b4", + "7506": "\uc2dc", + "7507": "\uc790", + "7508": "\uc11c", + "7509": "\ub85c", + "7510": "\ud574", + "7511": "\ub9ac", + "7512": "\uc694", + "7513": "\uc0ac", + "7514": "\u2581\ubb50", + "7515": "\uc778", + "7516": "\uac8c", + "7517": "\uc5d0\uc11c", + "7518": "\u2581\uc774\uc81c", + "7519": "\uc815", + "7520": "\ud558", + "7521": "\u2581\uc5b4", + "7522": "\u2581\uac70", + "7523": "\ud558\ub294", + "7524": "\ub098", + "7525": "\ub300", + "7526": "\u2581\uc880", + "7527": "\ud558\uace0", + "7528": "\ub9cc", + "7529": "\u2581\uc218", + "7530": "\u2581\uc544", + "7531": "\uc7a5", + "7532": "\uba74", + "7533": "\uc73c\ub85c", + "7534": "\uc6d0", + "7535": "\uc57c", + "7536": "\uc8fc", + "7537": "\uacfc", + "7538": "\uc0c1", + "7539": "\uad6c", + "7540": "\uc2a4", + "7541": "\uc77c", + "7542": "\u2581\uadf8\ub7f0", + "7543": "\ub77c", + "7544": "\uc218", + "7545": "\ud560", + "7546": "\uc544", + "7547": "\ub4e4", + "7548": "\u2581\uc774\ub7f0", + "7549": "\u2581\uc608", + "7550": "\uac70", + "7551": "\u2581\uc9c0\uae08", + "7552": "\uc131", + "7553": "\u2581\ubcf4", + "7554": "\u2581\uc548", + "7555": "\ubcf4", + "7556": "\u2581\ub610", + "7557": "\ub3d9", + "7558": "\uc18c", + "7559": "\uc2e0", + "7560": "\u2581\uc788\ub294", + "7561": "\uc2ed", + "7562": "\u2581\uac83", + "7563": "\uac04", + "7564": "\uc81c", + "7565": "\ub294\ub370", + "7566": "\uac74", + "7567": "\u2581\ub300", + "7568": "\ubd80", + "7569": "\ud654", + "7570": "\uc804", + "7571": "\u2581\uc804", + "7572": "\u2581\uc774\ub807\uac8c", + "7573": "\u2581\uc77c", + "7574": "\u2581\uadfc\ub370", + "7575": "\ub4e4\uc774", + "7576": "\u2581\uadf8\ub798\uc11c", + "7577": "\ub370", + "7578": "\ud588", + "7579": "\uce58", + "7580": "\uc120", + "7581": "\ub4dc", + "7582": "\u2581\ub9ce\uc774", + "7583": "\uc138", + "7584": "\uc9c4", + "7585": "\uc5f0", + "7586": "\uc5ec", + "7587": "\uad00", + "7588": "\ubd84", + "7589": "\u2581\ub124", + "7590": "\ub9c8", + "7591": "\uc624", + "7592": "\ubbf8", + "7593": "\uc704", + "7594": "\uc8e0", + "7595": "\uc2b5\ub2c8\ub2e4", + "7596": "\uacc4", + "7597": "\uc2dd", + "7598": "\ubb34", + "7599": "\uc788", + "7600": "\ubb38", + "7601": "\ub2f9", + "7602": "\uc7ac", + "7603": "\ub144", + "7604": "\uccb4", + "7605": "\u2581\ub098", + "7606": "\uc640", + "7607": "\uc6b0", + "7608": "\ub77c\uace0", + "7609": "\uc2e4", + "7610": "\u2581\ub54c", + "7611": "\ub2e8", + "7612": "\ud1b5", + "7613": "\uc601", + "7614": "\u2581\uc8fc", + "7615": "\uc801", + "7616": "\uba85", + "7617": "\u2581\uc54a", + "7618": "\u2581\ub9d0", + "7619": "\u2581\uc624", + "7620": "\u2581\uc788\ub2e4", + "7621": "\ud574\uc11c", + "7622": "\ub824", + "7623": "\u2581\uc5b4\ub5a4", + "7624": "\ubc29", + "7625": "\uc0b0", + "7626": "\u2581\uc6b0\ub9ac", + "7627": "\ucc28", + "7628": "\u2581\uc800", + "7629": "\ubb3c", + "7630": "\ub2c8", + "7631": "\u2581\uc544\ub2c8", + "7632": "\u2581\ub354", + "7633": "\u2581\uc0ac", + "7634": "\ubc18", + "7635": "\ub2c8\ub2e4", + "7636": "\uc810", + "7637": "\u2581\ube44", + "7638": "\ud2b8", + "7639": "\u2581\uc74c", + "7640": "\uc6a9", + "7641": "\uc5c5", + "7642": "\uacbd", + "7643": "\uc0dd", + "7644": "\uc801\uc73c\ub85c", + "7645": "\uacf5", + "7646": "\u2581\ub0b4", + "7647": "\u2581\uadf8\ub9ac\uace0", + "7648": "\uad6d", + "7649": "\ub7ec", + "7650": "\uc548", + "7651": "\ube44", + "7652": "\uae4c\uc9c0", + "7653": "\ub2c8\uae4c", + "7654": "\uae08", + "7655": "\uc6b4", + "7656": "\u2581\uc774\uac8c", + "7657": "\u2581\uacf5", + "7658": "\ub0b4", + "7659": "\ud68c", + "7660": "\u2581\uc798", + "7661": "\ud558\uac8c", + "7662": "\ud589", + "7663": "\uc870", + "7664": "\ubaa8", + "7665": "\uac10", + "7666": "\uac00\uc9c0\uace0", + "7667": "\u2581\ub9c9", + "7668": "\uc9d1", + "7669": "\ub41c", + "7670": "\uac83", + "7671": "\ubc1c", + "7672": "\ud559", + "7673": "\uc2ec", + "7674": "\ub358", + "7675": "\ubc31", + "7676": "\u2581\uc720", + "7677": "\ub77c\ub294", + "7678": "\ub0a8", + "7679": "\u2581\ub54c\ubb38\uc5d0", + "7680": "\u2581\uadf8\ub7ec\ub2c8\uae4c", + "7681": "\ub418\ub294", + "7682": "\uc785\ub2c8\ub2e4", + "7683": "\ud0c0", + "7684": "\uad50", + "7685": "\u2581\ub4e4\uc5b4", + "7686": "\u2581\uc5c6", + "7687": "\uc5b4\uc694", + "7688": "\ubc95", + "7689": "\uc801\uc778", + "7690": "\uc5ed", + "7691": "\u2581\uc0dd\uac01", + "7692": "\ub9e4", + "7693": "\ubbfc", + "7694": "\ud55c\ub2e4", + "7695": "\u2581\uac19\uc740", + "7696": "\u2581\uadf8\ub0e5", + "7697": "\ubc30", + "7698": "\ub974", + "7699": "\u2581\ub418", + "7700": "\ubd80\ubd84", + "7701": "\uc721", + "7702": "\u2581\uc598\uae30", + "7703": "\ud638", + "7704": "\ud504", + "7705": "\ub0a0", + "7706": "\u2581\ubabb", + "7707": "\u2581\uc0ac\uc2e4", + "7708": "\uac70\ub4e0\uc694", + "7709": "\ucc9c", + "7710": "\ub4f1", + "7711": "\u2581\uc5b4\ub5bb\uac8c", + "7712": "\u2581\uc81c", + "7713": "\uc9c0\ub9cc", + "7714": "\ud788", + "7715": "\u2581\uc81c\uac00", + "7716": "\u2581\uadf8\ub807\uac8c", + "7717": "\ub354", + "7718": "\uad8c", + "7719": "\ud558\uba74", + "7720": "\ucd9c", + "7721": "\ub2e4\uace0", + "7722": "\ub2ec", + "7723": "\uaca0", + "7724": "\uc791", + "7725": "\uc785", + "7726": "\u2581\uc800\ub294", + "7727": "\ud574\uc57c", + "7728": "\u2581\ubd80", + "7729": "\u2581\uc9c4\uc9dc", + "7730": "\ud45c", + "7731": "\uc9c1", + "7732": "\uc591", + "7733": "\u2581\ubc14", + "7734": "\ud569\ub2c8\ub2e4", + "7735": "\uc0b4", + "7736": "\ub825", + "7737": "\uc5c8", + "7738": "\ud588\ub2e4", + "7739": "\u2581\ub108\ubb34", + "7740": "\u2581\uac00\uc7a5", + "7741": "\u2581\uc870", + "7742": "\ud314", + "7743": "\uc911", + "7744": "\ub2d8", + "7745": "\u2581\ub0b4\uac00", + "7746": "\uc720", + "7747": "\ub798", + "7748": "\ubc84", + "7749": "\ubc88", + "7750": "\uac1c", + "7751": "\ud6c4", + "7752": "\uc796\uc544\uc694", + "7753": "\ud558\uc9c0", + "7754": "\ud53c", + "7755": "\uc885", + "7756": "\ub124", + "7757": "\ud604", + "7758": "\u2581\uc788\uc2b5\ub2c8\ub2e4", + "7759": "\u2581\uc88b", + "7760": "\ub9de", + "7761": "\ub09c", + "7762": "\uac19", + "7763": "\u2581\uad49\uc7a5\ud788", + "7764": "\u2581\uc911", + "7765": "\ucd94", + "7766": "\uc6d4", + "7767": "\uc5d0\ub294", + "7768": "\uccad", + "7769": "\uc18d", + "7770": "\u2581\uc0ac\ub78c", + "7771": "\ubc1b", + "7772": "\uc9c8", + "7773": "\ub178", + "7774": "\ud615", + "7775": "\u2581\uac78", + "7776": "\uad70", + "7777": "\uc600", + "7778": "\ud30c", + "7779": "\ub514", + "7780": "\ubcf8", + "7781": "\ub3fc", + "7782": "\ub108", + "7783": "\u2581\uadf8\ub7ec\uba74", + "7784": "\u2581\ubd88", + "7785": "\u2581\ub450", + "7786": "\u2581\uc624\ub298", + "7787": "\u2581\uac1c", + "7788": "\ucd5c", + "7789": "\u2581\uc0bc", + "7790": "\ud06c", + "7791": "\ub410", + "7792": "\ud3b8", + "7793": "\ucabd", + "7794": "\ud310", + "7795": "\ub54c", + "7796": "\u2581\ub418\uac8c", + "7797": "\u2581\ub098\ub294", + "7798": "\ubd80\ud130", + "7799": "\ub791", + "7800": "\u2581\uadf8\uac70", + "7801": "\u2581\ub300\ud574\uc11c", + "7802": "\u2581\uc815\ub3c4", + "7803": "\ub808", + "7804": "\u2581\uae40", + "7805": "\u2581\uc774\uac70", + "7806": "\u2581\uc788\uace0", + "7807": "\u2581\uac15", + "7808": "\u2581\ub300\ud55c", + "7809": "\uc73c\uba74", + "7810": "\u2581\uadf8\uac8c", + "7811": "\u2581\ubb38\uc81c", + "7812": "\ud3ec", + "7813": "\ubaa9", + "7814": "\uacb0", + "7815": "\uc900", + "7816": "\ud0dc", + "7817": "\u2581\ud558\ub098", + "7818": "\uc678", + "7819": "\uc528", + "7820": "\uc796\uc544", + "7821": "\uc784", + "7822": "\uce60", + "7823": "\uc5f4", + "7824": "\ubcc0", + "7825": "\ub41c\ub2e4", + "7826": "\uc608\uc694", + "7827": "\ud0a4", + "7828": "\ubc15", + "7829": "\u2581\uadf8\ub807", + "7830": "\uae4c", + "7831": "\u2581\ub9d0\uc500", + "7832": "\u2581\uc870\uae08", + "7833": "\ud130", + "7834": "\u2581\uc6b0\ub9ac\uac00", + "7835": "\uc57d", + "7836": "\uc778\ub370", + "7837": "\uae34", + "7838": "\ub9ce", + "7839": "\ud558\uae30", + "7840": "\ub4e0", + "7841": "\u2581\uc57d\uac04", + "7842": "\u2581\uc788\uc5c8", + "7843": "\u2581\ub420", + "7844": "\uaca9", + "7845": "\uc6cc", + "7846": "\ub4e4\uc740", + "7847": "\ud558\ub2e4", + "7848": "\u2581\ub2e4\ub978", + "7849": "\uba39", + "7850": "\u2581\uc815\ub9d0", + "7851": "\u2581\uc65c", + "7852": "\uba74\uc11c", + "7853": "\uc220", + "7854": "\ud569", + "7855": "\uc99d", + "7856": "\u2581\uacc4\uc18d", + "7857": "\uce74", + "7858": "\u2581\uacbd\uc6b0", + "7859": "\ud3c9", + "7860": "\ub0d0", + "7861": "\uc774\ub2e4", + "7862": "\ubd24", + "7863": "\ub4e4\uc744", + "7864": "\uc11d", + "7865": "\uac01", + "7866": "\ubcf4\ub2e4", + "7867": "\ubd84\ub4e4", + "7868": "\uadfc", + "7869": "\ub9b0", + "7870": "\ubcfc", + "7871": "\uae09", + "7872": "\uc54c", + "7873": "\uc124", + "7874": "\uc558", + "7875": "\ub418", + "7876": "\ucd08", + "7877": "\uc4f0", + "7878": "\uc74c", + "7879": "\u2581\ub098\uc624", + "7880": "\uc73c", + "7881": "\uc62c", + "7882": "\uc838", + "7883": "\ucc45", + "7884": "\ud655", + "7885": "\uac08", + "7886": "\ub3c8", + "7887": "\u2581\uc788\ub294\ub370", + "7888": "\ubcf5", + "7889": "\uc751", + "7890": "\ub418\uace0", + "7891": "\uc904", + "7892": "\u2581\ub9ce\uc740", + "7893": "\ub839", + "7894": "\ud5a5", + "7895": "\uac70\uc8e0", + "7896": "\u2581\ubcf4\uba74", + "7897": "\ub8e8", + "7898": "\uc5b8", + "7899": "\uc808", + "7900": "\uc5d0\uc11c\ub294", + "7901": "\ud2f0", + "7902": "\u2581\ud55c\uad6d", + "7903": "\ud1a0", + "7904": "\ud55c\ud14c", + "7905": "\u2581\ub9de\uc544", + "7906": "\uc5d0\uac8c", + "7907": "\u2581\uadf8\ub7f0\ub370", + "7908": "\ub2e4\ub294", + "7909": "\u2581\uc0c1\ud669", + "7910": "\u2581\uadf8\ub7ec", + "7911": "\uc774\ub77c\uace0", + "7912": "\ub8cc", + "7913": "\uc774\ub098", + "7914": "\u2581\uc5ec\uae30", + "7915": "\ubc14", + "7916": "\u2581\uc544\uc774", + "7917": "\uc560", + "7918": "\ub300\ub85c", + "7919": "\u2581\uac70\uae30", + "7920": "\u2581\uc88b\uc544", + "7921": "\ucc38", + "7922": "\uace0\uc694", + "7923": "\uadf8", + "7924": "\uba74\uc740", + "7925": "\uc0bc", + "7926": "\uad6c\uc694", + "7927": "\ub984", + "7928": "\ucc98\ub7fc", + "7929": "\ub2f4", + "7930": "\u2581\uc788\uc744", + "7931": "\u2581\uc88b\uc740", + "7932": "\ud488", + "7933": "\uc800", + "7934": "\uc2b9", + "7935": "\u2581\ubbf8\uad6d", + "7936": "\u2581\uac19\uc560", + "7937": "\ud558\uc2dc", + "7938": "\ubcd1", + "7939": "\ud658", + "7940": "\u2581\ud544\uc694", + "7941": "\u2581\uc0ac\ub78c\ub4e4", + "7942": "\uc9c0\ub294", + "7943": "\uc545", + "7944": "\u2581\ud55c\ubc88", + "7945": "\u2581\uc778\uc81c", + "7946": "\ub860", + "7947": "\uc21c", + "7948": "\uc628", + "7949": "\ucc98", + "7950": "\uc84c", + "7951": "\uc168", + "7952": "\u2581\uadf8\ub54c", + "7953": "\ub450", + "7954": "\uac14", + "7955": "\uc190", + "7956": "\uc6b8", + "7957": "\ubc8c", + "7958": "\ucf54", + "7959": "\u2581\uadf8\ub2c8\uae4c", + "7960": "\ucde8", + "7961": "\u2581\uc788\uc5b4", + "7962": "\uc804\uc5d0", + "7963": "\u2581\uac83\uc774", + "7964": "\ub204", + "7965": "\u2581\uc0ac\ub78c\ub4e4\uc774", + "7966": "\u2581\uc790\uae30", + "7967": "\ub838", + "7968": "\u2581\uc544\ub2c8\ub77c", + "7969": "\uc608", + "7970": "\ud22c", + "7971": "\uc2b5\ub2c8\uae4c", + "7972": "\u2581\uc77c\ub2e8", + "7973": "\u2581\uc5c6\ub294", + "7974": "\ud070", + "7975": "\u2581\uc0dd\uac01\uc744", + "7976": "\ub978", + "7977": "\uc0c8", + "7978": "\uae38", + "7979": "\ud0dd", + "7980": "\ud50c", + "7981": "\uac81", + "7982": "\u2581\uc694\uc998", + "7983": "\u2581\uadf8\ub7fc", + "7984": "\uba38", + "7985": "\u2581\ubb54\uac00", + "7986": "\uc2f6", + "7987": "\uac80", + "7988": "\uba54", + "7989": "\uac70\ub098", + "7990": "\ub780", + "7991": "\ub3c5", + "7992": "\uba87", + "7993": "\uc654", + "7994": "\ubd81", + "7995": "\ud588\uc2b5\ub2c8\ub2e4", + "7996": "\u2581\uadf8\ub798", + "7997": "\uacf3", + "7998": "\uc871", + "7999": "\uaca8", + "8000": "\ube60", + "8001": "\u2581\ubcf4\ub2c8\uae4c", + "8002": "\u2581\uc815\ubd80", + "8003": "\ub9dd", + "8004": "\uc788\ub294", + "8005": "\ub098\uc694", + "8006": "\uc6c0", + "8007": "\u2581\uc0ac\ub78c\uc774", + "8008": "\u2581\uc598\uae30\ub97c", + "8009": "\ub193", + "8010": "\ud2b9", + "8011": "\u2581\uac83\ub3c4", + "8012": "\u2581\uc774\uc57c\uae30", + "8013": "\uad11", + "8014": "\uba70", + "8015": "\uac15", + "8016": "\ud558\uba74\uc11c", + "8017": "\ub85d", + "8018": "\ub9d0", + "8019": "\ube0c", + "8020": "\u2581\uad00\ub828", + "8021": "\u2581\uc2dc\uc791", + "8022": "\uae00", + "8023": "\ud588\ub358", + "8024": "\u2581\uacbd\uc81c", + "8025": "\uc644", + "8026": "\uaca0\ub2e4", + "8027": "\uaca0\uc2b5\ub2c8\ub2e4", + "8028": "\u2581\uce5c\uad6c", + "8029": "\u2581\uad6d\ubbfc", + "8030": "\u2581\uadf8\uac83", + "8031": "\ubd10", + "8032": "\ud65c", + "8033": "\ub192", + "8034": "\u2581\uc788\uc5b4\uc694", + "8035": "\uc774\ub77c\ub294", + "8036": "\u2581\ub2e4\uc2dc", + "8037": "\u2581\uc5ec\ub7ec", + "8038": "\ucd1d", + "8039": "\uc7a1", + "8040": "\ub2a5", + "8041": "\ud56d", + "8042": "\ub958", + "8043": "\uaddc", + "8044": "\ub530", + "8045": "\ucc44", + "8046": "\uc874", + "8047": "\ub9bd", + "8048": "\uce5c", + "8049": "\uc7c1", + "8050": "\ub298", + "8051": "\ubc94", + "8052": "\ubcc4", + "8053": "\uce21", + "8054": "\ud14c", + "8055": "\ucca0", + "8056": "\ub531", + "8057": "\uc5d4", + "8058": "\uc5b5", + "8059": "\ub05d", + "8060": "\ub77d", + "8061": "\ub9b4", + "8062": "\ucc3d", + "8063": "\uadf9", + "8064": "\uc918", + "8065": "\ud611", + "8066": "\ud328", + "8067": "\ucee4", + "8068": "\uc55e", + "8069": "\ub3cc", + "8070": "\ucda9", + "8071": "\uc0c9", + "8072": "\ub208", + "8073": "\uc154", + "8074": "\uc775", + "8075": "\uc811", + "8076": "\uc1a1", + "8077": "\ud798", + "8078": "\ub0ac", + "8079": "\uafb8", + "8080": "\uaed8", + "8081": "\uc2f8", + "8082": "\ub420", + "8083": "\ub2f5", + "8084": "\ud5d8", + "8085": "\uce68", + "8086": "\uc728", + "8087": "\ub7fd", + "8088": "\ud398", + "8089": "\uac78", + "8090": "\ub4a4", + "8091": "\ud76c", + "8092": "\ucf1c", + "8093": "\ubab0", + "8094": "\ud600", + "8095": "\uc368", + "8096": "\ud300", + "8097": "\uc158", + "8098": "\ucd95", + "8099": "\ub7c9", + "8100": "\ud63c", + "8101": "\uccd0", + "8102": "\ub4dd", + "8103": "\ubc00", + "8104": "\ubca0", + "8105": "\ud669", + "8106": "\ud3ed", + "8107": "\ub140", + "8108": "\uc27d", + "8109": "\ud2c0", + "8110": "\ud6a8", + "8111": "\uace8", + "8112": "\ubd88", + "8113": "\ub78c", + "8114": "\ub840", + "8115": "\ub290", + "8116": "\uc988", + "8117": "\ube14", + "8118": "\ub180", + "8119": "\ud568", + "8120": "\ub5a8", + "8121": "\ud154", + "8122": "\ud5c8", + "8123": "\ub17c", + "8124": "\uad81", + "8125": "\ub9bc", + "8126": "\ud0c4", + "8127": "\ub7f4", + "8128": "\uacac", + "8129": "\uc529", + "8130": "\ub7f0", + "8131": "\ub5a0", + "8132": "\ub118", + "8133": "\ud480", + "8134": "\uc8fd", + "8135": "\uc8c4", + "8136": "\uc2b5", + "8137": "\ud575", + "8138": "\uadc0", + "8139": "\uc61b", + "8140": "\uc724", + "8141": "\ud64d", + "8142": "\ub07c", + "8143": "\ub18d", + "8144": "\ub828", + "8145": "\uac16", + "8146": "\uccab", + "8147": "\ub458", + "8148": "\ud639", + "8149": "\uc5e0", + "8150": "\uc9d5", + "8151": "\ud6c8", + "8152": "\ud0c8", + "8153": "\ucf00", + "8154": "\uaf2d", + "8155": "\ub960", + "8156": "\ud601", + "8157": "\uc12f", + "8158": "\ucc29", + "8159": "\ud734", + "8160": "\ubd09", + "8161": "\ud074", + "8162": "\uc5fc", + "8163": "\ubc1d", + "8164": "\ud48d", + "8165": "\uc2ac", + "8166": "\ubd99", + "8167": "\uace1", + "8168": "\uc5bc", + "8169": "\ucc0d", + "8170": "\ubfd0", + "8171": "\uc9dc", + "8172": "\ubab8", + "8173": "\uc7a0", + "8174": "\ub123", + "8175": "\ub79c", + "8176": "\uc26c", + "8177": "\ub4ef", + "8178": "\ub110", + "8179": "\ub790", + "8180": "\ubc0f", + "8181": "\uc655", + "8182": "\ud37c", + "8183": "\uc555", + "8184": "\ub9db", + "8185": "\ub0ae", + "8186": "\ud0c1", + "8187": "\uc561", + "8188": "\uc92c", + "8189": "\ucc2c", + "8190": "\uc9f8", + "8191": "\ud544", + "8192": "\uc288", + "8193": "\uc13c", + "8194": "\ub099", + "8195": "\ud3d0", + "8196": "\ub73b", + "8197": "\uac11", + "8198": "\uc695", + "8199": "\ud3f0", + "8200": "\uc77d", + "8201": "\uc5c6", + "8202": "\ud2bc", + "8203": "\uc6c3", + "8204": "\ub9e8", + "8205": "\uce35", + "8206": "\ucc3e", + "8207": "\uc219", + "8208": "\ud1f4", + "8209": "\uad74", + "8210": "\uade0", + "8211": "\uc6e0", + "8212": "\ud765", + "8213": "\ub150", + "8214": "\ub4e3", + "8215": "\uc637", + "8216": "\ub0c8", + "8217": "\ud754", + "8218": "\ud61c", + "8219": "\uc554", + "8220": "\uac1d", + "8221": "\uc5d8", + "8222": "\ub274", + "8223": "\uae68", + "8224": "\ubb58", + "8225": "\ub04c", + "8226": "\ub6f0", + "8227": "\uc2eb", + "8228": "\ub054", + "8229": "\uce90", + "8230": "\ub2d0", + "8231": "\uacbc", + "8232": "\uc559", + "8233": "\ud750", + "8234": "\ub7b5", + "8235": "\ubcbd", + "8236": "\uc598", + "8237": "\ub9c9", + "8238": "\uaebc", + "8239": "\uc9d3", + "8240": "\uc990", + "8241": "\ucc30", + "8242": "\uba3c", + "8243": "\uc4f8", + "8244": "\ub355", + "8245": "\uae50", + "8246": "\ubc25", + "8247": "\uc5c7", + "8248": "\ub728", + "8249": "\ucdb0", + "8250": "\ubd05", + "8251": "\ubbff", + "8252": "\ub807", + "8253": "\uce59", + "8254": "\ub0b8", + "8255": "\ubc24", + "8256": "\uc9dd", + "8257": "\uc5bb", + "8258": "\uc794", + "8259": "\uc058", + "8260": "\ud0ac", + "8261": "\ud138", + "8262": "\uc6b1", + "8263": "\uac12", + "8264": "\ub9c1", + "8265": "\uc88c", + "8266": "\ube4c", + "8267": "\ucee8", + "8268": "\ub86d", + "8269": "\ud5cc", + "8270": "\uac54", + "8271": "\ucfe0", + "8272": "\ud305", + "8273": "\ube7c", + "8274": "\uc228", + "8275": "\uce20", + "8276": "\uc5c4", + "8277": "\ud614", + "8278": "\ub545", + "8279": "\uafc8", + "8280": "\ub2e5", + "8281": "\ub0bc", + "8282": "\uacf1", + "8283": "\uc0b6", + "8284": "\ub9e5", + "8285": "\uc98c", + "8286": "\uc606", + "8287": "\uc54a", + "8288": "\uad34", + "8289": "\ube48", + "8290": "\ud134", + "8291": "\uc625", + "8292": "\uc6e8", + "8293": "\ud0a8", + "8294": "\ub9ad", + "8295": "\ud32c", + "8296": "\ud5e4", + "8297": "\ud718", + "8298": "\uc12d", + "8299": "\uba40", + "8300": "\uce6d", + "8301": "\uc05c", + "8302": "\ub0a9", + "8303": "\ub1cc", + "8304": "\ucc99", + "8305": "\uad73", + "8306": "\uc37c", + "8307": "\ub4ed", + "8308": "\uaef4", + "8309": "\ub9e1", + "8310": "\uc1fc", + "8311": "\uace4", + "8312": "\ube68", + "8313": "\ucce4", + "8314": "\uc568", + "8315": "\ucbe4", + "8316": "\ub313", + "8317": "\ub179", + "8318": "\uc989", + "8319": "\ucf58", + "8320": "\ub2a6", + "8321": "\ube5b", + "8322": "\ud608", + "8323": "\ubf51", + "8324": "\uae4a", + "8325": "\ub538", + "8326": "\uc4f4", + "8327": "\uaf43", + "8328": "\ud39c", + "8329": "\ub04a", + "8330": "\ud3b4", + "8331": "\ub78d", + "8332": "\ud648", + "8333": "\ub0c9", + "8334": "\ud508", + "8335": "\ub220", + "8336": "\ud0d5", + "8337": "\ubc11", + "8338": "\uae54", + "8339": "\ub8b0", + "8340": "\ucc0c", + "8341": "\ub800", + "8342": "\ud551", + "8343": "\ud758", + "8344": "\uce7c", + "8345": "\ucb49", + "8346": "\uc2f1", + "8347": "\uc2b7", + "8348": "\ub35c", + "8349": "\uafd4", + "8350": "\ubb54", + "8351": "\ub799", + "8352": "\uc0ad", + "8353": "\ud478", + "8354": "\ub86f", + "8355": "\ub529", + "8356": "\ucf69", + "8357": "\ud68d", + "8358": "\ubd07", + "8359": "\ud150", + "8360": "\ub8f9", + "8361": "\ub9f9", + "8362": "\ud31d", + "8363": "\uc2fc", + "8364": "\uc878", + "8365": "\ubca4", + "8366": "\ub044", + "8367": "\ub80c", + "8368": "\ub7ab", + "8369": "\ubba4", + "8370": "\ud610", + "8371": "\ud0b9", + "8372": "\uc313", + "8373": "\uc635", + "8374": "\ud78c", + "8375": "\ucf13", + "8376": "\ud380", + "8377": "\ud3f4", + "8378": "\uc820", + "8379": "\ubc97", + "8380": "\ucd09", + "8381": "\uace7", + "8382": "\uacaa", + "8383": "\ub113", + "8384": "\uc783", + "8385": "\ubca8", + "8386": "\ub1a8", + "8387": "\ubd04", + "8388": "\ubb3b", + "8389": "\ub784", + "8390": "\uba58", + "8391": "\uceec", + "8392": "\ud761", + "8393": "\ucd98", + "8394": "\uba4b", + "8395": "\ucd0c", + "8396": "\ub378", + "8397": "\uc12c", + "8398": "\ucc59", + "8399": "\ucf30", + "8400": "\uae5d", + "8401": "\ud578", + "8402": "\ud649", + "8403": "\ucd78", + "8404": "\ucf5c", + "8405": "\uaf64", + "8406": "\uc9d0", + "8407": "\uc22b", + "8408": "\uc998", + "8409": "\ub454", + "8410": "\ucef5", + "8411": "\uc194", + "8412": "\uae0d", + "8413": "\ub7ac", + "8414": "\ud3fc", + "8415": "\ub141", + "8416": "\ub5bb", + "8417": "\ud329", + "8418": "\ub5a1", + "8419": "\uaf2c", + "8420": "\ud799", + "8421": "\uc0c0", + "8422": "\uca54", + "8423": "\uaf08", + "8424": "\ud0d0", + "8425": "\ucea0", + "8426": "\uc2b4", + "8427": "\ubfcc", + "8428": "\uc9da", + "8429": "\uc1c4", + "8430": "\ubb18", + "8431": "\ub9bf", + "8432": "\uc564", + "8433": "\ud640", + "8434": "\uc14b", + "8435": "\ud1a1", + "8436": "\uc130", + "8437": "\uc78a", + "8438": "\ub465", + "8439": "\ub2eb", + "8440": "\ucda4", + "8441": "\ube59", + "8442": "\ubaac", + "8443": "\uaf3c", + "8444": "\ub7a8", + "8445": "\ube75", + "8446": "\uc2a8", + "8447": "\ub7fc", + "8448": "\ud3bc", + "8449": "\uc140", + "8450": "\ub864", + "8451": "\ub82c", + "8452": "\ud0d1", + "8453": "\uc384", + "8454": "\ub137", + "8455": "\ub4b7", + "8456": "\uc5d1", + "8457": "\ub7ed", + "8458": "\ucef4", + "8459": "\ub611", + "8460": "\ub2dd", + "8461": "\ud5ec", + "8462": "\ucca8", + "8463": "\ub904", + "8464": "\ub51c", + "8465": "\uae5c", + "8466": "\ud2f1", + "8467": "\ub744", + "8468": "\uc0e4", + "8469": "\ube45", + "8470": "\ub834", + "8471": "\uc81d", + "8472": "\uca4c", + "8473": "\uc300", + "8474": "\ubc0d", + "8475": "\ud751", + "8476": "\ub194", + "8477": "\uac77", + "8478": "\uba78", + "8479": "\ud1a4", + "8480": "\uc5fd", + "8481": "\ud050", + "8482": "\ucd2c", + "8483": "\ucb64", + "8484": "\ud29c", + "8485": "\ud790", + "8486": "\uc88b", + "8487": "\ub959", + "8488": "\ube5a", + "8489": "\ucf8c", + "8490": "\uafc0", + "8491": "\ud540", + "8492": "\ub871", + "8493": "\ub2cc", + "8494": "\ub5bc", + "8495": "\uba48", + "8496": "\ub550", + "8497": "\ud034", + "8498": "\uae40", + "8499": "\ub985", + "8500": "\ub048", + "8501": "\ub20c", + "8502": "\uc30d", + "8503": "\ubb35", + "8504": "\ub534", + "8505": "\uc789", + "8506": "\ubbc0", + "8507": "\ub369", + "8508": "\uc6c5", + "8509": "\uc5c9", + "8510": "\ub0ab", + "8511": "\ud280", + "8512": "\uc67c", + "8513": "\ub0c4", + "8514": "\uaf3d", + "8515": "\uacb8", + "8516": "\ubc16", + "8517": "\ub10c", + "8518": "\uc148", + "8519": "\ub0e5", + "8520": "\uafbc", + "8521": "\ub188", + "8522": "\uba4d", + "8523": "\ubabd", + "8524": "\ubd95", + "8525": "\uacb9", + "8526": "\ubc34", + "8527": "\uc950", + "8528": "\ub080", + "8529": "\ubb50", + "8530": "\ub8f0", + "8531": "\ub809", + "8532": "\ub561", + "8533": "\ub69c", + "8534": "\ub8f8", + "8535": "\ubcbc", + "8536": "\ub2c9", + "8537": "\ub9d8", + "8538": "\ud15c", + "8539": "\ub2f7", + "8540": "\ub518", + "8541": "\ub0ad", + "8542": "\uc11e", + "8543": "\uc6ec", + "8544": "\uce78", + "8545": "\uc787", + "8546": "\uc881", + "8547": "\ub989", + "8548": "\uc571", + "8549": "\ub9fa", + "8550": "\ud6fc", + "8551": "\ubed4", + "8552": "\uac24", + "8553": "\ub010", + "8554": "\ud6cc", + "8555": "\ub084", + "8556": "\ub36e", + "8557": "\uc3d8", + "8558": "\ub057", + "8559": "\ubf08", + "8560": "\ucabc", + "8561": "\uc798", + "8562": "\ubb36", + "8563": "\ub154", + "8564": "\ud53d", + "8565": "\ucca9", + "8566": "\ucef8", + "8567": "\ub2ed", + "8568": "\uc735", + "8569": "\uc1e0", + "8570": "\ud2f4", + "8571": "\ub374", + "8572": "\uc90d", + "8573": "\ud14d", + "8574": "\ubdd4", + "8575": "\uc6f9", + "8576": "\uc0f5", + "8577": "\ub2ff", + "8578": "\ubdf0", + "8579": "\ub367", + "8580": "\ubbf9", + "8581": "\ub364", + "8582": "\ub42c", + "8583": "\uc796", + "8584": "\uacfd", + "8585": "\uad04", + "8586": "\uad1c", + "8587": "\ub8ec", + "8588": "\ub987", + "8589": "\ubd59", + "8590": "\ub760", + "8591": "\ub8e1", + "8592": "\ub155", + "8593": "\ub4ec", + "8594": "\ubabb", + "8595": "\uaf34", + "8596": "\ub69d", + "8597": "\ub801", + "8598": "\ucc2e", + "8599": "\ub610", + "8600": "\ub96d", + "8601": "\uc15c", + "8602": "\ud31f", + "8603": "\ud33d", + "8604": "\ubed0", + "8605": "\uc2f9", + "8606": "\ud0d3", + "8607": "\ub451", + "8608": "\ud1b1", + "8609": "\ucf64", + "8610": "\ub6b1", + "8611": "\uae0b", + "8612": "\uba64", + "8613": "\ub9d9", + "8614": "\uc824", + "8615": "\uc708", + "8616": "\uac90", + "8617": "\ud587", + "8618": "\ube57", + "8619": "\uacf0", + "8620": "\uae65", + "8621": "\uba5c", + "8622": "\ub0af", + "8623": "\uc639", + "8624": "\ucfe8", + "8625": "\ubcf6", + "8626": "\uc232", + "8627": "\ub365", + "8628": "\ubc1f", + "8629": "\ud2f8", + "8630": "\ud2c8", + "8631": "\ub52a", + "8632": "\uae4e", + "8633": "\uac89", + "8634": "\uce69", + "8635": "\ub480", + "8636": "\uc717", + "8637": "\uc090", + "8638": "\uc575", + "8639": "\ub125", + "8640": "\uafe8", + "8641": "\ubb49", + "8642": "\uc22d", + "8643": "\ud321", + "8644": "\ubc45", + "8645": "\ud56b", + "8646": "\ud749", + "8647": "\uce94", + "8648": "\ub46c", + "8649": "\ub540", + "8650": "\uc53b", + "8651": "\ud6a1", + "8652": "\ucfc4", + "8653": "\ubc2d", + "8654": "\uc369", + "8655": "\ub014", + "8656": "\uac31", + "8657": "\ubc38", + "8658": "\ud514", + "8659": "\uc880", + "8660": "\ud1a8", + "8661": "\uc580", + "8662": "\ube61", + "8663": "\uaecf", + "8664": "\ub258", + "8665": "\ub2ee", + "8666": "\ub1e8", + "8667": "\ud131", + "8668": "\ub304", + "8669": "\ud760", + "8670": "\ub7ad", + "8671": "\uc78e", + "8672": "\ub835", + "8673": "\ube8f", + "8674": "\ucad9", + "8675": "\ub0c5", + "8676": "\uc557", + "8677": "\uca4d", + "8678": "\ud770", + "8679": "\uad49", + "8680": "\ud584", + "8681": "\uada4", + "8682": "\uae43", + "8683": "\uc90c", + "8684": "\uc0d8", + "8685": "\ub55c", + "8686": "\uc0cc", + "8687": "\ucdc4", + "8688": "\ud0f1", + "8689": "\ud0a5", + "8690": "\ubc85", + "8691": "\uc3e0", + "8692": "\uc74d", + "8693": "\ubccd", + "8694": "\ud230", + "8695": "\uca0c", + "8696": "\ube10", + "8697": "\ub3d5", + "8698": "\ud140", + "8699": "\uc9e4", + "8700": "\ucef7", + "8701": "\uc0bd", + "8702": "\uaf42", + "8703": "\ub837", + "8704": "\uc139", + "8705": "\ud54f", + "8706": "\ud5e8", + "8707": "\uad7d", + "8708": "\ub8e9", + "8709": "\ucda5", + "8710": "\ub2e6", + "8711": "\ub7a9", + "8712": "\ud47c", + "8713": "\uc660", + "8714": "\ub3cb", + "8715": "\ud30d", + "8716": "\ucc14", + "8717": "\ub72c", + "8718": "\ud5f7", + "8719": "\ubaab", + "8720": "\ud399", + "8721": "\ubd93", + "8722": "\ud0e0", + "8723": "\ub72f", + "8724": "\uc149", + "8725": "\uad90", + "8726": "\ub625", + "8727": "\uae41", + "8728": "\ud0d4", + "8729": "\uba55", + "8730": "\uc816", + "8731": "\ub4c0", + "8732": "\ud4e8", + "8733": "\ub9f5", + "8734": "\uc587", + "8735": "\uc068", + "8736": "\uaecd", + "8737": "\uac13", + "8738": "\ub109", + "8739": "\uc9f1", + "8740": "\uce6b", + "8741": "\ud759", + "8742": "\uaf49", + "8743": "\uc501", + "8744": "\ub428", + "8745": "\ud3a0", + "8746": "\ubf40", + "8747": "\uac07", + "8748": "\uc465", + "8749": "\ud5d0", + "8750": "\ub299", + "8751": "\uc500", + "8752": "\ubc40", + "8753": "\ub618", + "8754": "\uc370", + "8755": "\ud23c", + "8756": "\ub95c", + "8757": "\ub86c", + "8758": "\ubed7", + "8759": "\ud301", + "8760": "\ud48b", + "8761": "\uc274", + "8762": "\ucea1", + "8763": "\ub584", + "8764": "\uc0f7", + "8765": "\uc539", + "8766": "\uc7a3", + "8767": "\uc3f4", + "8768": "\ubb47", + "8769": "\uc270", + "8770": "\ub81b", + "8771": "\uc65c", + "8772": "\ud729", + "8773": "\uc36c", + "8774": "\uc5ce", + "8775": "\ud5db", + "8776": "\ubfd4", + "8777": "\uc27c", + "8778": "\uc813", + "8779": "\ub729", + "8780": "\uc719", + "8781": "\uc29b", + "8782": "\uc170", + "8783": "\uc19f", + "8784": "\uc9e0", + "8785": "\ud6d4", + "8786": "\uc6f0", + "8787": "\uc634", + "8788": "\ud384", + "8789": "\uaf41", + "8790": "\ub730", + "8791": "\ubf55", + "8792": "\ub2ac", + "8793": "\ucc1c", + "8794": "\ud391", + "8795": "\ubbac", + "8796": "\uccbc", + "8797": "\ud241", + "8798": "\ub5b4", + "8799": "\ub284", + "8800": "\ub291", + "8801": "\ucf08", + "8802": "\ud0b4", + "8803": "\uc3d9", + "8804": "\uce98", + "8805": "\uad7c", + "8806": "\ud22d", + "8807": "\ub968", + "8808": "\ub6f8", + "8809": "\uc5ff", + "8810": "\uc610", + "8811": "\ubd90", + "8812": "\uc7ad", + "8813": "\ub315", + "8814": "\uafc9", + "8815": "\ucf67", + "8816": "\ud479", + "8817": "\uc70c", + "8818": "\ucffc", + "8819": "\uac9f", + "8820": "\uc060", + "8821": "\uc5ee", + "8822": "\ud69f", + "8823": "\uad7f", + "8824": "\uae61", + "8825": "\ub2d9", + "8826": "\uc2e3", + "8827": "\ucf55", + "8828": "\ubc43", + "8829": "\uc5b9", + "8830": "\uc9ec", + "8831": "\ud234", + "8832": "\ubee5", + "8833": "\ud07c", + "8834": "\uc290", + "8835": "\uc7a6", + "8836": "\ud720", + "8837": "\uc19c", + "8838": "\ud38c", + "8839": "\uca61", + "8840": "\ud5dd", + "8841": "\ud1b0", + "8842": "\uc0d0", + "8843": "\uae01", + "8844": "\ud31c", + "8845": "\ube54", + "8846": "\ub3d7", + "8847": "\uac2f", + "8848": "\ucf04", + "8849": "\ub7ff", + "8850": "\ub301", + "8851": "\ub310", + "8852": "\uc570", + "8853": "\ud0ed", + "8854": "\ube90", + "8855": "\ub308", + "8856": "\ub2db", + "8857": "\ud6c5", + "8858": "\ube7d", + "8859": "\ub12c", + "8860": "\uc9d6", + "8861": "\ub460", + "8862": "\uce84", + "8863": "\ub461", + "8864": "\ucac4", + "8865": "\ub528", + "8866": "\ubca1", + "8867": "\uc7a4", + "8868": "\ud004", + "8869": "\ubfdc", + "8870": "\uac2d", + "8871": "\ub3d4", + "8872": "\ucf70", + "8873": "\uc653", + "8874": "\uc96c", + "8875": "\ub314", + "8876": "\ub5b3", + "8877": "\ub0b1", + "8878": "\ud168", + "8879": "\ubcd5", + "8880": "\ub38c", + "8881": "\ud30e", + "8882": "\uad88", + "8883": "\ud0b5", + "8884": "\uadc4", + "8885": "\ucc10", + "8886": "\ucc1d", + "8887": "\uae4d", + "8888": "\ub7b4", + "8889": "\ud145", + "8890": "\ube80", + "8891": "\ud325", + "8892": "\ubd48", + "8893": "\uba67", + "8894": "\uc52c", + "8895": "\ubc99", + "8896": "\ubcb3", + "8897": "\uc1a5", + "8898": "\uc82f", + "8899": "\ub6f4", + "8900": "\ucb48", + "8901": "\ub810", + "8902": "\uc250", + "8903": "\uaec4", + "8904": "\uc584", + "8905": "\uc5e3", + "8906": "\uc324", + "8907": "\uc3dc", + "8908": "\ucff5", + "8909": "\ud0b7", + "8910": "\uc648", + "8911": "\ub764", + "8912": "\ube74", + "8913": "\ube84", + "8914": "\uafc7", + "8915": "\ub525", + "8916": "\ub544", + "8917": "\ub818", + "8918": "\ud54d", + "8919": "\uc378", + "8920": "\ub5a4", + "8921": "\ub215", + "8922": "\ub9f7", + "8923": "\ucc3c", + "8924": "\uc308", + "8925": "\ub2a0", + "8926": "\uc999", + "8927": "\uaf30", + "8928": "\ub371", + "8929": "\uc20d", + "8930": "\uc5ca", + "8931": "\uc0bf", + "8932": "\uaf80", + "8933": "\ub9e3", + "8934": "\uc7bc", + "8935": "\uac40", + "8936": "\ud018", + "8937": "\uc464", + "8938": "\ubfb0", + "8939": "\uca50", + "8940": "\ubb44", + "8941": "\uade4", + "8942": "\ub797", + "8943": "\uca0b", + "8944": "\ucee5", + "8945": "\uaff0", + "8946": "\uc123", + "8947": "\uc379", + "8948": "\ud3c8", + "8949": "\ud0ec", + "8950": "\ub2aa", + "8951": "\ub738", + "8952": "\uce89", + "8953": "\ubab9", + "8954": "\uc298", + "8955": "\ub975", + "8956": "\uc4f1", + "8957": "\ud6d7", + "8958": "\ub9cf", + "8959": "\ud15d", + "8960": "\ub51b", + "8961": "\ucc39", + "8962": "\ubb8c", + "8963": "\uce04", + "8964": "\ud3ad", + "8965": "\ube64", + "8966": "\ub08c", + "8967": "\ubed8", + "8968": "\uc6c1", + "8969": "\uafb9", + "8970": "\ub205", + "8971": "\uc6cd", + "8972": "\ud4f0", + "8973": "\uca5c", + "8974": "\uc21f", + "8975": "\uac94", + "8976": "\ud038", + "8977": "\ucf65", + "8978": "\ub8fb", + "8979": "\ub515", + "8980": "\ube91", + "8981": "\uc167", + "8982": "\uceeb", + "8983": "\uc388", + "8984": "\ubf18", + "8985": "\ub128", + "8986": "\ucc4c", + "8987": "\ud3ab", + "8988": "\ud390", + "8989": "\ucbd4", + "8990": "\ud5c9", + "8991": "\ud2ac", + "8992": "\ub9f4", + "8993": "\uc58c", + "8994": "\ud143", + "8995": "\uc69c", + "8996": "\ubed1", + "8997": "\uce85", + "8998": "\ubc0b", + "8999": "\uc574", + "9000": "\ud295", + "9001": "\ub214", + "9002": "\uc0f9", + "9003": "\ubc09", + "9004": "\uac71", + "9005": "\uae45", + "9006": "\uc408", + "9007": "\ud3c4", + "9008": "\uc204", + "9009": "\ub4c8", + "9010": "\ubee3", + "9011": "\ubc27", + "9012": "\uac20", + "9013": "\ud401", + "9014": "\uaf5d", + "9015": "\uce30", + "9016": "\ucfe1", + "9017": "\ucfe4", + "9018": "\ucf10", + "9019": "\uaecc", + "9020": "\uce75", + "9021": "\ub6a4", + "9022": "\ucc60", + "9023": "\uadd3", + "9024": "\ub11b", + "9025": "\ud6d1", + "9026": "\ud37d", + "9027": "\uc329", + "9028": "\uae7c", + "9029": "\ucea3", + "9030": "\ubea8", + "9031": "\ud6e8", + "9032": "\ub78f", + "9033": "\uccb8", + "9034": "\uc0ec", + "9035": "\ucb10", + "9036": "\uae6c", + "9037": "\uca84", + "9038": "\uc5cc", + "9039": "\ub07d", + "9040": "\uc9f0", + "9041": "\uac38", + "9042": "\uadc8", + "9043": "\ub385", + "9044": "\ub748", + "9045": "\uac4d", + "9046": "\ub08d", + "9047": "\ucf85", + "9048": "\ud0e4", + "9049": "\ud17c", + "9050": "\ub311", + "9051": "\ucc3b", + "9052": "\uad18", + "9053": "\uc73d", + "9054": "\uc309", + "9055": "\ub527", + "9056": "\ub7a0", + "9057": "\ucc21", + "9058": "\uafcb", + "9059": "\ub00c", + "9060": "\ubb63", + "9061": "\ub524", + "9062": "\ud64b", + "9063": "\ub8fd", + "9064": "\ud338", + "9065": "\ud5e5", + "9066": "\uaebd", + "9067": "\uafcd", + "9068": "\ud058", + "9069": "\ucef9", + "9070": "\uac1b", + "9071": "\uae70", + "9072": "\uc314", + "9073": "\ub775", + "9074": "\ud6e4", + "9075": "\uc53d", + "9076": "\ud141", + "9077": "\ub93c", + "9078": "\uc20f", + "9079": "\uc9ed", + "9080": "\ubbc4", + "9081": "\uc258", + "9082": "\ud33b", + "9083": "\uaed0", + "9084": "\uacf6", + "9085": "\ub400", + "9086": "\uc14c", + "9087": "\uca98", + "9088": "\uc641", + "9089": "\uc90f", + "9090": "\ucdcc", + "9091": "\ud330", + "9092": "\uc9ca", + "9093": "\uc60c", + "9094": "\uc37d", + "9095": "\uc83c", + "9096": "\ud719", + "9097": "\uba71", + "9098": "\uc2a5", + "9099": "\ucf20", + "9100": "\ub217", + "9101": "\uc954", + "9102": "\uc2ef", + "9103": "\ub796", + "9104": "\ubcb5", + "9105": "\uc0db", + "9106": "\uc7c8", + "9107": "\ucb50", + "9108": "\ucda7", + "9109": "\ub5d0", + "9110": "\ucc57", + "9111": "\uc178", + "9112": "\uc6dc", + "9113": "\ubc08", + "9114": "\ud248", + "9115": "\ud57c", + "9116": "\ubf48", + "9117": "\uc5e5", + "9118": "\ubd4c", + "9119": "\uaf48", + "9120": "\uc330", + "9121": "\ubd5c", + "9122": "\uaf3f", + "9123": "\ube73", + "9124": "\uc7b0", + "9125": "\ud2a0", + "9126": "\uc605", + "9127": "\uaec0", + "9128": "\uc37b", + "9129": "\uc538", + "9130": "\ucac0", + "9131": "\ub5c0", + "9132": "\ubfe1", + "9133": "\uc886", + "9134": "\uc42c", + "9135": "\ub761", + "9136": "\uc0e8", + "9137": "\uad82", + "9138": "\uac30", + "9139": "\ube55", + "9140": "\uccc7", + "9141": "\ub554", + "9142": "\uba69", + "9143": "\uba4e", + "9144": "\ubb88", + "9145": "\ub0c7", + "9146": "\ud000", + "9147": "\ud035", + "9148": "\ub4d0", + "9149": "\ud2bf", + "9150": "\uc573", + "9151": "\ud2a4", + "9152": "\ub3db", + "9153": "\uc607", + "9154": "\ud6e0", + "9155": "\ub5b5", + "9156": "\ubcd0", + "9157": "\uae7d", + "9158": "\uad9c", + "9159": "\uc211", + "9160": "\ubbc8", + "9161": "\ubd40", + "9162": "\ucf24", + "9163": "\ub289", + "9164": "\ucc48", + "9165": "\uc22f", + "9166": "\ubc28", + "9167": "\ucad1", + "9168": "\ub380", + "9169": "\ud5f9", + "9170": "\ub819", + "9171": "\ub138", + "9172": "\ub15c", + "9173": "\uc29d", + "9174": "\uc3ed", + "9175": "\ud54c", + "9176": "\uc58f", + "9177": "\ub9f8", + "9178": "\uc7bd", + "9179": "\ubf41", + "9180": "\uc468", + "9181": "\uc698", + "9182": "\ub119", + "9183": "\ub9ec", + "9184": "\ud188", + "9185": "\uba84", + "9186": "\uad38", + "9187": "\ubafc", + "9188": "\ucad2", + "9189": "\ub158", + "9190": "\uc958", + "9191": "\uac84", + "9192": "\uae60", + "9193": "\ubeb4", + "9194": "\ub135", + "9195": "\ubfcd", + "9196": "\ub020", + "9197": "\ud3a9", + "9198": "\uc174", + "9199": "\ucc58", + "9200": "\ub189", + "9201": "\ucd10", + "9202": "\uc5e1", + "9203": "\ub381", + "9204": "\uc0a5", + "9205": "\ucf78", + "9206": "\uc8e4", + "9207": "\ucf71", + "9208": "\ub74c", + "9209": "\ubccf", + "9210": "\uac2c", + "9211": "\ub0b5", + "9212": "\uc6a4", + "9213": "\ucc55", + "9214": "\uae7b", + "9215": "\uca30", + "9216": "\ub1fd", + "9217": "\uc38c", + "9218": "\ube8c", + "9219": "\uac85", + "9220": "\uc705", + "9221": "\uce87", + "9222": "\uc7b4", + "9223": "\uceac", + "9224": "\uc714", + "9225": "\ub768", + "9226": "\ub11c", + "9227": "\ubb4d", + "9228": "\ubd87", + "9229": "\ucea5", + "9230": "\ub383", + "9231": "\uc7c0", + "9232": "\ub700", + "9233": "\ubdf4", + "9234": "\uc58d", + "9235": "\ub404", + "9236": "\ud03c", + "9237": "\ud144", + "9238": "\ubdf8", + "9239": "\ub844", + "9240": "\uc82d", + "9241": "\uc730", + "9242": "\ud6d9", + "9243": "\ubd2c", + "9244": "\ud667", + "9245": "\ucd28", + "9246": "\uae31", + "9247": "\ub5b0", + "9248": "\ubb45", + "9249": "\ud5c0", + "9250": "\uafce", + "9251": "\uba49", + "9252": "\ucd25", + "9253": "\ucea4", + "9254": "\ud590", + "9255": "\uc6e1", + "9256": "\uccb5", + "9257": "\ud38d", + "9258": "\uc530", + "9259": "\uc200", + "9260": "\uce24", + "9261": "\uc229", + "9262": "\uc19d", + "9263": "\uc398", + "9264": "\ud789", + "9265": "\ubeec", + "9266": "\ud73c", + "9267": "\ub0d4", + "9268": "\uc0dc", + "9269": "\ub134", + "9270": "\uc094", + "9271": "\ud5f8", + "9272": "\uac9c", + "9273": "\uc234", + "9274": "\uc82c", + "9275": "\uacc8", + "9276": "\ub11d", + "9277": "\ub588", + "9278": "\uc894", + "9279": "\ud5f4", + "9280": "\uc61c", + "9281": "\uc974", + "9282": "\uc9fc", + "9283": "\uac17", + "9284": "\ub599", + "9285": "\ub9df", + "9286": "\uba53", + "9287": "\uca5d", + "9288": "\uc74f", + "9289": "\ud339", + "9290": "\uc289", + "9291": "\uaf10", + "9292": "\uc0fe", + "9293": "\uc3bc", + "9294": "\ud3ff", + "9295": "\ud489", + "9296": "\uacd8", + "9297": "\uc479", + "9298": "\uac8b", + "9299": "\ub8d4", + "9300": "\ud690", + "9301": "\ubc0e", + "9302": "\ud71c", + "9303": "\ub0e0", + "9304": "\ud5d9", + "9305": "\uaea0", + "9306": "\ubf09", + "9307": "\uc883", + "9308": "\uc0e5", + "9309": "\ub4f8", + "9310": "\ub81d", + "9311": "\ubbd0", + "9312": "\uadff", + "9313": "\ub0d8", + "9314": "\ub40f", + "9315": "\uc6e9", + "9316": "\uca08", + "9317": "\ucb58", + "9318": "\ub463", + "9319": "\ub3e0", + "9320": "\ud06d", + "9321": "\uc0d9", + "9322": "\ud56c", + "9323": "\uc53c", + "9324": "\uc619", + "9325": "\uc394", + "9326": "\uca09", + "9327": "\ub5f4", + "9328": "\ub9dc", + "9329": "\uc14d", + "9330": "\ud3a8", + "9331": "\uc9f9", + "9332": "\ub614", + "9333": "\uacbb", + "9334": "\uc8c8", + "9335": "\ub664", + "9336": "\ucc1f", + "9337": "\uaedc", + "9338": "\ud23d", + "9339": "\uae5f", + "9340": "\uc84d", + "9341": "\ub878", + "9342": "\ud2c9", + "9343": "\ubc0c", + "9344": "\uad54", + "9345": "\uaf07", + "9346": "\ub543", + "9347": "\ub81c", + "9348": "\ub877", + "9349": "\ub879", + "9350": "\uc315", + "9351": "\ucb2c", + "9352": "\uc651", + "9353": "\ud79d", + "9354": "\ud460", + "9355": "\uae37", + "9356": "\uba04", + "9357": "\uae84", + "9358": "\uc2f0", + "9359": "\uc6df", + "9360": "\ud763", + "9361": "\ubba8", + "9362": "\ubfb1", + "9363": "\uc248", + "9364": "\uc814", + "9365": "\ud081", + "9366": "\uc345", + "9367": "\uc0a3", + "9368": "\uacef", + "9369": "\uc0b5", + "9370": "\ub139", + "9371": "\ucb14", + "9372": "\uae79", + "9373": "\uaf4c", + "9374": "\ud1b3", + "9375": "\ud3c5", + "9376": "\ucff1", + "9377": "\uc8d7", + "9378": "\uce61", + "9379": "\ucacd", + "9380": "\ubca7", + "9381": "\ub620", + "9382": "\ub594", + "9383": "\ud207", + "9384": "\uc79b", + "9385": "\uc098", + "9386": "\uc448", + "9387": "\ubb00", + "9388": "\uc18e", + "9389": "\ub5cd", + "9390": "\uaf79", + "9391": "\uc62f", + "9392": "\ub2a1", + "9393": "\ubd91", + "9394": "\uad1e", + "9395": "\uac02", + "9396": "\ube7b", + "9397": "\uc88d", + "9398": "\uc36a", + "9399": "\uc318", + "9400": "\ucc3f", + "9401": "\uacd7", + "9402": "\ud711", + "9403": "\ucc27", + "9404": "\ub754", + "9405": "\ub5ab", + "9406": "\uc80b", + "9407": "\uc62d", + "9408": "\ud613", + "9409": "\uc27f", + "9410": "\uc6f8", + "9411": "\ud25c", + "9412": "\ud0c9", + "9413": "\ubb90", + "9414": "\ub5cf", + "9415": "\ucad8", + "9416": "\uc54e", + "9417": "\ub2fb", + "9418": "\uca44", + "9419": "\ub701", + "9420": "\uc59c", + "9421": "\uc1f3", + "9422": "\ub055", + "9423": "\ud651", + "9424": "\ud0ef", + "9425": "\ucbe7", + "9426": "\ub269", + "9427": "\ud284", + "9428": "\ud744", + "9429": "\ub260", + "9430": "\uc737", + "9431": "\ubb3d", + "9432": "\ud0f0", + "9433": "\uc091", + "9434": "\uac58", + "9435": "\ud585", + "9436": "\ube8d", + "9437": "\uacea", + "9438": "\ud683", + "9439": "\ud2d4", + "9440": "\uc9e2", + "9441": "\ubc9b", + "9442": "\ubfc5", + "9443": "\uc5f7", + "9444": "\ub091", + "9445": "\uc100", + "9446": "\uac09", + "9447": "\uafe9", + "9448": "\uc733", + "9449": "\uc251", + "9450": "\ud2cb", + "9451": "\uc74a", + "9452": "\ub4b9", + "9453": "\uad2d", + "9454": "\ubf1b", + "9455": "\ub739", + "9456": "\uc887", + "9457": "\ub01c", + "9458": "\ube70", + "9459": "\ub3a0", + "9460": "\uc7bf", + "9461": "\ub10b", + "9462": "\ud288", + "9463": "\ub4e6", + "9464": "\ud565", + "9465": "\ub755", + "9466": "\uc2ad", + "9467": "\uc0c5", + "9468": "\uc410", + "9469": "\ub059", + "9470": "\ud515", + "9471": "\uc231", + "9472": "\uca0d", + "9473": "\ub2f3", + "9474": "\ub36b", + "9475": "\ubd50", + "9476": "\uc069", + "9477": "\ub01d", + "9478": "\uad0c", + "9479": "\uc597", + "9480": "\ucb59", + "9481": "\ubf50", + "9482": "\ubee4", + "9483": "\uc595", + "9484": "\uc30c", + "9485": "\ub5c4", + "9486": "\ubc9a", + "9487": "\uc0f4", + "9488": "\ubc49", + "9489": "\ucacc", + "9490": "\uc9d9", + "9491": "\uad76", + "9492": "\ud769", + "9493": "\ub25c", + "9494": "\ucd18", + "9495": "\ub0a1", + "9496": "\uc50c", + "9497": "\ub234", + "9498": "\ucc22", + "9499": "\ud320", + "9500": "\uaed1", + "9501": "\uc5bd", + "9502": "\uad75", + "9503": "\ud07d", + "9504": "\uc553", + "9505": "\ub918", + "9506": "\uacc1", + "9507": "\uc7e4", + "9508": "\ubd89", + "9509": "\ub053", + "9510": "\ub9d1", + "9511": "\ub98e", + "9512": "\ub09a", + "9513": "\ucd1b", + "9514": "\uaebe", + "9515": "\uac1a", + "9516": "\ucc54", + "9517": "\ubb61", + "9518": "\ub560", + "9519": "\ub6ab", + "9520": "\uc633", + "9521": "\ucad3", + "9522": "\uc3df", + "9523": "\uc62e", + "9524": "\ub35f", + "9525": "\ub0b3", + "9526": "\uc549", + "9527": "\uc80a", + "9528": "\uc9e7", + "9529": "\ub429", + "9530": "\ucf2f", + "9531": "\ud290", + "9532": "", + "9533": "ene", + "9534": "\u2581ble", + "9535": "ikk", + "9536": "opp", + "9537": "\u2581Han", + "9538": "\u2581Den", + "9539": "unn", + "9540": "\u2581han", + "9541": "asjon", + "9542": "\u2581word", + "9543": "\u2581werd", + "9544": "", + "9545": "eg", + "9546": "\u2581ikkje", + "9547": "\u2581bok", + "9548": "lik", + "9549": "\u2581eit", + "9550": "s\u00e5", + "9551": "kk", + "9552": "\u2581nok", + "9553": "\u2581god", + "9554": "\u2581lese", + "9555": "dde", + "9556": "inga", + "9557": "\u2581denn", + "9558": "inn", + "9559": "kkje", + "9560": "dig", + "9561": "tid", + "9562": "\u2581b\u00f8ke", + "9563": "ord", + "9564": "\u2581tru", + "9565": "skje", + "9566": "\u2581sei", + "9567": "ller", + "9568": "\u2581fle", + "9569": "skriv", + "9570": "\u2581heil", + "9571": "wy", + "9572": "\u015a", + "9573": "\u0141", + "9574": "\u0179", + "9575": "\u017b", + "9576": "car", + "9577": "t\u00e3o", + "9578": "ia", + "9579": "\u2581foi", + "9580": "ito", + "9581": "ram", + "9582": "fa", + "9583": "\u2581meu", + "9584": "\u00e7a", + "9585": "\u2581dois", + "9586": "a\u00e7\u00e3o", + "9587": "\u2581ter", + "9588": "n\u00e7a", + "9589": "\u2581compra", + "9590": "\u2581mil", + "9591": "\u2581minha", + "9592": "\u2581passa", + "9593": "\u2581casa", + "9594": "\u00c3", + "9595": "\u00b7", + "9596": "", + "9597": "das", + "9598": "\u2581s\u00e3o", + "9599": "\u2581Pa", + "9600": "tura", + "9601": "\u2581ser", + "9602": "\u2581Ele", + "9603": "forma", + "9604": "\u2581Esta", + "9605": "\u00f5es", + "9606": "\u2581pelo", + "9607": "tua", + "9608": "\u2581pela", + "9609": "mar", + "9610": "\u2581Foi", + "9611": "\u2581foram", + "9612": "este", + "9613": "\u2581Um", + "9614": "\u2581S\u00e3o", + "9615": "\u2581entre", + "9616": "fun", + "9617": "agem", + "9618": "gua", + "9619": "\u2581Brasil", + "9620": "\u2581grande", + "9621": "icos", + "9622": "\u2581cidade", + "9623": "inda", + "9624": "\u2581Este", + "9625": "\u2581maior", + "9626": "\u2581brasileiro", + "9627": "\u2581munic\u00edpio", + "9628": "\u2581nome", + "9629": "\u2581encontra", + "9630": "amb\u00e9m", + "9631": "\u2581Sua", + "9632": "\u2581tr\u00eas", + "9633": "\u2581\u0421", + "9634": "\u2581\u0410", + "9635": "\u2581\u041a", + "9636": "\u0431\u0435", + "9637": "\u2581\u041e", + "9638": "\u0441\u0435", + "9639": "\u2581\u041f", + "9640": "\u2581\u043c\u043d\u0435", + "9641": "\u2581\u043e\u043d", + "9642": "\u0446\u0430", + "9643": "\u043d\u0438\u0435", + "9644": "\u0436\u0430", + "9645": "\u0441\u0442\u044c", + "9646": "\u043f\u0443", + "9647": "\u043c\u044b", + "9648": "\u0441\u043a\u0430", + "9649": "\u0441\u0430", + "9650": "\u2581\u0442\u0435\u0431\u044f", + "9651": "\u0433\u0438", + "9652": "\u2581\u0444\u0438\u043b\u044c\u043c", + "9653": "\u0442\u0440\u0435", + "9654": "\u0433\u0440\u0430", + "9655": "\u043c\u0435\u0440", + "9656": "\u0448\u0430", + "9657": "\u2581\u0412\u043a\u043b\u044e\u0447\u0438", + "9658": "\u043b\u0441\u044f", + "9659": "\u0449\u0438", + "9660": "\u2581\u0441\u0435\u0437\u043e\u043d", + "9661": "\u2581\u041a\u0430\u043a", + "9662": "\u2581\u0441\u043c\u043e\u0442\u0440\u0435\u0448\u043a\u0435", + "9663": "\u2581\u0421\u0431\u0435\u0440", + "9664": "\u2581\u0422\u0432", + "9665": "\u2581\u041d\u0435", + "9666": "\u2581\u0414\u0436\u043e\u0439", + "9667": "\u2581\u043e\u0434\u0438\u043d", + "9668": "\u2581\u0410\u0444\u0438\u043d\u0430", + "9669": "\u2581\u041c\u0430", + "9670": "\u2581\u0441\u0435\u043c\u044c", + "9671": "\u2581\u0422\u0430", + "9672": "\u2581\u0421\u0430\u043b\u044e\u0442", + "9673": "\u0431\u043e\u043b\u044c\u0448", + "9674": "\u0441\u043a\u0438\u0439", + "9675": "\u2581\u043f\u044f\u0442\u044c", + "9676": "\u2581\u0441\u0435\u0440\u0438\u0430\u043b", + "9677": "\u2581\u0447\u0435\u0442\u044b\u0440\u0435", + "9678": "\u043a\u043b\u044e\u0447", + "9679": "\u2581\u0448\u0435\u0441\u0442\u044c", + "9680": "\u0438\u0442\u0441\u044f", + "9681": "\u2581\u0432\u043e\u0441\u0435\u043c\u044c", + "9682": "\u2581\u0432\u043e\u043e\u0431\u0449\u0435", + "9683": "\u2581\u041f\u043e\u043a\u0430\u0436\u0438", + "9684": "\u2581\u043f\u043e\u0442\u043e\u043c\u0443", + "9685": "\u2581\u0434\u0432\u0430\u0434\u0446\u0430\u0442\u044c", + "9686": "\u2581\u043a\u0430\u043d\u0430\u043b", + "9687": "\u2581\u0432\u043a\u043b\u044e\u0447\u0438", + "9688": "\u2581\u0440\u0430\u0431\u043e\u0442", + "9689": "\u2581\u043a\u0430\u0440\u0442", + "9690": "\u0438\u0448\u044c", + "9691": "\u2581\u0434\u0435\u043d\u044c", + "9692": "\u042b", + "9693": "ska", + "9694": "var", + "9695": "", + "9696": "\u2581\u0e32", + "9697": "\u2581\u0e19", + "9698": "\u2581\u0e23", + "9699": "\u2581\u0e01", + "9700": "\u2581\u0e2d", + "9701": "\u0e40", + "9702": "\u2581\u0e48", + "9703": "\u2581\u0e07", + "9704": "\u0e31", + "9705": "\u2581\u0e21", + "9706": "\u2581\u0e49", + "9707": "\u2581\u0e22", + "9708": "\u2581\u0e35", + "9709": "\u2581\u0e25", + "9710": "\u2581\u0e27", + "9711": "\u2581\u0e14", + "9712": "\u2581\u0e17", + "9713": "\u2581\u0e2a", + "9714": "\u2581\u0e15", + "9715": "\u2581\u0e34", + "9716": "\u2581\u0e1a", + "9717": "\u2581\u0e1b", + "9718": "\u2581\u0e30", + "9719": "\u2581\u0e2b", + "9720": "\u0e41", + "9721": "\u2581\u0e04", + "9722": "\u2581\u0e08", + "9723": "\u2581\u0e02", + "9724": "\u0e43", + "9725": "\u0e44", + "9726": "\u0e37", + "9727": "\u2581\u0e1e", + "9728": "\u2581\u0e0a", + "9729": "\u2581\u0e47", + "9730": "\u2581\u0e39", + "9731": "\u2581\u0e38", + "9732": "\u2581\u0e4c", + "9733": "\u0e42", + "9734": "\u0e4d", + "9735": "\u2581\u0e36", + "9736": "\u2581\u0e28", + "9737": "\u2581\u0e16", + "9738": "\u2581\u0e0b", + "9739": "\u0e1c", + "9740": "\u2581\u0e20", + "9741": "\u2581\u0e29", + "9742": "\u2581\u0e13", + "9743": "\u2581\u0e18", + "9744": "\u2581\u0e0d", + "9745": "\u0e32", + "9746": "\u0e19", + "9747": "\u2581\u0e1f", + "9748": "\u0e23", + "9749": "\u0e01", + "9750": "\u0e2d", + "9751": "\u0e48", + "9752": "\u0e07", + "9753": "\u0e21", + "9754": "\u0e49", + "9755": "\u0e09", + "9756": "\u0e22", + "9757": "\u2581\u0e10", + "9758": "\u0e35", + "9759": "\u0e25", + "9760": "\u0e27", + "9761": "\u0e14", + "9762": "\u0e17", + "9763": "\u2581\u0e1d", + "9764": "\u0e2a", + "9765": "\u0e15", + "9766": "\u0e34", + "9767": "\u0e1a", + "9768": "\u2581\u0e2e", + "9769": "\u0e1b", + "9770": "\u0e30", + "9771": "\u0e2b", + "9772": "\u0e24", + "9773": "\u0e04", + "9774": "\u0e08", + "9775": "\u2581\u0e0f", + "9776": "\u0e12", + "9777": "\u0e02", + "9778": "\u0e1e", + "9779": "\u0e0a", + "9780": "\u0e47", + "9781": "\u0e39", + "9782": "\u0e38", + "9783": "\u0e4c", + "9784": "\u0e4a", + "9785": "\u2581\u0e2c", + "9786": "\u2581\u0e0e", + "9787": "\u0e11", + "9788": "\u0e36", + "9789": "\u0e28", + "9790": "\u0e16", + "9791": "\u0e0b", + "9792": "\u0e20", + "9793": "\u2581\u0e4b", + "9794": "\u0e29", + "9795": "\u0e13", + "9796": "\u0e18", + "9797": "\u0e0d", + "9798": "\u2581\u0e06", + "9799": "\u0e1f", + "9800": "\u0e10", + "9801": "\u0e1d", + "9802": "\u0e2e", + "9803": "\u0e0c", + "9804": "\u0e0f", + "9805": "\u0e2c", + "9806": "\u0e0e", + "9807": "\u0e45", + "9808": "\u0e4b", + "9809": "\u0e06", + "9810": "\u2581\u0e46", + "9811": "\u0e03", + "9812": "\u0e3a", + "9813": "\u0e05", + "9814": "\u0e46", + "9815": "", + "9816": "\u015f", + "9817": "\u011f", + "9818": "ya", + "9819": "\u2581ve", + "9820": "lar", + "9821": "\u2581bir", + "9822": "l\u0131", + "9823": "d\u0131", + "9824": "ler", + "9825": "ye", + "9826": "s\u0131", + "9827": "lar\u0131", + "9828": "leri", + "9829": "\u0131nda", + "9830": "t\u0131", + "9831": "\u2581bu", + "9832": "lan", + "9833": "ara", + "9834": "\u2581Bu", + "9835": "inde", + "9836": "\u0131n\u0131", + "9837": "y\u0131", + "9838": "yo", + "9839": "d\u00fc", + "9840": "\u2581olarak", + "9841": "\u2581i\u00e7in", + "9842": "maktad\u0131r", + "9843": "ar\u0131", + "9844": "\u2581ba\u015f", + "9845": "\u015e", + "9846": "\u011e", + "9847": "", + "9848": "\u2581\u987b", + "9849": "\u2581\u8d28", + "9850": "\u2581\u6237", + "9851": "\u2581\u4e91", + "9852": "\u2581\u697c", + "9853": "\u2581\u77f3", + "9854": "\u2581\u5ba1", + "9855": "\u2581\u663e", + "9856": "\u2581\u7559", + "9857": "\u2581\u5c3d", + "9858": "\u2581\u96f7", + "9859": "\u2581\u6597", + "9860": "\u2581\u667a", + "9861": "\u2581\u6740", + "9862": "\u2581\u62ec", + "9863": "\u2581\u6267", + "9864": "\u2581\u6548", + "9865": "\u2581\u9752", + "9866": "\u2581\u5584", + "9867": "\u2581\u793c", + "9868": "\u2581\u9760", + "9869": "\u2581\u674e", + "9870": "\u2581\u9ec4", + "9871": "\u2581\u54cd", + "9872": "\u2581\u8425", + "9873": "\u2581\u8865", + "9874": "\u2581\u52bf", + "9875": "\u2581\u8db3", + "9876": "\u2581\u6781", + "9877": "\u2581\u6c5f", + "9878": "\u2581\u7701", + "9879": "\u2581\u9999", + "9880": "\u2581\u7a76", + "9881": "\u2581\u8ffd", + "9882": "\u2581\u7ef4", + "9883": "\u2581\u7fa4", + "9884": "\u2581\u5347", + "9885": "\u2581\u7c73", + "9886": "\u2581\u4ebf", + "9887": "\u2581\u5e1d", + "9888": "\u2581\u7968", + "9889": "\u2581\u5b9d", + "9890": "\u2581\u62cd", + "9891": "\u2581\u613f", + "9892": "\u2581\u7075", + "9893": "\u2581\u6b66", + "9894": "\u2581\u6562", + "9895": "\u2581\u5df4", + "9896": "\u2581\u53e5", + "9897": "\u2581\u5f8b", + "9898": "\u2581\u5c14", + "9899": "\u2581\u72d7", + "9900": "\u2581\u68c0", + "9901": "\u2581\u8f7b", + "9902": "\u2581\u4f01", + "9903": "\u2581\u7b56", + "9904": "\u2047", + "9905": "\u962e", + "9906": "\u6c22", + "9907": "\u53f5", + "9908": "\u8859", + "9909": "\u6cf8", + "9910": "\u90af", + "9911": "\u9c7f", + "9912": "\u95f0", + "9913": "\u6c82", + "9914": "\u5315", + "9915": "\u6860", + "9916": "\u90a1", + "9917": "\u99a5", + "9918": "\u6fee", + "9919": "\u988d", + "9920": "\u5c8c", + "9921": "\u5162", + "9922": "\u8340", + "9923": "\u7fdf", + "9924": "\u86af", + "9925": "\u6d3c", + "9926": "\u7f8c", + "9927": "\u627c", + "9928": "\u8543", + "9929": "\u86df", + "9930": "\u9b13", + "9931": "\u6538", + "9932": "\u5e27", + "9933": "\u9050", + "9934": "\u81fb", + "9935": "\u61ca", + "9936": "\u6d9f", + "9937": 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"\u630e", + "10036": "\u8037", + "10037": "\u817c", + "10038": "\u82aa", + "10039": "\u7619", + "10040": "\u9e9d", + "10041": "\u5b34", + "10042": "\u606a", + "10043": "\u8fe2", + "10044": "\u63a3", + "10045": "\u7ff1", + "10046": "\u9cd5", + "10047": "\u90ac", + "10048": "\u9b03", + "10049": "\u83e1", + "10050": "\u9068", + "10051": "\u577b", + "10052": "\u62c4", + "10053": "\u91ba", + "10054": "\u9e35", + "10055": "\u5b62", + "10056": "\u8862", + "10057": "\u6dbf", + "10058": "\u8c19", + "10059": "\u5156", + "10060": "\u8343", + "10061": "\u9773", + "10062": "\u665f", + "10063": "\u6f2f", + "10064": "\u86d0", + "10065": "\u86f3", + "10066": "\u92ae", + "10067": "\u59a9", + "10068": "\u6b92", + "10069": "\u7f42", + "10070": "\u8012", + "10071": "\u8c06", + "10072": "\u8c00", + "10073": "\u8f72", + "10074": "\u9713", + "10075": "\u83b4", + "10076": "\u96bd", + "10077": "\u6f7a", + "10078": "\u8e0c", + "10079": "\u90eb", + "10080": "\u5555", + "10081": "\u77d7", + "10082": "\u7a88", + "10083": "\u89de", + "10084": "\u94ec", + "10085": "\u988c", + "10086": "\u5d82", + "10087": "\u6da3", + "10088": "\u6e49", + "10089": "\u81ca", + "10090": "\u5522", + "10091": "\u6026", + "10092": "\u9aa5", + "10093": "\u6ee6", + "10094": "\u76f1", + "10095": "\u9e8b", + "10096": "\u535e", + "10097": "\u8200", + "10098": "\u916e", + "10099": "\u93d6", + "10100": "\u951a", + "10101": "\u9aa1", + "10102": "\u9ed4", + "10103": "\u6cf1", + "10104": "\u73de", + "10105": "\u74ef", + "10106": "\u77bf", + "10107": "\u9cb6", + "10108": "\u6175", + "10109": "\u6886", + "10110": "\u6ee2", + "10111": "\u8d5d", + "10112": "\u5a40", + "10113": "\u6c26", + "10114": "\u6dec", + "10115": "\u724d", + "10116": "\u740a", + "10117": "\u8446", + "10118": "\u57ed", + "10119": "\u8707", + "10120": "\u9642", + "10121": "\u62c8", + "10122": "\u7751", + "10123": "\u7ee5", + "10124": "\u8ddb", + "10125": "\u9122", + "10126": "\u5639", + "10127": "\u5d02", + "10128": "\u642a", + "10129": "\u655d", + "10130": "\u8c49", + "10131": "\u8d45", + "10132": "\u98d2", + "10133": "\u5c91", + "10134": "\u7ba9", + "10135": "\u87a8", + "10136": "\u6e0c", + "10137": "\u961a", + "10138": "\u998a", + "10139": "\u704f", + "10140": "\u70b7", + "10141": "\u712f", + "10142": "\u752c", + "10143": "\u8748", + "10144": "\u55e4", + "10145": "\u5cb7", + "10146": "\u62bf", + "10147": "\u6d9e", + "10148": "\u75b8", + "10149": "\u779f", + "10150": "\u7eb0", + "10151": "\u701b", + "10152": "\u75c9", + "10153": "\u7601", + "10154": "\u8368", + "10155": "\u88c6", + "10156": "\u9e51", + "10157": "\u5b6c", + "10158": "\u7c0b", + "10159": "\u7ec9", + "10160": "\u8331", + "10161": "\u839c", + "10162": "\u86d4", + "10163": "\u6800", + "10164": "\u72f0", + "10165": "\u78a3", + "10166": "\u909d", + "10167": "\u94c6", + "10168": "\u6cad", + "10169": "\u80e5", + "10170": "\u858f", + "10171": "\u8941", + "10172": "\u8f76", + "10173": "\u9537", + "10174": "\u504c", + "10175": "\u57c2", + "10176": "\u6035", + "10177": "\u6cd4", + "10178": "\u80db", + "10179": "\u5482", + "10180": "\u5676", + "10181": "\u5d27", + "10182": "\u623e", + "10183": "\u781d", + "10184": "\u8d2e", + "10185": "\u6715", + "10186": "\u6773", + "10187": "\u705e", + "10188": "\u7a37", + "10189": "\u8e2e", + "10190": "\u9506", + "10191": "\u542e", + "10192": "\u6525", + "10193": "\u6bd3", + "10194": "\u6ca3", + "10195": "\u85ff", + "10196": "\u88f1", + "10197": "\u4fda", + "10198": "\u51bd", + "10199": "\u77ec", + "10200": "\u852b", + "10201": "\u998f", + "10202": "\u7812", + "10203": "\u8983", + "10204": "\u8e09", + "10205": "\u949c", + "10206": "\u57a4", + "10207": "\u6dde", + "10208": "\u891a", + "10209": "\u8e52", + "10210": "\u8e69", + "10211": "\u90dc", + "10212": "\u6c68", + "10213": "\u7548", + "10214": "\u75e8", + "10215": "\u7823", + "10216": "\u785a", + "10217": "\u8c1f", + "10218": "\u9528", + "10219": "\u5773", + "10220": "\u57ad", + "10221": "\u5b51", + "10222": "\u5d4b", + "10223": "\u5d99", + "10224": "\u664c", + "10225": "\u6654", + "10226": "\u684e", + "10227": "\u6c85", + "10228": "\u6dc5", + "10229": "\u6ed8", + "10230": "\u714a", + "10231": "\u7284", + "10232": "\u7ea8", + "10233": "\u8188", + "10234": "\u9563", + "10235": "\u510b", + "10236": "\u51c7", + "10237": "\u5d03", + "10238": "\u5fe4", + "10239": "\u6004", + "10240": "\u6a28", + "10241": "\u7430", + "10242": "\u75fc", + "10243": "\u8238", + "10244": "\u853a", + "10245": "\u87cb", + "10246": "\u94a8", + "10247": "\u94e8", + "10248": "\u9cb3", + "10249": "\u9edd", + "10250": "\u4f91", + "10251": "\u5d06", + "10252": "\u69ab", + "10253": "\u72b8", + "10254": "\u742c", + "10255": "\u7eeb", + "10256": "\u8d48", + "10257": "\u909b", + "10258": "\u9995", + "10259": "\u9a77", + "10260": "\u56cd", + "10261": "\u57a1", + "10262": "\u59dd", + "10263": "\u6414", + "10264": "\u6ddd", + "10265": "\u6f78", + "10266": "\u70c3", + "10267": "\u73b3", + "10268": "\u73ee", + "10269": "\u768b", + "10270": "\u8174", + "10271": "\u8dec", + "10272": "\u9ca0", + "10273": "\u9f2c", + "10274": "\u4f22", + "10275": "\u5043", + "10276": "\u5d4a", + "10277": "\u60b1", + "10278": "\u63e9", + "10279": "\u6636", + "10280": "\u6ceb", + "10281": "\u6da0", + "10282": "\u6e6b", + "10283": "\u784c", + "10284": "\u7aa8", + "10285": "\u7ed4", + "10286": "\u7fb8", + "10287": "\u8148", + "10288": "\u8671", + "10289": "\u8d30", + "10290": "\u8db5", + "10291": "\u948e", + "10292": "\u94f7", + "10293": "\u4f2b", + "10294": "\u57a9", + "10295": "\u57dd", + "10296": "\u59af", + "10297": "\u5a09", + "10298": "\u626a", + "10299": "\u63ae", + "10300": "\u6d2e", + "10301": "\u6d43", + "10302": "\u7173", + "10303": "\u737e", + "10304": "\u73f2", + "10305": "\u7583", + "10306": "\u7800", + "10307": "\u7b71", + "10308": "\u7da6", + "10309": "\u826e", + "10310": "\u8306", + "10311": "\u891b", + "10312": "\u8bd3", + "10313": "\u8c94", + "10314": "\u902f", + "10315": "\u90e7", + "10316": "\u539d", + "10317": "\u56d4", + "10318": "\u584d", + "10319": "\u5889", + "10320": "\u5a9e", + "10321": "\u5f9c", + "10322": "\u6387", + "10323": "\u63b8", + "10324": "\u665e", + "10325": "\u66b9", + "10326": "\u6cee", + "10327": "\u6e9f", + "10328": "\u6f5e", + "10329": "\u7287", + "10330": "\u749f", + "10331": "\u7747", + "10332": "\u82cb", + "10333": "\u83c0", + "10334": "\u8473", + "10335": "\u8dda", + "10336": "\u90c5", + "10337": "\u94b4", + "10338": "\u9f39", + "10339": "\u4edf", + "10340": "\u4f97", + "10341": "\u4ffe", + "10342": "\u53c1", + "10343": "\u573b", + "10344": "\u5785", + "10345": "\u59a4", + "10346": "\u65cc", + "10347": "\u67b3", + "10348": "\u6954", + "10349": "\u6978", + "10350": "\u6e86", + "10351": "\u6fc2", + "10352": "\u77f8", + "10353": "\u7efb", + "10354": "\u7f31", + "10355": "\u8153", + "10356": "\u84e5", + "10357": "\u8c11", + "10358": "\u8c15", + "10359": "\u8e31", + "10360": "\u9099", + "10361": "\u94af", + "10362": "\u9512", + "10363": "\u95f3", + "10364": "\u9621", + "10365": "\u98a2", + "10366": "\u9a90", + "10367": "\u9cad", + "10368": "\u9cb7", + "10369": "\u9e5e", + "10370": "\u52ad", + "10371": "\u5575", + "10372": "\u5d47", + "10373": "\u5eb9", + "10374": "\u62da", + "10375": "\u65fb", + "10376": "\u67de", + "10377": "\u6a2f", + "10378": "\u6e8f", + "10379": "\u6f8d", + "10380": "\u740f", + "10381": "\u7762", + "10382": "\u7837", + "10383": "\u795a", + "10384": "\u7afd", + "10385": "\u82e1", + "10386": "\u8347", + "10387": "\u8385", + "10388": "\u8572", + "10389": "\u8731", + "10390": "\u87ca", + "10391": "\u88e8", + "10392": "\u89d0", + "10393": "\u8bc3", + "10394": "\u8c27", + "10395": "\u9095", + "10396": "\u90d3", + "10397": "\u9170", + "10398": "\u94d6", + "10399": "\u94df", + "10400": "\u954c", + "10401": "\u9606", + "10402": "\u9615", + "10403": "\u96d2", + "10404": "\u9701", + "10405": "\u9acb", + "10406": "\u9c85", + "10407": "\u9c91", + "10408": "\u9ca2", + "10409": "\u9eb8", + "10410": "\u523d", + "10411": "\u5511", + "10412": "\u559f", + "10413": "\u55ea", + "10414": "\u5658", + "10415": "\u56f9", + "10416": "\u572a", + "10417": "\u579a", + "10418": "\u57f8", + "10419": "\u5807", + "10420": "\u5aeb", + "10421": "\u5b17", + "10422": "\u5b5b", + "10423": "\u5b73", + "10424": "\u5cc1", + "10425": "\u5d6c", + "10426": "\u5f0b", + "10427": "\u60bb", + "10428": "\u625e", + "10429": "\u6448", + "10430": "\u64ba", + "10431": "\u64d8", + "10432": "\u6710", + "10433": "\u680e", + "10434": "\u6c8f", + "10435": "\u6d60", + "10436": "\u6de6", + "10437": "\u6e11", + "10438": "\u6f4b", + "10439": "\u7094", + "10440": "\u7117", + "10441": "\u7118", + "10442": "\u7168", + "10443": "\u7424", + "10444": "\u742e", + "10445": "\u7477", + "10446": "\u759d", + "10447": "\u75bd", + "10448": "\u7aa0", + "10449": "\u7cbc", + "10450": "\u7ebe", + "10451": "\u7f19", + "10452": "\u7f54", + "10453": "\u816d", + "10454": "\u830c", + "10455": "\u832f", + "10456": "\u8360", + "10457": "\u8438", + "10458": "\u8788", + "10459": "\u8872", + "10460": "\u8c2f", + "10461": "\u8e3a", + "10462": "\u8f6b", + "10463": "\u90b3", + "10464": "\u90ef", + "10465": "\u94e3", + "10466": "\u94e9", + "10467": "\u94f0", + "10468": "\u9532", + "10469": "\u9616", + "10470": "\u9708", + "10471": "\u9aa0", + "10472": "\u9ecd", + "10473": "\u4dae", + "10474": "\u4ee1", + "10475": "\u5053", + "10476": "\u520d", + "10477": "\u525c", + "10478": "\u5416", + "10479": "\u549d", + "10480": "\u54bb", + "10481": "\u54c2", + "10482": "\u5537", + "10483": "\u5581", + "10484": "\u55c4", + "10485": "\u562d", + "10486": "\u5659", + "10487": "\u5739", + "10488": "\u5769", + "10489": "\u57c7", + "10490": "\u57d5", + "10491": "\u57da", + "10492": "\u59ab", + "10493": "\u5a0c", + "10494": "\u5ada", + "10495": "\u5b71", + "10496": "\u5b93", + "10497": "\u5c05", + "10498": "\u5d9d", + "10499": "\u5f2d", + "10500": "\u6006", + "10501": "\u603f", + "10502": "\u6041", + "10503": "\u6078", + "10504": "\u6266", + "10505": "\u678b", + "10506": "\u690b", + "10507": "\u6a3e", + "10508": "\u6bc2", + "10509": "\u6c4a", + "10510": "\u6c69", + "10511": "\u6ce0", + "10512": "\u6d39", + "10513": "\u6d48", + "10514": "\u7113", + "10515": "\u727e", + "10516": "\u73b9", + "10517": "\u73d9", + "10518": "\u75a3", + "10519": "\u75b4", + "10520": "\u7633", + "10521": "\u772c", + "10522": "\u77fd", + "10523": "\u79e3", + "10524": "\u7b33", + "10525": "\u7be6", + "10526": "\u7c7c", + "10527": "\u7cb2", + "10528": "\u7ec0", + "10529": "\u7ecb", + "10530": "\u82a9", + "10531": "\u84e6", + "10532": "\u8821", + "10533": "\u8934", + "10534": "\u8a3e", + "10535": "\u8ba3", + "10536": "\u8bd8", + "10537": "\u8dba", + "10538": "\u8e2f", + "10539": "\u8e5a", + "10540": "\u8e85", + "10541": "\u8f78", + "10542": "\u9021", + "10543": "\u9150", + "10544": "\u9487", + "10545": "\u94b2", + "10546": "\u94e7", + "10547": "\u9509", + "10548": "\u951f", + "10549": "\u95e9", + "10550": "\u9697", + "10551": "\u9880", + "10552": "\u98e7", + "10553": "\u9ac2", + "10554": "\u9b49", + "10555": "\u9cdf", + "10556": "\u9e22", + "10557": "\uff21", + "10558": "\u9980", + "10559": "\u966c", + "10560": "\u8914", + "10561": "\u7596", + "10562": "\u68c2", + "10563": "\u6677", + "10564": "\u643d", + "10565": "\u9011", + "10566": "\u82f7", + "10567": "\u783c", + "10568": "\u76c5", + "10569": "\u746d", + "10570": "\u61b7", + "10571": "\u5fff", + "10572": "\u5c50", + "10573": "\u5c15", + "10574": "\u586c", + "10575": "\u500c", + "10576": "\u8df9", + "10577": "\u845a", + "10578": "\u6b93", + "10579": "\u51bc", + "10580": "\u50ee", + "10581": "\u8f73", + "10582": "\u8df6", + "10583": "\u8dce", + "10584": "\u8c85", + "10585": "\u831b", + "10586": "\u73fa", + "10587": "\u67d2", + "10588": "\u4f76", + "10589": "\u94e1", + "10590": "\u7cb3", + "10591": "\u71ee", + "10592": "\u67b0", + "10593": "\u547b", + "10594": "\u9534", + "10595": "\u5cd2", + "10596": "\u551b", + "10597": "\u9c9f", + "10598": "\u9a9d", + "10599": "\u975b", + "10600": "\u8db8", + "10601": "\u8019", + "10602": "\u78b4", + "10603": "\u71ca", + "10604": "\u6dd6", + "10605": "\u948f", + "10606": "\u886e", + "10607": "\u7428", + "10608": "\u5f89", + "10609": "\u5501", + "10610": "\u80d7", + "10611": "\u7ecc", + "10612": "\u5a4a", + "10613": "\u54ad", + "10614": "\u9a85", + "10615": "\u794e", + "10616": "\u7663", + "10617": "\u72d2", + "10618": "\u90ba", + "10619": "\u87c0", + "10620": "\u7a1e", + "10621": "\u6e4e", + "10622": "\u659b", + "10623": "\u688f", + "10624": "\u679e", + "10625": "\u9549", + "10626": "\u7bb4", + "10627": "\u7166", + "10628": "\u55d4", + "10629": "\u82e3", + "10630": "\u7fca", + "10631": "\u765c", + "10632": "\u8e7c", + "10633": "\u86c6", + "10634": "\u7441", + "10635": "\u6600", + "10636": "\u9a9e", + "10637": "\u77fe", + "10638": "\u749e", + "10639": "\u6849", + "10640": "\u5d58", + "10641": "\u5662", + "10642": "\u8bb4", + "10643": "\u7691", + "10644": "\u73c9", + "10645": "\u835a", + "10646": "\u7fce", + "10647": "\u5a75", + "10648": "\u8d53", + "10649": "\u7f30", + "10650": "\u7f28", + "10651": "\u7620", + "10652": "\u61cb", + "10653": "\u789c", + "10654": "\u70e9", + "10655": "\u5b37", + "10656": "\u5472", + "10657": "\u9e4c", + "10658": "\u9604", + "10659": "\u9555", + "10660": "\u7b60", + "10661": "\u7080", + "10662": "\u6c1f", + "10663": "\u5729", + "10664": "\u71a0", + "10665": "\u6f2a", + "10666": "\u6b46", + "10667": "\u64c0", + "10668": "\u9a9c", + "10669": "\u956d", + "10670": "\u8d4a", + "10671": "\u83c1", + "10672": "\u7bea", + "10673": "\u7708", + "10674": "\u5ffb", + "10675": "\u5b40", + "10676": "\u85dc", + "10677": "\u70f7", + "10678": "\u5bb8", + "10679": "\u504e", + "10680": "\u9539", + "10681": "\u94c9", + "10682": "\u8913", + "10683": "\u768e", + "10684": "\u72b7", + "10685": "\u7292", + "10686": "\u55d6", + "10687": "\u9e5c", + "10688": "\u950c", + "10689": "\u73cf", + "10690": "\u85d3", + "10691": "\u8dc4", + "10692": "\u69ad", + "10693": "\u5ad4", + "10694": "\u5a23", + "10695": "\u8d3b", + "10696": "\u870d", + "10697": "\u7f04", + "10698": "\u7738", + "10699": "\u7719", + "10700": "\u9e6b", + "10701": "\u8734", + "10702": "\u81ba", + "10703": "\u762a", + "10704": "\u6c93", + "10705": "\u6593", + "10706": "\u64de", + "10707": "\u5d2e", + "10708": "\u9541", + "10709": "\u7eab", + "10710": "\u789a", + "10711": "\u6862", + "10712": "\u98da", + "10713": "\u840b", + "10714": "\u7131", + "10715": "\u6a35", + "10716": "\u576f", + "10717": "\u5636", + "10718": "\u954a", + "10719": "\u8869", + "10720": "\u86f9", + "10721": "\u83a0", + "10722": "\u783e", + "10723": "\u6e0d", + "10724": "\u6be1", + "10725": "\u65ef", + "10726": "\u579b", + "10727": "\u9530", + "10728": "\u915a", + "10729": "\u9ccd", + "10730": "\u9968", + "10731": "\u94c0", + "10732": "\u5ccb", + "10733": "\u9a9b", + "10734": "\u8169", + "10735": "\u754a", + "10736": "\u5530", + "10737": "\u4ede", + "10738": "\u9609", + "10739": "\u72de", + "10740": "\u6631", + "10741": "\u6421", + "10742": "\u8f67", + "10743": "\u81e7", + "10744": "\u7a95", + "10745": "\u781a", + "10746": "\u70ca", + "10747": "\u6963", + "10748": "\u5fe1", + "10749": "\u9e42", + "10750": "\u6868", + "10751": "\u645e", + "10752": "\u612b", + "10753": "\u949b", + "10754": "\u797a", + "10755": "\u8e76", + "10756": "\u6043", + "10757": "\u5477", + "10758": "\u7b06", + "10759": "\u62a1", + "10760": "\u5ff1", + "10761": "\u5b05", + "10762": "\u520e", + "10763": "\u94b5", + "10764": "\u8ba7", + "10765": "\u86c0", + "10766": "\u6748", + "10767": "\u992e", + "10768": "\u948a", + "10769": "\u7f0e", + "10770": "\u954d", + "10771": "\u89ce", + "10772": "\u5a67", + "10773": "\u98a7", + "10774": "\u989a", + "10775": "\u874c", + "10776": "\u810d", + "10777": "\u55f2", + "10778": "\u5323", + "10779": "\u9f8a", + "10780": "\u82de", + "10781": "\u9cab", + "10782": "\u8e8f", + "10783": "\u8885", + "10784": "\u7ee2", + "10785": "\u5a7a", + "10786": "\u94ff", + "10787": "\u86b1", + "10788": "\u7bd1", + "10789": "\u94e4", + "10790": "\u8113", + "10791": "\u5ab2", + "10792": "\u94c4", + "10793": "\u7bab", + "10794": "\u5a06", + "10795": "\u4f58", + "10796": "\u90b0", + "10797": "\u83ba", + "10798": "\u7f22", + "10799": "\u6410", + "10800": "\u916f", + "10801": "\u8426", + "10802": "\u6f3e", + "10803": "\u6c7e", + "10804": "\u6bfd", + "10805": "\u8902", + "10806": "\u6684", + "10807": "\u9e3e", + "10808": "\u9b1f", + "10809": "\u7f07", + "10810": "\u7bd3", + "10811": "\u6cfe", + "10812": "\u8d73", + "10813": "\u8146", + "10814": "\u9cdd", + "10815": "\u97ec", + "10816": "\u950f", + "10817": "\u8be9", + "10818": "\u79f8", + "10819": "\u622c", + "10820": "\u89d1", + "10821": "\u8559", + "10822": "\u9e6d", + "10823": "\u86a4", + "10824": "\u828a", + "10825": "\u780c", + "10826": "\u7352", + "10827": "\u6b87", + "10828": "\u5942", + "10829": "\u94a3", + "10830": "\u8191", + "10831": "\u7cd7", + "10832": "\u76f9", + "10833": "\u73a5", + "10834": "\u9083", + "10835": "\u8713", + "10836": "\u71ce", + "10837": "\u5567", + "10838": "\u7f44", + "10839": "\u873b", + "10840": "\u776c", + "10841": "\u732c", + "10842": "\u9984", + "10843": "\u7696", + "10844": "\u5140", + "10845": "\u970e", + "10846": "\u84d3", + "10847": "\u7634", + "10848": "\u75eb", + "10849": "\u9550", + "10850": "\u8936", + "10851": "\u8dfa", + "10852": "\u70ec", + "10853": "\u6cd3", + "10854": "\u9535", + "10855": "\u8bb7", + "10856": "\u86aa", + "10857": "\u79c6", + "10858": "\u6cde", + "10859": "\u9cd7", + "10860": "\u8725", + "10861": "\u7085", + "10862": "\u65ee", + "10863": "\u6382", + "10864": "\u58d1", + "10865": "\u54a3", + "10866": "\u9b47", + "10867": "\u7898", + "10868": "\u7699", + "10869": "\u5bd0", + "10870": "\u7600", + "10871": "\u6005", + "10872": "\u869d", + "10873": "\u8398", + "10874": "\u5bf0", + "10875": "\u832c", + "10876": "\u51a2", + "10877": "\u9cde", + "10878": "\u9573", + "10879": "\u8f8d", + "10880": "\u9a8b", + "10881": "\u85b0", + "10882": "\u7b75", + "10883": "\u76ce", + "10884": "\u6988", + "10885": "\u5498", + "10886": "\u4fac", + "10887": "\u8f98", + "10888": "\u812f", + "10889": "\u695e", + "10890": "\u997d", + "10891": "\u82ef", + "10892": "\u9ab0", + "10893": "\u970f", + "10894": "\u8722", + "10895": "\u6d54", + "10896": "\u631b", + "10897": "\u5a04", + "10898": "\u60b4", + "10899": "\u5a55", + "10900": "\u55b3", + "10901": "\u557e", + "10902": "\u8baa", + "10903": "\u5e1b", + "10904": "\u5b7a", + "10905": "\u8bcb", + "10906": "\u8bff", + "10907": "\u78fa", + "10908": "\u7693", + "10909": "\u62f4", + "10910": "\u709c", + "10911": "\u5e44", + "10912": "\u5d3d", + "10913": "\u50a5", + "10914": "\u9cc5", + "10915": "\u94ee", + "10916": "\u6da7", + "10917": "\u94be", + "10918": "\u819b", + "10919": "\u7d0a", + "10920": "\u75e7", + "10921": "\u728a", + "10922": "\u6d5a", + "10923": "\u9163", + "10924": "\u6479", + "10925": "\u5e62", + "10926": "\u5ced", + "10927": "\u59e3", + "10928": "\u5406", + "10929": "\u7ead", + "10930": "\u8301", + "10931": "\u6ec7", + "10932": "\u4f57", + "10933": "\u9035", + "10934": "\u6e4d", + "10935": "\u8c29", + "10936": "\u836b", + "10937": "\u7a96", + "10938": "\u715c", + "10939": "\u9955", + "10940": "\u9062", + "10941": "\u67ad", + "10942": "\u60e6", + "10943": "\u8f7c", + "10944": "\u7bf1", + "10945": "\u7abf", + "10946": "\u795b", + "10947": "\u54fd", + "10948": "\u9e43", + "10949": "\u7c41", + "10950": "\u69b7", + "10951": "\u6635", + "10952": "\u5657", + "10953": "\u8925", + "10954": "\u7638", + "10955": "\u6cef", + "10956": "\u5b5c", + "10957": "\u8bb9", + "10958": "\u8537", + "10959": "\u729f", + "10960": "\u5c96", + "10961": "\u9791", + "10962": "\u91c9", + "10963": "\u8e4b", + "10964": "\u7c91", + "10965": "\u6d93", + "10966": "\u6cf7", + "10967": "\u9c88", + "10968": "\u988a", + "10969": "\u6d19", + "10970": "\u952d", + "10971": "\u7116", + "10972": "\u60ec", + "10973": "\u9a6e", + "10974": "\u998b", + "10975": "\u6995", + "10976": "\u996f", + "10977": "\u9776", + "10978": "\u9542", + "10979": "\u6cb1", + "10980": "\u6452", + "10981": "\u54d0", + "10982": "\u9eef", + "10983": "\u8be7", + "10984": "\u64ac", + "10985": "\u94d0", + "10986": "\u83cf", + "10987": "\u5671", + "10988": "\u82ae", + "10989": "\u739f", + "10990": "\u6dae", + "10991": "\u94c2", + "10992": "\u80ed", + "10993": "\u7459", + "10994": "\u5c79", + "10995": "\u55dd", + "10996": "\u9cd6", + "10997": "\u9602", + "10998": "\u5693", + "10999": "\u86a3", + "11000": "\u7c7d", + "11001": "\u7095", + "11002": "\u568f", + "11003": "\u8fe9", + "11004": "\u9981", + "11005": "\u72c8", + "11006": "\u631d", + "11007": "\u95f5", + "11008": "\u8c0f", + "11009": "\u7bc6", + "11010": "\u75a1", + "11011": "\u6dfc", + "11012": "\u631e", + "11013": "\u61e6", + "11014": "\u6059", + "11015": "\u5f5d", + "11016": "\u5958", + "11017": "\u4f36", + "11018": "\u6dcc", + "11019": "\u9ccc", + "11020": "\u80ef", + "11021": "\u6c74", + "11022": "\u9497", + "11023": "\u8de4", + "11024": "\u68e3", + "11025": "\u6657", + "11026": "\u5fd1", + "11027": "\u56f1", + "11028": "\u7405", + "11029": "\u5f99", + "11030": "\u7f9a", + "11031": "\u6a90", + "11032": "\u853c", + "11033": "\u8334", + "11034": "\u9997", + "11035": "\u8c1b", + "11036": "\u7444", + "11037": "\u6866", + "11038": "\u64b5", + "11039": "\u9e25", + "11040": "\u87b3", + "11041": "\u7edb", + "11042": "\u7ea3", + "11043": "\u7a57", + "11044": "\u69bb", + "11045": "\u6942", + "11046": "\u607a", + "11047": "\u592f", + "11048": "\u54ee", + "11049": "\u9e2f", + "11050": "\u60fa", + "11051": "\u9131", + "11052": "\u8f84", + "11053": "\u567c", + "11054": "\u53ae", + "11055": "\u533e", + "11056": "\u5014", + "11057": "\u7736", + "11058": "\u6829", + "11059": "\u664f", + "11060": "\u55d2", + "11061": "\u4f7c", + "11062": "\u6376", + "11063": "\u9a81", + "11064": "\u9504", + "11065": "\u80eb", + "11066": "\u9977", + "11067": "\u7b8d", + "11068": "\u70e8", + "11069": "\u8892", + "11070": "\u7578", + "11071": "\u60ee", + "11072": "\u7357", + "11073": "\u6ed5", + "11074": "\u5e3c", + "11075": "\u74a8", + "11076": "\u667e", + "11077": "\u8df7", + "11078": "\u62a8", + "11079": "\u74ee", + "11080": "\u82c7", + "11081": "\u621b", + "11082": "\u8e6c", + "11083": "\u556c", + "11084": "\u4f5f", + "11085": "\u5c9a", + "11086": "\u5b1b", + "11087": "\u956f", + "11088": "\u7f81", + "11089": "\u98d3", + "11090": "\u905b", + "11091": "\u6e85", + "11092": "\u9522", + "11093": "\u8386", + "11094": "\u63b3", + "11095": "\u7172", + "11096": "\u9698", + "11097": "\u6f4d", + "11098": "\u8be3", + "11099": "\u5c49", + "11100": "\u5b5a", + "11101": "\u4f70", + "11102": "\u9a6f", + "11103": "\u66a8", + "11104": "\u4fd1", + "11105": "\u835f", + "11106": "\u5cea", + "11107": "\u9890", + "11108": "\u919b", + "11109": "\u62e3", + "11110": "\u87d2", + "11111": "\u6ca5", + "11112": "\u6096", + "11113": "\u9ae6", + "11114": "\u63b7", + "11115": "\u4ee8", + "11116": "\u998d", + "11117": "\u94e0", + "11118": "\u75ca", + "11119": "\u6fd1", + "11120": "\u5623", + "11121": "\u8693", + "11122": "\u7830", + "11123": "\u8dc6", + "11124": "\u6d52", + "11125": "\u5ce5", + "11126": "\u4ea2", + "11127": "\u7329", + "11128": "\u6c76", + "11129": "\u79ba", + "11130": "\u73d1", + "11131": "\u53fc", + "11132": "\u8638", + "11133": "\u9e20", + "11134": "\u7fe9", + "11135": "\u7f24", + "11136": "\u7c27", + "11137": "\u747e", + "11138": "\u552c", + "11139": "\u748b", + "11140": "\u68a7", + "11141": "\u75f1", + "11142": "\u9a6d", + "11143": "\u741b", + "11144": "\u6c2a", + "11145": "\u84bf", + "11146": "\u78f7", + "11147": "\u949d", + "11148": "\u8fab", + "11149": "\u84df", + "11150": "\u7cb1", + "11151": "\u67b8", + "11152": "\u8717", + "11153": "\u7a98", + "11154": "\u9975", + "11155": "\u5228", + "11156": "\u7629", + "11157": "\u54c6", + "11158": "\u88f4", + "11159": "\u804b", + "11160": "\u7316", + "11161": "\u80e7", + "11162": "\u609a", + "11163": "\u8884", + "11164": "\u8364", + "11165": "\u80fa", + "11166": "\u6805", + "11167": "\u5fd2", + "11168": "\u9611", + "11169": "\u8f97", + "11170": "\u8e1d", + "11171": "\u6fd2", + "11172": "\u6d31", + "11173": "\u6a71", + "11174": "\u9a7f", + "11175": "\u7b5d", + "11176": "\u85c9", + "11177": "\u7ede", + "11178": "\u6bcb", + "11179": "\u80f0", + "11180": "\u70fd", + "11181": "\u701a", + "11182": "\u8f99", + "11183": "\u5ae6", + "11184": "\u6f7c", + "11185": "\u6e0e", + "11186": "\u6e32", + "11187": "\u55f7", + "11188": "\u7a20", + "11189": "\u5ad6", + "11190": "\u622e", + "11191": "\u6b83", + "11192": "\u9a78", + "11193": "\u8d58", + "11194": "\u56b7", + "11195": "\u5a34", + "11196": "\u5586", + "11197": "\u8327", + "11198": "\u7f2a", + "11199": "\u9e49", + "11200": "\u9abc", + "11201": "\u7f15", + "11202": "\u5dcd", + "11203": "\u9e66", + "11204": "\u8d43", + "11205": "\u8715", + "11206": "\u6ea5", + "11207": "\u7b03", + "11208": "\u952f", + "11209": "\u94b0", + "11210": "\u9a79", + "11211": "\u8c82", + "11212": "\u766b", + "11213": "\u759a", + "11214": "\u8708", + "11215": "\u5412", + "11216": "\u9704", + "11217": "\u968d", + "11218": "\u9e33", + "11219": "\u7eca", + "11220": "\u6da1", + "11221": "\u5e37", + "11222": "\u94db", + "11223": "\u4fea", + "11224": "\u9716", + "11225": "\u8517", + "11226": "\u692d", + "11227": "\u6e89", + "11228": "\u5ce6", + "11229": "\u5a05", + "11230": "\u532e", + "11231": "\u6994", + "11232": "\u4fd0", + "11233": "\u541d", + "11234": "\u8bec", + "11235": "\u97ed", + "11236": "\u4fde", + "11237": "\u70ef", + "11238": "\u574d", + "11239": "\u7599", + "11240": "\u6cae", + "11241": "\u7750", + "11242": "\u6c55", + "11243": "\u50a3", + "11244": "\u9885", + "11245": "\u865e", + "11246": "\u9619", + "11247": "\u7487", + "11248": "\u8bdf", + "11249": "\u659f", + "11250": "\u816e", + "11251": "\u70af", + "11252": "\u6b7c", + "11253": "\u90f8", + "11254": "\u75f9", + "11255": "\u66e6", + "11256": "\u64c2", + "11257": "\u9525", + "11258": "\u8eac", + "11259": "\u772f", + "11260": "\u8c4c", + "11261": "\u8bfd", + "11262": "\u60eb", + "11263": "\u9e4a", + "11264": "\u854a", + "11265": "\u6151", + "11266": "\u7ec5", + "11267": "\u64d2", + "11268": "\u6342", + "11269": "\u7efd", + "11270": "\u5b70", + "11271": "\u6664", + "11272": "\u5d2d", + "11273": "\u6f62", + "11274": "\u5e42", + "11275": "\u62e7", + "11276": "\u80ae", + "11277": "\u9176", + "11278": "\u6c2e", + "11279": "\u566c", + "11280": "\u9893", + "11281": "\u821c", + "11282": "\u683e", + "11283": "\u9523", + "11284": "\u86e4", + "11285": "\u9ac5", + "11286": "\u95eb", + "11287": "\u6cf5", + "11288": "\u996a", + "11289": "\u6002", + "11290": "\u814c", + "11291": "\u9cb8", + "11292": "\u752d", + "11293": "\u57a6", + "11294": "\u5180", + "11295": "\u78c5", + "11296": "\u5f29", + "11297": "\u796f", + "11298": "\u68ad", + "11299": "\u6615", + "11300": "\u4fa5", + "11301": "\u6123", + "11302": "\u77aa", + "11303": "\u6da4", + "11304": "\u68f1", + "11305": "\u7eef", + "11306": "\u6f9c", + "11307": "\u59d7", + "11308": "\u85d5", + "11309": "\u973e", + "11310": "\u9502", + "11311": "\u9540", + "11312": "\u6c79", + "11313": "\u9ca4", + "11314": "\u6e43", + "11315": "\u7c07", + "11316": "\u6e3a", + "11317": "\u9074", + "11318": "\u4e4d", + "11319": "\u6273", + "11320": "\u8018", + "11321": "\u9102", + "11322": "\u75ae", + "11323": "\u9ab7", + "11324": "\u8680", + "11325": "\u8042", + "11326": "\u75a4", + "11327": "\u6de4", + "11328": "\u5777", + "11329": "\u79fd", + "11330": "\u77a9", + "11331": "\u97f6", + "11332": "\u94a7", + "11333": "\u87d1", + "11334": "\u8335", + "11335": "\u829c", + "11336": "\u620c", + "11337": "\u52b5", + "11338": "\u5520", + "11339": "\u7eee", + "11340": "\u6d4a", + "11341": "\u6f13", + "11342": "\u6ba1", + "11343": "\u7728", + "11344": "\u60ed", + "11345": "\u502a", + "11346": "\u715e", + "11347": "\u6ed4", + "11348": "\u5018", + "11349": "\u67ab", + "11350": "\u6f88", + "11351": "\u5b7d", + "11352": "\u96f3", + "11353": "\u6c28", + "11354": "\u7ef0", + "11355": "\u8f95", + "11356": "\u9551", + "11357": "\u7184", + "11358": "\u6064", + "11359": "\u631a", + "11360": "\u98a4", + "11361": "\u778c", + "11362": "\u56e7", + "11363": "\u8bb3", + "11364": "\u75ea", + "11365": "\u70c1", + "11366": "\u7f94", + "11367": "\u79c3", + "11368": "\u6177", + "11369": "\u5c94", + "11370": "\u6f33", + "11371": "\u75de", + "11372": "\u5f64", + "11373": "\u69a8", + "11374": "\u76cf", + "11375": "\u6c90", + "11376": "\u68e0", + "11377": "\u5d34", + "11378": "\u575e", + "11379": "\u5429", + "11380": "\u6808", + "11381": "\u67e0", + "11382": "\u6556", + "11383": "\u4f88", + "11384": "\u7faf", + "11385": "\u6e1d", + "11386": "\u7ef7", + "11387": "\u7eb6", + "11388": "\u7cef", + "11389": "\u8354", + "11390": "\u6dc6", + "11391": "\u9661", + "11392": "\u4fcf", + "11393": "\u58a9", + "11394": "\u7cbd", + "11395": "\u67ec", + "11396": "\u5600", + "11397": "\u53a5", + "11398": "\u5254", + "11399": "\u903e", + "11400": "\u7fb2", + "11401": "\u8beb", + "11402": "\u7f00", + "11403": "\u5768", + "11404": "\u8d42", + "11405": "\u603c", + "11406": "\u5669", + "11407": "\u9647", + "11408": "\u94a6", + "11409": "\u94a0", + "11410": "\u5527", + "11411": "\u51ff", + "11412": "\u55e1", + "11413": "\u5431", + "11414": "\u5349", + "11415": "\u5455", + "11416": "\u6c5b", + "11417": "\u5f08", + "11418": "\u79e7", + "11419": "\u7cd9", + "11420": "\u7115", + "11421": "\u6da9", + "11422": "\u7d6e", + "11423": "\u7490", + "11424": "\u6d95", + "11425": "\u75b5", + "11426": "\u8110", + "11427": "\u6c13", + "11428": "\u7fbf", + "11429": "\u8c24", + "11430": "\u8759", + "11431": "\u904f", + "11432": "\u8760", + "11433": "\u7076", + "11434": "\u6789", + "11435": "\u54a9", + "11436": "\u61f5", + "11437": "\u5a6a", + "11438": "\u60d5", + "11439": "\u8bc5", + "11440": "\u5580", + "11441": "\u6320", + "11442": "\u9753", + "11443": "\u90dd", + "11444": "\u6cfb", + "11445": "\u97e7", + "11446": "\u618b", + "11447": "\u94dd", + "11448": "\u777f", + "11449": "\u5189", + "11450": "\u7a8d", + "11451": "\u78be", + "11452": "\u60f6", + "11453": "\u6f47", + "11454": "\u5dc5", + "11455": "\u9668", + "11456": "\u73ba", + "11457": "\u8d63", + "11458": "\u9c8d", + "11459": "\u54d7", + "11460": "\u7ca4", + "11461": "\u5a25", + "11462": "\u56e4", + "11463": "\u7011", + "11464": "\u68d5", + "11465": "\u53fd", + "11466": "\u710a", + "11467": "\u9e3d", + "11468": "\u6292", + "11469": "\u527f", + "11470": "\u82df", + "11471": "\u915d", + "11472": "\u8046", + "11473": "\u7845", + "11474": "\u7779", + "11475": "\u8782", + "11476": "\u6252", + "11477": "\u4eb5", + "11478": "\u9508", + "11479": "\u4e10", + "11480": "\u731d", + "11481": "\u964b", + "11482": "\u8845", + "11483": "\u599e", + "11484": "\u5478", + "11485": "\u7f1a", + "11486": "\u9a87", + "11487": "\u9f9a", + "11488": "\u5241", + "11489": "\u73ae", + "11490": "\u7785", + "11491": "\u4fd8", + "11492": "\u6986", + "11493": "\u5a76", + "11494": "\u761f", + "11495": "\u655b", + "11496": "\u8747", + "11497": "\u4fed", + "11498": "\u9556", + "11499": "\u9a8f", + "11500": "\u51f3", + "11501": "\u501a", + "11502": "\u5578", + "11503": "\u7b77", + "11504": "\u7ef8", + "11505": "\u6caa", + "11506": "\u886b", + "11507": "\u7455", + "11508": "\u6d3d", + "11509": "\u89c5", + "11510": "\u818a", + "11511": "\u4f6c", + "11512": "\u7f2e", + "11513": "\u63ba", + "11514": "\u80f3", + "11515": "\u7682", + "11516": "\u90a2", + "11517": "\u7ed2", + "11518": "\u78b1", + "11519": "\u7aa5", + "11520": "\u66a7", + "11521": "\u61c8", + "11522": "\u69df", + "11523": "\u56a3", + "11524": "\u7caa", + "11525": "\u9499", + "11526": "\u846b", + "11527": "\u5201", + "11528": "\u54d2", + "11529": "\u90b9", + "11530": "\u6a61", + "11531": "\u8165", + "11532": "\u9985", + "11533": "\u77f6", + "11534": "\u9cc4", + "11535": "\u545b", + "11536": "\u61ac", + "11537": "\u76b1", + "11538": "\u55b1", + "11539": "\u960e", + "11540": "\u55e6", + "11541": "\u96ef", + "11542": "\u5570", + "11543": "\u7a9c", + "11544": "\u9992", + "11545": "\u655e", + "11546": "\u8d41", + "11547": "\u7980", + "11548": "\u6402", + "11549": "\u5288", + "11550": "\u8038", + "11551": "\u8574", + "11552": "\u7bf7", + "11553": "\u8c41", + "11554": "\u8214", + "11555": "\u6bd9", + "11556": "\u7aa6", + "11557": "\u565c", + "11558": "\u8a79", + "11559": "\u762b", + "11560": "\u5f6a", + "11561": "\u6380", + "11562": "\u94f2", + "11563": "\u987d", + "11564": "\u7be1", + "11565": "\u4e53", + "11566": "\u9600", + "11567": "\u5a1f", + "11568": "\u946b", + "11569": "\u5e1c", + "11570": "\u4e2b", + "11571": "\u9ad3", + "11572": "\u6ca6", + "11573": "\u53e8", + "11574": "\u9576", + "11575": "\u55d3", + "11576": "\u8bf2", + "11577": "\u548f", + "11578": "\u997a", + "11579": "\u9e26", + "11580": "\u6984", + "11581": "\u5e90", + "11582": "\u864f", + "11583": "\u9a86", + "11584": "\u874e", + "11585": "\u54d4", + "11586": "\u8f7f", + "11587": "\u63cd", + "11588": "\u61a8", + "11589": "\u4f84", + "11590": "\u9165", + "11591": "\u8e39", + "11592": "\u6a44", + "11593": "\u7eba", + "11594": "\u516e", + "11595": "\u70db", + "11596": "\u60af", + "11597": "\u8783", + "11598": "\u8424", + "11599": "\u53a2", + "11600": "\u6ca7", + "11601": "\u5543", + "11602": "\u8f9c", + "11603": "\u7f55", + "11604": "\u9972", + "11605": "\u8c1c", + "11606": "\u5364", + "11607": "\u6d47", + "11608": "\u57d4", + "11609": "\u7426", + "11610": "\u8469", + "11611": "\u6073", + "11612": "\u7b0b", + "11613": "\u5490", + "11614": "\u5c7f", + "11615": "\u949e", + "11616": "\u8bc0", + "11617": "\u96cf", + "11618": "\u63b0", + "11619": "\u9610", + "11620": "\u5c4e", + "11621": "\u5495", + "11622": "\u6467", + "11623": "\u9ecf", + "11624": "\u6441", + "11625": "\u6055", + "11626": "\u7f09", + "11627": "\u6e24", + "11628": "\u7eac", + "11629": "\u64b8", + "11630": "\u840d", + "11631": "\u6512", + "11632": "\u64ce", + "11633": "\u7741", + "11634": "\u70b3", + "11635": "\u4e52", + "11636": "\u7ad6", + "11637": "\u7f14", + "11638": "\u4ed1", + "11639": "\u95f8", + "11640": "\u8be1", + "11641": "\u5564", + "11642": "\u7410", + "11643": "\u8682", + "11644": "\u8774", + "11645": "\u5955", + "11646": "\u8c34", + "11647": "\u63fd", + "11648": "\u53ee", + "11649": "\u7ece", + "11650": "\u77eb", + "11651": "\u6363", + "11652": "\u6b47", + "11653": "\u888d", + "11654": "\u8c0d", + "11655": "\u67a3", + "11656": "\u55b5", + "11657": "\u9ca8", + "11658": "\u8bcf", + "11659": "\u5960", + "11660": "\u5029", + "11661": "\u8e6d", + "11662": "\u64a9", + "11663": "\u7fd8", + "11664": "\u4fa8", + "11665": "\u8f90", + "11666": "\u7792", + "11667": "\u7130", + "11668": "\u9965", + "11669": "\u54a6", + "11670": "\u889c", + "11671": "\u634d", + "11672": "\u6a0a", + "11673": "\u95fd", + "11674": "\u94f8", + "11675": "\u58f6", + "11676": "\u8611", + "11677": "\u7f38", + "11678": "\u90b5", + "11679": "\u76d4", + "11680": "\u7096", + "11681": "\u6f8e", + "11682": "\u8c2c", + "11683": "\u6dc7", + "11684": "\u94c5", + "11685": "\u5d1b", + "11686": "\u803f", + "11687": "\u63e3", + "11688": "\u7504", + "11689": "\u575d", + "11690": "\u4ea9", + "11691": "\u9631", + "11692": "\u96a7", + "11693": "\u7538", + "11694": "\u5c27", + "11695": "\u78d5", + "11696": "\u6233", + "11697": "\u6ee4", + "11698": "\u8bb6", + "11699": "\u7574", + "11700": "\u917f", + "11701": "\u8206", + "11702": "\u5c82", + "11703": "\u5ac2", + "11704": "\u707f", + "11705": "\u886c", + "11706": "\u75d8", + "11707": "\u8393", + "11708": "\u549a", + "11709": "\u5fcf", + "11710": "\u9882", + "11711": "\u9521", + "11712": "\u563b", + "11713": "\u5188", + "11714": "\u7ee3", + "11715": "\u8d31", + "11716": "\u7eb1", + "11717": "\u96cd", + "11718": "\u98d9", + "11719": "\u7737", + "11720": "\u7784", + "11721": "\u5195", + "11722": "\u5ed6", + "11723": "\u62e2", + "11724": "\u6390", + "11725": "\u6d51", + "11726": "\u69c3", + "11727": "\u9489", + "11728": "\u6487", + "11729": "\u9a74", + "11730": "\u6ee5", + "11731": "\u88f9", + "11732": "\u545c", + "11733": "\u5e10", + "11734": "\u7aed", + "11735": "\u8d3f", + "11736": "\u6d46", + "11737": "\u8116", + "11738": "\u5306", + "11739": "\u9a7c", + "11740": "\u859b", + "11741": "\u9b44", + "11742": "\u8bf5", + "11743": "\u5792", + "11744": "\u7f05", + "11745": "\u8e66", + "11746": "\u9709", + "11747": "\u63ea", + "11748": "\u5784", + "11749": "\u5300", + "11750": "\u7ea4", + "11751": "\u6405", + "11752": "\u574e", + "11753": "\u7a3b", + "11754": "\u6869", + "11755": "\u73ab", + "11756": "\u8367", + "11757": "\u7a91", + "11758": "\u54d1", + "11759": "\u6413", + "11760": "\u94ed", + "11761": "\u5151", + "11762": "\u8086", + "11763": "\u5494", + "11764": "\u575f", + "11765": "\u56ca", + "11766": "\u9a70", + "11767": "\u77a7", + "11768": "\u58e4", + "11769": "\u5bde", + "11770": "\u9887", + "11771": "\u62ce", + "11772": "\u65f7", + "11773": "\u8721", + "11774": "\u7fa1", + "11775": "\u5594", + "11776": "\u6d85", + "11777": "\u94a5", + "11778": "\u7199", + "11779": "\u6495", + "11780": "\u70eb", + "11781": "\u9a73", + "11782": "\u7f06", + "11783": "\u8e48", + "11784": "\u77bb", + "11785": "\u7470", + "11786": "\u8854", + "11787": "\u803b", + "11788": "\u8681", + "11789": "\u95fa", + "11790": "\u6346", + "11791": "\u9877", + "11792": "\u5858", + "11793": "\u7476", + "11794": "\u8c2d", + "11795": "\u83b9", + "11796": "\u743c", + "11797": "\u62e6", + "11798": "\u7a46", + "11799": "\u83e0", + "11800": "\u54aa", + "11801": "\u68f5", + "11802": "\u8bbd", + "11803": "\u5ae9", + "11804": "\u8bdb", + "11805": "\u57ae", + "11806": "\u5499", + "11807": "\u9e64", + "11808": "\u74f7", + "11809": "\u9e70", + "11810": "\u5021", + "11811": "\u5471", + "11812": "\u964c", + "11813": "\u6084", + "11814": "\u70d8", + "11815": "\u62f1", + "11816": "\u62ef", + "11817": "\u8231", + "11818": "\u71b9", + "11819": "\u5de9", + "11820": "\u6d4f", + "11821": "\u7529", + "11822": "\u9888", + "11823": "\u5c61", + "11824": "\u62fd", + "11825": "\u584c", + "11826": "\u8d2c", + "11827": "\u8822", + "11828": "\u82ac", + "11829": "\u7ef5", + "11830": "\u5308", + "11831": "\u640f", + "11832": "\u8d4e", + "11833": "\u658b", + "11834": "\u8c10", + "11835": "\u852c", + "11836": "\u800d", + "11837": "\u789f", + "11838": "\u83c7", + "11839": "\u4e1b", + "11840": "\u5de2", + "11841": "\u5e18", + "11842": "\u83bd", + "11843": "\u5bc7", + "11844": "\u88d9", + "11845": "\u8c6b", + "11846": "\u64c5", + "11847": "\u4f63", + "11848": "\u567b", + "11849": "\u9976", + "11850": "\u6e17", + "11851": "\u953b", + "11852": "\u8be0", + "11853": "\u8482", + "11854": "\u52fa", + "11855": "\u96b6", + "11856": "\u5a77", + "11857": "\u8d9f", + "11858": "\u6401", + "11859": "\u561f", + "11860": "\u5760", + "11861": "\u594e", + "11862": "\u814a", + "11863": "\u6cfc", + "11864": "\u532a", + "11865": "\u9510", + "11866": "\u54e9", + "11867": "\u8270", + "11868": "\u5428", + "11869": "\u8c23", + "11870": "\u59ec", + "11871": "\u4fa3", + "11872": "\u6fa1", + "11873": "\u69db", + "11874": "\u8346", + "11875": "\u72e0", + "11876": "\u6e23", + "11877": "\u9655", + "11878": "\u638f", + "11879": "\u5f17", + "11880": "\u8c0a", + "11881": "\u9881", + "11882": "\u6500", + "11883": "\u6124", + "11884": "\u5992", + "11885": "\u94a9", + "11886": "\u80c0", + "11887": "\u625b", + "11888": "\u6254", + "11889": "\u51d1", + "11890": "\u70ab", + "11891": "\u57ab", + "11892": "\u94ae", + "11893": "\u5783", + "11894": "\u9e45", + "11895": "\u6127", + "11896": "\u50f5", + "11897": "\u6e34", + "11898": "\u632a", + "11899": "\u8c05", + "11900": "\u94c3", + "11901": "\u7b3c", + "11902": "\u8dea", + "11903": "\u745c", + "11904": "\u6e83", + "11905": "\u60ac", + "11906": "\u8d3e", + "11907": "\u6b79", + "11908": "\u9f7f", + "11909": "\u8d81", + "11910": "\u63a9", + "11911": "\u8bbc", + "11912": "\u8d29", + "11913": "\u6ee9", + "11914": "\u9524", + "11915": "\u76ef", + "11916": "\u6251", + "11917": "\u727a", + "11918": "\u58f3", + "11919": "\u573e", + "11920": "\u52cb", + "11921": "\u54fc", + "11922": "\u763e", + "11923": "\u82cd", + "11924": "\u59ae", + "11925": "\u9896", + "11926": "\u9614", + "11927": "\u718f", + "11928": "\u778e", + "11929": "\u6e0a", + "11930": "\u5764", + "11931": "\u9e23", + "11932": "\u6108", + "11933": "\u900a", + "11934": "\u817b", + "11935": "\u9a84", + "11936": "\u8d1e", + "11937": "\u5524", + "11938": "\u97f5", + "11939": "\u5a74", + "11940": "\u6cbe", + "11941": "\u97e6", + "11942": "\u98a0", + "11943": "\u68cd", + "11944": "\u4e54", + "11945": "\u5c4c", + "11946": "\u8083", + "11947": "\u80c1", + "11948": "\u5f6d", + "11949": "\u78ca", + "11950": "\u556a", + "11951": "\u53a6", + "11952": "\u742a", + "11953": "\u7ef3", + "11954": "\u59ca", + "11955": "\u9a9a", + "11956": "\u7eb2", + "11957": "\u8f96", + "11958": "\u867e", + "11959": "\u8c0e", + "11960": "\u8881", + "11961": "\u7f20", + "11962": "\u7f50", + "11963": "\u5be8", + "11964": "\u5e9e", + "11965": "\u95ef", + "11966": "\u5a07", + "11967": "\u8e72", + "11968": "\u53ed", + "11969": "\u5e15", + "11970": "\u8427", + "11971": "\u5401", + "11972": "\u745f", + "11973": "\u6c1b", + "11974": "\u838e", + "11975": "\u6454", + "11976": "\u76fc", + "11977": "\u5ab3", + "11978": "\u95f7", + "11979": "\u635e", + "11980": "\u4ff1", + "11981": "\u9e3f", + "11982": "\u9e4f", + "11983": "\u9988", + "11984": "\u7545", + "11985": "\u8c26", + "11986": "\u5509", + "11987": "\u62a0", + "11988": "\u8fc8", + "11989": "\u7b5b", + "11990": "\u8d3a", + "11991": "\u841d", + "11992": "\u8c28", + "11993": "\u7ebd", + "11994": "\u7239", + "11995": "\u80be", + "11996": "\u9aa4", + "11997": "\u51af", + "11998": "\u626d", + "11999": "\u5587", + "12000": "\u7816", + "12001": "\u8bde", + "12002": "\u65a9", + "12003": "\u72ee", + "12004": "\u7f62", + "12005": "\u8bf1", + "12006": "\u5492", + "12007": "\u7855", + "12008": "\u7f1d", + "12009": "\u6345", + "12010": "\u9a71", + "12011": "\u55bb", + "12012": "\u76d0", + "12013": "\u8fbd", + "12014": "\u54c4", + "12015": "\u9171", + "12016": "\u62e8", + "12017": "\u53f9", + "12018": "\u60e9", + "12019": "\u6cdb", + "12020": "\u5986", + "12021": "\u9601", + "12022": "\u6ee8", + "12023": "\u4fa6", + "12024": "\u6021", + "12025": "\u5978", + "12026": "\u5733", + "12027": "\u7b28", + "12028": "\u8eba", + "12029": "\u5179", + "12030": "\u6b67", + "12031": "\u4ed7", + "12032": "\u7fc5", + "12033": "\u7a9d", + "12034": "\u7ff0", + "12035": "\u5c97", + "12036": "\u88e4", + "12037": "\u7ed8", + "12038": "\u8bc8", + "12039": "\u9971", + "12040": "\u8b6c", + "12041": "\u8f69", + "12042": "\u8d2b", + "12043": "\u77e3", + "12044": "\u6323", + "12045": "\u67ef", + "12046": "\u8dc3", + "12047": "\u9493", + "12048": "\u63ed", + "12049": "\u6361", + "12050": "\u59e8", + "12051": "\u81c2", + "12052": "\u8db4", + "12053": "\u98d8", + "12054": "\u4eff", + "12055": "\u8f74", + "12056": "\u5939", + "12057": "\u758f", + "12058": "\u7838", + "12059": "\u94bb", + "12060": "\u54a7", + "12061": "\u80bf", + "12062": "\u997f", + "12063": "\u626f", + "12064": "\u7eb9", + "12065": "\u644a", + "12066": "\u4f2a", + "12067": "\u8c31", + "12068": "\u8d2f", + "12069": "\u809a", + "12070": "\u7f34", + "12071": "\u8361", + "12072": "\u629b", + "12073": "\u80a0", + "12074": "\u5415", + "12075": "\u5cad", + "12076": "\u78b3", + "12077": "\u90bb", + "12078": "\u9a7b", + "12079": "\u9e2d", + "12080": "\u629a", + "12081": "\u5154", + "12082": "\u7ea0", + "12083": "\u9f9f", + "12084": "\u71ac", + "12085": "\u5435", + "12086": "\u6d53", + "12087": "\u503e", + "12088": "\u5395", + "12089": "\u6d82", + "12090": "\u4fe9", + "12091": "\u9093", + "12092": "\u96fe", + "12093": "\u7eb5", + "12094": "\u5367", + "12095": "\u80a4", + "12096": "\u4e27", + "12097": "\u80f6", + "12098": "\u80d6", + "12099": "\u6377", + "12100": "\u6db5", + "12101": "\u8d60", + "12102": "\u8d4c", + "12103": "\u90ae", + "12104": "\u6655", + "12105": "\u7bee", + "12106": "\u5362", + "12107": "\u7ed1", + "12108": "\u575b", + "12109": "\u7978", + "12110": "\u83b2", + "12111": "\u6760", + "12112": "\u730e", + "12113": "\u8f70", + "12114": "\u53e0", + "12115": "\u5c38", + "12116": "\u67dc", + "12117": "\u5821", + "12118": "\u5242", + "12119": "\u607c", + "12120": "\u5220", + "12121": "\u594b", + "12122": "\u6296", + "12123": "\u70e4", + "12124": "\u5f7b", + "12125": "\u9189", + "12126": "\u950b", + "12127": "\u7cdf", + "12128": "\u6746", + "12129": "\u4f1e", + "12130": "\u7eb7", + "12131": "\u538c", + "12132": "\u6846", + "12133": "\u680f", + "12134": "\u4f69", + "12135": "\u529d", + "12136": "\u901b", + "12137": "\u918b", + "12138": "\u8be6", + "12139": "\u8273", + "12140": "\u70bc", + "12141": "\u522e", + "12142": "\u6062", + "12143": "\u5938", + "12144": "\u9012", + "12145": "\u739b", + "12146": "\u5c18", + "12147": "\u8d50", + "12148": "\u8fdf", + "12149": "\u83f2", + "12150": "\u8d4b", + "12151": "\u75af", + "12152": "\u7efc", + "12153": "\u8350", + "12154": "\u6cea", + "12155": "\u5c34", + "12156": "\u507f", + "12157": "\u6324", + "12158": "\u50a8", + "12159": "\u8fc1", + "12160": "\u9677", + "12161": "\u9a76", + "12162": "\u8230", + "12163": "\u5457", + "12164": "\u72b9", + "12165": "\u52b2", + "12166": "\u624e", + "12167": "\u518c", + "12168": "\u7275", + "12169": "\u7b79", + "12170": "\u50bb", + "12171": "\u8f89", + "12172": "\u6668", + "12173": "\u4ed3", + "12174": "\u8e22", + "12175": "\u9970", + "12176": "\u7f69", + "12177": "\u51bb", + "12178": "\u7ed5", + "12179": "\u55b7", + "12180": "\u7eea", + "12181": "\u8d54", + "12182": "\u780d", + "12183": "\u8d21", + "12184": "\u8e29", + "12185": "\u6491", + "12186": "\u4fa7", + "12187": "\u95f2", + "12188": "\u8fa9", + "12189": "\u6b49", + "12190": "\u5baa", + "12191": "\u94dc", + "12192": "\u94fe", + "12193": "\u6c27", + "12194": "\u817e", + "12195": "\u9f84", + "12196": "\u5a31", + "12197": "\u8f86", + "12198": "\u8d2a", + "12199": "\u89c8", + "12200": "\u5899", + "12201": "\u9274", + "12202": "\u5561", + "12203": "\u8109", + "12204": "\u5413", + "12205": "\u72f1", + "12206": "\u517d", + "12207": "\u7a97", + "12208": "\u5f2f", + "12209": "\u70ae", + "12210": "\u54a8", + "12211": "\u5fe7", + "12212": "\u96d5", + "12213": "\u5ba0", + "12214": "\u5c2c", + "12215": "\u6e14", + "12216": "\u806a", + "12217": "\u77ff", + "12218": "\u94fa", + "12219": "\u684c", + "12220": "\u6bc1", + "12221": "\u6735", + "12222": "\u88ad", + "12223": "\u6270", + "12224": "\u5a1c", + "12225": "\u9526", + "12226": "\u6321", + "12227": "\u680b", + "12228": "\u903b", + "12229": "\u90d1", + "12230": "\u568e", + "12231": "\u51ef", + "12232": "\u8f68", + "12233": "\u5e99", + "12234": "\u51ed", + "12235": "\u62df", + "12236": "\u5c1d", + "12237": "\u5565", + "12238": "\u55e8", + "12239": "\u6cfd", + "12240": "\u731c", + "12241": "\u5085", + "12242": "\u5141", + "12243": "\u95f9", + "12244": "\u9ed8", + "12245": "\u7a77", + "12246": "\u5466", + "12247": "\u7f13", + "12248": "\u9e1f", + "12249": "\u7f29", + "12250": "\u8d38", + "12251": "\u8eb2", + "12252": "\u8d4f", + "12253": "\u626c", + "12254": "\u7cd5", + "12255": "\u649e", + "12256": "\u8d37", + "12257": "\u593a", + "12258": "\u8212", + "12259": "\u5fc6", + "12260": "\u6d01", + "12261": "\u61d2", + "12262": "\u6c47", + "12263": "\u8f85", + "12264": "\u62d6", + "12265": "\u8bd1", + "12266": "\u788e", + "12267": "\u4f19", + "12268": "\u4eea", + "12269": "\u5496", + "12270": "\u6e10", + "12271": "\u8d24", + "12272": "\u810f", + "12273": "\u996e", + "12274": "\u6478", + "12275": "\u9080", + "12276": "\u8f88", + "12277": "\u563f", + "12278": "\u6653", + "12279": "\u62e5", + "12280": "\u9897", + "12281": "\u5a03", + "12282": "\u5e05", + "12283": "\u8d56", + "12284": "\u62c6", + "12285": "\u5e9f", + "12286": "\u70c2", + "12287": "\u9605", + "12288": "\u9a91", + "12289": "\u6c61", + "12290": "\u63d2", + "12291": "\u8fea", + "12292": "\u82f9", + "12293": "\u8bca", + "12294": "\u8d26", + "12295": "\u6682", + "12296": "\u7a23", + "12297": "\u9a7e", + "12298": "\u62fc", + "12299": "\u987f", + "12300": "\u9a82", + "12301": "\u8bfa", + "12302": "\u6c89", + "12303": "\u5582", + "12304": "\u5bbe", + "12305": "\u62ac", + "12306": "\u503a", + "12307": "\u51e4", + "12308": "\u8d8b", + "12309": "\u5385", + "12310": "\u7237", + "12311": "\u6865", + "12312": "\u6444", + "12313": "\u6269", + "12314": "\u9505", + "12315": "\u8ba2", + "12316": "\u9501", + "12317": "\u4e4c", + "12318": "\u4e30", + "12319": "\u9738", + "12320": "\u4f26", + "12321": "\u626b", + "12322": "\u8bda", + "12323": "\u9c9c", + "12324": "\u9057", + "12325": "\u9f50", + "12326": "\u6446", + "12327": "\u5434", + "12328": "\u9690", + "12329": "\u7840", + "12330": "\u5bbd", + "12331": "\u5706", + "12332": "\u78b0", + "12333": "\u60ef", + "12334": "\u4ecd", + "12335": "\u60ca", + "12336": "\u654c", + "12337": "\u997c", + "12338": "\u6325", + "12339": "\u6770", + "12340": "\u9c81", + "12341": "\u7ee9", + "12342": "\u62a2", + "12343": "\u8d3c", + "12344": "\u5e86", + "12345": "\u6c64", + "12346": "\u560e", + "12347": "\u8d1d", + "12348": "\u5f03", + "12349": "\u6316", + "12350": "\u955c", + "12351": "\u558a", + "12352": "\u5269", + "12353": "\u5077", + "12354": "\u9635", + "12355": "\u989c", + "12356": "\u8363", + "12357": "\u7f5a", + "12358": "\u54df", + "12359": "\u8f91", + "12360": "\u9634", + "12361": "\u7eaf", + "12362": "\u7b7e", + "12363": "\u6eda", + "12364": "\u84dd", + "12365": "\u7f18", + "12366": "\u8be2", + "12367": "\u6d89", + "12368": "\u9a97", + "12369": "\u7ade", + "12370": "\u8dcc", + "12371": "\u5761", + "12372": "\u8bbf", + "12373": "\u707e", + "12374": "\u95ed", + "12375": "\u9875", + "12376": "\u94a2", + "12377": "\u4f30", + "12378": "\u82cf", + "12379": "\u5e01", + "12380": "\u5251", + "12381": "\u5e93", + "12382": "\u706d", + "12383": "\u6302", + "12384": "\u8fdd", + "12385": "\u552e", + "12386": "\u5b81", + "12387": "\u6263", + "12388": "\u575a", + "12389": "\u6768", + "12390": "\u8d62", + "12391": "\u4e1d", + "12392": "\u55bd", + "12393": "\u67aa", + "12394": "\u8d5a", + "12395": "\u5708", + "12396": "\u7eb3", + "12397": "\u8d34", + "12398": "\u7597", + "12399": "\u5389", + "12400": "\u8f6f", + "12401": "\u6c9f", + "12402": "\u8bd7", + "12403": "\u8d5e", + "12404": "\u70df", + "12405": "\u8d25", + "12406": "\u8651", + "12407": "\u65c1", + "12408": "\u635f", + "12409": "\u54af", + "12410": "\u6742", + "12411": "\u7f3a", + "12412": "\u5976", + "12413": "\u5c9b", + "12414": "\u4e61", + "12415": "\u7ec7", + "12416": "\u70e7", + "12417": "\u989d", + "12418": "\u51c0", + "12419": "\u952e", + "12420": "\u9547", + "12421": "\u8138", + "12422": "\u7a33", + "12423": "\u6863", + "12424": "\u8f7d", + "12425": "\u5979", + "12426": "\u7a0d", + "12427": "\u8bf8", + "12428": "\u7f16", + "12429": "\u8d75", + "12430": "\u7334", + "12431": "\u6447", + "12432": "\u5170", + "12433": "\u54b1", + "12434": "\u4ec5", + "12435": "\u5218", + "12436": "\u8c0b", + "12437": "\u7adf", + "12438": "\u542f", + "12439": "\u68a6", + "12440": "\u4f1f", + "12441": "\u4e34", + "12442": "\u7edc", + "12443": "\u5b59", + "12444": "\u97e9", + "12445": "\u8f6e", + "12446": "\u6da8", + "12447": "\u5bfb", + "12448": "\u9500", + "12449": "\u8bef", + "12450": "\u5382", + "12451": "\u91ca", + "12452": "\u7ecd", + "12453": "\u4e8f", + "12454": "\u9636", + "12455": "\u8bad", + "12456": "\u8d2d", + "12457": "\u95ea", + "12458": "\u641c", + "12459": "\u9646", + "12460": "\u52b3", + "12461": "\u4e3d", + "12462": "\u5f39", + "12463": "\u6076", + "12464": "\u53bf", + "12465": "\u7801", + "12466": "\u4e22", + "12467": "\u5f02", + "12468": "\u8d27", + "12469": "\u6bd5", + "12470": "\u9891", + "12471": "\u8428", + "12472": "\u6293", + "12473": "\u5956", + "12474": "\u7b14", + "12475": "\u6000", + "12476": "\u8f93", + "12477": "\u6811", + "12478": "\u7eaa", + "12479": "\u996d", + "12480": "\u70e6", + "12481": "\u7eff", + "12482": "\u51b0", + "12483": "\u80dc", + "12484": "\u62e9", + "12485": "\u7238", + "12486": "\u51fb", + "12487": "\u95fb", + "12488": "\u574f", + "12489": "\u94c1", + "12490": "\u83b7", + "12491": "\u987e", + "12492": "\u56f4", + "12493": "\u8d23", + "12494": "\u60a8", + "12495": "\u9002", + "12496": "\u5f52", + "12497": "\u8bc4", + "12498": "\u76d8", + "12499": "\u9e21", + "12500": "\u5e7a", + "12501": "\u804c", + "12502": "\u79ef", + "12503": "\u827a", + "12504": "\u9488", + "12505": "\u8d76", + "12506": "\u8111", + "12507": "\u5174", + "12508": "\u8d22", + "12509": "\u519c", + "12510": "\u7d27", + "12511": "\u987a", + "12512": "\u56ed", + "12513": "\u6d4b", + "12514": "\u8baf", + "12515": "\u5f55", + "12516": "\u8d35", + "12517": "\u538b", + "12518": "\u94f6", + "12519": "\u8303", + "12520": "\u9648", + "12521": "\u5267", + "12522": "\u7ec3", + "12523": "\u76d1", + "12524": "\u534f", + "12525": "\u51cf", + "12526": "\u8bcd", + "12527": "\u5450", + "12528": "\u4f18", + "12529": "\u949f", + "12530": "\u5c81", + "12531": "\u4e25", + "12532": "\u7ec6", + "12533": "\u6c49", + "12534": "\u8d1f", + "12535": "\u76d6", + "12536": "\u836f", + "12537": "\u4e9a", + "12538": "\u9876", + "12539": "\u4f24", + "12540": "\u5c42", + "12541": "\u70ed", + "12542": "\u8f7b", + "12543": "\u68c0", + "12544": "\u5c14", + "12545": "\u7075", + "12546": "\u4ebf", + "12547": "\u7ef4", + "12548": "\u6781", + "12549": "\u8865", + "12550": "\u8425", + "12551": "\u54cd", + "12552": "\u9760", + "12553": "\u6548", + "12554": "\u6267", + "12555": "\u6740", + "12556": "\u663e", + "12557": "\u5ba1", + "12558": "\u8d28", + "12559": "\u987b", + "12560": "\u6784", + "12561": "\u5723", + "12562": "\u8c13", + "12563": "\u5356", + "12564": "\u54e5", + "12565": "\u4eb2", + "12566": "\u6d4e", + "12567": "\u7edd", + "12568": "\u9c7c", + "12569": "\u9669", + "12570": "\u8bfb", + "12571": "\u8bfe", + "12572": "\u7f57", + "12573": "\u867d", + "12574": "\u98de", + "12575": "\u5b69", + "12576": "\u5361", + "12577": "\u536b", + "12578": "\u503c", + "12579": "\u62a4", + "12580": "\u8c08", + "12581": "\u6015", + "12582": "\u9a8c", + "12583": "\u8d5b", + "12584": "\u620f", + "12585": "\u7ee7", + "12586": "\u8054", + "12587": "\u9633", + "12588": "\u5212", + "12589": "\u521b", + "12590": "\u665a", + "12591": "\u589e", + "12592": "\u8bc9", + "12593": "\u8bd5", + "12594": "\u8bc6", + "12595": "\u8dd1", + "12596": "\u9884", + "12597": "\u73af", + "12598": "\u8bb8", + "12599": "\u61c2", + "12600": "\u6001", + "12601": "\u9879", + "12602": "\u56e2", + "12603": "\u5bab", + "12604": "\u5907", + "12605": "\u79bb", + "12606": "\u9f99", + "12607": "\u8ba8", + "12608": "\u9645", + "12609": "\u7b80", + "12610": "\u517b", + "12611": "\u5bfc", + "12612": "\u4e3e", + "12613": "\u5757", + "12614": "\u961f", + "12615": "\u8fde", + "12616": "\u672f", + "12617": "\u5386", + "12618": "\u56fe", + "12619": "\u5219", + "12620": "\u8bc1", + "12621": "\u8bed", + "12622": "\u62dc", + "12623": "\u4e13", + "12624": "\u7ea2", + "12625": "\u6362", + "12626": "\u4f17", + "12627": "\u6b65", + "12628": "\u7ea7", + "12629": "\u6743", + "12630": "\u4e60", + "12631": "\u67e5", + "12632": "\u590d", + "12633": "\u513f", + "12634": "\u51b5", + "12635": "\u51b3", + "12636": "\u9886", + "12637": "\u8fbe", + "12638": "\u6807", + "12639": "\u6b22", + "12640": "\u7ec4", + "12641": "\u641e", + "12642": "\u7c7b", + "12643": "\u7eed", + "12644": "\u53e6", + "12645": "\u5988", + "12646": "\u5e7f", + "12647": "\u534e", + "12648": "\u4e50", + "12649": "\u89c4", + "12650": "\u4f20", + "12651": "\u786e", + "12652": "\u8282", + "12653": "\u4e49", + "12654": "\u561e", + "12655": "\u9519", + "12656": "\u7ea6", + "12657": "\u89c6", + "12658": "\u519b", + "12659": "\u54c7", + "12660": "\u6218", + "12661": "\u5f3a", + "12662": "\u8bae", + "12663": "\u6536", + "12664": "\u89c2", + "12665": "\u8c01", + "12666": "\u4ef7", + "12667": "\u8f6c", + "12668": "\u8fd0", + "12669": "\u62ff", + "12670": "\u52a1", + "12671": "\u6389", + "12672": "\u5e76", + "12673": "\u7f51", + "12674": "\u8fdc", + "12675": "\u6ee1", + "12676": "\u7ebf", + "12677": "\u96be", + "12678": "\u603b", + "12679": "\u94b1", + "12680": "\u7edf", + "12681": "\u5e2e", + "12682": "\u8ba1", + "12683": "\u98ce", + "12684": "\u95e8", + "12685": "\u7231", + "12686": "\u5f20", + "12687": "\u5440", + "12688": "\u9a6c", + "12689": "\u627e", + "12690": "\u6c14", + "12691": "\u529e", + "12692": "\u8bbe", + "12693": "\u5e26", + "12694": "\u4e70", + "12695": "\u5904", + "12696": "\u62a5", + "12697": "\u9009", + "12698": "\u8ba4", + "12699": "\u8bba", + "12700": "\u4e66", + "12701": "\u89c1", + "12702": "\u8f66", + "12703": "\u7ed3", + "12704": "\u5355", + "12705": "\u8bb0", + "12706": "\u6bcf", + "12707": "\u591f", + "12708": "\u8c03", + "12709": "\u4ea7", + "12710": "\u542c", + "12711": "\u5566", + "12712": "\u8c22", + "12713": "\u8bf6", + "12714": "\u5458", + "12715": "\u55ef", + "12716": "\u8f83", + "12717": "\u7535", + "12718": "\u8d44", + "12719": "\u53d8", + "12720": "\u65e0", + "12721": "\u522b", + "12722": "\u573a", + "12723": "\u54ce", + "12724": "\u5417", + "12725": "\u8ba9", + "12726": "\u8be5", + "12727": "\u4ece", + "12728": "\u5427", + "12729": "\u4e1a", + "12730": "\u9898", + "12731": "\u600e", + "12732": "\u95f4", + "12733": "\u4e1c", + "12734": "\u561b", + "12735": "\u5e94", + "12736": "\u957f", + "12737": "\u8fdb", + "12738": "\u521a", + "12739": "\u52a8", + "12740": "\u5173", + "12741": "\u8fb9", + "12742": "\u89c9", + "12743": "\u800c", + "12744": "\u53d1", + "12745": "\u7ecf", + "12746": "\u8bdd", + "12747": "\u79cd", + "12748": "\u8bb2", + "12749": "\u5f00", + "12750": "\u5b83", + "12751": "\u5b9e", + "12752": "\u7ed9", + "12753": "\u505a", + "12754": "\u8ddf", + "12755": "\u73b0", + "12756": "\u8fc7", + "12757": "\u5443", + "12758": "\u5f88", + "12759": "\u54e6", + "12760": "\u65f6", + "12761": "\u8fd8", + "12762": "\u5462", + "12763": "\u8bf4", + "12764": "\u4e3a", + "12765": "\u4e48", + "12766": "\u4eec", + "12767": "\u554a", + "12768": "\u4f60", + "12769": "\u8fd9", + "12770": "\u3da7", + "12771": "\u4f5a", + "12772": "\u4f5d", + "12773": "\u4fdf", + "12774": "\u5048", + "12775": "\u507b", + "12776": "\u52d0", + "12777": "\u530f", + "12778": "\u5372", + "12779": "\u540b", + "12780": "\u54d5", + "12781": "\u5533", + "12782": "\u5572", + "12783": "\u5576", + "12784": "\u55eb", + "12785": "\u55ec", + "12786": "\u560f", + "12787": "\u56d7", + "12788": "\u56eb", + "12789": "\u56ef", + "12790": "\u56f5", + "12791": "\u5704", + "12792": "\u576d", + "12793": "\u578c", + "12794": "\u5803", + "12795": "\u5914", + "12796": "\u5941", + "12797": "\u59aa", + "12798": "\u5a08", + "12799": "\u5ad2", + "12800": "\u5d5b", + "12801": "\u5e54", + "12802": "\u5fea", + "12803": "\u602b", + "12804": "\u60ad", + "12805": "\u61d1", + "12806": "\u620d", + "12807": "\u6217", + "12808": "\u6427", + "12809": "\u6555", + "12810": "\u65d6", + "12811": "\u661d", + "12812": "\u668c", + "12813": "\u66be", + "12814": "\u66c8", + "12815": "\u67a5", + "12816": "\u67c3", + "12817": "\u680c", + "12818": "\u6874", + "12819": "\u6877", + "12820": "\u6901", + "12821": "\u691f", + "12822": "\u6924", + "12823": "\u6934", + "12824": "\u6989", + "12825": "\u69ed", + "12826": "\u6a50", + "12827": "\u6a97", + "12828": "\u6b38", + "12829": "\u6b59", + "12830": "\u6b81", + "12831": "\u6b9a", + "12832": "\u6c1a", + "12833": "\u6c24", + "12834": "\u6c32", + "12835": "\u6cd6", + "12836": "\u6cfa", + "12837": "\u6d3a", + "12838": "\u6d50", + "12839": "\u6d91", + "12840": "\u6ef9", + "12841": "\u6f2d", + "12842": "\u6f46", + "12843": "\u6fa7", + "12844": "\u6fb6", + "12845": "\u70c0", + "12846": "\u71b5", + "12847": "\u7260", + "12848": "\u7301", + "12849": "\u7339", + "12850": "\u736d", + "12851": "\u7391", + "12852": "\u73e5", + "12853": "\u7622", + "12854": "\u765e", + "12855": "\u77cd", + "12856": "\u782d", + "12857": "\u7852", + "12858": "\u7856", + "12859": "\u78f4", + "12860": "\u7a39", + "12861": "\u7b38", + "12862": "\u7bfc", + "12863": "\u7ea1", + "12864": "\u7f1b", + "12865": "\u7f2c", + "12866": "\u7fa7", + "12867": "\u8004", + "12868": "\u800b", + "12869": "\u801c", + "12870": "\u802a", + "12871": "\u80b1", + "12872": "\u81ec", + "12873": "\u824b", + "12874": "\u827f", + "12875": "\u8297", + "12876": "\u82be", + "12877": "\u8333", + "12878": "\u833c", + "12879": "\u835b", + "12880": "\u8378", + "12881": "\u83d8", + "12882": "\u84af", + "12883": "\u84c1", + "12884": "\u85b7", + "12885": "\u85e0", + "12886": "\u86ac", + "12887": "\u86b4", + "12888": "\u86c9", + "12889": "\u877d", + "12890": "\u87c6", + "12891": "\u880a", + "12892": "\u89e5", + "12893": "\u8be4", + "12894": "\u8bf9", + "12895": "\u8c16", + "12896": "\u8c18", + "12897": "\u8c2e", + "12898": "\u8c36", + "12899": "\u8c47", + "12900": "\u8c62", + "12901": "\u8c89", + "12902": "\u8d32", + "12903": "\u8d49", + "12904": "\u8e41", + "12905": "\u8e49", + "12906": "\u8f8a", + "12907": "\u900b", + "12908": "\u9051", + "12909": "\u90c7", + "12910": "\u915e", + "12911": "\u9490", + "12912": "\u9492", + "12913": "\u94bc", + "12914": "\u94cb", + "12915": "\u94cd", + "12916": "\u94d1", + "12917": "\u9511", + "12918": "\u954f", + "12919": "\u9554", + "12920": "\u95fe", + "12921": "\u9649", + "12922": "\u972a", + "12923": "\u9751", + "12924": "\u97eb", + "12925": "\u98a6", + "12926": "\u990d", + "12927": "\u9974", + "12928": "\u9991", + "12929": "\u9a88", + "12930": "\u9a93", + "12931": "\u9aba", + "12932": "\u9acc", + "12933": "\u9aef", + "12934": "\u9b5f", + "12935": "\u9cbc", + "12936": "\u9cc3", + "12937": "\u9e29", + "12938": "\u9e2a", + "12939": "\u9e2b", + "12940": "\u9e41", + "12941": "\u9e67", + "12942": "\u9e73", + "12943": "\uff0c", + "12944": "", + "12945": "\u2581t", + "12946": "\u2581\u0111", + "12947": "nh", + "12948": "\u2581th", + "12949": "\u2581ch", + "12950": "\u2581nh", + "12951": "\u2581kh", + "12952": "\u2581ng", + "12953": "\u2581g", + "12954": "\u00f4ng", + "12955": "\u2581ph", + "12956": "\u2581r", + "12957": "\u2581gi", + "12958": "\u1eddi", + "12959": "\u00ean", + "12960": "\u2581c\u00e1", + "12961": "\u2581v\u00e0", + "12962": "\u2581c\u00f3", + "12963": "i\u1ec7", + "12964": "\u1ed9t", + "12965": "\u2581kh\u00f4ng", + "12966": "\u00f4i", + "12967": "i\u1ebf", + "12968": "\u2581m\u1ed9t", + "12969": "\u1edbi", + "12970": "\u1ee7a", + "12971": "\u2581c\u1ee7a", + "12972": "\u2581x", + "12973": "\u01b0\u1eddi", + "12974": "\u01b0\u1ee3", + "12975": "\u00ecnh", + "12976": "\u1ea5t", + "12977": "\u1ea1i", + "12978": "uy", + "12979": "\u00e0y", + "12980": "\u2581ng\u01b0\u1eddi", + "12981": "ong", + "12982": "anh", + "12983": "\u01b0\u1ee3c", + "12984": "i\u1ec1", + "12985": "\u2581\u0111\u01b0\u1ee3c", + "12986": "\u2581n\u00f3", + "12987": "\u1eefng", + "12988": "\u2581cho", + "12989": "\u1ea5y", + "12990": "\u2581nh\u01b0", + "12991": "\u2581ngh", + "12992": "\u2581m\u00e0", + "12993": "\u2581t\u00f4i", + "12994": "\u01b0\u01a1", + "12995": "\u1ea3i", + "12996": "\u2581nh\u1eefng", + "12997": "\u2581th\u00ec", + "12998": "\u00e2y", + "12999": "ao", + "13000": "\u2581\u0111\u00e3", + "13001": "\u1ea7n", + "13002": "\u2581c\u00e1i", + "13003": "\u2581\u0111\u00f3", + "13004": "\u2581\u0111i", + "13005": "\u2581v\u1edbi", + "13006": "\u01b0\u1edb", + "13007": "\u2581trong", + "13008": "\u2581c\u00e1c", + "13009": "i\u1ec1u", + "13010": "\u2581n\u00e0y", + "13011": "\u0169ng", + "13012": "\u00fang", + "13013": "\u0103m", + "13014": "\u1ed3i", + "13015": "\u1ea1n", + "13016": "\u2581anh", + "13017": "\u01b0", + "13018": "\u1ebf", + "13019": "\u1ea1", + "13020": "\u1ed9", + "13021": "\u1edd", + "13022": "\u1ea3", + "13023": "\u1ea5", + "13024": "\u1ed1", + "13025": "\u1edb", + "13026": "\u1ec7", + "13027": "\u1ec1", + "13028": "\u1ec3", + "13029": "\u01a1", + "13030": "\u1ee7", + "13031": "\u1ead", + "13032": "\u1ee3", + "13033": "\u1ea7", + "13034": "\u1ecb", + "13035": "\u1eef", + "13036": "\u1ee9", + "13037": "\u1ef1", + "13038": "\u1ecd", + "13039": "\u1ed3", + "13040": "\u1edf", + "13041": "\u1eaf", + "13042": "\u1eeb", + "13043": "\u1ee5", + "13044": "\u0169", + "13045": "\u1ed5", + "13046": "\u1eb7", + "13047": "\u1ebd", + "13048": "\u1eb1", + "13049": "\u0110", + "13050": "\u1ec9", + "13051": "\u1ecf", + "13052": "\u1eed", + "13053": "\u0129", + "13054": "\u1ed7", + "13055": "\u1eab", + "13056": "\u1eb9", + "13057": "\u1ea9", + "13058": "\u1ec5", + "13059": "\u1ebb", + "13060": "\u1eb3", + "13061": "\u1ef9", + "13062": "\u1ee1", + "13063": "\u1ef3", + "13064": "\u1ef7", + "13065": "\u1eb5", + "13066": "\u1ede", + "13067": "\u1ef5", + "13068": "\u1ea4", + "13069": "\u00dd", + "13070": "\u1eea", + "13071": "\u0102", + "13072": "\u1edc", + "13073": "\u1ea2", + "13074": "\u1ed2", + "13075": "\u01a0", + "13076": "\u01af", + "13077": "\u1ee8", + "13078": "\u1ed0", + "13079": "\u1eda", + "13080": "\u1ee6", + "13081": "\u1ea8", + "13082": "\u1eae", + "13083": "\u1ed4", + "13084": "\u1ef6", + "13085": "\u1ebe", + "13086": "\u1ef2" +} \ No newline at end of file diff --git a/multilingual/2240ms/decoder.mlmodelc/analytics/coremldata.bin b/multilingual/2240ms/decoder.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..646ff8b67c27f7e1035df022ec9e0691346d2780 --- /dev/null +++ b/multilingual/2240ms/decoder.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9c32520b84ded2c698000854a77228adf394db522b5a3c25f7737415aae7ed0d +size 243 diff --git a/multilingual/2240ms/decoder.mlmodelc/coremldata.bin b/multilingual/2240ms/decoder.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..b50e1931d2e3334018b12d034c8ff8a57896a265 --- /dev/null +++ b/multilingual/2240ms/decoder.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fa812bb65dd2a3bef6acf584b2abd5d0f26f4d09afaccf6c5dfd41e630d0fd1b +size 433 diff --git a/multilingual/2240ms/decoder.mlmodelc/model.mil b/multilingual/2240ms/decoder.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..697235107988a50dadcf7b2334d72723c3d73048 --- /dev/null +++ b/multilingual/2240ms/decoder.mlmodelc/model.mil @@ -0,0 +1,64 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.5.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] +{ + func main(tensor c_in, tensor h_in, tensor token, tensor token_length) { + int32 y_axis_0 = const()[name = string("y_axis_0"), val = int32(0)]; + int32 y_batch_dims_0 = const()[name = string("y_batch_dims_0"), val = int32(0)]; + bool y_validate_indices_0 = const()[name = string("y_validate_indices_0"), val = bool(false)]; + tensor module_prediction_embed_weight_to_fp16 = const()[name = string("module_prediction_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + string token_to_int16_dtype_0 = const()[name = string("token_to_int16_dtype_0"), val = string("int16")]; + tensor token_to_int16 = cast(dtype = token_to_int16_dtype_0, x = token)[name = string("cast_8")]; + tensor y_cast_fp16_cast_uint16 = gather(axis = y_axis_0, batch_dims = y_batch_dims_0, indices = token_to_int16, validate_indices = y_validate_indices_0, x = module_prediction_embed_weight_to_fp16)[name = string("y_cast_fp16_cast_uint16")]; + tensor input_3_perm_0 = const()[name = string("input_3_perm_0"), val = tensor([1, 0, 2])]; + int32 split_0_num_splits_0 = const()[name = string("split_0_num_splits_0"), val = int32(2)]; + int32 split_0_axis_0 = const()[name = string("split_0_axis_0"), val = int32(0)]; + string h_in_to_fp16_dtype_0 = const()[name = string("h_in_to_fp16_dtype_0"), val = string("fp16")]; + tensor h_in_to_fp16 = cast(dtype = h_in_to_fp16_dtype_0, x = h_in)[name = string("cast_7")]; + tensor split_0_cast_fp16_0, tensor split_0_cast_fp16_1 = split(axis = split_0_axis_0, num_splits = split_0_num_splits_0, x = h_in_to_fp16)[name = string("split_0_cast_fp16")]; + int32 split_1_num_splits_0 = const()[name = string("split_1_num_splits_0"), val = int32(2)]; + int32 split_1_axis_0 = const()[name = string("split_1_axis_0"), val = int32(0)]; + string c_in_to_fp16_dtype_0 = const()[name = string("c_in_to_fp16_dtype_0"), val = string("fp16")]; + tensor c_in_to_fp16 = cast(dtype = c_in_to_fp16_dtype_0, x = c_in)[name = string("cast_6")]; + tensor split_1_cast_fp16_0, tensor split_1_cast_fp16_1 = split(axis = split_1_axis_0, num_splits = split_1_num_splits_0, x = c_in_to_fp16)[name = string("split_1_cast_fp16")]; + tensor input_lstm_layer_0_lstm_h0_squeeze_axes_0 = const()[name = string("input_lstm_layer_0_lstm_h0_squeeze_axes_0"), val = tensor([0])]; + tensor input_lstm_layer_0_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_lstm_layer_0_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_0)[name = string("input_lstm_layer_0_lstm_h0_squeeze_cast_fp16")]; + tensor input_lstm_layer_0_lstm_c0_squeeze_axes_0 = const()[name = string("input_lstm_layer_0_lstm_c0_squeeze_axes_0"), val = tensor([0])]; + tensor input_lstm_layer_0_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_lstm_layer_0_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_0)[name = string("input_lstm_layer_0_lstm_c0_squeeze_cast_fp16")]; + string input_lstm_layer_0_direction_0 = const()[name = string("input_lstm_layer_0_direction_0"), val = string("forward")]; + bool input_lstm_layer_0_output_sequence_0 = const()[name = string("input_lstm_layer_0_output_sequence_0"), val = bool(true)]; + string input_lstm_layer_0_recurrent_activation_0 = const()[name = string("input_lstm_layer_0_recurrent_activation_0"), val = string("sigmoid")]; + string input_lstm_layer_0_cell_activation_0 = const()[name = string("input_lstm_layer_0_cell_activation_0"), val = string("tanh")]; + string input_lstm_layer_0_activation_0 = const()[name = string("input_lstm_layer_0_activation_0"), val = string("tanh")]; + tensor concat_1_to_fp16 = const()[name = string("concat_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16752768)))]; + tensor concat_2_to_fp16 = const()[name = string("concat_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20029632)))]; + tensor concat_0_to_fp16 = const()[name = string("concat_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23306496)))]; + tensor input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = y_cast_fp16_cast_uint16)[name = string("transpose_2")]; + tensor input_lstm_layer_0_cast_fp16_0, tensor input_lstm_layer_0_cast_fp16_1, tensor input_lstm_layer_0_cast_fp16_2 = lstm(activation = input_lstm_layer_0_activation_0, bias = concat_0_to_fp16, cell_activation = input_lstm_layer_0_cell_activation_0, direction = input_lstm_layer_0_direction_0, initial_c = input_lstm_layer_0_lstm_c0_squeeze_cast_fp16, initial_h = input_lstm_layer_0_lstm_h0_squeeze_cast_fp16, output_sequence = input_lstm_layer_0_output_sequence_0, recurrent_activation = input_lstm_layer_0_recurrent_activation_0, weight_hh = concat_2_to_fp16, weight_ih = concat_1_to_fp16, x = input_3_cast_fp16)[name = string("input_lstm_layer_0_cast_fp16")]; + tensor input_lstm_h0_squeeze_axes_0 = const()[name = string("input_lstm_h0_squeeze_axes_0"), val = tensor([0])]; + tensor input_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_1)[name = string("input_lstm_h0_squeeze_cast_fp16")]; + tensor input_lstm_c0_squeeze_axes_0 = const()[name = string("input_lstm_c0_squeeze_axes_0"), val = tensor([0])]; + tensor input_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_1)[name = string("input_lstm_c0_squeeze_cast_fp16")]; + string input_direction_0 = const()[name = string("input_direction_0"), val = string("forward")]; + bool input_output_sequence_0 = const()[name = string("input_output_sequence_0"), val = bool(true)]; + string input_recurrent_activation_0 = const()[name = string("input_recurrent_activation_0"), val = string("sigmoid")]; + string input_cell_activation_0 = const()[name = string("input_cell_activation_0"), val = string("tanh")]; + string input_activation_0 = const()[name = string("input_activation_0"), val = string("tanh")]; + tensor concat_4_to_fp16 = const()[name = string("concat_4_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23311680)))]; + tensor concat_5_to_fp16 = const()[name = string("concat_5_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26588544)))]; + tensor concat_3_to_fp16 = const()[name = string("concat_3_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29865408)))]; + tensor input_cast_fp16_0, tensor input_cast_fp16_1, tensor input_cast_fp16_2 = lstm(activation = input_activation_0, bias = concat_3_to_fp16, cell_activation = input_cell_activation_0, direction = input_direction_0, initial_c = input_lstm_c0_squeeze_cast_fp16, initial_h = input_lstm_h0_squeeze_cast_fp16, output_sequence = input_output_sequence_0, recurrent_activation = input_recurrent_activation_0, weight_hh = concat_5_to_fp16, weight_ih = concat_4_to_fp16, x = input_lstm_layer_0_cast_fp16_0)[name = string("input_cast_fp16")]; + int32 obj_3_axis_0 = const()[name = string("obj_3_axis_0"), val = int32(0)]; + tensor obj_3_cast_fp16 = stack(axis = obj_3_axis_0, values = (input_lstm_layer_0_cast_fp16_1, input_cast_fp16_1))[name = string("obj_3_cast_fp16")]; + string obj_3_cast_fp16_to_fp32_dtype_0 = const()[name = string("obj_3_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + int32 obj_axis_0 = const()[name = string("obj_axis_0"), val = int32(0)]; + tensor obj_cast_fp16 = stack(axis = obj_axis_0, values = (input_lstm_layer_0_cast_fp16_2, input_cast_fp16_2))[name = string("obj_cast_fp16")]; + string obj_cast_fp16_to_fp32_dtype_0 = const()[name = string("obj_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 2, 0])]; + string transpose_0_cast_fp16_to_fp32_dtype_0 = const()[name = string("transpose_0_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor transpose_0_cast_fp16 = transpose(perm = transpose_0_perm_0, x = input_cast_fp16_0)[name = string("transpose_1")]; + tensor decoder_out = cast(dtype = transpose_0_cast_fp16_to_fp32_dtype_0, x = transpose_0_cast_fp16)[name = string("cast_3")]; + tensor c_out = cast(dtype = obj_cast_fp16_to_fp32_dtype_0, x = obj_cast_fp16)[name = string("cast_4")]; + tensor h_out = cast(dtype = obj_3_cast_fp16_to_fp32_dtype_0, x = obj_3_cast_fp16)[name = string("cast_5")]; + tensor token_length_tmp = identity(x = token_length)[name = string("token_length_tmp")]; + } -> (decoder_out, h_out, c_out); +} \ No newline at end of file diff --git a/multilingual/2240ms/decoder.mlmodelc/weights/weight.bin b/multilingual/2240ms/decoder.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..8b094b9de3ad890a887d85c198a8ee88800323fa --- /dev/null +++ b/multilingual/2240ms/decoder.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2e49d029739602d265719f0040fcf5328c007a1ed62d0c6ea621e0b2aaeb9a64 +size 29870592 diff --git a/multilingual/2240ms/decoder.mlpackage/Data/com.apple.CoreML/model.mlmodel b/multilingual/2240ms/decoder.mlpackage/Data/com.apple.CoreML/model.mlmodel new file mode 100644 index 0000000000000000000000000000000000000000..3b86cf52f95837fc5e90f3e8cf6373bde60c5ad1 --- /dev/null +++ b/multilingual/2240ms/decoder.mlpackage/Data/com.apple.CoreML/model.mlmodel @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c26ee345b7763ed9f217561572b1386719956de5b58e9174f5586926b4ab85c5 +size 10360 diff --git a/multilingual/2240ms/decoder.mlpackage/Data/com.apple.CoreML/weights/weight.bin b/multilingual/2240ms/decoder.mlpackage/Data/com.apple.CoreML/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..8b094b9de3ad890a887d85c198a8ee88800323fa --- /dev/null +++ b/multilingual/2240ms/decoder.mlpackage/Data/com.apple.CoreML/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2e49d029739602d265719f0040fcf5328c007a1ed62d0c6ea621e0b2aaeb9a64 +size 29870592 diff --git a/multilingual/2240ms/decoder.mlpackage/Manifest.json b/multilingual/2240ms/decoder.mlpackage/Manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..1ac968d8fcfbdfdead1150534e3677d2f045db51 --- /dev/null +++ b/multilingual/2240ms/decoder.mlpackage/Manifest.json @@ -0,0 +1,18 @@ +{ + "fileFormatVersion": "1.0.0", + "itemInfoEntries": { + "542DC13B-08DF-47C7-AAAA-C2F9DE67BB37": { + "author": "com.apple.CoreML", + "description": "CoreML Model Weights", + "name": "weights", + "path": "com.apple.CoreML/weights" + }, + "8B23B00A-4F60-49E4-B460-719FB6B05887": { + "author": "com.apple.CoreML", + "description": "CoreML Model Specification", + "name": "model.mlmodel", + "path": "com.apple.CoreML/model.mlmodel" + } + }, + "rootModelIdentifier": "8B23B00A-4F60-49E4-B460-719FB6B05887" +} diff --git a/multilingual/2240ms/decoder_joint.mlmodelc/analytics/coremldata.bin b/multilingual/2240ms/decoder_joint.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..11215e473da8d2cc24d47de983947493613791d7 --- /dev/null +++ b/multilingual/2240ms/decoder_joint.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3b314763ced4d2bd27484b8ec2a9c60939b724f8ab60b32d29ad0c03f6192599 +size 243 diff --git a/multilingual/2240ms/decoder_joint.mlmodelc/coremldata.bin b/multilingual/2240ms/decoder_joint.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..a036c0b966fa4c57af9b0d7699bfb5e37c53f4d4 --- /dev/null +++ b/multilingual/2240ms/decoder_joint.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:729baa5678fde0b9fa3e46044cb8eafcb96249bff4c306740e1c40ce326b7101 +size 454 diff --git a/multilingual/2240ms/decoder_joint.mlmodelc/model.mil b/multilingual/2240ms/decoder_joint.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..e0c611c93d86fefc0f5758164fe03174da291441 --- /dev/null +++ b/multilingual/2240ms/decoder_joint.mlmodelc/model.mil @@ -0,0 +1,83 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.5.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] +{ + func main(tensor c_in, tensor encoder, tensor h_in, tensor token, tensor token_length) { + int32 y_axis_0 = const()[name = string("y_axis_0"), val = int32(0)]; + int32 y_batch_dims_0 = const()[name = string("y_batch_dims_0"), val = int32(0)]; + bool y_validate_indices_0 = const()[name = string("y_validate_indices_0"), val = bool(false)]; + tensor decoder_module_prediction_embed_weight_to_fp16 = const()[name = string("decoder_module_prediction_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + string token_to_int16_dtype_0 = const()[name = string("token_to_int16_dtype_0"), val = string("int16")]; + tensor token_to_int16 = cast(dtype = token_to_int16_dtype_0, x = token)[name = string("cast_9")]; + tensor y_cast_fp16_cast_uint16 = gather(axis = y_axis_0, batch_dims = y_batch_dims_0, indices = token_to_int16, validate_indices = y_validate_indices_0, x = decoder_module_prediction_embed_weight_to_fp16)[name = string("y_cast_fp16_cast_uint16")]; + tensor input_3_perm_0 = const()[name = string("input_3_perm_0"), val = tensor([1, 0, 2])]; + int32 split_0_num_splits_0 = const()[name = string("split_0_num_splits_0"), val = int32(2)]; + int32 split_0_axis_0 = const()[name = string("split_0_axis_0"), val = int32(0)]; + string h_in_to_fp16_dtype_0 = const()[name = string("h_in_to_fp16_dtype_0"), val = string("fp16")]; + tensor h_in_to_fp16 = cast(dtype = h_in_to_fp16_dtype_0, x = h_in)[name = string("cast_8")]; + tensor split_0_cast_fp16_0, tensor split_0_cast_fp16_1 = split(axis = split_0_axis_0, num_splits = split_0_num_splits_0, x = h_in_to_fp16)[name = string("split_0_cast_fp16")]; + int32 split_1_num_splits_0 = const()[name = string("split_1_num_splits_0"), val = int32(2)]; + int32 split_1_axis_0 = const()[name = string("split_1_axis_0"), val = int32(0)]; + string c_in_to_fp16_dtype_0 = const()[name = string("c_in_to_fp16_dtype_0"), val = string("fp16")]; + tensor c_in_to_fp16 = cast(dtype = c_in_to_fp16_dtype_0, x = c_in)[name = string("cast_7")]; + tensor split_1_cast_fp16_0, tensor split_1_cast_fp16_1 = split(axis = split_1_axis_0, num_splits = split_1_num_splits_0, x = c_in_to_fp16)[name = string("split_1_cast_fp16")]; + tensor input_5_lstm_layer_0_lstm_h0_squeeze_axes_0 = const()[name = string("input_5_lstm_layer_0_lstm_h0_squeeze_axes_0"), val = tensor([0])]; + tensor input_5_lstm_layer_0_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_5_lstm_layer_0_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_0)[name = string("input_5_lstm_layer_0_lstm_h0_squeeze_cast_fp16")]; + tensor input_5_lstm_layer_0_lstm_c0_squeeze_axes_0 = const()[name = string("input_5_lstm_layer_0_lstm_c0_squeeze_axes_0"), val = tensor([0])]; + tensor input_5_lstm_layer_0_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_5_lstm_layer_0_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_0)[name = string("input_5_lstm_layer_0_lstm_c0_squeeze_cast_fp16")]; + string input_5_lstm_layer_0_direction_0 = const()[name = string("input_5_lstm_layer_0_direction_0"), val = string("forward")]; + bool input_5_lstm_layer_0_output_sequence_0 = const()[name = string("input_5_lstm_layer_0_output_sequence_0"), val = bool(true)]; + string input_5_lstm_layer_0_recurrent_activation_0 = const()[name = string("input_5_lstm_layer_0_recurrent_activation_0"), val = string("sigmoid")]; + string input_5_lstm_layer_0_cell_activation_0 = const()[name = string("input_5_lstm_layer_0_cell_activation_0"), val = string("tanh")]; + string input_5_lstm_layer_0_activation_0 = const()[name = string("input_5_lstm_layer_0_activation_0"), val = string("tanh")]; + tensor concat_1_to_fp16 = const()[name = string("concat_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16752768)))]; + tensor concat_2_to_fp16 = const()[name = string("concat_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20029632)))]; + tensor concat_0_to_fp16 = const()[name = string("concat_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23306496)))]; + tensor input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = y_cast_fp16_cast_uint16)[name = string("transpose_4")]; + tensor input_5_lstm_layer_0_cast_fp16_0, tensor input_5_lstm_layer_0_cast_fp16_1, tensor input_5_lstm_layer_0_cast_fp16_2 = lstm(activation = input_5_lstm_layer_0_activation_0, bias = concat_0_to_fp16, cell_activation = input_5_lstm_layer_0_cell_activation_0, direction = input_5_lstm_layer_0_direction_0, initial_c = input_5_lstm_layer_0_lstm_c0_squeeze_cast_fp16, initial_h = input_5_lstm_layer_0_lstm_h0_squeeze_cast_fp16, output_sequence = input_5_lstm_layer_0_output_sequence_0, recurrent_activation = input_5_lstm_layer_0_recurrent_activation_0, weight_hh = concat_2_to_fp16, weight_ih = concat_1_to_fp16, x = input_3_cast_fp16)[name = string("input_5_lstm_layer_0_cast_fp16")]; + tensor input_5_lstm_h0_squeeze_axes_0 = const()[name = string("input_5_lstm_h0_squeeze_axes_0"), val = tensor([0])]; + tensor input_5_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_5_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_1)[name = string("input_5_lstm_h0_squeeze_cast_fp16")]; + tensor input_5_lstm_c0_squeeze_axes_0 = const()[name = string("input_5_lstm_c0_squeeze_axes_0"), val = tensor([0])]; + tensor input_5_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_5_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_1)[name = string("input_5_lstm_c0_squeeze_cast_fp16")]; + string input_5_direction_0 = const()[name = string("input_5_direction_0"), val = string("forward")]; + bool input_5_output_sequence_0 = const()[name = string("input_5_output_sequence_0"), val = bool(true)]; + string input_5_recurrent_activation_0 = const()[name = string("input_5_recurrent_activation_0"), val = string("sigmoid")]; + string input_5_cell_activation_0 = const()[name = string("input_5_cell_activation_0"), val = string("tanh")]; + string input_5_activation_0 = const()[name = string("input_5_activation_0"), val = string("tanh")]; + tensor concat_4_to_fp16 = const()[name = string("concat_4_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23311680)))]; + tensor concat_5_to_fp16 = const()[name = string("concat_5_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26588544)))]; + tensor concat_3_to_fp16 = const()[name = string("concat_3_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29865408)))]; + tensor input_5_cast_fp16_0, tensor input_5_cast_fp16_1, tensor input_5_cast_fp16_2 = lstm(activation = input_5_activation_0, bias = concat_3_to_fp16, cell_activation = input_5_cell_activation_0, direction = input_5_direction_0, initial_c = input_5_lstm_c0_squeeze_cast_fp16, initial_h = input_5_lstm_h0_squeeze_cast_fp16, output_sequence = input_5_output_sequence_0, recurrent_activation = input_5_recurrent_activation_0, weight_hh = concat_5_to_fp16, weight_ih = concat_4_to_fp16, x = input_5_lstm_layer_0_cast_fp16_0)[name = string("input_5_cast_fp16")]; + int32 obj_3_axis_0 = const()[name = string("obj_3_axis_0"), val = int32(0)]; + tensor obj_3_cast_fp16 = stack(axis = obj_3_axis_0, values = (input_5_lstm_layer_0_cast_fp16_1, input_5_cast_fp16_1))[name = string("obj_3_cast_fp16")]; + string obj_3_cast_fp16_to_fp32_dtype_0 = const()[name = string("obj_3_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + int32 obj_axis_0 = const()[name = string("obj_axis_0"), val = int32(0)]; + tensor obj_cast_fp16 = stack(axis = obj_axis_0, values = (input_5_lstm_layer_0_cast_fp16_2, input_5_cast_fp16_2))[name = string("obj_cast_fp16")]; + string obj_cast_fp16_to_fp32_dtype_0 = const()[name = string("obj_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor transpose_1_perm_0 = const()[name = string("transpose_1_perm_0"), val = tensor([1, 0, 2])]; + tensor input_7_perm_0 = const()[name = string("input_7_perm_0"), val = tensor([0, 2, 1])]; + string encoder_to_fp16_dtype_0 = const()[name = string("encoder_to_fp16_dtype_0"), val = string("fp16")]; + tensor joint_module_enc_weight_to_fp16 = const()[name = string("joint_module_enc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29870592)))]; + tensor joint_module_enc_bias_to_fp16 = const()[name = string("joint_module_enc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31181376)))]; + tensor encoder_to_fp16 = cast(dtype = encoder_to_fp16_dtype_0, x = encoder)[name = string("cast_4")]; + tensor input_7_cast_fp16 = transpose(perm = input_7_perm_0, x = encoder_to_fp16)[name = string("transpose_2")]; + tensor linear_0_cast_fp16 = linear(bias = joint_module_enc_bias_to_fp16, weight = joint_module_enc_weight_to_fp16, x = input_7_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor joint_module_pred_weight_to_fp16 = const()[name = string("joint_module_pred_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31182720)))]; + tensor joint_module_pred_bias_to_fp16 = const()[name = string("joint_module_pred_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32001984)))]; + tensor transpose_1_cast_fp16 = transpose(perm = transpose_1_perm_0, x = input_5_cast_fp16_0)[name = string("transpose_3")]; + tensor linear_1_cast_fp16 = linear(bias = joint_module_pred_bias_to_fp16, weight = joint_module_pred_weight_to_fp16, x = transpose_1_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_79_axes_0 = const()[name = string("op_79_axes_0"), val = tensor([2])]; + tensor var_79_cast_fp16 = expand_dims(axes = var_79_axes_0, x = linear_0_cast_fp16)[name = string("op_79_cast_fp16")]; + tensor var_80_axes_0 = const()[name = string("op_80_axes_0"), val = tensor([1])]; + tensor var_80_cast_fp16 = expand_dims(axes = var_80_axes_0, x = linear_1_cast_fp16)[name = string("op_80_cast_fp16")]; + tensor input_11_cast_fp16 = add(x = var_79_cast_fp16, y = var_80_cast_fp16)[name = string("input_11_cast_fp16")]; + tensor input_13_cast_fp16 = relu(x = input_11_cast_fp16)[name = string("input_13_cast_fp16")]; + tensor joint_module_joint_net_2_weight_to_fp16 = const()[name = string("joint_module_joint_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32003328)))]; + tensor joint_module_joint_net_2_bias_to_fp16 = const()[name = string("joint_module_joint_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48756032)))]; + tensor linear_2_cast_fp16 = linear(bias = joint_module_joint_net_2_bias_to_fp16, weight = joint_module_joint_net_2_weight_to_fp16, x = input_13_cast_fp16)[name = string("linear_2_cast_fp16")]; + string linear_2_cast_fp16_to_fp32_dtype_0 = const()[name = string("linear_2_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor logits = cast(dtype = linear_2_cast_fp16_to_fp32_dtype_0, x = linear_2_cast_fp16)[name = string("cast_3")]; + tensor c_out = cast(dtype = obj_cast_fp16_to_fp32_dtype_0, x = obj_cast_fp16)[name = string("cast_5")]; + tensor h_out = cast(dtype = obj_3_cast_fp16_to_fp32_dtype_0, x = obj_3_cast_fp16)[name = string("cast_6")]; + tensor token_length_tmp = identity(x = token_length)[name = string("token_length_tmp")]; + } -> (logits, h_out, c_out); +} \ No newline at end of file diff --git a/multilingual/2240ms/decoder_joint.mlmodelc/weights/weight.bin b/multilingual/2240ms/decoder_joint.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..c9ff4a76fa3fff489b780debbcd78bdc261f1489 --- /dev/null +++ b/multilingual/2240ms/decoder_joint.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f283edec035d616e9e1372419dd9bfd8a2de2c92a70b23f6a0f5ed93366ebb03 +size 48782272 diff --git a/multilingual/2240ms/decoder_joint.mlpackage/Data/com.apple.CoreML/model.mlmodel b/multilingual/2240ms/decoder_joint.mlpackage/Data/com.apple.CoreML/model.mlmodel new 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0000000000000000000000000000000000000000..0bb38a83354a8f578eda404248b0895118b55ad5 --- /dev/null +++ b/multilingual/2240ms/decoder_joint_noencproj.mlmodelc/model.mil @@ -0,0 +1,91 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.10.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] +{ + func main(tensor c_in, tensor encoder_proj, tensor h_in, tensor token, tensor token_length) { + int32 y_batch_dims_0 = const()[name = string("y_batch_dims_0"), val = int32(0)]; + bool y_validate_indices_0 = const()[name = string("y_validate_indices_0"), val = bool(false)]; + tensor decoder_module_prediction_embed_weight_to_fp16 = const()[name = string("decoder_module_prediction_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + string token_to_int16_dtype_0 = const()[name = string("token_to_int16_dtype_0"), val = string("int16")]; + string cast_1_dtype_0 = const()[name = string("cast_1_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor token_to_int16 = cast(dtype = token_to_int16_dtype_0, x = token)[name = string("cast_10")]; + tensor cast_1 = cast(dtype = cast_1_dtype_0, x = token_to_int16)[name = string("cast_9")]; + tensor greater_equal_0 = greater_equal(x = cast_1, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(13088)]; + tensor add_2 = add(x = cast_1, y = slice_by_index_0)[name = string("add_2")]; + tensor select_0 = select(a = cast_1, b = add_2, cond = greater_equal_0)[name = string("select_0")]; + int32 y_cast_fp16_cast_uint16_axis_0 = const()[name = string("y_cast_fp16_cast_uint16_axis_0"), val = int32(0)]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_8")]; + tensor y_cast_fp16_cast_uint16_cast_uint16 = gather(axis = y_cast_fp16_cast_uint16_axis_0, batch_dims = y_batch_dims_0, indices = select_0_to_int16, validate_indices = y_validate_indices_0, x = decoder_module_prediction_embed_weight_to_fp16)[name = string("y_cast_fp16_cast_uint16_cast_uint16")]; + tensor input_3_perm_0 = const()[name = string("input_3_perm_0"), val = tensor([1, 0, 2])]; + int32 split_0_num_splits_0 = const()[name = string("split_0_num_splits_0"), val = int32(2)]; + int32 split_0_axis_0 = const()[name = string("split_0_axis_0"), val = int32(0)]; + string h_in_to_fp16_dtype_0 = const()[name = string("h_in_to_fp16_dtype_0"), val = string("fp16")]; + tensor h_in_to_fp16 = cast(dtype = h_in_to_fp16_dtype_0, x = h_in)[name = string("cast_7")]; + tensor split_0_cast_fp16_0, tensor split_0_cast_fp16_1 = split(axis = split_0_axis_0, num_splits = split_0_num_splits_0, x = h_in_to_fp16)[name = string("split_0_cast_fp16")]; + int32 split_1_num_splits_0 = const()[name = string("split_1_num_splits_0"), val = int32(2)]; + int32 split_1_axis_0 = const()[name = string("split_1_axis_0"), val = int32(0)]; + string c_in_to_fp16_dtype_0 = const()[name = string("c_in_to_fp16_dtype_0"), val = string("fp16")]; + tensor c_in_to_fp16 = cast(dtype = c_in_to_fp16_dtype_0, x = c_in)[name = string("cast_6")]; + tensor split_1_cast_fp16_0, tensor split_1_cast_fp16_1 = split(axis = split_1_axis_0, num_splits = split_1_num_splits_0, x = c_in_to_fp16)[name = string("split_1_cast_fp16")]; + tensor input_5_lstm_layer_0_lstm_h0_squeeze_axes_0 = const()[name = string("input_5_lstm_layer_0_lstm_h0_squeeze_axes_0"), val = tensor([0])]; + tensor input_5_lstm_layer_0_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_5_lstm_layer_0_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_0)[name = string("input_5_lstm_layer_0_lstm_h0_squeeze_cast_fp16")]; + tensor input_5_lstm_layer_0_lstm_c0_squeeze_axes_0 = const()[name = string("input_5_lstm_layer_0_lstm_c0_squeeze_axes_0"), val = tensor([0])]; + tensor input_5_lstm_layer_0_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_5_lstm_layer_0_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_0)[name = string("input_5_lstm_layer_0_lstm_c0_squeeze_cast_fp16")]; + string input_5_lstm_layer_0_direction_0 = const()[name = string("input_5_lstm_layer_0_direction_0"), val = string("forward")]; + bool input_5_lstm_layer_0_output_sequence_0 = const()[name = string("input_5_lstm_layer_0_output_sequence_0"), val = bool(true)]; + string input_5_lstm_layer_0_recurrent_activation_0 = const()[name = string("input_5_lstm_layer_0_recurrent_activation_0"), val = string("sigmoid")]; + string input_5_lstm_layer_0_cell_activation_0 = const()[name = string("input_5_lstm_layer_0_cell_activation_0"), val = string("tanh")]; + string input_5_lstm_layer_0_activation_0 = const()[name = string("input_5_lstm_layer_0_activation_0"), val = string("tanh")]; + tensor concat_1_to_fp16 = const()[name = string("concat_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16752768)))]; + tensor concat_2_to_fp16 = const()[name = string("concat_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20029632)))]; + tensor concat_0_to_fp16 = const()[name = string("concat_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23306496)))]; + tensor input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = y_cast_fp16_cast_uint16_cast_uint16)[name = string("transpose_3")]; + tensor input_5_lstm_layer_0_cast_fp16_0, tensor input_5_lstm_layer_0_cast_fp16_1, tensor input_5_lstm_layer_0_cast_fp16_2 = lstm(activation = input_5_lstm_layer_0_activation_0, bias = concat_0_to_fp16, cell_activation = input_5_lstm_layer_0_cell_activation_0, direction = input_5_lstm_layer_0_direction_0, initial_c = input_5_lstm_layer_0_lstm_c0_squeeze_cast_fp16, initial_h = input_5_lstm_layer_0_lstm_h0_squeeze_cast_fp16, output_sequence = input_5_lstm_layer_0_output_sequence_0, recurrent_activation = input_5_lstm_layer_0_recurrent_activation_0, weight_hh = concat_2_to_fp16, weight_ih = concat_1_to_fp16, x = input_3_cast_fp16)[name = string("input_5_lstm_layer_0_cast_fp16")]; + tensor input_5_lstm_h0_squeeze_axes_0 = const()[name = string("input_5_lstm_h0_squeeze_axes_0"), val = tensor([0])]; + tensor input_5_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_5_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_1)[name = string("input_5_lstm_h0_squeeze_cast_fp16")]; + tensor input_5_lstm_c0_squeeze_axes_0 = const()[name = string("input_5_lstm_c0_squeeze_axes_0"), val = tensor([0])]; + tensor input_5_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_5_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_1)[name = string("input_5_lstm_c0_squeeze_cast_fp16")]; + string input_5_direction_0 = const()[name = string("input_5_direction_0"), val = string("forward")]; + bool input_5_output_sequence_0 = const()[name = string("input_5_output_sequence_0"), val = bool(true)]; + string input_5_recurrent_activation_0 = const()[name = string("input_5_recurrent_activation_0"), val = string("sigmoid")]; + string input_5_cell_activation_0 = const()[name = string("input_5_cell_activation_0"), val = string("tanh")]; + string input_5_activation_0 = const()[name = string("input_5_activation_0"), val = string("tanh")]; + tensor concat_4_to_fp16 = const()[name = string("concat_4_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23311680)))]; + tensor concat_5_to_fp16 = const()[name = string("concat_5_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26588544)))]; + tensor concat_3_to_fp16 = const()[name = string("concat_3_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29865408)))]; + tensor input_5_cast_fp16_0, tensor input_5_cast_fp16_1, tensor input_5_cast_fp16_2 = lstm(activation = input_5_activation_0, bias = concat_3_to_fp16, cell_activation = input_5_cell_activation_0, direction = input_5_direction_0, initial_c = input_5_lstm_c0_squeeze_cast_fp16, initial_h = input_5_lstm_h0_squeeze_cast_fp16, output_sequence = input_5_output_sequence_0, recurrent_activation = input_5_recurrent_activation_0, weight_hh = concat_5_to_fp16, weight_ih = concat_4_to_fp16, x = input_5_lstm_layer_0_cast_fp16_0)[name = string("input_5_cast_fp16")]; + int32 obj_3_axis_0 = const()[name = string("obj_3_axis_0"), val = int32(0)]; + tensor obj_3_cast_fp16 = stack(axis = obj_3_axis_0, values = (input_5_lstm_layer_0_cast_fp16_1, input_5_cast_fp16_1))[name = string("obj_3_cast_fp16")]; + string obj_3_cast_fp16_to_fp32_dtype_0 = const()[name = string("obj_3_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + int32 obj_axis_0 = const()[name = string("obj_axis_0"), val = int32(0)]; + tensor obj_cast_fp16 = stack(axis = obj_axis_0, values = (input_5_lstm_layer_0_cast_fp16_2, input_5_cast_fp16_2))[name = string("obj_cast_fp16")]; + string obj_cast_fp16_to_fp32_dtype_0 = const()[name = string("obj_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor transpose_1_perm_0 = const()[name = string("transpose_1_perm_0"), val = tensor([1, 0, 2])]; + tensor joint_module_pred_weight_to_fp16 = const()[name = string("joint_module_pred_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29870592)))]; + tensor joint_module_pred_bias_to_fp16 = const()[name = string("joint_module_pred_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30689856)))]; + tensor transpose_1_cast_fp16 = transpose(perm = transpose_1_perm_0, x = input_5_cast_fp16_0)[name = string("transpose_2")]; + tensor linear_0_cast_fp16 = linear(bias = joint_module_pred_bias_to_fp16, weight = joint_module_pred_weight_to_fp16, x = transpose_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor f_axes_0 = const()[name = string("f_axes_0"), val = tensor([2])]; + string encoder_proj_to_fp16_dtype_0 = const()[name = string("encoder_proj_to_fp16_dtype_0"), val = string("fp16")]; + tensor encoder_proj_to_fp16 = cast(dtype = encoder_proj_to_fp16_dtype_0, x = encoder_proj)[name = string("cast_3")]; + tensor f_cast_fp16 = expand_dims(axes = f_axes_0, x = encoder_proj_to_fp16)[name = string("f_cast_fp16")]; + tensor g_axes_0 = const()[name = string("g_axes_0"), val = tensor([1])]; + tensor g_cast_fp16 = expand_dims(axes = g_axes_0, x = linear_0_cast_fp16)[name = string("g_cast_fp16")]; + tensor input_9_cast_fp16 = add(x = f_cast_fp16, y = g_cast_fp16)[name = string("input_9_cast_fp16")]; + tensor input_11_cast_fp16 = relu(x = input_9_cast_fp16)[name = string("input_11_cast_fp16")]; + tensor joint_module_joint_net_2_weight_to_fp16 = const()[name = string("joint_module_joint_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30691200)))]; + tensor joint_module_joint_net_2_bias_to_fp16 = const()[name = string("joint_module_joint_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47443904)))]; + tensor linear_1_cast_fp16 = linear(bias = joint_module_joint_net_2_bias_to_fp16, weight = joint_module_joint_net_2_weight_to_fp16, x = input_11_cast_fp16)[name = string("linear_1_cast_fp16")]; + int32 var_83 = const()[name = string("op_83"), val = int32(-1)]; + tensor var_85_softmax_cast_fp16 = softmax(axis = var_83, x = linear_1_cast_fp16)[name = string("op_85_softmax_cast_fp16")]; + fp32 var_85_epsilon_0 = const()[name = string("op_85_epsilon_0"), val = fp32(0x1p-149)]; + tensor var_85_cast_fp16 = log(epsilon = var_85_epsilon_0, x = var_85_softmax_cast_fp16)[name = string("op_85_cast_fp16")]; + string var_85_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_85_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor logits = cast(dtype = var_85_cast_fp16_to_fp32_dtype_0, x = var_85_cast_fp16)[name = string("cast_2")]; + tensor c_out = cast(dtype = obj_cast_fp16_to_fp32_dtype_0, x = obj_cast_fp16)[name = string("cast_4")]; + tensor h_out = cast(dtype = obj_3_cast_fp16_to_fp32_dtype_0, x = obj_3_cast_fp16)[name = string("cast_5")]; + tensor token_length_tmp = identity(x = token_length)[name = string("token_length_tmp")]; + } -> (logits, h_out, c_out); +} \ No newline at end of file diff --git a/multilingual/2240ms/decoder_joint_noencproj.mlmodelc/weights/weight.bin b/multilingual/2240ms/decoder_joint_noencproj.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..a04dee90b1d0140bff13a5c28dd8d3b4054a8679 --- /dev/null +++ b/multilingual/2240ms/decoder_joint_noencproj.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version 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0000000000000000000000000000000000000000..a04dee90b1d0140bff13a5c28dd8d3b4054a8679 --- /dev/null +++ b/multilingual/2240ms/decoder_joint_noencproj.mlpackage/Data/com.apple.CoreML/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c7ddfe0cb3be2e2896258d91a95483b898e8a274c49fee256e8effd86dc64dda +size 47470144 diff --git a/multilingual/2240ms/decoder_joint_noencproj.mlpackage/Manifest.json b/multilingual/2240ms/decoder_joint_noencproj.mlpackage/Manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..d76312490c0a14638c4bb8ed46a75c55349985e3 --- /dev/null +++ b/multilingual/2240ms/decoder_joint_noencproj.mlpackage/Manifest.json @@ -0,0 +1,18 @@ +{ + "fileFormatVersion": "1.0.0", + "itemInfoEntries": { + "0D1E34D7-EC88-4977-9F50-17C13013772E": { + "author": "com.apple.CoreML", + "description": "CoreML Model Weights", + "name": "weights", + "path": "com.apple.CoreML/weights" + }, + "4F1B4339-60A2-4F3E-9F0C-B5B2CB77BDAE": { + "author": "com.apple.CoreML", + "description": "CoreML Model Specification", + "name": "model.mlmodel", + "path": "com.apple.CoreML/model.mlmodel" + } + }, + "rootModelIdentifier": "4F1B4339-60A2-4F3E-9F0C-B5B2CB77BDAE" +} diff --git a/multilingual/2240ms/encoder.mlmodelc/analytics/coremldata.bin b/multilingual/2240ms/encoder.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..cf222b05b3795ceca6d95b114574c50f82d5a9fc --- /dev/null +++ b/multilingual/2240ms/encoder.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cb008a0254143d32e93073d5db48272287719969c062c9ac55514b11dc699a3f +size 243 diff --git a/multilingual/2240ms/encoder.mlmodelc/coremldata.bin b/multilingual/2240ms/encoder.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..c2f88eab42da4e6a9f3cee2cecfc5530230a543f --- /dev/null +++ b/multilingual/2240ms/encoder.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f6d3ff445101c11cf8e04953dff1d7779a5dc9e855d4befd64f66087c34c4969 +size 633 diff --git a/multilingual/2240ms/encoder.mlmodelc/model.mil b/multilingual/2240ms/encoder.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..0b61772b118bff8a3d2b83d31f02ad558e04cde4 --- /dev/null +++ b/multilingual/2240ms/encoder.mlmodelc/model.mil @@ -0,0 +1,4434 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}})] +{ + func main(tensor cache_channel, tensor cache_len, tensor cache_time, tensor mel, tensor mel_length, tensor prompt_id) { + tensor value_3_perm_0 = const()[name = string("value_3_perm_0"), val = tensor([1, 0, 2, 3])]; + string cache_channel_to_fp16_dtype_0 = const()[name = string("cache_channel_to_fp16_dtype_0"), val = string("fp16")]; + tensor value_5_perm_0 = const()[name = string("value_5_perm_0"), val = tensor([1, 0, 2, 3])]; + string cache_time_to_fp16_dtype_0 = const()[name = string("cache_time_to_fp16_dtype_0"), val = string("fp16")]; + int32 var_59 = const()[name = string("op_59"), val = int32(-1)]; + int32 var_68 = const()[name = string("op_68"), val = int32(1)]; + tensor x_1_perm_0 = const()[name = string("x_1_perm_0"), val = tensor([0, 2, 1])]; + string mel_to_fp16_dtype_0 = const()[name = string("mel_to_fp16_dtype_0"), val = string("fp16")]; + tensor tensor_1_axes_0 = const()[name = string("tensor_1_axes_0"), val = tensor([1])]; + tensor mel_to_fp16 = cast(dtype = mel_to_fp16_dtype_0, x = mel)[name = string("cast_21")]; + tensor x_1_cast_fp16 = transpose(perm = x_1_perm_0, x = mel_to_fp16)[name = string("transpose_367")]; + tensor tensor_1_cast_fp16 = expand_dims(axes = tensor_1_axes_0, x = x_1_cast_fp16)[name = string("tensor_1_cast_fp16")]; + tensor expand_dims_0 = const()[name = string("expand_dims_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor var_137_axes_0 = const()[name = string("op_137_axes_0"), val = tensor([1])]; + tensor var_137 = expand_dims(axes = var_137_axes_0, x = mel_length)[name = string("op_137")]; + tensor time_mask_1 = less(x = expand_dims_0, y = var_137)[name = string("time_mask_1")]; + tensor var_139_axes_0 = const()[name = string("op_139_axes_0"), val = tensor([-1])]; + tensor var_139 = expand_dims(axes = var_139_axes_0, x = time_mask_1)[name = string("op_139")]; + tensor var_141_reps_0 = const()[name = string("op_141_reps_0"), val = tensor([1, 1, 128])]; + tensor var_141 = tile(reps = var_141_reps_0, x = var_139)[name = string("op_141")]; + tensor var_147_axes_0 = const()[name = string("op_147_axes_0"), val = tensor([1])]; + string mask_1_to_fp16_dtype_0 = const()[name = string("mask_1_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_141_to_fp16 = cast(dtype = mask_1_to_fp16_dtype_0, x = var_141)[name = string("cast_20")]; + tensor var_147_cast_fp16 = expand_dims(axes = var_147_axes_0, x = var_141_to_fp16)[name = string("op_147_cast_fp16")]; + tensor input_1_cast_fp16 = mul(x = tensor_1_cast_fp16, y = var_147_cast_fp16)[name = string("input_1_cast_fp16")]; + tensor input_3_pad_0 = const()[name = string("input_3_pad_0"), val = tensor([0, 0, 0, 0, 2, 1, 2, 1])]; + string input_3_mode_0 = const()[name = string("input_3_mode_0"), val = string("constant")]; + fp16 const_9_to_fp16 = const()[name = string("const_9_to_fp16"), val = fp16(0x0p+0)]; + tensor input_3_cast_fp16 = pad(constant_val = const_9_to_fp16, mode = input_3_mode_0, pad = input_3_pad_0, x = input_1_cast_fp16)[name = string("input_3_cast_fp16")]; + string tensor_3_pad_type_0 = const()[name = string("tensor_3_pad_type_0"), val = string("valid")]; + tensor tensor_3_strides_0 = const()[name = string("tensor_3_strides_0"), val = tensor([2, 2])]; + tensor tensor_3_pad_0 = const()[name = string("tensor_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor tensor_3_dilations_0 = const()[name = string("tensor_3_dilations_0"), val = tensor([1, 1])]; + int32 tensor_3_groups_0 = const()[name = string("tensor_3_groups_0"), val = int32(1)]; + tensor encoder_pre_encode_conv_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3456))))[name = string("encoder_pre_encode_conv_0_weight_to_fp16_quantized")]; + tensor encoder_pre_encode_conv_0_bias_to_fp16 = const()[name = string("encoder_pre_encode_conv_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4032)))]; + tensor tensor_3_cast_fp16 = conv(bias = encoder_pre_encode_conv_0_bias_to_fp16, dilations = tensor_3_dilations_0, groups = tensor_3_groups_0, pad = tensor_3_pad_0, pad_type = tensor_3_pad_type_0, strides = tensor_3_strides_0, weight = encoder_pre_encode_conv_0_weight_to_fp16_quantized, x = input_3_cast_fp16)[name = string("tensor_3_cast_fp16")]; + string current_lengths_1_to_fp16_dtype_0 = const()[name = string("current_lengths_1_to_fp16_dtype_0"), val = string("fp16")]; + fp16 var_160_promoted_to_fp16 = const()[name = string("op_160_promoted_to_fp16"), val = fp16(0x1p+1)]; + tensor mel_length_to_fp16 = cast(dtype = current_lengths_1_to_fp16_dtype_0, x = mel_length)[name = string("cast_19")]; + tensor var_161_cast_fp16 = add(x = mel_length_to_fp16, y = var_160_promoted_to_fp16)[name = string("op_161_cast_fp16")]; + fp16 var_162_promoted_to_fp16 = const()[name = string("op_162_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_163_cast_fp16 = add(x = var_161_cast_fp16, y = var_162_promoted_to_fp16)[name = string("op_163_cast_fp16")]; + fp16 var_164_promoted_to_fp16 = const()[name = string("op_164_promoted_to_fp16"), val = fp16(0x1.8p+1)]; + tensor var_165_cast_fp16 = sub(x = var_163_cast_fp16, y = var_164_promoted_to_fp16)[name = string("op_165_cast_fp16")]; + fp16 var_56_promoted_to_fp16 = const()[name = string("op_56_promoted_to_fp16"), val = fp16(0x1p+1)]; + tensor floor_div_0_cast_fp16 = floor_div(x = var_165_cast_fp16, y = var_56_promoted_to_fp16)[name = string("floor_div_0_cast_fp16")]; + fp16 var_167_promoted_to_fp16 = const()[name = string("op_167_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor current_lengths_3_cast_fp16 = add(x = floor_div_0_cast_fp16, y = var_167_promoted_to_fp16)[name = string("current_lengths_3_cast_fp16")]; + string lengths_19_dtype_0 = const()[name = string("lengths_19_dtype_0"), val = string("int32")]; + tensor expand_dims_1 = const()[name = string("expand_dims_1"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4608)))]; + tensor var_176_axes_0 = const()[name = string("op_176_axes_0"), val = tensor([1])]; + tensor current_lengths_3_cast_fp16_to_int32 = cast(dtype = lengths_19_dtype_0, x = current_lengths_3_cast_fp16)[name = string("cast_18")]; + tensor var_176 = expand_dims(axes = var_176_axes_0, x = current_lengths_3_cast_fp16_to_int32)[name = string("op_176")]; + tensor time_mask_3 = less(x = expand_dims_1, y = var_176)[name = string("time_mask_3")]; + tensor var_178_axes_0 = const()[name = string("op_178_axes_0"), val = tensor([-1])]; + tensor var_178 = expand_dims(axes = var_178_axes_0, x = time_mask_3)[name = string("op_178")]; + tensor var_180_reps_0 = const()[name = string("op_180_reps_0"), val = tensor([1, 1, 65])]; + tensor var_180 = tile(reps = var_180_reps_0, x = var_178)[name = string("op_180")]; + tensor var_186_axes_0 = const()[name = string("op_186_axes_0"), val = tensor([1])]; + string mask_3_to_fp16_dtype_0 = const()[name = string("mask_3_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_180_to_fp16 = cast(dtype = mask_3_to_fp16_dtype_0, x = var_180)[name = string("cast_17")]; + tensor var_186_cast_fp16 = expand_dims(axes = var_186_axes_0, x = var_180_to_fp16)[name = string("op_186_cast_fp16")]; + tensor expanded_mask_3_reps_0 = const()[name = string("expanded_mask_3_reps_0"), val = tensor([1, 256, 1, 1])]; + tensor expanded_mask_3_cast_fp16 = tile(reps = expanded_mask_3_reps_0, x = var_186_cast_fp16)[name = string("expanded_mask_3_cast_fp16")]; + tensor input_5_cast_fp16 = mul(x = tensor_3_cast_fp16, y = expanded_mask_3_cast_fp16)[name = string("input_5_cast_fp16")]; + tensor tensor_5_cast_fp16 = relu(x = input_5_cast_fp16)[name = string("tensor_5_cast_fp16")]; + tensor input_7_cast_fp16 = mul(x = tensor_5_cast_fp16, y = expanded_mask_3_cast_fp16)[name = string("input_7_cast_fp16")]; + tensor input_9_pad_0 = const()[name = string("input_9_pad_0"), val = tensor([0, 0, 0, 0, 2, 1, 2, 1])]; + string input_9_mode_0 = const()[name = string("input_9_mode_0"), val = string("constant")]; + fp16 const_23_to_fp16 = const()[name = string("const_23_to_fp16"), val = fp16(0x0p+0)]; + tensor input_9_cast_fp16 = pad(constant_val = const_23_to_fp16, mode = input_9_mode_0, pad = input_9_pad_0, x = input_7_cast_fp16)[name = string("input_9_cast_fp16")]; + string tensor_7_pad_type_0 = const()[name = string("tensor_7_pad_type_0"), val = string("valid")]; + tensor tensor_7_strides_0 = const()[name = string("tensor_7_strides_0"), val = tensor([2, 2])]; + int32 tensor_7_groups_0 = const()[name = string("tensor_7_groups_0"), val = int32(256)]; + tensor tensor_7_pad_0 = const()[name = string("tensor_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor tensor_7_dilations_0 = const()[name = string("tensor_7_dilations_0"), val = tensor([1, 1])]; + tensor encoder_pre_encode_conv_2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5184))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7552))))[name = string("encoder_pre_encode_conv_2_weight_to_fp16_quantized")]; + tensor encoder_pre_encode_conv_2_bias_to_fp16 = const()[name = string("encoder_pre_encode_conv_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8128)))]; + tensor tensor_7_cast_fp16 = conv(bias = encoder_pre_encode_conv_2_bias_to_fp16, dilations = tensor_7_dilations_0, groups = tensor_7_groups_0, pad = tensor_7_pad_0, pad_type = tensor_7_pad_type_0, strides = tensor_7_strides_0, weight = encoder_pre_encode_conv_2_weight_to_fp16_quantized, x = input_9_cast_fp16)[name = string("tensor_7_cast_fp16")]; + fp16 var_208_promoted_to_fp16 = const()[name = string("op_208_promoted_to_fp16"), val = fp16(0x1p+1)]; + tensor var_209_cast_fp16 = add(x = current_lengths_3_cast_fp16, y = var_208_promoted_to_fp16)[name = string("op_209_cast_fp16")]; + fp16 var_210_promoted_to_fp16 = const()[name = string("op_210_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_211_cast_fp16 = add(x = var_209_cast_fp16, y = var_210_promoted_to_fp16)[name = string("op_211_cast_fp16")]; + fp16 var_212_promoted_to_fp16 = const()[name = string("op_212_promoted_to_fp16"), val = fp16(0x1.8p+1)]; + tensor var_213_cast_fp16 = sub(x = var_211_cast_fp16, y = var_212_promoted_to_fp16)[name = string("op_213_cast_fp16")]; + fp16 var_56_promoted_1_to_fp16 = const()[name = string("op_56_promoted_1_to_fp16"), val = fp16(0x1p+1)]; + tensor floor_div_1_cast_fp16 = floor_div(x = var_213_cast_fp16, y = var_56_promoted_1_to_fp16)[name = string("floor_div_1_cast_fp16")]; + fp16 var_215_promoted_to_fp16 = const()[name = string("op_215_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor current_lengths_5_cast_fp16 = add(x = floor_div_1_cast_fp16, y = var_215_promoted_to_fp16)[name = string("current_lengths_5_cast_fp16")]; + string lengths_21_dtype_0 = const()[name = string("lengths_21_dtype_0"), val = string("int32")]; + tensor expand_dims_2 = const()[name = string("expand_dims_2"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8704)))]; + tensor var_224_axes_0 = const()[name = string("op_224_axes_0"), val = tensor([1])]; + tensor current_lengths_5_cast_fp16_to_int32 = cast(dtype = lengths_21_dtype_0, x = current_lengths_5_cast_fp16)[name = string("cast_16")]; + tensor var_224 = expand_dims(axes = var_224_axes_0, x = current_lengths_5_cast_fp16_to_int32)[name = string("op_224")]; + tensor time_mask_5 = less(x = expand_dims_2, y = var_224)[name = string("time_mask_5")]; + tensor var_226_axes_0 = const()[name = string("op_226_axes_0"), val = tensor([-1])]; + tensor var_226 = expand_dims(axes = var_226_axes_0, x = time_mask_5)[name = string("op_226")]; + tensor var_228_reps_0 = const()[name = string("op_228_reps_0"), val = tensor([1, 1, 33])]; + tensor var_228 = tile(reps = var_228_reps_0, x = var_226)[name = string("op_228")]; + tensor var_234_axes_0 = const()[name = string("op_234_axes_0"), val = tensor([1])]; + string mask_5_to_fp16_dtype_0 = const()[name = string("mask_5_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_228_to_fp16 = cast(dtype = mask_5_to_fp16_dtype_0, x = var_228)[name = string("cast_15")]; + tensor var_234_cast_fp16 = expand_dims(axes = var_234_axes_0, x = var_228_to_fp16)[name = string("op_234_cast_fp16")]; + tensor expanded_mask_7_reps_0 = const()[name = string("expanded_mask_7_reps_0"), val = tensor([1, 256, 1, 1])]; + tensor expanded_mask_7_cast_fp16 = tile(reps = expanded_mask_7_reps_0, x = var_234_cast_fp16)[name = string("expanded_mask_7_cast_fp16")]; + tensor input_11_cast_fp16 = mul(x = tensor_7_cast_fp16, y = expanded_mask_7_cast_fp16)[name = string("input_11_cast_fp16")]; + string tensor_9_pad_type_0 = const()[name = string("tensor_9_pad_type_0"), val = string("valid")]; + tensor tensor_9_strides_0 = const()[name = string("tensor_9_strides_0"), val = tensor([1, 1])]; + tensor tensor_9_pad_0 = const()[name = string("tensor_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor tensor_9_dilations_0 = const()[name = string("tensor_9_dilations_0"), val = tensor([1, 1])]; + int32 tensor_9_groups_0 = const()[name = string("tensor_9_groups_0"), val = int32(1)]; + tensor encoder_pre_encode_conv_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9024))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(74624))))[name = string("encoder_pre_encode_conv_3_weight_to_fp16_quantized")]; + tensor encoder_pre_encode_conv_3_bias_to_fp16 = const()[name = string("encoder_pre_encode_conv_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75200)))]; + tensor tensor_9_cast_fp16 = conv(bias = encoder_pre_encode_conv_3_bias_to_fp16, dilations = tensor_9_dilations_0, groups = tensor_9_groups_0, pad = tensor_9_pad_0, pad_type = tensor_9_pad_type_0, strides = tensor_9_strides_0, weight = encoder_pre_encode_conv_3_weight_to_fp16_quantized, x = input_11_cast_fp16)[name = string("tensor_9_cast_fp16")]; + tensor input_13_cast_fp16 = mul(x = tensor_9_cast_fp16, y = expanded_mask_7_cast_fp16)[name = string("input_13_cast_fp16")]; + tensor tensor_11_cast_fp16 = relu(x = input_13_cast_fp16)[name = string("tensor_11_cast_fp16")]; + tensor input_15_cast_fp16 = mul(x = tensor_11_cast_fp16, y = expanded_mask_7_cast_fp16)[name = string("input_15_cast_fp16")]; + tensor input_17_pad_0 = const()[name = string("input_17_pad_0"), val = tensor([0, 0, 0, 0, 2, 1, 2, 1])]; + string input_17_mode_0 = const()[name = string("input_17_mode_0"), val = string("constant")]; + fp16 const_41_to_fp16 = const()[name = string("const_41_to_fp16"), val = fp16(0x0p+0)]; + tensor input_17_cast_fp16 = pad(constant_val = const_41_to_fp16, mode = input_17_mode_0, pad = input_17_pad_0, x = input_15_cast_fp16)[name = string("input_17_cast_fp16")]; + string tensor_13_pad_type_0 = const()[name = string("tensor_13_pad_type_0"), val = string("valid")]; + tensor tensor_13_strides_0 = const()[name = string("tensor_13_strides_0"), val = tensor([2, 2])]; + int32 tensor_13_groups_0 = const()[name = string("tensor_13_groups_0"), val = int32(256)]; + tensor tensor_13_pad_0 = const()[name = string("tensor_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor tensor_13_dilations_0 = const()[name = string("tensor_13_dilations_0"), val = tensor([1, 1])]; + tensor encoder_pre_encode_conv_5_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75776))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78144))))[name = string("encoder_pre_encode_conv_5_weight_to_fp16_quantized")]; + tensor encoder_pre_encode_conv_5_bias_to_fp16 = const()[name = string("encoder_pre_encode_conv_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78720)))]; + tensor tensor_13_cast_fp16 = conv(bias = encoder_pre_encode_conv_5_bias_to_fp16, dilations = tensor_13_dilations_0, groups = tensor_13_groups_0, pad = tensor_13_pad_0, pad_type = tensor_13_pad_type_0, strides = tensor_13_strides_0, weight = encoder_pre_encode_conv_5_weight_to_fp16_quantized, x = input_17_cast_fp16)[name = string("tensor_13_cast_fp16")]; + fp16 var_271_promoted_to_fp16 = const()[name = string("op_271_promoted_to_fp16"), val = fp16(0x1p+1)]; + tensor var_272_cast_fp16 = add(x = current_lengths_5_cast_fp16, y = var_271_promoted_to_fp16)[name = string("op_272_cast_fp16")]; + fp16 var_273_promoted_to_fp16 = const()[name = string("op_273_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_274_cast_fp16 = add(x = var_272_cast_fp16, y = var_273_promoted_to_fp16)[name = string("op_274_cast_fp16")]; + fp16 var_275_promoted_to_fp16 = const()[name = string("op_275_promoted_to_fp16"), val = fp16(0x1.8p+1)]; + tensor var_276_cast_fp16 = sub(x = var_274_cast_fp16, y = var_275_promoted_to_fp16)[name = string("op_276_cast_fp16")]; + fp16 var_56_promoted_2_to_fp16 = const()[name = string("op_56_promoted_2_to_fp16"), val = fp16(0x1p+1)]; + tensor floor_div_2_cast_fp16 = floor_div(x = var_276_cast_fp16, y = var_56_promoted_2_to_fp16)[name = string("floor_div_2_cast_fp16")]; + fp16 var_278_promoted_to_fp16 = const()[name = string("op_278_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor current_lengths_cast_fp16 = add(x = floor_div_2_cast_fp16, y = var_278_promoted_to_fp16)[name = string("current_lengths_cast_fp16")]; + string lengths_dtype_0 = const()[name = string("lengths_dtype_0"), val = string("int32")]; + tensor expand_dims_3 = const()[name = string("expand_dims_3"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79296)))]; + tensor var_287_axes_0 = const()[name = string("op_287_axes_0"), val = tensor([1])]; + tensor current_lengths_cast_fp16_to_int32 = cast(dtype = lengths_dtype_0, x = current_lengths_cast_fp16)[name = string("cast_14")]; + tensor var_287 = expand_dims(axes = var_287_axes_0, x = current_lengths_cast_fp16_to_int32)[name = string("op_287")]; + tensor time_mask = less(x = expand_dims_3, y = var_287)[name = string("time_mask")]; + tensor var_289_axes_0 = const()[name = string("op_289_axes_0"), val = tensor([-1])]; + tensor var_289 = expand_dims(axes = var_289_axes_0, x = time_mask)[name = string("op_289")]; + tensor var_291_reps_0 = const()[name = string("op_291_reps_0"), val = tensor([1, 1, 17])]; + tensor var_291 = tile(reps = var_291_reps_0, x = var_289)[name = string("op_291")]; + tensor var_297_axes_0 = const()[name = string("op_297_axes_0"), val = tensor([1])]; + string mask_7_to_fp16_dtype_0 = const()[name = string("mask_7_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_291_to_fp16 = cast(dtype = mask_7_to_fp16_dtype_0, x = var_291)[name = string("cast_13")]; + tensor var_297_cast_fp16 = expand_dims(axes = var_297_axes_0, x = var_291_to_fp16)[name = string("op_297_cast_fp16")]; + tensor expanded_mask_13_reps_0 = const()[name = string("expanded_mask_13_reps_0"), val = tensor([1, 256, 1, 1])]; + tensor expanded_mask_13_cast_fp16 = tile(reps = expanded_mask_13_reps_0, x = var_297_cast_fp16)[name = string("expanded_mask_13_cast_fp16")]; + tensor input_19_cast_fp16 = mul(x = tensor_13_cast_fp16, y = expanded_mask_13_cast_fp16)[name = string("input_19_cast_fp16")]; + string tensor_15_pad_type_0 = const()[name = string("tensor_15_pad_type_0"), val = string("valid")]; + tensor tensor_15_strides_0 = const()[name = string("tensor_15_strides_0"), val = tensor([1, 1])]; + tensor tensor_15_pad_0 = const()[name = string("tensor_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor tensor_15_dilations_0 = const()[name = string("tensor_15_dilations_0"), val = tensor([1, 1])]; + int32 tensor_15_groups_0 = const()[name = string("tensor_15_groups_0"), val = int32(1)]; + tensor encoder_pre_encode_conv_6_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145088))))[name = string("encoder_pre_encode_conv_6_weight_to_fp16_quantized")]; + tensor encoder_pre_encode_conv_6_bias_to_fp16 = const()[name = string("encoder_pre_encode_conv_6_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145664)))]; + tensor tensor_15_cast_fp16 = conv(bias = encoder_pre_encode_conv_6_bias_to_fp16, dilations = tensor_15_dilations_0, groups = tensor_15_groups_0, pad = tensor_15_pad_0, pad_type = tensor_15_pad_type_0, strides = tensor_15_strides_0, weight = encoder_pre_encode_conv_6_weight_to_fp16_quantized, x = input_19_cast_fp16)[name = string("tensor_15_cast_fp16")]; + tensor input_21_cast_fp16 = mul(x = tensor_15_cast_fp16, y = expanded_mask_13_cast_fp16)[name = string("input_21_cast_fp16")]; + tensor tensor_cast_fp16 = relu(x = input_21_cast_fp16)[name = string("tensor_cast_fp16")]; + tensor x_3_cast_fp16 = mul(x = tensor_cast_fp16, y = expanded_mask_13_cast_fp16)[name = string("x_3_cast_fp16")]; + tensor var_331_perm_0 = const()[name = string("op_331_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_332 = const()[name = string("op_332"), val = tensor([1, 30, -1])]; + tensor var_331_cast_fp16 = transpose(perm = var_331_perm_0, x = x_3_cast_fp16)[name = string("transpose_366")]; + tensor input_23_cast_fp16 = reshape(shape = var_332, x = var_331_cast_fp16)[name = string("input_23_cast_fp16")]; + tensor encoder_pre_encode_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146240))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4602752))))[name = string("encoder_pre_encode_out_weight_to_fp16_quantized")]; + tensor encoder_pre_encode_out_bias_to_fp16 = const()[name = string("encoder_pre_encode_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4604864)))]; + tensor linear_0_cast_fp16 = linear(bias = encoder_pre_encode_out_bias_to_fp16, weight = encoder_pre_encode_out_weight_to_fp16_quantized, x = input_23_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_342_begin_0 = const()[name = string("op_342_begin_0"), val = tensor([0, 2, 0])]; + tensor var_342_end_0 = const()[name = string("op_342_end_0"), val = tensor([1, 30, 1024])]; + tensor var_342_end_mask_0 = const()[name = string("op_342_end_mask_0"), val = tensor([true, true, true])]; + tensor var_342_cast_fp16 = slice_by_index(begin = var_342_begin_0, end = var_342_end_0, end_mask = var_342_end_mask_0, x = linear_0_cast_fp16)[name = string("op_342_cast_fp16")]; + int32 var_344 = const()[name = string("op_344"), val = int32(2)]; + tensor var_345 = sub(x = current_lengths_cast_fp16_to_int32, y = var_344)[name = string("op_345")]; + string var_345_promoted_to_fp16_dtype_0 = const()[name = string("op_345_promoted_to_fp16_dtype_0"), val = string("fp16")]; + fp16 var_62_promoted_to_fp16 = const()[name = string("op_62_promoted_to_fp16"), val = fp16(0x0p+0)]; + fp16 const_61_to_fp16 = const()[name = string("const_61_to_fp16"), val = fp16(inf)]; + tensor var_345_to_fp16 = cast(dtype = var_345_promoted_to_fp16_dtype_0, x = var_345)[name = string("cast_12")]; + tensor clip_0_cast_fp16 = clip(alpha = var_62_promoted_to_fp16, beta = const_61_to_fp16, x = var_345_to_fp16)[name = string("clip_0_cast_fp16")]; + tensor max_audio_length_1 = const()[name = string("max_audio_length_1"), val = tensor([28])]; + fp16 var_361_promoted_to_fp16 = const()[name = string("op_361_promoted_to_fp16"), val = fp16(0x1.5p+5)]; + tensor padding_length_cast_fp16 = add(x = clip_0_cast_fp16, y = var_361_promoted_to_fp16)[name = string("padding_length_cast_fp16")]; + int32 const_63 = const()[name = string("const_63"), val = int32(-1)]; + tensor var_363 = mul(x = cache_len, y = const_63)[name = string("op_363")]; + int32 var_364 = const()[name = string("op_364"), val = int32(42)]; + tensor offset = add(x = var_363, y = var_364)[name = string("offset")]; + tensor var_404_axes_0 = const()[name = string("op_404_axes_0"), val = tensor([-1])]; + tensor var_404_cast_fp16 = expand_dims(axes = var_404_axes_0, x = padding_length_cast_fp16)[name = string("op_404_cast_fp16")]; + tensor var_403_promoted_to_fp16 = const()[name = string("op_403_promoted_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4606976)))]; + tensor pad_mask_1_cast_fp16 = less(x = var_403_promoted_to_fp16, y = var_404_cast_fp16)[name = string("pad_mask_1_cast_fp16")]; + tensor expand_dims_5 = const()[name = string("expand_dims_5"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4607232)))]; + tensor var_410_axes_0 = const()[name = string("op_410_axes_0"), val = tensor([-1])]; + tensor var_410 = expand_dims(axes = var_410_axes_0, x = offset)[name = string("op_410")]; + tensor pad_mask_off = greater_equal(x = expand_dims_5, y = var_410)[name = string("pad_mask_off")]; + tensor pad_mask_3 = logical_and(x = pad_mask_off, y = pad_mask_1_cast_fp16)[name = string("pad_mask_3")]; + tensor var_413_axes_0 = const()[name = string("op_413_axes_0"), val = tensor([1])]; + tensor var_413 = expand_dims(axes = var_413_axes_0, x = pad_mask_3)[name = string("op_413")]; + tensor var_414 = const()[name = string("op_414"), val = tensor([1, 70, 1])]; + tensor pad_mask_for_att_mask_1 = tile(reps = var_414, x = var_413)[name = string("pad_mask_for_att_mask_1")]; + tensor var_416_perm_0 = const()[name = string("op_416_perm_0"), val = tensor([0, 2, 1])]; + tensor var_416 = transpose(perm = var_416_perm_0, x = pad_mask_for_att_mask_1)[name = string("transpose_365")]; + tensor pad_mask_for_att_mask = logical_and(x = pad_mask_for_att_mask_1, y = var_416)[name = string("pad_mask_for_att_mask")]; + tensor const_71 = const()[name = string("const_71"), val = tensor([[[true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, 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true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true]]])]; + tensor att_mask_9 = logical_and(x = pad_mask_for_att_mask, y = const_71)[name = string("att_mask_9")]; + tensor att_mask = logical_not(x = att_mask_9)[name = string("att_mask")]; + tensor pad_mask_5 = logical_not(x = pad_mask_3)[name = string("pad_mask_5")]; + tensor pad_mask_begin_0 = const()[name = string("pad_mask_begin_0"), val = tensor([0, 42])]; + tensor pad_mask_end_0 = const()[name = string("pad_mask_end_0"), val = tensor([1, 70])]; + tensor pad_mask_end_mask_0 = const()[name = string("pad_mask_end_mask_0"), val = tensor([true, true])]; + tensor pad_mask = slice_by_index(begin = pad_mask_begin_0, end = pad_mask_end_0, end_mask = pad_mask_end_mask_0, x = pad_mask_5)[name = string("pad_mask")]; + tensor mask_9_begin_0 = const()[name = string("mask_9_begin_0"), val = tensor([0, 42, 0])]; + tensor mask_9_end_0 = const()[name = string("mask_9_end_0"), val = tensor([1, 70, 70])]; + tensor mask_9_end_mask_0 = const()[name = string("mask_9_end_mask_0"), val = tensor([true, true, true])]; + tensor mask_9 = slice_by_index(begin = mask_9_begin_0, end = mask_9_end_0, end_mask = mask_9_end_mask_0, x = att_mask)[name = string("mask_9")]; + tensor cache_1_begin_0 = const()[name = string("cache_1_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor cache_1_end_0 = const()[name = string("cache_1_end_0"), val = tensor([1, 1, 42, 1024])]; + tensor cache_1_end_mask_0 = const()[name = string("cache_1_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_1_squeeze_mask_0 = const()[name = string("cache_1_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_channel_to_fp16 = cast(dtype = cache_channel_to_fp16_dtype_0, x = cache_channel)[name = string("cast_11")]; + tensor value_3_cast_fp16 = transpose(perm = value_3_perm_0, x = cache_channel_to_fp16)[name = string("transpose_364")]; + tensor cache_1_cast_fp16 = slice_by_index(begin = cache_1_begin_0, end = cache_1_end_0, end_mask = cache_1_end_mask_0, squeeze_mask = cache_1_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_1_cast_fp16")]; + tensor cache_3_begin_0 = const()[name = string("cache_3_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor cache_3_end_0 = const()[name = string("cache_3_end_0"), val = tensor([1, 1, 1024, 8])]; + tensor cache_3_end_mask_0 = const()[name = string("cache_3_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_3_squeeze_mask_0 = const()[name = string("cache_3_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_time_to_fp16 = cast(dtype = cache_time_to_fp16_dtype_0, x = cache_time)[name = string("cast_10")]; + tensor value_5_cast_fp16 = transpose(perm = value_5_perm_0, x = cache_time_to_fp16)[name = string("transpose_363")]; + tensor cache_3_cast_fp16 = slice_by_index(begin = cache_3_begin_0, end = cache_3_end_0, end_mask = cache_3_end_mask_0, squeeze_mask = cache_3_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_3_cast_fp16")]; + tensor input_27_axes_0 = const()[name = string("input_27_axes_0"), val = tensor([-1])]; + tensor encoder_layers_0_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_0_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4607616)))]; + tensor encoder_layers_0_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_0_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4609728)))]; + fp16 var_42_to_fp16 = const()[name = string("op_42_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_27_cast_fp16 = layer_norm(axes = input_27_axes_0, beta = encoder_layers_0_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_0_norm_feed_forward1_weight_to_fp16, x = var_342_cast_fp16)[name = string("input_27_cast_fp16")]; + tensor encoder_layers_0_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4611840))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8806208))))[name = string("encoder_layers_0_feed_forward1_linear1_weight_to_fp16_quantized")]; + tensor encoder_layers_0_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_0_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8814464)))]; + tensor linear_1_cast_fp16 = linear(bias = encoder_layers_0_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_0_feed_forward1_linear1_weight_to_fp16_quantized, x = input_27_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor input_31_cast_fp16 = silu(x = linear_1_cast_fp16)[name = string("input_31_cast_fp16")]; + tensor encoder_layers_0_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8822720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13017088))))[name = string("encoder_layers_0_feed_forward1_linear2_weight_to_fp16_quantized")]; + tensor encoder_layers_0_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_0_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13019200)))]; + tensor linear_2_cast_fp16 = linear(bias = encoder_layers_0_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_0_feed_forward1_linear2_weight_to_fp16_quantized, x = input_31_cast_fp16)[name = string("linear_2_cast_fp16")]; + fp16 var_455_to_fp16 = const()[name = string("op_455_to_fp16"), val = fp16(0x1p-1)]; + tensor var_456_cast_fp16 = mul(x = linear_2_cast_fp16, y = var_455_to_fp16)[name = string("op_456_cast_fp16")]; + tensor input_37_cast_fp16 = add(x = var_342_cast_fp16, y = var_456_cast_fp16)[name = string("input_37_cast_fp16")]; + tensor key_1_axes_0 = const()[name = string("key_1_axes_0"), val = tensor([-1])]; + tensor encoder_layers_0_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_0_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13021312)))]; + tensor encoder_layers_0_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_0_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13023424)))]; + tensor key_1_cast_fp16 = layer_norm(axes = key_1_axes_0, beta = encoder_layers_0_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_0_norm_self_att_weight_to_fp16, x = input_37_cast_fp16)[name = string("key_1_cast_fp16")]; + bool input_39_interleave_0 = const()[name = string("input_39_interleave_0"), val = bool(false)]; + tensor input_39_cast_fp16 = concat(axis = var_68, interleave = input_39_interleave_0, values = (cache_1_cast_fp16, key_1_cast_fp16))[name = string("input_39_cast_fp16")]; + tensor var_478_begin_0 = const()[name = string("op_478_begin_0"), val = tensor([0, 28, 0])]; + tensor var_478_end_0 = const()[name = string("op_478_end_0"), val = tensor([1, 42, 1024])]; + tensor var_478_end_mask_0 = const()[name = string("op_478_end_mask_0"), val = tensor([true, true, true])]; + tensor var_478_cast_fp16 = slice_by_index(begin = var_478_begin_0, end = var_478_end_0, end_mask = var_478_end_mask_0, x = cache_1_cast_fp16)[name = string("op_478_cast_fp16")]; + bool var_484_interleave_0 = const()[name = string("op_484_interleave_0"), val = bool(false)]; + tensor var_484_cast_fp16 = concat(axis = var_68, interleave = var_484_interleave_0, values = (var_478_cast_fp16, key_1_cast_fp16))[name = string("op_484_cast_fp16")]; + tensor encoder_layers_0_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13025536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14074176))))[name = string("encoder_layers_0_self_attn_linear_q_weight_to_fp16_quantized")]; + tensor encoder_layers_0_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_0_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14076288)))]; + tensor linear_3_cast_fp16 = linear(bias = encoder_layers_0_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_0_self_attn_linear_q_weight_to_fp16_quantized, x = key_1_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_489 = const()[name = string("op_489"), val = tensor([1, -1, 8, 128])]; + tensor q_1_cast_fp16 = reshape(shape = var_489, x = linear_3_cast_fp16)[name = string("q_1_cast_fp16")]; + tensor encoder_layers_0_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14078400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15127040))))[name = string("encoder_layers_0_self_attn_linear_k_weight_to_fp16_quantized")]; + tensor encoder_layers_0_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_0_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15129152)))]; + tensor linear_4_cast_fp16 = linear(bias = encoder_layers_0_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_0_self_attn_linear_k_weight_to_fp16_quantized, x = input_39_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_494 = const()[name = string("op_494"), val = tensor([1, -1, 8, 128])]; + tensor k_1_cast_fp16 = reshape(shape = var_494, x = linear_4_cast_fp16)[name = string("k_1_cast_fp16")]; + tensor encoder_layers_0_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15131264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16179904))))[name = string("encoder_layers_0_self_attn_linear_v_weight_to_fp16_quantized")]; + tensor encoder_layers_0_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_0_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16182016)))]; + tensor linear_5_cast_fp16 = linear(bias = encoder_layers_0_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_0_self_attn_linear_v_weight_to_fp16_quantized, x = input_39_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_499 = const()[name = string("op_499"), val = tensor([1, -1, 8, 128])]; + tensor v_1_cast_fp16 = reshape(shape = var_499, x = linear_5_cast_fp16)[name = string("v_1_cast_fp16")]; + tensor value_9_perm_0 = const()[name = string("value_9_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_0_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_0_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16184128)))]; + tensor var_512_cast_fp16 = add(x = q_1_cast_fp16, y = encoder_layers_0_self_attn_pos_bias_u_to_fp16)[name = string("op_512_cast_fp16")]; + tensor encoder_layers_0_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_0_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16186240)))]; + tensor var_514_cast_fp16 = add(x = q_1_cast_fp16, y = encoder_layers_0_self_attn_pos_bias_v_to_fp16)[name = string("op_514_cast_fp16")]; + tensor q_with_bias_v_1_perm_0 = const()[name = string("q_with_bias_v_1_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_7_transpose_x_0 = const()[name = string("x_7_transpose_x_0"), val = bool(false)]; + bool x_7_transpose_y_0 = const()[name = string("x_7_transpose_y_0"), val = bool(false)]; + tensor op_516_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16188352))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16330752))))[name = string("op_516_to_fp16_quantized")]; + tensor q_with_bias_v_1_cast_fp16 = transpose(perm = q_with_bias_v_1_perm_0, x = var_514_cast_fp16)[name = string("transpose_362")]; + tensor x_7_cast_fp16 = matmul(transpose_x = x_7_transpose_x_0, transpose_y = x_7_transpose_y_0, x = q_with_bias_v_1_cast_fp16, y = op_516_to_fp16_quantized)[name = string("x_7_cast_fp16")]; + tensor x_9_pad_0 = const()[name = string("x_9_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_9_mode_0 = const()[name = string("x_9_mode_0"), val = string("constant")]; + fp16 const_79_to_fp16 = const()[name = string("const_79_to_fp16"), val = fp16(0x0p+0)]; + tensor x_9_cast_fp16 = pad(constant_val = const_79_to_fp16, mode = x_9_mode_0, pad = x_9_pad_0, x = x_7_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor var_524 = const()[name = string("op_524"), val = tensor([1, 8, -1, 28])]; + tensor x_11_cast_fp16 = reshape(shape = var_524, x = x_9_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor var_528_begin_0 = const()[name = string("op_528_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_528_end_0 = const()[name = string("op_528_end_0"), val = tensor([1, 8, 140, 28])]; + tensor var_528_end_mask_0 = const()[name = string("op_528_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_528_cast_fp16 = slice_by_index(begin = var_528_begin_0, end = var_528_end_0, end_mask = var_528_end_mask_0, x = x_11_cast_fp16)[name = string("op_528_cast_fp16")]; + tensor var_529 = const()[name = string("op_529"), val = tensor([1, 8, 28, 139])]; + tensor matrix_bd_1_cast_fp16 = reshape(shape = var_529, x = var_528_cast_fp16)[name = string("matrix_bd_1_cast_fp16")]; + bool matrix_ac_1_transpose_x_0 = const()[name = string("matrix_ac_1_transpose_x_0"), val = bool(false)]; + bool matrix_ac_1_transpose_y_0 = const()[name = string("matrix_ac_1_transpose_y_0"), val = bool(false)]; + tensor transpose_96_perm_0 = const()[name = string("transpose_96_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_97_perm_0 = const()[name = string("transpose_97_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_97 = transpose(perm = transpose_97_perm_0, x = k_1_cast_fp16)[name = string("transpose_360")]; + tensor transpose_96 = transpose(perm = transpose_96_perm_0, x = var_512_cast_fp16)[name = string("transpose_361")]; + tensor matrix_ac_1_cast_fp16 = matmul(transpose_x = matrix_ac_1_transpose_x_0, transpose_y = matrix_ac_1_transpose_y_0, x = transpose_96, y = transpose_97)[name = string("matrix_ac_1_cast_fp16")]; + tensor matrix_bd_3_begin_0 = const()[name = string("matrix_bd_3_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_3_end_0 = const()[name = string("matrix_bd_3_end_0"), val = tensor([1, 8, 28, 70])]; + tensor matrix_bd_3_end_mask_0 = const()[name = string("matrix_bd_3_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_3_cast_fp16 = slice_by_index(begin = matrix_bd_3_begin_0, end = matrix_bd_3_end_0, end_mask = matrix_bd_3_end_mask_0, x = matrix_bd_1_cast_fp16)[name = string("matrix_bd_3_cast_fp16")]; + tensor var_538_cast_fp16 = add(x = matrix_ac_1_cast_fp16, y = matrix_bd_3_cast_fp16)[name = string("op_538_cast_fp16")]; + fp16 _inversed_scores_1_y_0_to_fp16 = const()[name = string("_inversed_scores_1_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_1_cast_fp16 = mul(x = var_538_cast_fp16, y = _inversed_scores_1_y_0_to_fp16)[name = string("_inversed_scores_1_cast_fp16")]; + tensor mask_11_axes_0 = const()[name = string("mask_11_axes_0"), val = tensor([1])]; + tensor mask_11 = expand_dims(axes = mask_11_axes_0, x = mask_9)[name = string("mask_11")]; + fp16 var_45_to_fp16 = const()[name = string("op_45_to_fp16"), val = fp16(-0x1.388p+13)]; + tensor scores_3_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_1_cast_fp16, cond = mask_11)[name = string("scores_3_cast_fp16")]; + tensor var_544_cast_fp16 = softmax(axis = var_59, x = scores_3_cast_fp16)[name = string("op_544_cast_fp16")]; + fp16 var_44_to_fp16 = const()[name = string("op_44_to_fp16"), val = fp16(0x0p+0)]; + tensor input_41_cast_fp16 = select(a = var_44_to_fp16, b = var_544_cast_fp16, cond = mask_11)[name = string("input_41_cast_fp16")]; + bool x_13_transpose_x_0 = const()[name = string("x_13_transpose_x_0"), val = bool(false)]; + bool x_13_transpose_y_0 = const()[name = string("x_13_transpose_y_0"), val = bool(false)]; + tensor value_9_cast_fp16 = transpose(perm = value_9_perm_0, x = v_1_cast_fp16)[name = string("transpose_359")]; + tensor x_13_cast_fp16 = matmul(transpose_x = x_13_transpose_x_0, transpose_y = x_13_transpose_y_0, x = input_41_cast_fp16, y = value_9_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor var_548_perm_0 = const()[name = string("op_548_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_549 = const()[name = string("op_549"), val = tensor([1, -1, 1024])]; + tensor var_548_cast_fp16 = transpose(perm = var_548_perm_0, x = x_13_cast_fp16)[name = string("transpose_358")]; + tensor input_43_cast_fp16 = reshape(shape = var_549, x = var_548_cast_fp16)[name = string("input_43_cast_fp16")]; + tensor encoder_layers_0_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16331136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17379776))))[name = string("encoder_layers_0_self_attn_linear_out_weight_to_fp16_quantized")]; + tensor encoder_layers_0_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_0_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17381888)))]; + tensor linear_7_cast_fp16 = linear(bias = encoder_layers_0_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_0_self_attn_linear_out_weight_to_fp16_quantized, x = input_43_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor input_47_cast_fp16 = add(x = input_37_cast_fp16, y = linear_7_cast_fp16)[name = string("input_47_cast_fp16")]; + tensor x_17_axes_0 = const()[name = string("x_17_axes_0"), val = tensor([-1])]; + tensor encoder_layers_0_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_0_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17384000)))]; + tensor encoder_layers_0_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_0_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17386112)))]; + tensor x_17_cast_fp16 = layer_norm(axes = x_17_axes_0, beta = encoder_layers_0_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_0_norm_conv_weight_to_fp16, x = input_47_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor input_49_perm_0 = const()[name = string("input_49_perm_0"), val = tensor([0, 2, 1])]; + string input_51_pad_type_0 = const()[name = string("input_51_pad_type_0"), val = string("valid")]; + tensor input_51_strides_0 = const()[name = string("input_51_strides_0"), val = tensor([1])]; + tensor input_51_pad_0 = const()[name = string("input_51_pad_0"), val = tensor([0, 0])]; + tensor input_51_dilations_0 = const()[name = string("input_51_dilations_0"), val = tensor([1])]; + int32 input_51_groups_0 = const()[name = string("input_51_groups_0"), val = int32(1)]; + tensor encoder_layers_0_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17388224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19485440))))[name = string("encoder_layers_0_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_49_cast_fp16 = transpose(perm = input_49_perm_0, x = x_17_cast_fp16)[name = string("transpose_357")]; + tensor input_51_cast_fp16 = conv(dilations = input_51_dilations_0, groups = input_51_groups_0, pad = input_51_pad_0, pad_type = input_51_pad_type_0, strides = input_51_strides_0, weight = encoder_layers_0_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_49_cast_fp16)[name = string("input_51_cast_fp16")]; + int32 x_19_split_num_splits_0 = const()[name = string("x_19_split_num_splits_0"), val = int32(2)]; + int32 x_19_split_axis_0 = const()[name = string("x_19_split_axis_0"), val = int32(1)]; + tensor x_19_split_cast_fp16_0, tensor x_19_split_cast_fp16_1 = split(axis = x_19_split_axis_0, num_splits = x_19_split_num_splits_0, x = input_51_cast_fp16)[name = string("x_19_split_cast_fp16")]; + tensor x_19_split_1_sigmoid_cast_fp16 = sigmoid(x = x_19_split_cast_fp16_1)[name = string("x_19_split_1_sigmoid_cast_fp16")]; + tensor x_19_cast_fp16 = mul(x = x_19_split_cast_fp16_0, y = x_19_split_1_sigmoid_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor var_575_axes_0 = const()[name = string("op_575_axes_0"), val = tensor([1])]; + tensor var_575 = expand_dims(axes = var_575_axes_0, x = pad_mask)[name = string("op_575")]; + tensor input_53_cast_fp16 = select(a = var_44_to_fp16, b = x_19_cast_fp16, cond = var_575)[name = string("input_53_cast_fp16")]; + bool new_x_3_interleave_0 = const()[name = string("new_x_3_interleave_0"), val = bool(false)]; + tensor new_x_3_cast_fp16 = concat(axis = var_59, interleave = new_x_3_interleave_0, values = (cache_3_cast_fp16, input_53_cast_fp16))[name = string("new_x_3_cast_fp16")]; + tensor var_588_begin_0 = const()[name = string("op_588_begin_0"), val = tensor([0, 0, 28])]; + tensor var_588_end_0 = const()[name = string("op_588_end_0"), val = tensor([1, 1024, 36])]; + tensor var_588_end_mask_0 = const()[name = string("op_588_end_mask_0"), val = tensor([true, true, true])]; + tensor var_588_cast_fp16 = slice_by_index(begin = var_588_begin_0, end = var_588_end_0, end_mask = var_588_end_mask_0, x = new_x_3_cast_fp16)[name = string("op_588_cast_fp16")]; + string x_21_pad_type_0 = const()[name = string("x_21_pad_type_0"), val = string("valid")]; + int32 x_21_groups_0 = const()[name = string("x_21_groups_0"), val = int32(1024)]; + tensor x_21_strides_0 = const()[name = string("x_21_strides_0"), val = tensor([1])]; + tensor x_21_pad_0 = const()[name = string("x_21_pad_0"), val = tensor([0, 0])]; + tensor x_21_dilations_0 = const()[name = string("x_21_dilations_0"), val = tensor([1])]; + tensor encoder_layers_0_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19489600))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19498880))))[name = string("encoder_layers_0_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_21_cast_fp16 = conv(dilations = x_21_dilations_0, groups = x_21_groups_0, pad = x_21_pad_0, pad_type = x_21_pad_type_0, strides = x_21_strides_0, weight = encoder_layers_0_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_3_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor input_55_perm_0 = const()[name = string("input_55_perm_0"), val = tensor([0, 2, 1])]; + tensor x_23_axes_0 = const()[name = string("x_23_axes_0"), val = tensor([-1])]; + tensor encoder_layers_0_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_0_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19500992)))]; + tensor encoder_layers_0_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_0_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19503104)))]; + tensor input_55_cast_fp16 = transpose(perm = input_55_perm_0, x = x_21_cast_fp16)[name = string("transpose_356")]; + tensor x_23_cast_fp16 = layer_norm(axes = x_23_axes_0, beta = encoder_layers_0_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_0_conv_batch_norm_weight_to_fp16, x = input_55_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor input_57_perm_0 = const()[name = string("input_57_perm_0"), val = tensor([0, 2, 1])]; + tensor input_57_cast_fp16 = transpose(perm = input_57_perm_0, x = x_23_cast_fp16)[name = string("transpose_355")]; + tensor input_59_cast_fp16 = silu(x = input_57_cast_fp16)[name = string("input_59_cast_fp16")]; + string x_25_pad_type_0 = const()[name = string("x_25_pad_type_0"), val = string("valid")]; + tensor x_25_strides_0 = const()[name = string("x_25_strides_0"), val = tensor([1])]; + tensor x_25_pad_0 = const()[name = string("x_25_pad_0"), val = tensor([0, 0])]; + tensor x_25_dilations_0 = const()[name = string("x_25_dilations_0"), val = tensor([1])]; + int32 x_25_groups_0 = const()[name = string("x_25_groups_0"), val = int32(1)]; + tensor encoder_layers_0_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19505216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20553856))))[name = string("encoder_layers_0_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_25_cast_fp16 = conv(dilations = x_25_dilations_0, groups = x_25_groups_0, pad = x_25_pad_0, pad_type = x_25_pad_type_0, strides = x_25_strides_0, weight = encoder_layers_0_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_59_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor input_61_perm_0 = const()[name = string("input_61_perm_0"), val = tensor([0, 2, 1])]; + tensor input_61_cast_fp16 = transpose(perm = input_61_perm_0, x = x_25_cast_fp16)[name = string("transpose_354")]; + tensor input_63_cast_fp16 = add(x = input_47_cast_fp16, y = input_61_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor input_65_axes_0 = const()[name = string("input_65_axes_0"), val = tensor([-1])]; + tensor encoder_layers_0_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_0_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20555968)))]; + tensor encoder_layers_0_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_0_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20558080)))]; + tensor input_65_cast_fp16 = layer_norm(axes = input_65_axes_0, beta = encoder_layers_0_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_0_norm_feed_forward2_weight_to_fp16, x = input_63_cast_fp16)[name = string("input_65_cast_fp16")]; + tensor encoder_layers_0_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20560192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24754560))))[name = string("encoder_layers_0_feed_forward2_linear1_weight_to_fp16_quantized")]; + tensor encoder_layers_0_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_0_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24762816)))]; + tensor linear_8_cast_fp16 = linear(bias = encoder_layers_0_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_0_feed_forward2_linear1_weight_to_fp16_quantized, x = input_65_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor input_69_cast_fp16 = silu(x = linear_8_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor encoder_layers_0_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24771072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28965440))))[name = string("encoder_layers_0_feed_forward2_linear2_weight_to_fp16_quantized")]; + tensor encoder_layers_0_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_0_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28967552)))]; + tensor linear_9_cast_fp16 = linear(bias = encoder_layers_0_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_0_feed_forward2_linear2_weight_to_fp16_quantized, x = input_69_cast_fp16)[name = string("linear_9_cast_fp16")]; + fp16 var_631_to_fp16 = const()[name = string("op_631_to_fp16"), val = fp16(0x1p-1)]; + tensor var_632_cast_fp16 = mul(x = linear_9_cast_fp16, y = var_631_to_fp16)[name = string("op_632_cast_fp16")]; + tensor input_75_cast_fp16 = add(x = input_63_cast_fp16, y = var_632_cast_fp16)[name = string("input_75_cast_fp16")]; + tensor input_77_axes_0 = const()[name = string("input_77_axes_0"), val = tensor([-1])]; + tensor encoder_layers_0_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_0_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28969664)))]; + tensor encoder_layers_0_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_0_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28971776)))]; + tensor input_77_cast_fp16 = layer_norm(axes = input_77_axes_0, beta = encoder_layers_0_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_0_norm_out_weight_to_fp16, x = input_75_cast_fp16)[name = string("input_77_cast_fp16")]; + tensor cache_5_begin_0 = const()[name = string("cache_5_begin_0"), val = tensor([1, 0, 0, 0])]; + tensor cache_5_end_0 = const()[name = string("cache_5_end_0"), val = tensor([2, 1, 42, 1024])]; + tensor cache_5_end_mask_0 = const()[name = string("cache_5_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_5_squeeze_mask_0 = const()[name = string("cache_5_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_5_cast_fp16 = slice_by_index(begin = cache_5_begin_0, end = cache_5_end_0, end_mask = cache_5_end_mask_0, squeeze_mask = cache_5_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_5_cast_fp16")]; + tensor cache_7_begin_0 = const()[name = string("cache_7_begin_0"), val = tensor([1, 0, 0, 0])]; + tensor cache_7_end_0 = const()[name = string("cache_7_end_0"), val = tensor([2, 1, 1024, 8])]; + tensor cache_7_end_mask_0 = const()[name = string("cache_7_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_7_squeeze_mask_0 = const()[name = string("cache_7_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_7_cast_fp16 = slice_by_index(begin = cache_7_begin_0, end = cache_7_end_0, end_mask = cache_7_end_mask_0, squeeze_mask = cache_7_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_7_cast_fp16")]; + tensor input_79_axes_0 = const()[name = string("input_79_axes_0"), val = tensor([-1])]; + tensor encoder_layers_1_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_1_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28973888)))]; + tensor encoder_layers_1_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_1_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28976000)))]; + tensor input_79_cast_fp16 = layer_norm(axes = input_79_axes_0, beta = encoder_layers_1_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_1_norm_feed_forward1_weight_to_fp16, x = input_77_cast_fp16)[name = string("input_79_cast_fp16")]; + tensor encoder_layers_1_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28978112))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33172480))))[name = string("encoder_layers_1_feed_forward1_linear1_weight_to_fp16_quantized")]; + tensor encoder_layers_1_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_1_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33180736)))]; + tensor linear_10_cast_fp16 = linear(bias = encoder_layers_1_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_1_feed_forward1_linear1_weight_to_fp16_quantized, x = input_79_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor input_83_cast_fp16 = silu(x = linear_10_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor encoder_layers_1_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33188992))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37383360))))[name = string("encoder_layers_1_feed_forward1_linear2_weight_to_fp16_quantized")]; + tensor encoder_layers_1_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_1_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37385472)))]; + tensor linear_11_cast_fp16 = linear(bias = encoder_layers_1_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_1_feed_forward1_linear2_weight_to_fp16_quantized, x = input_83_cast_fp16)[name = string("linear_11_cast_fp16")]; + fp16 var_668_to_fp16 = const()[name = string("op_668_to_fp16"), val = fp16(0x1p-1)]; + tensor var_669_cast_fp16 = mul(x = linear_11_cast_fp16, y = var_668_to_fp16)[name = string("op_669_cast_fp16")]; + tensor input_89_cast_fp16 = add(x = input_77_cast_fp16, y = var_669_cast_fp16)[name = string("input_89_cast_fp16")]; + tensor key_3_axes_0 = const()[name = string("key_3_axes_0"), val = tensor([-1])]; + tensor encoder_layers_1_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_1_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37387584)))]; + tensor encoder_layers_1_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_1_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37389696)))]; + tensor key_3_cast_fp16 = layer_norm(axes = key_3_axes_0, beta = encoder_layers_1_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_1_norm_self_att_weight_to_fp16, x = input_89_cast_fp16)[name = string("key_3_cast_fp16")]; + bool input_91_interleave_0 = const()[name = string("input_91_interleave_0"), val = bool(false)]; + tensor input_91_cast_fp16 = concat(axis = var_68, interleave = input_91_interleave_0, values = (cache_5_cast_fp16, key_3_cast_fp16))[name = string("input_91_cast_fp16")]; + tensor var_691_begin_0 = const()[name = string("op_691_begin_0"), val = tensor([0, 28, 0])]; + tensor var_691_end_0 = const()[name = string("op_691_end_0"), val = tensor([1, 42, 1024])]; + tensor var_691_end_mask_0 = const()[name = string("op_691_end_mask_0"), val = tensor([true, true, true])]; + tensor var_691_cast_fp16 = slice_by_index(begin = var_691_begin_0, end = var_691_end_0, end_mask = var_691_end_mask_0, x = cache_5_cast_fp16)[name = string("op_691_cast_fp16")]; + bool var_697_interleave_0 = const()[name = string("op_697_interleave_0"), val = bool(false)]; + tensor var_697_cast_fp16 = concat(axis = var_68, interleave = var_697_interleave_0, values = (var_691_cast_fp16, key_3_cast_fp16))[name = string("op_697_cast_fp16")]; + tensor encoder_layers_1_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37391808))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38440448))))[name = string("encoder_layers_1_self_attn_linear_q_weight_to_fp16_quantized")]; + tensor encoder_layers_1_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_1_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38442560)))]; + tensor linear_12_cast_fp16 = linear(bias = encoder_layers_1_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_1_self_attn_linear_q_weight_to_fp16_quantized, x = key_3_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_702 = const()[name = string("op_702"), val = tensor([1, -1, 8, 128])]; + tensor q_7_cast_fp16 = reshape(shape = var_702, x = linear_12_cast_fp16)[name = string("q_7_cast_fp16")]; + tensor encoder_layers_1_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38444672))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39493312))))[name = string("encoder_layers_1_self_attn_linear_k_weight_to_fp16_quantized")]; + tensor encoder_layers_1_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_1_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39495424)))]; + tensor linear_13_cast_fp16 = linear(bias = encoder_layers_1_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_1_self_attn_linear_k_weight_to_fp16_quantized, x = input_91_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_707 = const()[name = string("op_707"), val = tensor([1, -1, 8, 128])]; + tensor k_5_cast_fp16 = reshape(shape = var_707, x = linear_13_cast_fp16)[name = string("k_5_cast_fp16")]; + tensor encoder_layers_1_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39497536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40546176))))[name = string("encoder_layers_1_self_attn_linear_v_weight_to_fp16_quantized")]; + tensor encoder_layers_1_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_1_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40548288)))]; + tensor linear_14_cast_fp16 = linear(bias = encoder_layers_1_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_1_self_attn_linear_v_weight_to_fp16_quantized, x = input_91_cast_fp16)[name = string("linear_14_cast_fp16")]; + tensor var_712 = const()[name = string("op_712"), val = tensor([1, -1, 8, 128])]; + tensor v_3_cast_fp16 = reshape(shape = var_712, x = linear_14_cast_fp16)[name = string("v_3_cast_fp16")]; + tensor value_11_perm_0 = const()[name = string("value_11_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_1_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_1_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40550400)))]; + tensor var_725_cast_fp16 = add(x = q_7_cast_fp16, y = encoder_layers_1_self_attn_pos_bias_u_to_fp16)[name = string("op_725_cast_fp16")]; + tensor encoder_layers_1_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_1_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40552512)))]; + tensor var_727_cast_fp16 = add(x = q_7_cast_fp16, y = encoder_layers_1_self_attn_pos_bias_v_to_fp16)[name = string("op_727_cast_fp16")]; + tensor q_with_bias_v_3_perm_0 = const()[name = string("q_with_bias_v_3_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_33_transpose_x_0 = const()[name = string("x_33_transpose_x_0"), val = bool(false)]; + bool x_33_transpose_y_0 = const()[name = string("x_33_transpose_y_0"), val = bool(false)]; + tensor op_729_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40554624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40697024))))[name = string("op_729_to_fp16_quantized")]; + tensor q_with_bias_v_3_cast_fp16 = transpose(perm = q_with_bias_v_3_perm_0, x = var_727_cast_fp16)[name = string("transpose_353")]; + tensor x_33_cast_fp16 = matmul(transpose_x = x_33_transpose_x_0, transpose_y = x_33_transpose_y_0, x = q_with_bias_v_3_cast_fp16, y = op_729_to_fp16_quantized)[name = string("x_33_cast_fp16")]; + tensor x_35_pad_0 = const()[name = string("x_35_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_35_mode_0 = const()[name = string("x_35_mode_0"), val = string("constant")]; + fp16 const_92_to_fp16 = const()[name = string("const_92_to_fp16"), val = fp16(0x0p+0)]; + tensor x_35_cast_fp16 = pad(constant_val = const_92_to_fp16, mode = x_35_mode_0, pad = x_35_pad_0, x = x_33_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor var_737 = const()[name = string("op_737"), val = tensor([1, 8, -1, 28])]; + tensor x_37_cast_fp16 = reshape(shape = var_737, x = x_35_cast_fp16)[name = string("x_37_cast_fp16")]; + tensor var_741_begin_0 = const()[name = string("op_741_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_741_end_0 = const()[name = string("op_741_end_0"), val = tensor([1, 8, 140, 28])]; + tensor var_741_end_mask_0 = const()[name = string("op_741_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_741_cast_fp16 = slice_by_index(begin = var_741_begin_0, end = var_741_end_0, end_mask = var_741_end_mask_0, x = x_37_cast_fp16)[name = string("op_741_cast_fp16")]; + tensor var_742 = const()[name = string("op_742"), val = tensor([1, 8, 28, 139])]; + tensor matrix_bd_5_cast_fp16 = reshape(shape = var_742, x = var_741_cast_fp16)[name = string("matrix_bd_5_cast_fp16")]; + bool matrix_ac_3_transpose_x_0 = const()[name = string("matrix_ac_3_transpose_x_0"), val = bool(false)]; + bool matrix_ac_3_transpose_y_0 = const()[name = string("matrix_ac_3_transpose_y_0"), val = bool(false)]; + tensor transpose_98_perm_0 = const()[name = string("transpose_98_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_99_perm_0 = const()[name = string("transpose_99_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_99 = transpose(perm = transpose_99_perm_0, x = k_5_cast_fp16)[name = string("transpose_351")]; + tensor transpose_98 = transpose(perm = transpose_98_perm_0, x = var_725_cast_fp16)[name = string("transpose_352")]; + tensor matrix_ac_3_cast_fp16 = matmul(transpose_x = matrix_ac_3_transpose_x_0, transpose_y = matrix_ac_3_transpose_y_0, x = transpose_98, y = transpose_99)[name = string("matrix_ac_3_cast_fp16")]; + tensor matrix_bd_7_begin_0 = const()[name = string("matrix_bd_7_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_7_end_0 = const()[name = string("matrix_bd_7_end_0"), val = tensor([1, 8, 28, 70])]; + tensor matrix_bd_7_end_mask_0 = const()[name = string("matrix_bd_7_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_7_cast_fp16 = slice_by_index(begin = matrix_bd_7_begin_0, end = matrix_bd_7_end_0, end_mask = matrix_bd_7_end_mask_0, x = matrix_bd_5_cast_fp16)[name = string("matrix_bd_7_cast_fp16")]; + tensor var_751_cast_fp16 = add(x = matrix_ac_3_cast_fp16, y = matrix_bd_7_cast_fp16)[name = string("op_751_cast_fp16")]; + fp16 _inversed_scores_5_y_0_to_fp16 = const()[name = string("_inversed_scores_5_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_5_cast_fp16 = mul(x = var_751_cast_fp16, y = _inversed_scores_5_y_0_to_fp16)[name = string("_inversed_scores_5_cast_fp16")]; + tensor scores_7_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_5_cast_fp16, cond = mask_11)[name = string("scores_7_cast_fp16")]; + tensor var_757_cast_fp16 = softmax(axis = var_59, x = scores_7_cast_fp16)[name = string("op_757_cast_fp16")]; + tensor input_93_cast_fp16 = select(a = var_44_to_fp16, b = var_757_cast_fp16, cond = mask_11)[name = string("input_93_cast_fp16")]; + bool x_39_transpose_x_0 = const()[name = string("x_39_transpose_x_0"), val = bool(false)]; + bool x_39_transpose_y_0 = const()[name = string("x_39_transpose_y_0"), val = bool(false)]; + tensor value_11_cast_fp16 = transpose(perm = value_11_perm_0, x = v_3_cast_fp16)[name = string("transpose_350")]; + tensor x_39_cast_fp16 = matmul(transpose_x = x_39_transpose_x_0, transpose_y = x_39_transpose_y_0, x = input_93_cast_fp16, y = value_11_cast_fp16)[name = string("x_39_cast_fp16")]; + tensor var_761_perm_0 = const()[name = string("op_761_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_762 = const()[name = string("op_762"), val = tensor([1, -1, 1024])]; + tensor var_761_cast_fp16 = transpose(perm = var_761_perm_0, x = x_39_cast_fp16)[name = string("transpose_349")]; + tensor input_95_cast_fp16 = reshape(shape = var_762, x = var_761_cast_fp16)[name = string("input_95_cast_fp16")]; + tensor encoder_layers_1_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40697408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41746048))))[name = string("encoder_layers_1_self_attn_linear_out_weight_to_fp16_quantized")]; + tensor encoder_layers_1_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_1_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41748160)))]; + tensor linear_16_cast_fp16 = linear(bias = encoder_layers_1_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_1_self_attn_linear_out_weight_to_fp16_quantized, x = input_95_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor input_99_cast_fp16 = add(x = input_89_cast_fp16, y = linear_16_cast_fp16)[name = string("input_99_cast_fp16")]; + tensor x_43_axes_0 = const()[name = string("x_43_axes_0"), val = tensor([-1])]; + tensor encoder_layers_1_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_1_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41750272)))]; + tensor encoder_layers_1_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_1_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41752384)))]; + tensor x_43_cast_fp16 = layer_norm(axes = x_43_axes_0, beta = encoder_layers_1_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_1_norm_conv_weight_to_fp16, x = input_99_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor input_101_perm_0 = const()[name = string("input_101_perm_0"), val = tensor([0, 2, 1])]; + string input_103_pad_type_0 = const()[name = string("input_103_pad_type_0"), val = string("valid")]; + tensor input_103_strides_0 = const()[name = string("input_103_strides_0"), val = tensor([1])]; + tensor input_103_pad_0 = const()[name = string("input_103_pad_0"), val = tensor([0, 0])]; + tensor input_103_dilations_0 = const()[name = string("input_103_dilations_0"), val = tensor([1])]; + int32 input_103_groups_0 = const()[name = string("input_103_groups_0"), val = int32(1)]; + tensor encoder_layers_1_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41754496))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43851712))))[name = string("encoder_layers_1_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_101_cast_fp16 = transpose(perm = input_101_perm_0, x = x_43_cast_fp16)[name = string("transpose_348")]; + tensor input_103_cast_fp16 = conv(dilations = input_103_dilations_0, groups = input_103_groups_0, pad = input_103_pad_0, pad_type = input_103_pad_type_0, strides = input_103_strides_0, weight = encoder_layers_1_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_101_cast_fp16)[name = string("input_103_cast_fp16")]; + int32 x_45_split_num_splits_0 = const()[name = string("x_45_split_num_splits_0"), val = int32(2)]; + int32 x_45_split_axis_0 = const()[name = string("x_45_split_axis_0"), val = int32(1)]; + tensor x_45_split_cast_fp16_0, tensor x_45_split_cast_fp16_1 = split(axis = x_45_split_axis_0, num_splits = x_45_split_num_splits_0, x = input_103_cast_fp16)[name = string("x_45_split_cast_fp16")]; + tensor x_45_split_1_sigmoid_cast_fp16 = sigmoid(x = x_45_split_cast_fp16_1)[name = string("x_45_split_1_sigmoid_cast_fp16")]; + tensor x_45_cast_fp16 = mul(x = x_45_split_cast_fp16_0, y = x_45_split_1_sigmoid_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor input_105_cast_fp16 = select(a = var_44_to_fp16, b = x_45_cast_fp16, cond = var_575)[name = string("input_105_cast_fp16")]; + bool new_x_7_interleave_0 = const()[name = string("new_x_7_interleave_0"), val = bool(false)]; + tensor new_x_7_cast_fp16 = concat(axis = var_59, interleave = new_x_7_interleave_0, values = (cache_7_cast_fp16, input_105_cast_fp16))[name = string("new_x_7_cast_fp16")]; + tensor var_801_begin_0 = const()[name = string("op_801_begin_0"), val = tensor([0, 0, 28])]; + tensor var_801_end_0 = const()[name = string("op_801_end_0"), val = tensor([1, 1024, 36])]; + tensor var_801_end_mask_0 = const()[name = string("op_801_end_mask_0"), val = tensor([true, true, true])]; + tensor var_801_cast_fp16 = slice_by_index(begin = var_801_begin_0, end = var_801_end_0, end_mask = var_801_end_mask_0, x = new_x_7_cast_fp16)[name = string("op_801_cast_fp16")]; + string x_47_pad_type_0 = const()[name = string("x_47_pad_type_0"), val = string("valid")]; + int32 x_47_groups_0 = const()[name = string("x_47_groups_0"), val = int32(1024)]; + tensor x_47_strides_0 = const()[name = string("x_47_strides_0"), val = tensor([1])]; + tensor x_47_pad_0 = const()[name = string("x_47_pad_0"), val = tensor([0, 0])]; + tensor x_47_dilations_0 = const()[name = string("x_47_dilations_0"), val = tensor([1])]; + tensor encoder_layers_1_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43855872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43865152))))[name = string("encoder_layers_1_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_47_cast_fp16 = conv(dilations = x_47_dilations_0, groups = x_47_groups_0, pad = x_47_pad_0, pad_type = x_47_pad_type_0, strides = x_47_strides_0, weight = encoder_layers_1_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_7_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor input_107_perm_0 = const()[name = string("input_107_perm_0"), val = tensor([0, 2, 1])]; + tensor x_49_axes_0 = const()[name = string("x_49_axes_0"), val = tensor([-1])]; + tensor encoder_layers_1_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_1_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43867264)))]; + tensor encoder_layers_1_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_1_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43869376)))]; + tensor input_107_cast_fp16 = transpose(perm = input_107_perm_0, x = x_47_cast_fp16)[name = string("transpose_347")]; + tensor x_49_cast_fp16 = layer_norm(axes = x_49_axes_0, beta = encoder_layers_1_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_1_conv_batch_norm_weight_to_fp16, x = input_107_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor input_109_perm_0 = const()[name = string("input_109_perm_0"), val = tensor([0, 2, 1])]; + tensor input_109_cast_fp16 = transpose(perm = input_109_perm_0, x = x_49_cast_fp16)[name = string("transpose_346")]; + tensor input_111_cast_fp16 = silu(x = input_109_cast_fp16)[name = string("input_111_cast_fp16")]; + string x_51_pad_type_0 = const()[name = string("x_51_pad_type_0"), val = string("valid")]; + tensor x_51_strides_0 = const()[name = string("x_51_strides_0"), val = tensor([1])]; + tensor x_51_pad_0 = const()[name = string("x_51_pad_0"), val = tensor([0, 0])]; + tensor x_51_dilations_0 = const()[name = string("x_51_dilations_0"), val = tensor([1])]; + int32 x_51_groups_0 = const()[name = string("x_51_groups_0"), val = int32(1)]; + tensor encoder_layers_1_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43871488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44920128))))[name = string("encoder_layers_1_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_51_cast_fp16 = conv(dilations = x_51_dilations_0, groups = x_51_groups_0, pad = x_51_pad_0, pad_type = x_51_pad_type_0, strides = x_51_strides_0, weight = encoder_layers_1_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_111_cast_fp16)[name = string("x_51_cast_fp16")]; + tensor input_113_perm_0 = const()[name = string("input_113_perm_0"), val = tensor([0, 2, 1])]; + tensor input_113_cast_fp16 = transpose(perm = input_113_perm_0, x = x_51_cast_fp16)[name = string("transpose_345")]; + tensor input_115_cast_fp16 = add(x = input_99_cast_fp16, y = input_113_cast_fp16)[name = string("input_115_cast_fp16")]; + tensor input_117_axes_0 = const()[name = string("input_117_axes_0"), val = tensor([-1])]; + tensor encoder_layers_1_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_1_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44922240)))]; + tensor encoder_layers_1_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_1_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44924352)))]; + tensor input_117_cast_fp16 = layer_norm(axes = input_117_axes_0, beta = encoder_layers_1_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_1_norm_feed_forward2_weight_to_fp16, x = input_115_cast_fp16)[name = string("input_117_cast_fp16")]; + tensor encoder_layers_1_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44926464))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49120832))))[name = string("encoder_layers_1_feed_forward2_linear1_weight_to_fp16_quantized")]; + tensor encoder_layers_1_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_1_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49129088)))]; + tensor linear_17_cast_fp16 = linear(bias = encoder_layers_1_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_1_feed_forward2_linear1_weight_to_fp16_quantized, x = input_117_cast_fp16)[name = string("linear_17_cast_fp16")]; + tensor input_121_cast_fp16 = silu(x = linear_17_cast_fp16)[name = string("input_121_cast_fp16")]; + tensor encoder_layers_1_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49137344))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53331712))))[name = string("encoder_layers_1_feed_forward2_linear2_weight_to_fp16_quantized")]; + tensor encoder_layers_1_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_1_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53333824)))]; + tensor linear_18_cast_fp16 = linear(bias = encoder_layers_1_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_1_feed_forward2_linear2_weight_to_fp16_quantized, x = input_121_cast_fp16)[name = string("linear_18_cast_fp16")]; + fp16 var_844_to_fp16 = const()[name = string("op_844_to_fp16"), val = fp16(0x1p-1)]; + tensor var_845_cast_fp16 = mul(x = linear_18_cast_fp16, y = var_844_to_fp16)[name = string("op_845_cast_fp16")]; + tensor input_127_cast_fp16 = add(x = input_115_cast_fp16, y = var_845_cast_fp16)[name = string("input_127_cast_fp16")]; + tensor input_129_axes_0 = const()[name = string("input_129_axes_0"), val = tensor([-1])]; + tensor encoder_layers_1_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_1_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53335936)))]; + tensor encoder_layers_1_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_1_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53338048)))]; + tensor input_129_cast_fp16 = layer_norm(axes = input_129_axes_0, beta = encoder_layers_1_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_1_norm_out_weight_to_fp16, x = input_127_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor cache_9_begin_0 = const()[name = string("cache_9_begin_0"), val = tensor([2, 0, 0, 0])]; + tensor cache_9_end_0 = const()[name = string("cache_9_end_0"), val = tensor([3, 1, 42, 1024])]; + tensor cache_9_end_mask_0 = const()[name = string("cache_9_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_9_squeeze_mask_0 = const()[name = string("cache_9_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_9_cast_fp16 = slice_by_index(begin = cache_9_begin_0, end = cache_9_end_0, end_mask = cache_9_end_mask_0, squeeze_mask = cache_9_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_9_cast_fp16")]; + tensor cache_11_begin_0 = const()[name = string("cache_11_begin_0"), val = tensor([2, 0, 0, 0])]; + tensor cache_11_end_0 = const()[name = string("cache_11_end_0"), val = tensor([3, 1, 1024, 8])]; + tensor cache_11_end_mask_0 = const()[name = string("cache_11_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_11_squeeze_mask_0 = const()[name = string("cache_11_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_11_cast_fp16 = slice_by_index(begin = cache_11_begin_0, end = cache_11_end_0, end_mask = cache_11_end_mask_0, squeeze_mask = cache_11_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_11_cast_fp16")]; + tensor input_131_axes_0 = const()[name = string("input_131_axes_0"), val = tensor([-1])]; + tensor encoder_layers_2_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_2_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53340160)))]; + tensor encoder_layers_2_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_2_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53342272)))]; + tensor input_131_cast_fp16 = layer_norm(axes = input_131_axes_0, beta = encoder_layers_2_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_2_norm_feed_forward1_weight_to_fp16, x = input_129_cast_fp16)[name = string("input_131_cast_fp16")]; + tensor encoder_layers_2_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53344384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57538752))))[name = string("encoder_layers_2_feed_forward1_linear1_weight_to_fp16_quantized")]; + tensor encoder_layers_2_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_2_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57547008)))]; + tensor linear_19_cast_fp16 = linear(bias = encoder_layers_2_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_2_feed_forward1_linear1_weight_to_fp16_quantized, x = input_131_cast_fp16)[name = string("linear_19_cast_fp16")]; + tensor input_135_cast_fp16 = silu(x = linear_19_cast_fp16)[name = string("input_135_cast_fp16")]; + tensor encoder_layers_2_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57555264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61749632))))[name = string("encoder_layers_2_feed_forward1_linear2_weight_to_fp16_quantized")]; + tensor encoder_layers_2_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_2_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61751744)))]; + tensor linear_20_cast_fp16 = linear(bias = encoder_layers_2_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_2_feed_forward1_linear2_weight_to_fp16_quantized, x = input_135_cast_fp16)[name = string("linear_20_cast_fp16")]; + fp16 var_881_to_fp16 = const()[name = string("op_881_to_fp16"), val = fp16(0x1p-1)]; + tensor var_882_cast_fp16 = mul(x = linear_20_cast_fp16, y = var_881_to_fp16)[name = string("op_882_cast_fp16")]; + tensor input_141_cast_fp16 = add(x = input_129_cast_fp16, y = var_882_cast_fp16)[name = string("input_141_cast_fp16")]; + tensor key_5_axes_0 = const()[name = string("key_5_axes_0"), val = tensor([-1])]; + tensor encoder_layers_2_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_2_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61753856)))]; + tensor encoder_layers_2_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_2_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61755968)))]; + tensor key_5_cast_fp16 = layer_norm(axes = key_5_axes_0, beta = encoder_layers_2_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_2_norm_self_att_weight_to_fp16, x = input_141_cast_fp16)[name = string("key_5_cast_fp16")]; + bool input_143_interleave_0 = const()[name = string("input_143_interleave_0"), val = bool(false)]; + tensor input_143_cast_fp16 = concat(axis = var_68, interleave = input_143_interleave_0, values = (cache_9_cast_fp16, key_5_cast_fp16))[name = string("input_143_cast_fp16")]; + tensor var_904_begin_0 = const()[name = string("op_904_begin_0"), val = tensor([0, 28, 0])]; + tensor var_904_end_0 = const()[name = string("op_904_end_0"), val = tensor([1, 42, 1024])]; + tensor var_904_end_mask_0 = const()[name = string("op_904_end_mask_0"), val = tensor([true, true, true])]; + tensor var_904_cast_fp16 = slice_by_index(begin = var_904_begin_0, end = var_904_end_0, end_mask = var_904_end_mask_0, x = cache_9_cast_fp16)[name = string("op_904_cast_fp16")]; + bool var_910_interleave_0 = const()[name = string("op_910_interleave_0"), val = bool(false)]; + tensor var_910_cast_fp16 = concat(axis = var_68, interleave = var_910_interleave_0, values = (var_904_cast_fp16, key_5_cast_fp16))[name = string("op_910_cast_fp16")]; + tensor encoder_layers_2_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61758080))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62806720))))[name = string("encoder_layers_2_self_attn_linear_q_weight_to_fp16_quantized")]; + tensor encoder_layers_2_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_2_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62808832)))]; + tensor linear_21_cast_fp16 = linear(bias = encoder_layers_2_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_2_self_attn_linear_q_weight_to_fp16_quantized, x = key_5_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_915 = const()[name = string("op_915"), val = tensor([1, -1, 8, 128])]; + tensor q_13_cast_fp16 = reshape(shape = var_915, x = linear_21_cast_fp16)[name = string("q_13_cast_fp16")]; + tensor encoder_layers_2_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62810944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63859584))))[name = string("encoder_layers_2_self_attn_linear_k_weight_to_fp16_quantized")]; + tensor encoder_layers_2_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_2_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63861696)))]; + tensor linear_22_cast_fp16 = linear(bias = encoder_layers_2_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_2_self_attn_linear_k_weight_to_fp16_quantized, x = input_143_cast_fp16)[name = string("linear_22_cast_fp16")]; + tensor var_920 = const()[name = string("op_920"), val = tensor([1, -1, 8, 128])]; + tensor k_9_cast_fp16 = reshape(shape = var_920, x = linear_22_cast_fp16)[name = string("k_9_cast_fp16")]; + tensor encoder_layers_2_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63863808))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64912448))))[name = string("encoder_layers_2_self_attn_linear_v_weight_to_fp16_quantized")]; + tensor encoder_layers_2_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_2_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64914560)))]; + tensor linear_23_cast_fp16 = linear(bias = encoder_layers_2_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_2_self_attn_linear_v_weight_to_fp16_quantized, x = input_143_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor var_925 = const()[name = string("op_925"), val = tensor([1, -1, 8, 128])]; + tensor v_5_cast_fp16 = reshape(shape = var_925, x = linear_23_cast_fp16)[name = string("v_5_cast_fp16")]; + tensor value_13_perm_0 = const()[name = string("value_13_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_2_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_2_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64916672)))]; + tensor var_938_cast_fp16 = add(x = q_13_cast_fp16, y = encoder_layers_2_self_attn_pos_bias_u_to_fp16)[name = string("op_938_cast_fp16")]; + tensor encoder_layers_2_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_2_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64918784)))]; + tensor var_940_cast_fp16 = add(x = q_13_cast_fp16, y = encoder_layers_2_self_attn_pos_bias_v_to_fp16)[name = string("op_940_cast_fp16")]; + tensor q_with_bias_v_5_perm_0 = const()[name = string("q_with_bias_v_5_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_59_transpose_x_0 = const()[name = string("x_59_transpose_x_0"), val = bool(false)]; + bool x_59_transpose_y_0 = const()[name = string("x_59_transpose_y_0"), val = bool(false)]; + tensor op_942_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64920896))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65063296))))[name = string("op_942_to_fp16_quantized")]; + tensor q_with_bias_v_5_cast_fp16 = transpose(perm = q_with_bias_v_5_perm_0, x = var_940_cast_fp16)[name = string("transpose_344")]; + tensor x_59_cast_fp16 = matmul(transpose_x = x_59_transpose_x_0, transpose_y = x_59_transpose_y_0, x = q_with_bias_v_5_cast_fp16, y = op_942_to_fp16_quantized)[name = string("x_59_cast_fp16")]; + tensor x_61_pad_0 = const()[name = string("x_61_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_61_mode_0 = const()[name = string("x_61_mode_0"), val = string("constant")]; + fp16 const_105_to_fp16 = const()[name = string("const_105_to_fp16"), val = fp16(0x0p+0)]; + tensor x_61_cast_fp16 = pad(constant_val = const_105_to_fp16, mode = x_61_mode_0, pad = x_61_pad_0, x = x_59_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor var_950 = const()[name = string("op_950"), val = tensor([1, 8, -1, 28])]; + tensor x_63_cast_fp16 = reshape(shape = var_950, x = x_61_cast_fp16)[name = string("x_63_cast_fp16")]; + tensor var_954_begin_0 = const()[name = string("op_954_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_954_end_0 = const()[name = string("op_954_end_0"), val = tensor([1, 8, 140, 28])]; + tensor var_954_end_mask_0 = const()[name = string("op_954_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_954_cast_fp16 = slice_by_index(begin = var_954_begin_0, end = var_954_end_0, end_mask = var_954_end_mask_0, x = x_63_cast_fp16)[name = string("op_954_cast_fp16")]; + tensor var_955 = const()[name = string("op_955"), val = tensor([1, 8, 28, 139])]; + tensor matrix_bd_9_cast_fp16 = reshape(shape = var_955, x = var_954_cast_fp16)[name = string("matrix_bd_9_cast_fp16")]; + bool matrix_ac_5_transpose_x_0 = const()[name = string("matrix_ac_5_transpose_x_0"), val = bool(false)]; + bool matrix_ac_5_transpose_y_0 = const()[name = string("matrix_ac_5_transpose_y_0"), val = bool(false)]; + tensor transpose_100_perm_0 = const()[name = string("transpose_100_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_101_perm_0 = const()[name = string("transpose_101_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_101 = transpose(perm = transpose_101_perm_0, x = k_9_cast_fp16)[name = string("transpose_342")]; + tensor transpose_100 = transpose(perm = transpose_100_perm_0, x = var_938_cast_fp16)[name = string("transpose_343")]; + tensor matrix_ac_5_cast_fp16 = matmul(transpose_x = matrix_ac_5_transpose_x_0, transpose_y = matrix_ac_5_transpose_y_0, x = transpose_100, y = transpose_101)[name = string("matrix_ac_5_cast_fp16")]; + tensor matrix_bd_11_begin_0 = const()[name = string("matrix_bd_11_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_11_end_0 = const()[name = string("matrix_bd_11_end_0"), val = tensor([1, 8, 28, 70])]; + tensor matrix_bd_11_end_mask_0 = const()[name = string("matrix_bd_11_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_11_cast_fp16 = slice_by_index(begin = matrix_bd_11_begin_0, end = matrix_bd_11_end_0, end_mask = matrix_bd_11_end_mask_0, x = matrix_bd_9_cast_fp16)[name = string("matrix_bd_11_cast_fp16")]; + tensor var_964_cast_fp16 = add(x = matrix_ac_5_cast_fp16, y = matrix_bd_11_cast_fp16)[name = string("op_964_cast_fp16")]; + fp16 _inversed_scores_9_y_0_to_fp16 = const()[name = string("_inversed_scores_9_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_9_cast_fp16 = mul(x = var_964_cast_fp16, y = _inversed_scores_9_y_0_to_fp16)[name = string("_inversed_scores_9_cast_fp16")]; + tensor scores_11_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_9_cast_fp16, cond = mask_11)[name = string("scores_11_cast_fp16")]; + tensor var_970_cast_fp16 = softmax(axis = var_59, x = scores_11_cast_fp16)[name = string("op_970_cast_fp16")]; + tensor input_145_cast_fp16 = select(a = var_44_to_fp16, b = var_970_cast_fp16, cond = mask_11)[name = string("input_145_cast_fp16")]; + bool x_65_transpose_x_0 = const()[name = string("x_65_transpose_x_0"), val = bool(false)]; + bool x_65_transpose_y_0 = const()[name = string("x_65_transpose_y_0"), val = bool(false)]; + tensor value_13_cast_fp16 = transpose(perm = value_13_perm_0, x = v_5_cast_fp16)[name = string("transpose_341")]; + tensor x_65_cast_fp16 = matmul(transpose_x = x_65_transpose_x_0, transpose_y = x_65_transpose_y_0, x = input_145_cast_fp16, y = value_13_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor var_974_perm_0 = const()[name = string("op_974_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_975 = const()[name = string("op_975"), val = tensor([1, -1, 1024])]; + tensor var_974_cast_fp16 = transpose(perm = var_974_perm_0, x = x_65_cast_fp16)[name = string("transpose_340")]; + tensor input_147_cast_fp16 = reshape(shape = var_975, x = var_974_cast_fp16)[name = string("input_147_cast_fp16")]; + tensor encoder_layers_2_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65063680))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65850176))))[name = string("encoder_layers_2_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_2_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_2_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65850368)))]; + tensor linear_25_cast_fp16 = linear(bias = encoder_layers_2_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_2_self_attn_linear_out_weight_to_fp16_palettized, x = input_147_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor input_151_cast_fp16 = add(x = input_141_cast_fp16, y = linear_25_cast_fp16)[name = string("input_151_cast_fp16")]; + tensor x_69_axes_0 = const()[name = string("x_69_axes_0"), val = tensor([-1])]; + tensor encoder_layers_2_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_2_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65852480)))]; + tensor encoder_layers_2_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_2_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65854592)))]; + tensor x_69_cast_fp16 = layer_norm(axes = x_69_axes_0, beta = encoder_layers_2_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_2_norm_conv_weight_to_fp16, x = input_151_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor input_153_perm_0 = const()[name = string("input_153_perm_0"), val = tensor([0, 2, 1])]; + string input_155_pad_type_0 = const()[name = string("input_155_pad_type_0"), val = string("valid")]; + tensor input_155_strides_0 = const()[name = string("input_155_strides_0"), val = tensor([1])]; + tensor input_155_pad_0 = const()[name = string("input_155_pad_0"), val = tensor([0, 0])]; + tensor input_155_dilations_0 = const()[name = string("input_155_dilations_0"), val = tensor([1])]; + int32 input_155_groups_0 = const()[name = string("input_155_groups_0"), val = int32(1)]; + tensor encoder_layers_2_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65856704))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67953920))))[name = string("encoder_layers_2_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_153_cast_fp16 = transpose(perm = input_153_perm_0, x = x_69_cast_fp16)[name = string("transpose_339")]; + tensor input_155_cast_fp16 = conv(dilations = input_155_dilations_0, groups = input_155_groups_0, pad = input_155_pad_0, pad_type = input_155_pad_type_0, strides = input_155_strides_0, weight = encoder_layers_2_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_153_cast_fp16)[name = string("input_155_cast_fp16")]; + int32 x_71_split_num_splits_0 = const()[name = string("x_71_split_num_splits_0"), val = int32(2)]; + int32 x_71_split_axis_0 = const()[name = string("x_71_split_axis_0"), val = int32(1)]; + tensor x_71_split_cast_fp16_0, tensor x_71_split_cast_fp16_1 = split(axis = x_71_split_axis_0, num_splits = x_71_split_num_splits_0, x = input_155_cast_fp16)[name = string("x_71_split_cast_fp16")]; + tensor x_71_split_1_sigmoid_cast_fp16 = sigmoid(x = x_71_split_cast_fp16_1)[name = string("x_71_split_1_sigmoid_cast_fp16")]; + tensor x_71_cast_fp16 = mul(x = x_71_split_cast_fp16_0, y = x_71_split_1_sigmoid_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor input_157_cast_fp16 = select(a = var_44_to_fp16, b = x_71_cast_fp16, cond = var_575)[name = string("input_157_cast_fp16")]; + bool new_x_11_interleave_0 = const()[name = string("new_x_11_interleave_0"), val = bool(false)]; + tensor new_x_11_cast_fp16 = concat(axis = var_59, interleave = new_x_11_interleave_0, values = (cache_11_cast_fp16, input_157_cast_fp16))[name = string("new_x_11_cast_fp16")]; + tensor var_1014_begin_0 = const()[name = string("op_1014_begin_0"), val = tensor([0, 0, 28])]; + tensor var_1014_end_0 = const()[name = string("op_1014_end_0"), val = tensor([1, 1024, 36])]; + tensor var_1014_end_mask_0 = const()[name = string("op_1014_end_mask_0"), val = tensor([true, true, true])]; + tensor var_1014_cast_fp16 = slice_by_index(begin = var_1014_begin_0, end = var_1014_end_0, end_mask = var_1014_end_mask_0, x = new_x_11_cast_fp16)[name = string("op_1014_cast_fp16")]; + string x_73_pad_type_0 = const()[name = string("x_73_pad_type_0"), val = string("valid")]; + int32 x_73_groups_0 = const()[name = string("x_73_groups_0"), val = int32(1024)]; + tensor x_73_strides_0 = const()[name = string("x_73_strides_0"), val = tensor([1])]; + tensor x_73_pad_0 = const()[name = string("x_73_pad_0"), val = tensor([0, 0])]; + tensor x_73_dilations_0 = const()[name = string("x_73_dilations_0"), val = tensor([1])]; + tensor encoder_layers_2_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67958080))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67967360))))[name = string("encoder_layers_2_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_73_cast_fp16 = conv(dilations = x_73_dilations_0, groups = x_73_groups_0, pad = x_73_pad_0, pad_type = x_73_pad_type_0, strides = x_73_strides_0, weight = encoder_layers_2_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_11_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor input_159_perm_0 = const()[name = string("input_159_perm_0"), val = tensor([0, 2, 1])]; + tensor x_75_axes_0 = const()[name = string("x_75_axes_0"), val = tensor([-1])]; + tensor encoder_layers_2_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_2_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67969472)))]; + tensor encoder_layers_2_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_2_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67971584)))]; + tensor input_159_cast_fp16 = transpose(perm = input_159_perm_0, x = x_73_cast_fp16)[name = string("transpose_338")]; + tensor x_75_cast_fp16 = layer_norm(axes = x_75_axes_0, beta = encoder_layers_2_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_2_conv_batch_norm_weight_to_fp16, x = input_159_cast_fp16)[name = string("x_75_cast_fp16")]; + tensor input_161_perm_0 = const()[name = string("input_161_perm_0"), val = tensor([0, 2, 1])]; + tensor input_161_cast_fp16 = transpose(perm = input_161_perm_0, x = x_75_cast_fp16)[name = string("transpose_337")]; + tensor input_163_cast_fp16 = silu(x = input_161_cast_fp16)[name = string("input_163_cast_fp16")]; + string x_77_pad_type_0 = const()[name = string("x_77_pad_type_0"), val = string("valid")]; + tensor x_77_strides_0 = const()[name = string("x_77_strides_0"), val = tensor([1])]; + tensor x_77_pad_0 = const()[name = string("x_77_pad_0"), val = tensor([0, 0])]; + tensor x_77_dilations_0 = const()[name = string("x_77_dilations_0"), val = tensor([1])]; + int32 x_77_groups_0 = const()[name = string("x_77_groups_0"), val = int32(1)]; + tensor encoder_layers_2_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67973696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69022336))))[name = string("encoder_layers_2_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_77_cast_fp16 = conv(dilations = x_77_dilations_0, groups = x_77_groups_0, pad = x_77_pad_0, pad_type = x_77_pad_type_0, strides = x_77_strides_0, weight = encoder_layers_2_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_163_cast_fp16)[name = string("x_77_cast_fp16")]; + tensor input_165_perm_0 = const()[name = string("input_165_perm_0"), val = tensor([0, 2, 1])]; + tensor input_165_cast_fp16 = transpose(perm = input_165_perm_0, x = x_77_cast_fp16)[name = string("transpose_336")]; + tensor input_167_cast_fp16 = add(x = input_151_cast_fp16, y = input_165_cast_fp16)[name = string("input_167_cast_fp16")]; + tensor input_169_axes_0 = const()[name = string("input_169_axes_0"), val = tensor([-1])]; + tensor encoder_layers_2_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_2_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69024448)))]; + tensor encoder_layers_2_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_2_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69026560)))]; + tensor input_169_cast_fp16 = layer_norm(axes = input_169_axes_0, beta = encoder_layers_2_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_2_norm_feed_forward2_weight_to_fp16, x = input_167_cast_fp16)[name = string("input_169_cast_fp16")]; + tensor encoder_layers_2_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69028672))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72174464))))[name = string("encoder_layers_2_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_2_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_2_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72174656)))]; + tensor linear_26_cast_fp16 = linear(bias = encoder_layers_2_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_2_feed_forward2_linear1_weight_to_fp16_palettized, x = input_169_cast_fp16)[name = string("linear_26_cast_fp16")]; + tensor input_173_cast_fp16 = silu(x = linear_26_cast_fp16)[name = string("input_173_cast_fp16")]; + tensor encoder_layers_2_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72182912))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75328704))))[name = string("encoder_layers_2_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_2_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_2_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75328896)))]; + tensor linear_27_cast_fp16 = linear(bias = encoder_layers_2_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_2_feed_forward2_linear2_weight_to_fp16_palettized, x = input_173_cast_fp16)[name = string("linear_27_cast_fp16")]; + fp16 var_1057_to_fp16 = const()[name = string("op_1057_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1058_cast_fp16 = mul(x = linear_27_cast_fp16, y = var_1057_to_fp16)[name = string("op_1058_cast_fp16")]; + tensor input_179_cast_fp16 = add(x = input_167_cast_fp16, y = var_1058_cast_fp16)[name = string("input_179_cast_fp16")]; + tensor input_181_axes_0 = const()[name = string("input_181_axes_0"), val = tensor([-1])]; + tensor encoder_layers_2_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_2_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75331008)))]; + tensor encoder_layers_2_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_2_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75333120)))]; + tensor input_181_cast_fp16 = layer_norm(axes = input_181_axes_0, beta = encoder_layers_2_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_2_norm_out_weight_to_fp16, x = input_179_cast_fp16)[name = string("input_181_cast_fp16")]; + tensor cache_13_begin_0 = const()[name = string("cache_13_begin_0"), val = tensor([3, 0, 0, 0])]; + tensor cache_13_end_0 = const()[name = string("cache_13_end_0"), val = tensor([4, 1, 42, 1024])]; + tensor cache_13_end_mask_0 = const()[name = string("cache_13_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_13_squeeze_mask_0 = const()[name = string("cache_13_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_13_cast_fp16 = slice_by_index(begin = cache_13_begin_0, end = cache_13_end_0, end_mask = cache_13_end_mask_0, squeeze_mask = cache_13_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_13_cast_fp16")]; + tensor cache_15_begin_0 = const()[name = string("cache_15_begin_0"), val = tensor([3, 0, 0, 0])]; + tensor cache_15_end_0 = const()[name = string("cache_15_end_0"), val = tensor([4, 1, 1024, 8])]; + tensor cache_15_end_mask_0 = const()[name = string("cache_15_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_15_squeeze_mask_0 = const()[name = string("cache_15_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_15_cast_fp16 = slice_by_index(begin = cache_15_begin_0, end = cache_15_end_0, end_mask = cache_15_end_mask_0, squeeze_mask = cache_15_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_15_cast_fp16")]; + tensor input_183_axes_0 = const()[name = string("input_183_axes_0"), val = tensor([-1])]; + tensor encoder_layers_3_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_3_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75335232)))]; + tensor encoder_layers_3_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_3_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75337344)))]; + tensor input_183_cast_fp16 = layer_norm(axes = input_183_axes_0, beta = encoder_layers_3_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_3_norm_feed_forward1_weight_to_fp16, x = input_181_cast_fp16)[name = string("input_183_cast_fp16")]; + tensor encoder_layers_3_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75339456))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78485248))))[name = string("encoder_layers_3_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_3_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_3_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78485440)))]; + tensor linear_28_cast_fp16 = linear(bias = encoder_layers_3_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_3_feed_forward1_linear1_weight_to_fp16_palettized, x = input_183_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor input_187_cast_fp16 = silu(x = linear_28_cast_fp16)[name = string("input_187_cast_fp16")]; + tensor encoder_layers_3_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78493696))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81639488))))[name = string("encoder_layers_3_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_3_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_3_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81639680)))]; + tensor linear_29_cast_fp16 = linear(bias = encoder_layers_3_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_3_feed_forward1_linear2_weight_to_fp16_palettized, x = input_187_cast_fp16)[name = string("linear_29_cast_fp16")]; + fp16 var_1094_to_fp16 = const()[name = string("op_1094_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1095_cast_fp16 = mul(x = linear_29_cast_fp16, y = var_1094_to_fp16)[name = string("op_1095_cast_fp16")]; + tensor input_193_cast_fp16 = add(x = input_181_cast_fp16, y = var_1095_cast_fp16)[name = string("input_193_cast_fp16")]; + tensor key_7_axes_0 = const()[name = string("key_7_axes_0"), val = tensor([-1])]; + tensor encoder_layers_3_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_3_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81641792)))]; + tensor encoder_layers_3_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_3_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81643904)))]; + tensor key_7_cast_fp16 = layer_norm(axes = key_7_axes_0, beta = encoder_layers_3_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_3_norm_self_att_weight_to_fp16, x = input_193_cast_fp16)[name = string("key_7_cast_fp16")]; + bool input_195_interleave_0 = const()[name = string("input_195_interleave_0"), val = bool(false)]; + tensor input_195_cast_fp16 = concat(axis = var_68, interleave = input_195_interleave_0, values = (cache_13_cast_fp16, key_7_cast_fp16))[name = string("input_195_cast_fp16")]; + tensor var_1117_begin_0 = const()[name = string("op_1117_begin_0"), val = tensor([0, 28, 0])]; + tensor var_1117_end_0 = const()[name = string("op_1117_end_0"), val = tensor([1, 42, 1024])]; + tensor var_1117_end_mask_0 = const()[name = string("op_1117_end_mask_0"), val = tensor([true, true, true])]; + tensor var_1117_cast_fp16 = slice_by_index(begin = var_1117_begin_0, end = var_1117_end_0, end_mask = var_1117_end_mask_0, x = cache_13_cast_fp16)[name = string("op_1117_cast_fp16")]; + bool var_1123_interleave_0 = const()[name = string("op_1123_interleave_0"), val = bool(false)]; + tensor var_1123_cast_fp16 = concat(axis = var_68, interleave = var_1123_interleave_0, values = (var_1117_cast_fp16, key_7_cast_fp16))[name = string("op_1123_cast_fp16")]; + tensor encoder_layers_3_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81646016))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82432512))))[name = string("encoder_layers_3_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_3_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_3_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82432704)))]; + tensor linear_30_cast_fp16 = linear(bias = encoder_layers_3_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_3_self_attn_linear_q_weight_to_fp16_palettized, x = key_7_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor var_1128 = const()[name = string("op_1128"), val = tensor([1, -1, 8, 128])]; + tensor q_19_cast_fp16 = reshape(shape = var_1128, x = linear_30_cast_fp16)[name = string("q_19_cast_fp16")]; + tensor encoder_layers_3_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82434816))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83221312))))[name = string("encoder_layers_3_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_3_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_3_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83221504)))]; + tensor linear_31_cast_fp16 = linear(bias = encoder_layers_3_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_3_self_attn_linear_k_weight_to_fp16_palettized, x = input_195_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor var_1133 = const()[name = string("op_1133"), val = tensor([1, -1, 8, 128])]; + tensor k_13_cast_fp16 = reshape(shape = var_1133, x = linear_31_cast_fp16)[name = string("k_13_cast_fp16")]; + tensor encoder_layers_3_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83223616))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84010112))))[name = string("encoder_layers_3_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_3_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_3_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84010304)))]; + tensor linear_32_cast_fp16 = linear(bias = encoder_layers_3_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_3_self_attn_linear_v_weight_to_fp16_palettized, x = input_195_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor var_1138 = const()[name = string("op_1138"), val = tensor([1, -1, 8, 128])]; + tensor v_7_cast_fp16 = reshape(shape = var_1138, x = linear_32_cast_fp16)[name = string("v_7_cast_fp16")]; + tensor value_15_perm_0 = const()[name = string("value_15_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_3_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_3_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84012416)))]; + tensor var_1151_cast_fp16 = add(x = q_19_cast_fp16, y = encoder_layers_3_self_attn_pos_bias_u_to_fp16)[name = string("op_1151_cast_fp16")]; + tensor encoder_layers_3_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_3_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84014528)))]; + tensor var_1153_cast_fp16 = add(x = q_19_cast_fp16, y = encoder_layers_3_self_attn_pos_bias_v_to_fp16)[name = string("op_1153_cast_fp16")]; + tensor q_with_bias_v_7_perm_0 = const()[name = string("q_with_bias_v_7_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_85_transpose_x_0 = const()[name = string("x_85_transpose_x_0"), val = bool(false)]; + bool x_85_transpose_y_0 = const()[name = string("x_85_transpose_y_0"), val = bool(false)]; + tensor op_1155_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84016640))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84159040))))[name = string("op_1155_to_fp16_quantized")]; + tensor q_with_bias_v_7_cast_fp16 = transpose(perm = q_with_bias_v_7_perm_0, x = var_1153_cast_fp16)[name = string("transpose_335")]; + tensor x_85_cast_fp16 = matmul(transpose_x = x_85_transpose_x_0, transpose_y = x_85_transpose_y_0, x = q_with_bias_v_7_cast_fp16, y = op_1155_to_fp16_quantized)[name = string("x_85_cast_fp16")]; + tensor x_87_pad_0 = const()[name = string("x_87_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_87_mode_0 = const()[name = string("x_87_mode_0"), val = string("constant")]; + fp16 const_118_to_fp16 = const()[name = string("const_118_to_fp16"), val = fp16(0x0p+0)]; + tensor x_87_cast_fp16 = pad(constant_val = const_118_to_fp16, mode = x_87_mode_0, pad = x_87_pad_0, x = x_85_cast_fp16)[name = string("x_87_cast_fp16")]; + tensor var_1163 = const()[name = string("op_1163"), val = tensor([1, 8, -1, 28])]; + tensor x_89_cast_fp16 = reshape(shape = var_1163, x = x_87_cast_fp16)[name = string("x_89_cast_fp16")]; + tensor var_1167_begin_0 = const()[name = string("op_1167_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_1167_end_0 = const()[name = string("op_1167_end_0"), val = tensor([1, 8, 140, 28])]; + tensor var_1167_end_mask_0 = const()[name = string("op_1167_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1167_cast_fp16 = slice_by_index(begin = var_1167_begin_0, end = var_1167_end_0, end_mask = var_1167_end_mask_0, x = x_89_cast_fp16)[name = string("op_1167_cast_fp16")]; + tensor var_1168 = const()[name = string("op_1168"), val = tensor([1, 8, 28, 139])]; + tensor matrix_bd_13_cast_fp16 = reshape(shape = var_1168, x = var_1167_cast_fp16)[name = string("matrix_bd_13_cast_fp16")]; + bool matrix_ac_7_transpose_x_0 = const()[name = string("matrix_ac_7_transpose_x_0"), val = bool(false)]; + bool matrix_ac_7_transpose_y_0 = const()[name = string("matrix_ac_7_transpose_y_0"), val = bool(false)]; + tensor transpose_102_perm_0 = const()[name = string("transpose_102_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_103_perm_0 = const()[name = string("transpose_103_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_103 = transpose(perm = transpose_103_perm_0, x = k_13_cast_fp16)[name = string("transpose_333")]; + tensor transpose_102 = transpose(perm = transpose_102_perm_0, x = var_1151_cast_fp16)[name = string("transpose_334")]; + tensor matrix_ac_7_cast_fp16 = matmul(transpose_x = matrix_ac_7_transpose_x_0, transpose_y = matrix_ac_7_transpose_y_0, x = transpose_102, y = transpose_103)[name = string("matrix_ac_7_cast_fp16")]; + tensor matrix_bd_15_begin_0 = const()[name = string("matrix_bd_15_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_15_end_0 = const()[name = string("matrix_bd_15_end_0"), val = tensor([1, 8, 28, 70])]; + tensor matrix_bd_15_end_mask_0 = const()[name = string("matrix_bd_15_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_15_cast_fp16 = slice_by_index(begin = matrix_bd_15_begin_0, end = matrix_bd_15_end_0, end_mask = matrix_bd_15_end_mask_0, x = matrix_bd_13_cast_fp16)[name = string("matrix_bd_15_cast_fp16")]; + tensor var_1177_cast_fp16 = add(x = matrix_ac_7_cast_fp16, y = matrix_bd_15_cast_fp16)[name = string("op_1177_cast_fp16")]; + fp16 _inversed_scores_13_y_0_to_fp16 = const()[name = string("_inversed_scores_13_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_13_cast_fp16 = mul(x = var_1177_cast_fp16, y = _inversed_scores_13_y_0_to_fp16)[name = string("_inversed_scores_13_cast_fp16")]; + tensor scores_15_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_13_cast_fp16, cond = mask_11)[name = string("scores_15_cast_fp16")]; + tensor var_1183_cast_fp16 = softmax(axis = var_59, x = scores_15_cast_fp16)[name = string("op_1183_cast_fp16")]; + tensor input_197_cast_fp16 = select(a = var_44_to_fp16, b = var_1183_cast_fp16, cond = mask_11)[name = string("input_197_cast_fp16")]; + bool x_91_transpose_x_0 = const()[name = string("x_91_transpose_x_0"), val = bool(false)]; + bool x_91_transpose_y_0 = const()[name = string("x_91_transpose_y_0"), val = bool(false)]; + tensor value_15_cast_fp16 = transpose(perm = value_15_perm_0, x = v_7_cast_fp16)[name = string("transpose_332")]; + tensor x_91_cast_fp16 = matmul(transpose_x = x_91_transpose_x_0, transpose_y = x_91_transpose_y_0, x = input_197_cast_fp16, y = value_15_cast_fp16)[name = string("x_91_cast_fp16")]; + tensor var_1187_perm_0 = const()[name = string("op_1187_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1188 = const()[name = string("op_1188"), val = tensor([1, -1, 1024])]; + tensor var_1187_cast_fp16 = transpose(perm = var_1187_perm_0, x = x_91_cast_fp16)[name = string("transpose_331")]; + tensor input_199_cast_fp16 = reshape(shape = var_1188, x = var_1187_cast_fp16)[name = string("input_199_cast_fp16")]; + tensor encoder_layers_3_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84159424))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84945920))))[name = string("encoder_layers_3_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_3_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_3_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84946112)))]; + tensor linear_34_cast_fp16 = linear(bias = encoder_layers_3_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_3_self_attn_linear_out_weight_to_fp16_palettized, x = input_199_cast_fp16)[name = string("linear_34_cast_fp16")]; + tensor input_203_cast_fp16 = add(x = input_193_cast_fp16, y = linear_34_cast_fp16)[name = string("input_203_cast_fp16")]; + tensor x_95_axes_0 = const()[name = string("x_95_axes_0"), val = tensor([-1])]; + tensor encoder_layers_3_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_3_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84948224)))]; + tensor encoder_layers_3_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_3_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84950336)))]; + tensor x_95_cast_fp16 = layer_norm(axes = x_95_axes_0, beta = encoder_layers_3_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_3_norm_conv_weight_to_fp16, x = input_203_cast_fp16)[name = string("x_95_cast_fp16")]; + tensor input_205_perm_0 = const()[name = string("input_205_perm_0"), val = tensor([0, 2, 1])]; + string input_207_pad_type_0 = const()[name = string("input_207_pad_type_0"), val = string("valid")]; + tensor input_207_strides_0 = const()[name = string("input_207_strides_0"), val = tensor([1])]; + tensor input_207_pad_0 = const()[name = string("input_207_pad_0"), val = tensor([0, 0])]; + tensor input_207_dilations_0 = const()[name = string("input_207_dilations_0"), val = tensor([1])]; + int32 input_207_groups_0 = const()[name = string("input_207_groups_0"), val = int32(1)]; + tensor encoder_layers_3_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84952448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(87049664))))[name = string("encoder_layers_3_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_205_cast_fp16 = transpose(perm = input_205_perm_0, x = x_95_cast_fp16)[name = string("transpose_330")]; + tensor input_207_cast_fp16 = conv(dilations = input_207_dilations_0, groups = input_207_groups_0, pad = input_207_pad_0, pad_type = input_207_pad_type_0, strides = input_207_strides_0, weight = encoder_layers_3_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_205_cast_fp16)[name = string("input_207_cast_fp16")]; + int32 x_97_split_num_splits_0 = const()[name = string("x_97_split_num_splits_0"), val = int32(2)]; + int32 x_97_split_axis_0 = const()[name = string("x_97_split_axis_0"), val = int32(1)]; + tensor x_97_split_cast_fp16_0, tensor x_97_split_cast_fp16_1 = split(axis = x_97_split_axis_0, num_splits = x_97_split_num_splits_0, x = input_207_cast_fp16)[name = string("x_97_split_cast_fp16")]; + tensor x_97_split_1_sigmoid_cast_fp16 = sigmoid(x = x_97_split_cast_fp16_1)[name = string("x_97_split_1_sigmoid_cast_fp16")]; + tensor x_97_cast_fp16 = mul(x = x_97_split_cast_fp16_0, y = x_97_split_1_sigmoid_cast_fp16)[name = string("x_97_cast_fp16")]; + tensor input_209_cast_fp16 = select(a = var_44_to_fp16, b = x_97_cast_fp16, cond = var_575)[name = string("input_209_cast_fp16")]; + bool new_x_15_interleave_0 = const()[name = string("new_x_15_interleave_0"), val = bool(false)]; + tensor new_x_15_cast_fp16 = concat(axis = var_59, interleave = new_x_15_interleave_0, values = (cache_15_cast_fp16, input_209_cast_fp16))[name = string("new_x_15_cast_fp16")]; + tensor var_1227_begin_0 = const()[name = string("op_1227_begin_0"), val = tensor([0, 0, 28])]; + tensor var_1227_end_0 = const()[name = string("op_1227_end_0"), val = tensor([1, 1024, 36])]; + tensor var_1227_end_mask_0 = const()[name = string("op_1227_end_mask_0"), val = tensor([true, true, true])]; + tensor var_1227_cast_fp16 = slice_by_index(begin = var_1227_begin_0, end = var_1227_end_0, end_mask = var_1227_end_mask_0, x = new_x_15_cast_fp16)[name = string("op_1227_cast_fp16")]; + string x_99_pad_type_0 = const()[name = string("x_99_pad_type_0"), val = string("valid")]; + int32 x_99_groups_0 = const()[name = string("x_99_groups_0"), val = int32(1024)]; + tensor x_99_strides_0 = const()[name = string("x_99_strides_0"), val = tensor([1])]; + tensor x_99_pad_0 = const()[name = string("x_99_pad_0"), val = tensor([0, 0])]; + tensor x_99_dilations_0 = const()[name = string("x_99_dilations_0"), val = tensor([1])]; + tensor encoder_layers_3_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(87053824))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(87063104))))[name = string("encoder_layers_3_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_99_cast_fp16 = conv(dilations = x_99_dilations_0, groups = x_99_groups_0, pad = x_99_pad_0, pad_type = x_99_pad_type_0, strides = x_99_strides_0, weight = encoder_layers_3_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_15_cast_fp16)[name = string("x_99_cast_fp16")]; + tensor input_211_perm_0 = const()[name = string("input_211_perm_0"), val = tensor([0, 2, 1])]; + tensor x_101_axes_0 = const()[name = string("x_101_axes_0"), val = tensor([-1])]; + tensor encoder_layers_3_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_3_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(87065216)))]; + tensor encoder_layers_3_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_3_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(87067328)))]; + tensor input_211_cast_fp16 = transpose(perm = input_211_perm_0, x = x_99_cast_fp16)[name = string("transpose_329")]; + tensor x_101_cast_fp16 = layer_norm(axes = x_101_axes_0, beta = encoder_layers_3_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_3_conv_batch_norm_weight_to_fp16, x = input_211_cast_fp16)[name = string("x_101_cast_fp16")]; + tensor input_213_perm_0 = const()[name = string("input_213_perm_0"), val = tensor([0, 2, 1])]; + tensor input_213_cast_fp16 = transpose(perm = input_213_perm_0, x = x_101_cast_fp16)[name = string("transpose_328")]; + tensor input_215_cast_fp16 = silu(x = input_213_cast_fp16)[name = string("input_215_cast_fp16")]; + string x_103_pad_type_0 = const()[name = string("x_103_pad_type_0"), val = string("valid")]; + tensor x_103_strides_0 = const()[name = string("x_103_strides_0"), val = tensor([1])]; + tensor x_103_pad_0 = const()[name = string("x_103_pad_0"), val = tensor([0, 0])]; + tensor x_103_dilations_0 = const()[name = string("x_103_dilations_0"), val = tensor([1])]; + int32 x_103_groups_0 = const()[name = string("x_103_groups_0"), val = int32(1)]; + tensor encoder_layers_3_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(87069440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88118080))))[name = string("encoder_layers_3_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_103_cast_fp16 = conv(dilations = x_103_dilations_0, groups = x_103_groups_0, pad = x_103_pad_0, pad_type = x_103_pad_type_0, strides = x_103_strides_0, weight = encoder_layers_3_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_215_cast_fp16)[name = string("x_103_cast_fp16")]; + tensor input_217_perm_0 = const()[name = string("input_217_perm_0"), val = tensor([0, 2, 1])]; + tensor input_217_cast_fp16 = transpose(perm = input_217_perm_0, x = x_103_cast_fp16)[name = string("transpose_327")]; + tensor input_219_cast_fp16 = add(x = input_203_cast_fp16, y = input_217_cast_fp16)[name = string("input_219_cast_fp16")]; + tensor input_221_axes_0 = const()[name = string("input_221_axes_0"), val = tensor([-1])]; + tensor encoder_layers_3_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_3_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88120192)))]; + tensor encoder_layers_3_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_3_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88122304)))]; + tensor input_221_cast_fp16 = layer_norm(axes = input_221_axes_0, beta = encoder_layers_3_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_3_norm_feed_forward2_weight_to_fp16, x = input_219_cast_fp16)[name = string("input_221_cast_fp16")]; + tensor encoder_layers_3_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88124416))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91270208))))[name = string("encoder_layers_3_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_3_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_3_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91270400)))]; + tensor linear_35_cast_fp16 = linear(bias = encoder_layers_3_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_3_feed_forward2_linear1_weight_to_fp16_palettized, x = input_221_cast_fp16)[name = string("linear_35_cast_fp16")]; + tensor input_225_cast_fp16 = silu(x = linear_35_cast_fp16)[name = string("input_225_cast_fp16")]; + tensor encoder_layers_3_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91278656))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94424448))))[name = string("encoder_layers_3_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_3_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_3_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94424640)))]; + tensor linear_36_cast_fp16 = linear(bias = encoder_layers_3_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_3_feed_forward2_linear2_weight_to_fp16_palettized, x = input_225_cast_fp16)[name = string("linear_36_cast_fp16")]; + fp16 var_1270_to_fp16 = const()[name = string("op_1270_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1271_cast_fp16 = mul(x = linear_36_cast_fp16, y = var_1270_to_fp16)[name = string("op_1271_cast_fp16")]; + tensor input_231_cast_fp16 = add(x = input_219_cast_fp16, y = var_1271_cast_fp16)[name = string("input_231_cast_fp16")]; + tensor input_233_axes_0 = const()[name = string("input_233_axes_0"), val = tensor([-1])]; + tensor encoder_layers_3_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_3_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94426752)))]; + tensor encoder_layers_3_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_3_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94428864)))]; + tensor input_233_cast_fp16 = layer_norm(axes = input_233_axes_0, beta = encoder_layers_3_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_3_norm_out_weight_to_fp16, x = input_231_cast_fp16)[name = string("input_233_cast_fp16")]; + tensor cache_17_begin_0 = const()[name = string("cache_17_begin_0"), val = tensor([4, 0, 0, 0])]; + tensor cache_17_end_0 = const()[name = string("cache_17_end_0"), val = tensor([5, 1, 42, 1024])]; + tensor cache_17_end_mask_0 = const()[name = string("cache_17_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_17_squeeze_mask_0 = const()[name = string("cache_17_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_17_cast_fp16 = slice_by_index(begin = cache_17_begin_0, end = cache_17_end_0, end_mask = cache_17_end_mask_0, squeeze_mask = cache_17_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_17_cast_fp16")]; + tensor cache_19_begin_0 = const()[name = string("cache_19_begin_0"), val = tensor([4, 0, 0, 0])]; + tensor cache_19_end_0 = const()[name = string("cache_19_end_0"), val = tensor([5, 1, 1024, 8])]; + tensor cache_19_end_mask_0 = const()[name = string("cache_19_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_19_squeeze_mask_0 = const()[name = string("cache_19_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_19_cast_fp16 = slice_by_index(begin = cache_19_begin_0, end = cache_19_end_0, end_mask = cache_19_end_mask_0, squeeze_mask = cache_19_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_19_cast_fp16")]; + tensor input_235_axes_0 = const()[name = string("input_235_axes_0"), val = tensor([-1])]; + tensor encoder_layers_4_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_4_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94430976)))]; + tensor encoder_layers_4_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_4_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94433088)))]; + tensor input_235_cast_fp16 = layer_norm(axes = input_235_axes_0, beta = encoder_layers_4_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_4_norm_feed_forward1_weight_to_fp16, x = input_233_cast_fp16)[name = string("input_235_cast_fp16")]; + tensor encoder_layers_4_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94435200))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97580992))))[name = string("encoder_layers_4_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_4_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_4_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97581184)))]; + tensor linear_37_cast_fp16 = linear(bias = encoder_layers_4_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_4_feed_forward1_linear1_weight_to_fp16_palettized, x = input_235_cast_fp16)[name = string("linear_37_cast_fp16")]; + tensor input_239_cast_fp16 = silu(x = linear_37_cast_fp16)[name = string("input_239_cast_fp16")]; + tensor encoder_layers_4_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97589440))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100735232))))[name = string("encoder_layers_4_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_4_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_4_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100735424)))]; + tensor linear_38_cast_fp16 = linear(bias = encoder_layers_4_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_4_feed_forward1_linear2_weight_to_fp16_palettized, x = input_239_cast_fp16)[name = string("linear_38_cast_fp16")]; + fp16 var_1307_to_fp16 = const()[name = string("op_1307_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1308_cast_fp16 = mul(x = linear_38_cast_fp16, y = var_1307_to_fp16)[name = string("op_1308_cast_fp16")]; + tensor input_245_cast_fp16 = add(x = input_233_cast_fp16, y = var_1308_cast_fp16)[name = string("input_245_cast_fp16")]; + tensor key_9_axes_0 = const()[name = string("key_9_axes_0"), val = tensor([-1])]; + tensor encoder_layers_4_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_4_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100737536)))]; + tensor encoder_layers_4_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_4_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100739648)))]; + tensor key_9_cast_fp16 = layer_norm(axes = key_9_axes_0, beta = encoder_layers_4_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_4_norm_self_att_weight_to_fp16, x = input_245_cast_fp16)[name = string("key_9_cast_fp16")]; + bool input_247_interleave_0 = const()[name = string("input_247_interleave_0"), val = bool(false)]; + tensor input_247_cast_fp16 = concat(axis = var_68, interleave = input_247_interleave_0, values = (cache_17_cast_fp16, key_9_cast_fp16))[name = string("input_247_cast_fp16")]; + tensor var_1330_begin_0 = const()[name = string("op_1330_begin_0"), val = tensor([0, 28, 0])]; + tensor var_1330_end_0 = const()[name = string("op_1330_end_0"), val = tensor([1, 42, 1024])]; + tensor var_1330_end_mask_0 = const()[name = string("op_1330_end_mask_0"), val = tensor([true, true, true])]; + tensor var_1330_cast_fp16 = slice_by_index(begin = var_1330_begin_0, end = var_1330_end_0, end_mask = var_1330_end_mask_0, x = cache_17_cast_fp16)[name = string("op_1330_cast_fp16")]; + bool var_1336_interleave_0 = const()[name = string("op_1336_interleave_0"), val = bool(false)]; + tensor var_1336_cast_fp16 = concat(axis = var_68, interleave = var_1336_interleave_0, values = (var_1330_cast_fp16, key_9_cast_fp16))[name = string("op_1336_cast_fp16")]; + tensor encoder_layers_4_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100741760))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101528256))))[name = string("encoder_layers_4_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_4_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_4_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101528448)))]; + tensor linear_39_cast_fp16 = linear(bias = encoder_layers_4_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_4_self_attn_linear_q_weight_to_fp16_palettized, x = key_9_cast_fp16)[name = string("linear_39_cast_fp16")]; + tensor var_1341 = const()[name = string("op_1341"), val = tensor([1, -1, 8, 128])]; + tensor q_25_cast_fp16 = reshape(shape = var_1341, x = linear_39_cast_fp16)[name = string("q_25_cast_fp16")]; + tensor encoder_layers_4_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101530560))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102317056))))[name = string("encoder_layers_4_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_4_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_4_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102317248)))]; + tensor linear_40_cast_fp16 = linear(bias = encoder_layers_4_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_4_self_attn_linear_k_weight_to_fp16_palettized, x = input_247_cast_fp16)[name = string("linear_40_cast_fp16")]; + tensor var_1346 = const()[name = string("op_1346"), val = tensor([1, -1, 8, 128])]; + tensor k_17_cast_fp16 = reshape(shape = var_1346, x = linear_40_cast_fp16)[name = string("k_17_cast_fp16")]; + tensor encoder_layers_4_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102319360))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103105856))))[name = string("encoder_layers_4_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_4_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_4_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103106048)))]; + tensor linear_41_cast_fp16 = linear(bias = encoder_layers_4_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_4_self_attn_linear_v_weight_to_fp16_palettized, x = input_247_cast_fp16)[name = string("linear_41_cast_fp16")]; + tensor var_1351 = const()[name = string("op_1351"), val = tensor([1, -1, 8, 128])]; + tensor v_9_cast_fp16 = reshape(shape = var_1351, x = linear_41_cast_fp16)[name = string("v_9_cast_fp16")]; + tensor value_17_perm_0 = const()[name = string("value_17_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_4_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_4_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103108160)))]; + tensor var_1364_cast_fp16 = add(x = q_25_cast_fp16, y = encoder_layers_4_self_attn_pos_bias_u_to_fp16)[name = string("op_1364_cast_fp16")]; + tensor encoder_layers_4_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_4_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103110272)))]; + tensor var_1366_cast_fp16 = add(x = q_25_cast_fp16, y = encoder_layers_4_self_attn_pos_bias_v_to_fp16)[name = string("op_1366_cast_fp16")]; + tensor q_with_bias_v_9_perm_0 = const()[name = string("q_with_bias_v_9_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_111_transpose_x_0 = const()[name = string("x_111_transpose_x_0"), val = bool(false)]; + bool x_111_transpose_y_0 = const()[name = string("x_111_transpose_y_0"), val = bool(false)]; + tensor op_1368_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103112384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103254784))))[name = string("op_1368_to_fp16_quantized")]; + tensor q_with_bias_v_9_cast_fp16 = transpose(perm = q_with_bias_v_9_perm_0, x = var_1366_cast_fp16)[name = string("transpose_326")]; + tensor x_111_cast_fp16 = matmul(transpose_x = x_111_transpose_x_0, transpose_y = x_111_transpose_y_0, x = q_with_bias_v_9_cast_fp16, y = op_1368_to_fp16_quantized)[name = string("x_111_cast_fp16")]; + tensor x_113_pad_0 = const()[name = string("x_113_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_113_mode_0 = const()[name = string("x_113_mode_0"), val = string("constant")]; + fp16 const_131_to_fp16 = const()[name = string("const_131_to_fp16"), val = fp16(0x0p+0)]; + tensor x_113_cast_fp16 = pad(constant_val = const_131_to_fp16, mode = x_113_mode_0, pad = x_113_pad_0, x = x_111_cast_fp16)[name = string("x_113_cast_fp16")]; + tensor var_1376 = const()[name = string("op_1376"), val = tensor([1, 8, -1, 28])]; + tensor x_115_cast_fp16 = reshape(shape = var_1376, x = x_113_cast_fp16)[name = string("x_115_cast_fp16")]; + tensor var_1380_begin_0 = const()[name = string("op_1380_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_1380_end_0 = const()[name = string("op_1380_end_0"), val = tensor([1, 8, 140, 28])]; + tensor var_1380_end_mask_0 = const()[name = string("op_1380_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1380_cast_fp16 = slice_by_index(begin = var_1380_begin_0, end = var_1380_end_0, end_mask = var_1380_end_mask_0, x = x_115_cast_fp16)[name = string("op_1380_cast_fp16")]; + tensor var_1381 = const()[name = string("op_1381"), val = tensor([1, 8, 28, 139])]; + tensor matrix_bd_17_cast_fp16 = reshape(shape = var_1381, x = var_1380_cast_fp16)[name = string("matrix_bd_17_cast_fp16")]; + bool matrix_ac_9_transpose_x_0 = const()[name = string("matrix_ac_9_transpose_x_0"), val = bool(false)]; + bool matrix_ac_9_transpose_y_0 = const()[name = string("matrix_ac_9_transpose_y_0"), val = bool(false)]; + tensor transpose_104_perm_0 = const()[name = string("transpose_104_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_105_perm_0 = const()[name = string("transpose_105_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_105 = transpose(perm = transpose_105_perm_0, x = k_17_cast_fp16)[name = string("transpose_324")]; + tensor transpose_104 = transpose(perm = transpose_104_perm_0, x = var_1364_cast_fp16)[name = string("transpose_325")]; + tensor matrix_ac_9_cast_fp16 = matmul(transpose_x = matrix_ac_9_transpose_x_0, transpose_y = matrix_ac_9_transpose_y_0, x = transpose_104, y = transpose_105)[name = string("matrix_ac_9_cast_fp16")]; + tensor matrix_bd_19_begin_0 = const()[name = string("matrix_bd_19_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_19_end_0 = const()[name = string("matrix_bd_19_end_0"), val = tensor([1, 8, 28, 70])]; + tensor matrix_bd_19_end_mask_0 = const()[name = string("matrix_bd_19_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_19_cast_fp16 = slice_by_index(begin = matrix_bd_19_begin_0, end = matrix_bd_19_end_0, end_mask = matrix_bd_19_end_mask_0, x = matrix_bd_17_cast_fp16)[name = string("matrix_bd_19_cast_fp16")]; + tensor var_1390_cast_fp16 = add(x = matrix_ac_9_cast_fp16, y = matrix_bd_19_cast_fp16)[name = string("op_1390_cast_fp16")]; + fp16 _inversed_scores_17_y_0_to_fp16 = const()[name = string("_inversed_scores_17_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_17_cast_fp16 = mul(x = var_1390_cast_fp16, y = _inversed_scores_17_y_0_to_fp16)[name = string("_inversed_scores_17_cast_fp16")]; + tensor scores_19_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_17_cast_fp16, cond = mask_11)[name = string("scores_19_cast_fp16")]; + tensor var_1396_cast_fp16 = softmax(axis = var_59, x = scores_19_cast_fp16)[name = string("op_1396_cast_fp16")]; + tensor input_249_cast_fp16 = select(a = var_44_to_fp16, b = var_1396_cast_fp16, cond = mask_11)[name = string("input_249_cast_fp16")]; + bool x_117_transpose_x_0 = const()[name = string("x_117_transpose_x_0"), val = bool(false)]; + bool x_117_transpose_y_0 = const()[name = string("x_117_transpose_y_0"), val = bool(false)]; + tensor value_17_cast_fp16 = transpose(perm = value_17_perm_0, x = v_9_cast_fp16)[name = string("transpose_323")]; + tensor x_117_cast_fp16 = matmul(transpose_x = x_117_transpose_x_0, transpose_y = x_117_transpose_y_0, x = input_249_cast_fp16, y = value_17_cast_fp16)[name = string("x_117_cast_fp16")]; + tensor var_1400_perm_0 = const()[name = string("op_1400_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1401 = const()[name = string("op_1401"), val = tensor([1, -1, 1024])]; + tensor var_1400_cast_fp16 = transpose(perm = var_1400_perm_0, x = x_117_cast_fp16)[name = string("transpose_322")]; + tensor input_251_cast_fp16 = reshape(shape = var_1401, x = var_1400_cast_fp16)[name = string("input_251_cast_fp16")]; + tensor encoder_layers_4_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103255168))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(104041664))))[name = string("encoder_layers_4_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_4_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_4_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(104041856)))]; + tensor linear_43_cast_fp16 = linear(bias = encoder_layers_4_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_4_self_attn_linear_out_weight_to_fp16_palettized, x = input_251_cast_fp16)[name = string("linear_43_cast_fp16")]; + tensor input_255_cast_fp16 = add(x = input_245_cast_fp16, y = linear_43_cast_fp16)[name = string("input_255_cast_fp16")]; + tensor x_121_axes_0 = const()[name = string("x_121_axes_0"), val = tensor([-1])]; + tensor encoder_layers_4_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_4_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(104043968)))]; + tensor encoder_layers_4_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_4_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(104046080)))]; + tensor x_121_cast_fp16 = layer_norm(axes = x_121_axes_0, beta = encoder_layers_4_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_4_norm_conv_weight_to_fp16, x = input_255_cast_fp16)[name = string("x_121_cast_fp16")]; + tensor input_257_perm_0 = const()[name = string("input_257_perm_0"), val = tensor([0, 2, 1])]; + string input_259_pad_type_0 = const()[name = string("input_259_pad_type_0"), val = string("valid")]; + tensor input_259_strides_0 = const()[name = string("input_259_strides_0"), val = tensor([1])]; + tensor input_259_pad_0 = const()[name = string("input_259_pad_0"), val = tensor([0, 0])]; + tensor input_259_dilations_0 = const()[name = string("input_259_dilations_0"), val = tensor([1])]; + int32 input_259_groups_0 = const()[name = string("input_259_groups_0"), val = int32(1)]; + tensor encoder_layers_4_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(104048192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106145408))))[name = string("encoder_layers_4_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_257_cast_fp16 = transpose(perm = input_257_perm_0, x = x_121_cast_fp16)[name = string("transpose_321")]; + tensor input_259_cast_fp16 = conv(dilations = input_259_dilations_0, groups = input_259_groups_0, pad = input_259_pad_0, pad_type = input_259_pad_type_0, strides = input_259_strides_0, weight = encoder_layers_4_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_257_cast_fp16)[name = string("input_259_cast_fp16")]; + int32 x_123_split_num_splits_0 = const()[name = string("x_123_split_num_splits_0"), val = int32(2)]; + int32 x_123_split_axis_0 = const()[name = string("x_123_split_axis_0"), val = int32(1)]; + tensor x_123_split_cast_fp16_0, tensor x_123_split_cast_fp16_1 = split(axis = x_123_split_axis_0, num_splits = x_123_split_num_splits_0, x = input_259_cast_fp16)[name = string("x_123_split_cast_fp16")]; + tensor x_123_split_1_sigmoid_cast_fp16 = sigmoid(x = x_123_split_cast_fp16_1)[name = string("x_123_split_1_sigmoid_cast_fp16")]; + tensor x_123_cast_fp16 = mul(x = x_123_split_cast_fp16_0, y = x_123_split_1_sigmoid_cast_fp16)[name = string("x_123_cast_fp16")]; + tensor input_261_cast_fp16 = select(a = var_44_to_fp16, b = x_123_cast_fp16, cond = var_575)[name = string("input_261_cast_fp16")]; + bool new_x_19_interleave_0 = const()[name = string("new_x_19_interleave_0"), val = bool(false)]; + tensor new_x_19_cast_fp16 = concat(axis = var_59, interleave = new_x_19_interleave_0, values = (cache_19_cast_fp16, input_261_cast_fp16))[name = string("new_x_19_cast_fp16")]; + tensor var_1440_begin_0 = const()[name = string("op_1440_begin_0"), val = tensor([0, 0, 28])]; + tensor var_1440_end_0 = const()[name = string("op_1440_end_0"), val = tensor([1, 1024, 36])]; + tensor var_1440_end_mask_0 = const()[name = string("op_1440_end_mask_0"), val = tensor([true, true, true])]; + tensor var_1440_cast_fp16 = slice_by_index(begin = var_1440_begin_0, end = var_1440_end_0, end_mask = var_1440_end_mask_0, x = new_x_19_cast_fp16)[name = string("op_1440_cast_fp16")]; + string x_125_pad_type_0 = const()[name = string("x_125_pad_type_0"), val = string("valid")]; + int32 x_125_groups_0 = const()[name = string("x_125_groups_0"), val = int32(1024)]; + tensor x_125_strides_0 = const()[name = string("x_125_strides_0"), val = tensor([1])]; + tensor x_125_pad_0 = const()[name = string("x_125_pad_0"), val = tensor([0, 0])]; + tensor x_125_dilations_0 = const()[name = string("x_125_dilations_0"), val = tensor([1])]; + tensor encoder_layers_4_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106149568))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106158848))))[name = string("encoder_layers_4_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_125_cast_fp16 = conv(dilations = x_125_dilations_0, groups = x_125_groups_0, pad = x_125_pad_0, pad_type = x_125_pad_type_0, strides = x_125_strides_0, weight = encoder_layers_4_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_19_cast_fp16)[name = string("x_125_cast_fp16")]; + tensor input_263_perm_0 = const()[name = string("input_263_perm_0"), val = tensor([0, 2, 1])]; + tensor x_127_axes_0 = const()[name = string("x_127_axes_0"), val = tensor([-1])]; + tensor encoder_layers_4_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_4_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106160960)))]; + tensor encoder_layers_4_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_4_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106163072)))]; + tensor input_263_cast_fp16 = transpose(perm = input_263_perm_0, x = x_125_cast_fp16)[name = string("transpose_320")]; + tensor x_127_cast_fp16 = layer_norm(axes = x_127_axes_0, beta = encoder_layers_4_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_4_conv_batch_norm_weight_to_fp16, x = input_263_cast_fp16)[name = string("x_127_cast_fp16")]; + tensor input_265_perm_0 = const()[name = string("input_265_perm_0"), val = tensor([0, 2, 1])]; + tensor input_265_cast_fp16 = transpose(perm = input_265_perm_0, x = x_127_cast_fp16)[name = string("transpose_319")]; + tensor input_267_cast_fp16 = silu(x = input_265_cast_fp16)[name = string("input_267_cast_fp16")]; + string x_129_pad_type_0 = const()[name = string("x_129_pad_type_0"), val = string("valid")]; + tensor x_129_strides_0 = const()[name = string("x_129_strides_0"), val = tensor([1])]; + tensor x_129_pad_0 = const()[name = string("x_129_pad_0"), val = tensor([0, 0])]; + tensor x_129_dilations_0 = const()[name = string("x_129_dilations_0"), val = tensor([1])]; + int32 x_129_groups_0 = const()[name = string("x_129_groups_0"), val = int32(1)]; + tensor encoder_layers_4_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106165184))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(107213824))))[name = string("encoder_layers_4_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_129_cast_fp16 = conv(dilations = x_129_dilations_0, groups = x_129_groups_0, pad = x_129_pad_0, pad_type = x_129_pad_type_0, strides = x_129_strides_0, weight = encoder_layers_4_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_267_cast_fp16)[name = string("x_129_cast_fp16")]; + tensor input_269_perm_0 = const()[name = string("input_269_perm_0"), val = tensor([0, 2, 1])]; + tensor input_269_cast_fp16 = transpose(perm = input_269_perm_0, x = x_129_cast_fp16)[name = string("transpose_318")]; + tensor input_271_cast_fp16 = add(x = input_255_cast_fp16, y = input_269_cast_fp16)[name = string("input_271_cast_fp16")]; + tensor input_273_axes_0 = const()[name = string("input_273_axes_0"), val = tensor([-1])]; + tensor encoder_layers_4_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_4_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(107215936)))]; + tensor encoder_layers_4_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_4_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(107218048)))]; + tensor input_273_cast_fp16 = layer_norm(axes = input_273_axes_0, beta = encoder_layers_4_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_4_norm_feed_forward2_weight_to_fp16, x = input_271_cast_fp16)[name = string("input_273_cast_fp16")]; + tensor encoder_layers_4_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(107220160))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110365952))))[name = string("encoder_layers_4_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_4_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_4_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110366144)))]; + tensor linear_44_cast_fp16 = linear(bias = encoder_layers_4_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_4_feed_forward2_linear1_weight_to_fp16_palettized, x = input_273_cast_fp16)[name = string("linear_44_cast_fp16")]; + tensor input_277_cast_fp16 = silu(x = linear_44_cast_fp16)[name = string("input_277_cast_fp16")]; + tensor encoder_layers_4_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110374400))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113520192))))[name = string("encoder_layers_4_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_4_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_4_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113520384)))]; + tensor linear_45_cast_fp16 = linear(bias = encoder_layers_4_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_4_feed_forward2_linear2_weight_to_fp16_palettized, x = input_277_cast_fp16)[name = string("linear_45_cast_fp16")]; + fp16 var_1483_to_fp16 = const()[name = string("op_1483_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1484_cast_fp16 = mul(x = linear_45_cast_fp16, y = var_1483_to_fp16)[name = string("op_1484_cast_fp16")]; + tensor input_283_cast_fp16 = add(x = input_271_cast_fp16, y = var_1484_cast_fp16)[name = string("input_283_cast_fp16")]; + tensor input_285_axes_0 = const()[name = string("input_285_axes_0"), val = tensor([-1])]; + tensor encoder_layers_4_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_4_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113522496)))]; + tensor encoder_layers_4_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_4_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113524608)))]; + tensor input_285_cast_fp16 = layer_norm(axes = input_285_axes_0, beta = encoder_layers_4_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_4_norm_out_weight_to_fp16, x = input_283_cast_fp16)[name = string("input_285_cast_fp16")]; + tensor cache_21_begin_0 = const()[name = string("cache_21_begin_0"), val = tensor([5, 0, 0, 0])]; + tensor cache_21_end_0 = const()[name = string("cache_21_end_0"), val = tensor([6, 1, 42, 1024])]; + tensor cache_21_end_mask_0 = const()[name = string("cache_21_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_21_squeeze_mask_0 = const()[name = string("cache_21_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_21_cast_fp16 = slice_by_index(begin = cache_21_begin_0, end = cache_21_end_0, end_mask = cache_21_end_mask_0, squeeze_mask = cache_21_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_21_cast_fp16")]; + tensor cache_23_begin_0 = const()[name = string("cache_23_begin_0"), val = tensor([5, 0, 0, 0])]; + tensor cache_23_end_0 = const()[name = string("cache_23_end_0"), val = tensor([6, 1, 1024, 8])]; + tensor cache_23_end_mask_0 = const()[name = string("cache_23_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_23_squeeze_mask_0 = const()[name = string("cache_23_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_23_cast_fp16 = slice_by_index(begin = cache_23_begin_0, end = cache_23_end_0, end_mask = cache_23_end_mask_0, squeeze_mask = cache_23_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_23_cast_fp16")]; + tensor input_287_axes_0 = const()[name = string("input_287_axes_0"), val = tensor([-1])]; + tensor encoder_layers_5_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_5_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113526720)))]; + tensor encoder_layers_5_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_5_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113528832)))]; + tensor input_287_cast_fp16 = layer_norm(axes = input_287_axes_0, beta = encoder_layers_5_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_5_norm_feed_forward1_weight_to_fp16, x = input_285_cast_fp16)[name = string("input_287_cast_fp16")]; + tensor encoder_layers_5_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113530944))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116676736))))[name = string("encoder_layers_5_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_5_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_5_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116676928)))]; + tensor linear_46_cast_fp16 = linear(bias = encoder_layers_5_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_5_feed_forward1_linear1_weight_to_fp16_palettized, x = input_287_cast_fp16)[name = string("linear_46_cast_fp16")]; + tensor input_291_cast_fp16 = silu(x = linear_46_cast_fp16)[name = string("input_291_cast_fp16")]; + tensor encoder_layers_5_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116685184))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119830976))))[name = string("encoder_layers_5_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_5_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_5_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119831168)))]; + tensor linear_47_cast_fp16 = linear(bias = encoder_layers_5_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_5_feed_forward1_linear2_weight_to_fp16_palettized, x = input_291_cast_fp16)[name = string("linear_47_cast_fp16")]; + fp16 var_1520_to_fp16 = const()[name = string("op_1520_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1521_cast_fp16 = mul(x = linear_47_cast_fp16, y = var_1520_to_fp16)[name = string("op_1521_cast_fp16")]; + tensor input_297_cast_fp16 = add(x = input_285_cast_fp16, y = var_1521_cast_fp16)[name = string("input_297_cast_fp16")]; + tensor key_11_axes_0 = const()[name = string("key_11_axes_0"), val = tensor([-1])]; + tensor encoder_layers_5_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_5_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119833280)))]; + tensor encoder_layers_5_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_5_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119835392)))]; + tensor key_11_cast_fp16 = layer_norm(axes = key_11_axes_0, beta = encoder_layers_5_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_5_norm_self_att_weight_to_fp16, x = input_297_cast_fp16)[name = string("key_11_cast_fp16")]; + bool input_299_interleave_0 = const()[name = string("input_299_interleave_0"), val = bool(false)]; + tensor input_299_cast_fp16 = concat(axis = var_68, interleave = input_299_interleave_0, values = (cache_21_cast_fp16, key_11_cast_fp16))[name = string("input_299_cast_fp16")]; + tensor var_1543_begin_0 = const()[name = string("op_1543_begin_0"), val = tensor([0, 28, 0])]; + tensor var_1543_end_0 = const()[name = string("op_1543_end_0"), val = tensor([1, 42, 1024])]; + tensor var_1543_end_mask_0 = const()[name = string("op_1543_end_mask_0"), val = tensor([true, true, true])]; + tensor var_1543_cast_fp16 = slice_by_index(begin = var_1543_begin_0, end = var_1543_end_0, end_mask = var_1543_end_mask_0, x = cache_21_cast_fp16)[name = string("op_1543_cast_fp16")]; + bool var_1549_interleave_0 = const()[name = string("op_1549_interleave_0"), val = bool(false)]; + tensor var_1549_cast_fp16 = concat(axis = var_68, interleave = var_1549_interleave_0, values = (var_1543_cast_fp16, key_11_cast_fp16))[name = string("op_1549_cast_fp16")]; + tensor encoder_layers_5_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119837504))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120624000))))[name = string("encoder_layers_5_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_5_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_5_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120624192)))]; + tensor linear_48_cast_fp16 = linear(bias = encoder_layers_5_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_5_self_attn_linear_q_weight_to_fp16_palettized, x = key_11_cast_fp16)[name = string("linear_48_cast_fp16")]; + tensor var_1554 = const()[name = string("op_1554"), val = tensor([1, -1, 8, 128])]; + tensor q_31_cast_fp16 = reshape(shape = var_1554, x = linear_48_cast_fp16)[name = string("q_31_cast_fp16")]; + tensor encoder_layers_5_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120626304))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121412800))))[name = string("encoder_layers_5_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_5_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_5_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121412992)))]; + tensor linear_49_cast_fp16 = linear(bias = encoder_layers_5_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_5_self_attn_linear_k_weight_to_fp16_palettized, x = input_299_cast_fp16)[name = string("linear_49_cast_fp16")]; + tensor var_1559 = const()[name = string("op_1559"), val = tensor([1, -1, 8, 128])]; + tensor k_21_cast_fp16 = reshape(shape = var_1559, x = linear_49_cast_fp16)[name = string("k_21_cast_fp16")]; + tensor encoder_layers_5_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121415104))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122201600))))[name = string("encoder_layers_5_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_5_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_5_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122201792)))]; + tensor linear_50_cast_fp16 = linear(bias = encoder_layers_5_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_5_self_attn_linear_v_weight_to_fp16_palettized, x = input_299_cast_fp16)[name = string("linear_50_cast_fp16")]; + tensor var_1564 = const()[name = string("op_1564"), val = tensor([1, -1, 8, 128])]; + tensor v_11_cast_fp16 = reshape(shape = var_1564, x = linear_50_cast_fp16)[name = string("v_11_cast_fp16")]; + tensor value_19_perm_0 = const()[name = string("value_19_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_5_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_5_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122203904)))]; + tensor var_1577_cast_fp16 = add(x = q_31_cast_fp16, y = encoder_layers_5_self_attn_pos_bias_u_to_fp16)[name = string("op_1577_cast_fp16")]; + tensor encoder_layers_5_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_5_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122206016)))]; + tensor var_1579_cast_fp16 = add(x = q_31_cast_fp16, y = encoder_layers_5_self_attn_pos_bias_v_to_fp16)[name = string("op_1579_cast_fp16")]; + tensor q_with_bias_v_11_perm_0 = const()[name = string("q_with_bias_v_11_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_137_transpose_x_0 = const()[name = string("x_137_transpose_x_0"), val = bool(false)]; + bool x_137_transpose_y_0 = const()[name = string("x_137_transpose_y_0"), val = bool(false)]; + tensor op_1581_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122208128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122350528))))[name = string("op_1581_to_fp16_quantized")]; + tensor q_with_bias_v_11_cast_fp16 = transpose(perm = q_with_bias_v_11_perm_0, x = var_1579_cast_fp16)[name = string("transpose_317")]; + tensor x_137_cast_fp16 = matmul(transpose_x = x_137_transpose_x_0, transpose_y = x_137_transpose_y_0, x = q_with_bias_v_11_cast_fp16, y = op_1581_to_fp16_quantized)[name = string("x_137_cast_fp16")]; + tensor x_139_pad_0 = const()[name = string("x_139_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_139_mode_0 = const()[name = string("x_139_mode_0"), val = string("constant")]; + fp16 const_144_to_fp16 = const()[name = string("const_144_to_fp16"), val = fp16(0x0p+0)]; + tensor x_139_cast_fp16 = pad(constant_val = const_144_to_fp16, mode = x_139_mode_0, pad = x_139_pad_0, x = x_137_cast_fp16)[name = string("x_139_cast_fp16")]; + tensor var_1589 = const()[name = string("op_1589"), val = tensor([1, 8, -1, 28])]; + tensor x_141_cast_fp16 = reshape(shape = var_1589, x = x_139_cast_fp16)[name = string("x_141_cast_fp16")]; + tensor var_1593_begin_0 = const()[name = string("op_1593_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_1593_end_0 = const()[name = string("op_1593_end_0"), val = tensor([1, 8, 140, 28])]; + tensor var_1593_end_mask_0 = const()[name = string("op_1593_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1593_cast_fp16 = slice_by_index(begin = var_1593_begin_0, end = var_1593_end_0, end_mask = var_1593_end_mask_0, x = x_141_cast_fp16)[name = string("op_1593_cast_fp16")]; + tensor var_1594 = const()[name = string("op_1594"), val = tensor([1, 8, 28, 139])]; + tensor matrix_bd_21_cast_fp16 = reshape(shape = var_1594, x = var_1593_cast_fp16)[name = string("matrix_bd_21_cast_fp16")]; + bool matrix_ac_11_transpose_x_0 = const()[name = string("matrix_ac_11_transpose_x_0"), val = bool(false)]; + bool matrix_ac_11_transpose_y_0 = const()[name = string("matrix_ac_11_transpose_y_0"), val = bool(false)]; + tensor transpose_106_perm_0 = const()[name = string("transpose_106_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_107_perm_0 = const()[name = string("transpose_107_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_107 = transpose(perm = transpose_107_perm_0, x = k_21_cast_fp16)[name = string("transpose_315")]; + tensor transpose_106 = transpose(perm = transpose_106_perm_0, x = var_1577_cast_fp16)[name = string("transpose_316")]; + tensor matrix_ac_11_cast_fp16 = matmul(transpose_x = matrix_ac_11_transpose_x_0, transpose_y = matrix_ac_11_transpose_y_0, x = transpose_106, y = transpose_107)[name = string("matrix_ac_11_cast_fp16")]; + tensor matrix_bd_23_begin_0 = const()[name = string("matrix_bd_23_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_23_end_0 = const()[name = string("matrix_bd_23_end_0"), val = tensor([1, 8, 28, 70])]; + tensor matrix_bd_23_end_mask_0 = const()[name = string("matrix_bd_23_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_23_cast_fp16 = slice_by_index(begin = matrix_bd_23_begin_0, end = matrix_bd_23_end_0, end_mask = matrix_bd_23_end_mask_0, x = matrix_bd_21_cast_fp16)[name = string("matrix_bd_23_cast_fp16")]; + tensor var_1603_cast_fp16 = add(x = matrix_ac_11_cast_fp16, y = matrix_bd_23_cast_fp16)[name = string("op_1603_cast_fp16")]; + fp16 _inversed_scores_21_y_0_to_fp16 = const()[name = string("_inversed_scores_21_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_21_cast_fp16 = mul(x = var_1603_cast_fp16, y = _inversed_scores_21_y_0_to_fp16)[name = string("_inversed_scores_21_cast_fp16")]; + tensor scores_23_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_21_cast_fp16, cond = mask_11)[name = string("scores_23_cast_fp16")]; + tensor var_1609_cast_fp16 = softmax(axis = var_59, x = scores_23_cast_fp16)[name = string("op_1609_cast_fp16")]; + tensor input_301_cast_fp16 = select(a = var_44_to_fp16, b = var_1609_cast_fp16, cond = mask_11)[name = string("input_301_cast_fp16")]; + bool x_143_transpose_x_0 = const()[name = string("x_143_transpose_x_0"), val = bool(false)]; + bool x_143_transpose_y_0 = const()[name = string("x_143_transpose_y_0"), val = bool(false)]; + tensor value_19_cast_fp16 = transpose(perm = value_19_perm_0, x = v_11_cast_fp16)[name = string("transpose_314")]; + tensor x_143_cast_fp16 = matmul(transpose_x = x_143_transpose_x_0, transpose_y = x_143_transpose_y_0, x = input_301_cast_fp16, y = value_19_cast_fp16)[name = string("x_143_cast_fp16")]; + tensor var_1613_perm_0 = const()[name = string("op_1613_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1614 = const()[name = string("op_1614"), val = tensor([1, -1, 1024])]; + tensor var_1613_cast_fp16 = transpose(perm = var_1613_perm_0, x = x_143_cast_fp16)[name = string("transpose_313")]; + tensor input_303_cast_fp16 = reshape(shape = var_1614, x = var_1613_cast_fp16)[name = string("input_303_cast_fp16")]; + tensor encoder_layers_5_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122350912))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123137408))))[name = string("encoder_layers_5_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_5_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_5_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123137600)))]; + tensor linear_52_cast_fp16 = linear(bias = encoder_layers_5_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_5_self_attn_linear_out_weight_to_fp16_palettized, x = input_303_cast_fp16)[name = string("linear_52_cast_fp16")]; + tensor input_307_cast_fp16 = add(x = input_297_cast_fp16, y = linear_52_cast_fp16)[name = string("input_307_cast_fp16")]; + tensor x_147_axes_0 = const()[name = string("x_147_axes_0"), val = tensor([-1])]; + tensor encoder_layers_5_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_5_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123139712)))]; + tensor encoder_layers_5_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_5_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123141824)))]; + tensor x_147_cast_fp16 = layer_norm(axes = x_147_axes_0, beta = encoder_layers_5_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_5_norm_conv_weight_to_fp16, x = input_307_cast_fp16)[name = string("x_147_cast_fp16")]; + tensor input_309_perm_0 = const()[name = string("input_309_perm_0"), val = tensor([0, 2, 1])]; + string input_311_pad_type_0 = const()[name = string("input_311_pad_type_0"), val = string("valid")]; + tensor input_311_strides_0 = const()[name = string("input_311_strides_0"), val = tensor([1])]; + tensor input_311_pad_0 = const()[name = string("input_311_pad_0"), val = tensor([0, 0])]; + tensor input_311_dilations_0 = const()[name = string("input_311_dilations_0"), val = tensor([1])]; + int32 input_311_groups_0 = const()[name = string("input_311_groups_0"), val = int32(1)]; + tensor encoder_layers_5_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123143936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125241152))))[name = string("encoder_layers_5_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_309_cast_fp16 = transpose(perm = input_309_perm_0, x = x_147_cast_fp16)[name = string("transpose_312")]; + tensor input_311_cast_fp16 = conv(dilations = input_311_dilations_0, groups = input_311_groups_0, pad = input_311_pad_0, pad_type = input_311_pad_type_0, strides = input_311_strides_0, weight = encoder_layers_5_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_309_cast_fp16)[name = string("input_311_cast_fp16")]; + int32 x_149_split_num_splits_0 = const()[name = string("x_149_split_num_splits_0"), val = int32(2)]; + int32 x_149_split_axis_0 = const()[name = string("x_149_split_axis_0"), val = int32(1)]; + tensor x_149_split_cast_fp16_0, tensor x_149_split_cast_fp16_1 = split(axis = x_149_split_axis_0, num_splits = x_149_split_num_splits_0, x = input_311_cast_fp16)[name = string("x_149_split_cast_fp16")]; + tensor x_149_split_1_sigmoid_cast_fp16 = sigmoid(x = x_149_split_cast_fp16_1)[name = string("x_149_split_1_sigmoid_cast_fp16")]; + tensor x_149_cast_fp16 = mul(x = x_149_split_cast_fp16_0, y = x_149_split_1_sigmoid_cast_fp16)[name = string("x_149_cast_fp16")]; + tensor input_313_cast_fp16 = select(a = var_44_to_fp16, b = x_149_cast_fp16, cond = var_575)[name = string("input_313_cast_fp16")]; + bool new_x_23_interleave_0 = const()[name = string("new_x_23_interleave_0"), val = bool(false)]; + tensor new_x_23_cast_fp16 = concat(axis = var_59, interleave = new_x_23_interleave_0, values = (cache_23_cast_fp16, input_313_cast_fp16))[name = string("new_x_23_cast_fp16")]; + tensor var_1653_begin_0 = const()[name = string("op_1653_begin_0"), val = tensor([0, 0, 28])]; + tensor var_1653_end_0 = const()[name = string("op_1653_end_0"), val = tensor([1, 1024, 36])]; + tensor var_1653_end_mask_0 = const()[name = string("op_1653_end_mask_0"), val = tensor([true, true, true])]; + tensor var_1653_cast_fp16 = slice_by_index(begin = var_1653_begin_0, end = var_1653_end_0, end_mask = var_1653_end_mask_0, x = new_x_23_cast_fp16)[name = string("op_1653_cast_fp16")]; + string x_151_pad_type_0 = const()[name = string("x_151_pad_type_0"), val = string("valid")]; + int32 x_151_groups_0 = const()[name = string("x_151_groups_0"), val = int32(1024)]; + tensor x_151_strides_0 = const()[name = string("x_151_strides_0"), val = tensor([1])]; + tensor x_151_pad_0 = const()[name = string("x_151_pad_0"), val = tensor([0, 0])]; + tensor x_151_dilations_0 = const()[name = string("x_151_dilations_0"), val = tensor([1])]; + tensor encoder_layers_5_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125245312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125254592))))[name = string("encoder_layers_5_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_151_cast_fp16 = conv(dilations = x_151_dilations_0, groups = x_151_groups_0, pad = x_151_pad_0, pad_type = x_151_pad_type_0, strides = x_151_strides_0, weight = encoder_layers_5_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_23_cast_fp16)[name = string("x_151_cast_fp16")]; + tensor input_315_perm_0 = const()[name = string("input_315_perm_0"), val = tensor([0, 2, 1])]; + tensor x_153_axes_0 = const()[name = string("x_153_axes_0"), val = tensor([-1])]; + tensor encoder_layers_5_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_5_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125256704)))]; + tensor encoder_layers_5_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_5_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125258816)))]; + tensor input_315_cast_fp16 = transpose(perm = input_315_perm_0, x = x_151_cast_fp16)[name = string("transpose_311")]; + tensor x_153_cast_fp16 = layer_norm(axes = x_153_axes_0, beta = encoder_layers_5_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_5_conv_batch_norm_weight_to_fp16, x = input_315_cast_fp16)[name = string("x_153_cast_fp16")]; + tensor input_317_perm_0 = const()[name = string("input_317_perm_0"), val = tensor([0, 2, 1])]; + tensor input_317_cast_fp16 = transpose(perm = input_317_perm_0, x = x_153_cast_fp16)[name = string("transpose_310")]; + tensor input_319_cast_fp16 = silu(x = input_317_cast_fp16)[name = string("input_319_cast_fp16")]; + string x_155_pad_type_0 = const()[name = string("x_155_pad_type_0"), val = string("valid")]; + tensor x_155_strides_0 = const()[name = string("x_155_strides_0"), val = tensor([1])]; + tensor x_155_pad_0 = const()[name = string("x_155_pad_0"), val = tensor([0, 0])]; + tensor x_155_dilations_0 = const()[name = string("x_155_dilations_0"), val = tensor([1])]; + int32 x_155_groups_0 = const()[name = string("x_155_groups_0"), val = int32(1)]; + tensor encoder_layers_5_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125260928))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(126309568))))[name = string("encoder_layers_5_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_155_cast_fp16 = conv(dilations = x_155_dilations_0, groups = x_155_groups_0, pad = x_155_pad_0, pad_type = x_155_pad_type_0, strides = x_155_strides_0, weight = encoder_layers_5_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_319_cast_fp16)[name = string("x_155_cast_fp16")]; + tensor input_321_perm_0 = const()[name = string("input_321_perm_0"), val = tensor([0, 2, 1])]; + tensor input_321_cast_fp16 = transpose(perm = input_321_perm_0, x = x_155_cast_fp16)[name = string("transpose_309")]; + tensor input_323_cast_fp16 = add(x = input_307_cast_fp16, y = input_321_cast_fp16)[name = string("input_323_cast_fp16")]; + tensor input_325_axes_0 = const()[name = string("input_325_axes_0"), val = tensor([-1])]; + tensor encoder_layers_5_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_5_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(126311680)))]; + tensor encoder_layers_5_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_5_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(126313792)))]; + tensor input_325_cast_fp16 = layer_norm(axes = input_325_axes_0, beta = encoder_layers_5_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_5_norm_feed_forward2_weight_to_fp16, x = input_323_cast_fp16)[name = string("input_325_cast_fp16")]; + tensor encoder_layers_5_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(126315904))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129461696))))[name = string("encoder_layers_5_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_5_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_5_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129461888)))]; + tensor linear_53_cast_fp16 = linear(bias = encoder_layers_5_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_5_feed_forward2_linear1_weight_to_fp16_palettized, x = input_325_cast_fp16)[name = string("linear_53_cast_fp16")]; + tensor input_329_cast_fp16 = silu(x = linear_53_cast_fp16)[name = string("input_329_cast_fp16")]; + tensor encoder_layers_5_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129470144))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132615936))))[name = string("encoder_layers_5_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_5_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_5_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132616128)))]; + tensor linear_54_cast_fp16 = linear(bias = encoder_layers_5_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_5_feed_forward2_linear2_weight_to_fp16_palettized, x = input_329_cast_fp16)[name = string("linear_54_cast_fp16")]; + fp16 var_1696_to_fp16 = const()[name = string("op_1696_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1697_cast_fp16 = mul(x = linear_54_cast_fp16, y = var_1696_to_fp16)[name = string("op_1697_cast_fp16")]; + tensor input_335_cast_fp16 = add(x = input_323_cast_fp16, y = var_1697_cast_fp16)[name = string("input_335_cast_fp16")]; + tensor input_337_axes_0 = const()[name = string("input_337_axes_0"), val = tensor([-1])]; + tensor encoder_layers_5_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_5_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132618240)))]; + tensor encoder_layers_5_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_5_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132620352)))]; + tensor input_337_cast_fp16 = layer_norm(axes = input_337_axes_0, beta = encoder_layers_5_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_5_norm_out_weight_to_fp16, x = input_335_cast_fp16)[name = string("input_337_cast_fp16")]; + tensor cache_25_begin_0 = const()[name = string("cache_25_begin_0"), val = tensor([6, 0, 0, 0])]; + tensor cache_25_end_0 = const()[name = string("cache_25_end_0"), val = tensor([7, 1, 42, 1024])]; + tensor cache_25_end_mask_0 = const()[name = string("cache_25_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_25_squeeze_mask_0 = const()[name = string("cache_25_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_25_cast_fp16 = slice_by_index(begin = cache_25_begin_0, end = cache_25_end_0, end_mask = cache_25_end_mask_0, squeeze_mask = cache_25_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_25_cast_fp16")]; + tensor cache_27_begin_0 = const()[name = string("cache_27_begin_0"), val = tensor([6, 0, 0, 0])]; + tensor cache_27_end_0 = const()[name = string("cache_27_end_0"), val = tensor([7, 1, 1024, 8])]; + tensor cache_27_end_mask_0 = const()[name = string("cache_27_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_27_squeeze_mask_0 = const()[name = string("cache_27_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_27_cast_fp16 = slice_by_index(begin = cache_27_begin_0, end = cache_27_end_0, end_mask = cache_27_end_mask_0, squeeze_mask = cache_27_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_27_cast_fp16")]; + tensor input_339_axes_0 = const()[name = string("input_339_axes_0"), val = tensor([-1])]; + tensor encoder_layers_6_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_6_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132622464)))]; + tensor encoder_layers_6_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_6_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132624576)))]; + tensor input_339_cast_fp16 = layer_norm(axes = input_339_axes_0, beta = encoder_layers_6_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_6_norm_feed_forward1_weight_to_fp16, x = input_337_cast_fp16)[name = string("input_339_cast_fp16")]; + tensor encoder_layers_6_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132626688))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135772480))))[name = string("encoder_layers_6_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_6_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_6_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135772672)))]; + tensor linear_55_cast_fp16 = linear(bias = encoder_layers_6_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_6_feed_forward1_linear1_weight_to_fp16_palettized, x = input_339_cast_fp16)[name = string("linear_55_cast_fp16")]; + tensor input_343_cast_fp16 = silu(x = linear_55_cast_fp16)[name = string("input_343_cast_fp16")]; + tensor encoder_layers_6_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135780928))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138926720))))[name = string("encoder_layers_6_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_6_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_6_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138926912)))]; + tensor linear_56_cast_fp16 = linear(bias = encoder_layers_6_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_6_feed_forward1_linear2_weight_to_fp16_palettized, x = input_343_cast_fp16)[name = string("linear_56_cast_fp16")]; + fp16 var_1733_to_fp16 = const()[name = string("op_1733_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1734_cast_fp16 = mul(x = linear_56_cast_fp16, y = var_1733_to_fp16)[name = string("op_1734_cast_fp16")]; + tensor input_349_cast_fp16 = add(x = input_337_cast_fp16, y = var_1734_cast_fp16)[name = string("input_349_cast_fp16")]; + tensor key_13_axes_0 = const()[name = string("key_13_axes_0"), val = tensor([-1])]; + tensor encoder_layers_6_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_6_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138929024)))]; + tensor encoder_layers_6_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_6_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138931136)))]; + tensor key_13_cast_fp16 = layer_norm(axes = key_13_axes_0, beta = encoder_layers_6_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_6_norm_self_att_weight_to_fp16, x = input_349_cast_fp16)[name = string("key_13_cast_fp16")]; + bool input_351_interleave_0 = const()[name = string("input_351_interleave_0"), val = bool(false)]; + tensor input_351_cast_fp16 = concat(axis = var_68, interleave = input_351_interleave_0, values = (cache_25_cast_fp16, key_13_cast_fp16))[name = string("input_351_cast_fp16")]; + tensor var_1756_begin_0 = const()[name = string("op_1756_begin_0"), val = tensor([0, 28, 0])]; + tensor var_1756_end_0 = const()[name = string("op_1756_end_0"), val = tensor([1, 42, 1024])]; + tensor var_1756_end_mask_0 = const()[name = string("op_1756_end_mask_0"), val = tensor([true, true, true])]; + tensor var_1756_cast_fp16 = slice_by_index(begin = var_1756_begin_0, end = var_1756_end_0, end_mask = var_1756_end_mask_0, x = cache_25_cast_fp16)[name = string("op_1756_cast_fp16")]; + bool var_1762_interleave_0 = const()[name = string("op_1762_interleave_0"), val = bool(false)]; + tensor var_1762_cast_fp16 = concat(axis = var_68, interleave = var_1762_interleave_0, values = (var_1756_cast_fp16, key_13_cast_fp16))[name = string("op_1762_cast_fp16")]; + tensor encoder_layers_6_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138933248))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(139719744))))[name = string("encoder_layers_6_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_6_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_6_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(139719936)))]; + tensor linear_57_cast_fp16 = linear(bias = encoder_layers_6_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_6_self_attn_linear_q_weight_to_fp16_palettized, x = key_13_cast_fp16)[name = string("linear_57_cast_fp16")]; + tensor var_1767 = const()[name = string("op_1767"), val = tensor([1, -1, 8, 128])]; + tensor q_37_cast_fp16 = reshape(shape = var_1767, x = linear_57_cast_fp16)[name = string("q_37_cast_fp16")]; + tensor encoder_layers_6_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(139722048))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140508544))))[name = string("encoder_layers_6_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_6_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_6_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140508736)))]; + tensor linear_58_cast_fp16 = linear(bias = encoder_layers_6_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_6_self_attn_linear_k_weight_to_fp16_palettized, x = input_351_cast_fp16)[name = string("linear_58_cast_fp16")]; + tensor var_1772 = const()[name = string("op_1772"), val = tensor([1, -1, 8, 128])]; + tensor k_25_cast_fp16 = reshape(shape = var_1772, x = linear_58_cast_fp16)[name = string("k_25_cast_fp16")]; + tensor encoder_layers_6_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140510848))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141297344))))[name = string("encoder_layers_6_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_6_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_6_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141297536)))]; + tensor linear_59_cast_fp16 = linear(bias = encoder_layers_6_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_6_self_attn_linear_v_weight_to_fp16_palettized, x = input_351_cast_fp16)[name = string("linear_59_cast_fp16")]; + tensor var_1777 = const()[name = string("op_1777"), val = tensor([1, -1, 8, 128])]; + tensor v_13_cast_fp16 = reshape(shape = var_1777, x = linear_59_cast_fp16)[name = string("v_13_cast_fp16")]; + tensor value_21_perm_0 = const()[name = string("value_21_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_6_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_6_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141299648)))]; + tensor var_1790_cast_fp16 = add(x = q_37_cast_fp16, y = encoder_layers_6_self_attn_pos_bias_u_to_fp16)[name = string("op_1790_cast_fp16")]; + tensor encoder_layers_6_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_6_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141301760)))]; + tensor var_1792_cast_fp16 = add(x = q_37_cast_fp16, y = encoder_layers_6_self_attn_pos_bias_v_to_fp16)[name = string("op_1792_cast_fp16")]; + tensor q_with_bias_v_13_perm_0 = const()[name = string("q_with_bias_v_13_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_163_transpose_x_0 = const()[name = string("x_163_transpose_x_0"), val = bool(false)]; + bool x_163_transpose_y_0 = const()[name = string("x_163_transpose_y_0"), val = bool(false)]; + tensor op_1794_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141303872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141446272))))[name = string("op_1794_to_fp16_quantized")]; + tensor q_with_bias_v_13_cast_fp16 = transpose(perm = q_with_bias_v_13_perm_0, x = var_1792_cast_fp16)[name = string("transpose_308")]; + tensor x_163_cast_fp16 = matmul(transpose_x = x_163_transpose_x_0, transpose_y = x_163_transpose_y_0, x = q_with_bias_v_13_cast_fp16, y = op_1794_to_fp16_quantized)[name = string("x_163_cast_fp16")]; + tensor x_165_pad_0 = const()[name = string("x_165_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_165_mode_0 = const()[name = string("x_165_mode_0"), val = string("constant")]; + fp16 const_157_to_fp16 = const()[name = string("const_157_to_fp16"), val = fp16(0x0p+0)]; + tensor x_165_cast_fp16 = pad(constant_val = const_157_to_fp16, mode = x_165_mode_0, pad = x_165_pad_0, x = x_163_cast_fp16)[name = string("x_165_cast_fp16")]; + tensor var_1802 = const()[name = string("op_1802"), val = tensor([1, 8, -1, 28])]; + tensor x_167_cast_fp16 = reshape(shape = var_1802, x = x_165_cast_fp16)[name = string("x_167_cast_fp16")]; + tensor var_1806_begin_0 = const()[name = string("op_1806_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_1806_end_0 = const()[name = string("op_1806_end_0"), val = tensor([1, 8, 140, 28])]; + tensor var_1806_end_mask_0 = const()[name = string("op_1806_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1806_cast_fp16 = slice_by_index(begin = var_1806_begin_0, end = var_1806_end_0, end_mask = var_1806_end_mask_0, x = x_167_cast_fp16)[name = string("op_1806_cast_fp16")]; + tensor var_1807 = const()[name = string("op_1807"), val = tensor([1, 8, 28, 139])]; + tensor matrix_bd_25_cast_fp16 = reshape(shape = var_1807, x = var_1806_cast_fp16)[name = string("matrix_bd_25_cast_fp16")]; + bool matrix_ac_13_transpose_x_0 = const()[name = string("matrix_ac_13_transpose_x_0"), val = bool(false)]; + bool matrix_ac_13_transpose_y_0 = const()[name = string("matrix_ac_13_transpose_y_0"), val = bool(false)]; + tensor transpose_108_perm_0 = const()[name = string("transpose_108_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_109_perm_0 = const()[name = string("transpose_109_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_109 = transpose(perm = transpose_109_perm_0, x = k_25_cast_fp16)[name = string("transpose_306")]; + tensor transpose_108 = transpose(perm = transpose_108_perm_0, x = var_1790_cast_fp16)[name = string("transpose_307")]; + tensor matrix_ac_13_cast_fp16 = matmul(transpose_x = matrix_ac_13_transpose_x_0, transpose_y = matrix_ac_13_transpose_y_0, x = transpose_108, y = transpose_109)[name = string("matrix_ac_13_cast_fp16")]; + tensor matrix_bd_27_begin_0 = const()[name = string("matrix_bd_27_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_27_end_0 = const()[name = string("matrix_bd_27_end_0"), val = tensor([1, 8, 28, 70])]; + tensor matrix_bd_27_end_mask_0 = const()[name = string("matrix_bd_27_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_27_cast_fp16 = slice_by_index(begin = matrix_bd_27_begin_0, end = matrix_bd_27_end_0, end_mask = matrix_bd_27_end_mask_0, x = matrix_bd_25_cast_fp16)[name = string("matrix_bd_27_cast_fp16")]; + tensor var_1816_cast_fp16 = add(x = matrix_ac_13_cast_fp16, y = matrix_bd_27_cast_fp16)[name = string("op_1816_cast_fp16")]; + fp16 _inversed_scores_25_y_0_to_fp16 = const()[name = string("_inversed_scores_25_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_25_cast_fp16 = mul(x = var_1816_cast_fp16, y = _inversed_scores_25_y_0_to_fp16)[name = string("_inversed_scores_25_cast_fp16")]; + tensor scores_27_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_25_cast_fp16, cond = mask_11)[name = string("scores_27_cast_fp16")]; + tensor var_1822_cast_fp16 = softmax(axis = var_59, x = scores_27_cast_fp16)[name = string("op_1822_cast_fp16")]; + tensor input_353_cast_fp16 = select(a = var_44_to_fp16, b = var_1822_cast_fp16, cond = mask_11)[name = string("input_353_cast_fp16")]; + bool x_169_transpose_x_0 = const()[name = string("x_169_transpose_x_0"), val = bool(false)]; + bool x_169_transpose_y_0 = const()[name = string("x_169_transpose_y_0"), val = bool(false)]; + tensor value_21_cast_fp16 = transpose(perm = value_21_perm_0, x = v_13_cast_fp16)[name = string("transpose_305")]; + tensor x_169_cast_fp16 = matmul(transpose_x = x_169_transpose_x_0, transpose_y = x_169_transpose_y_0, x = input_353_cast_fp16, y = value_21_cast_fp16)[name = string("x_169_cast_fp16")]; + tensor var_1826_perm_0 = const()[name = string("op_1826_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1827 = const()[name = string("op_1827"), val = tensor([1, -1, 1024])]; + tensor var_1826_cast_fp16 = transpose(perm = var_1826_perm_0, x = x_169_cast_fp16)[name = string("transpose_304")]; + tensor input_355_cast_fp16 = reshape(shape = var_1827, x = var_1826_cast_fp16)[name = string("input_355_cast_fp16")]; + tensor encoder_layers_6_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141446656))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142233152))))[name = string("encoder_layers_6_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_6_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_6_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142233344)))]; + tensor linear_61_cast_fp16 = linear(bias = encoder_layers_6_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_6_self_attn_linear_out_weight_to_fp16_palettized, x = input_355_cast_fp16)[name = string("linear_61_cast_fp16")]; + tensor input_359_cast_fp16 = add(x = input_349_cast_fp16, y = linear_61_cast_fp16)[name = string("input_359_cast_fp16")]; + tensor x_173_axes_0 = const()[name = string("x_173_axes_0"), val = tensor([-1])]; + tensor encoder_layers_6_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_6_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142235456)))]; + tensor encoder_layers_6_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_6_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142237568)))]; + tensor x_173_cast_fp16 = layer_norm(axes = x_173_axes_0, beta = encoder_layers_6_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_6_norm_conv_weight_to_fp16, x = input_359_cast_fp16)[name = string("x_173_cast_fp16")]; + tensor input_361_perm_0 = const()[name = string("input_361_perm_0"), val = tensor([0, 2, 1])]; + string input_363_pad_type_0 = const()[name = string("input_363_pad_type_0"), val = string("valid")]; + tensor input_363_strides_0 = const()[name = string("input_363_strides_0"), val = tensor([1])]; + tensor input_363_pad_0 = const()[name = string("input_363_pad_0"), val = tensor([0, 0])]; + tensor input_363_dilations_0 = const()[name = string("input_363_dilations_0"), val = tensor([1])]; + int32 input_363_groups_0 = const()[name = string("input_363_groups_0"), val = int32(1)]; + tensor encoder_layers_6_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142239680))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144336896))))[name = string("encoder_layers_6_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_361_cast_fp16 = transpose(perm = input_361_perm_0, x = x_173_cast_fp16)[name = string("transpose_303")]; + tensor input_363_cast_fp16 = conv(dilations = input_363_dilations_0, groups = input_363_groups_0, pad = input_363_pad_0, pad_type = input_363_pad_type_0, strides = input_363_strides_0, weight = encoder_layers_6_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_361_cast_fp16)[name = string("input_363_cast_fp16")]; + int32 x_175_split_num_splits_0 = const()[name = string("x_175_split_num_splits_0"), val = int32(2)]; + int32 x_175_split_axis_0 = const()[name = string("x_175_split_axis_0"), val = int32(1)]; + tensor x_175_split_cast_fp16_0, tensor x_175_split_cast_fp16_1 = split(axis = x_175_split_axis_0, num_splits = x_175_split_num_splits_0, x = input_363_cast_fp16)[name = string("x_175_split_cast_fp16")]; + tensor x_175_split_1_sigmoid_cast_fp16 = sigmoid(x = x_175_split_cast_fp16_1)[name = string("x_175_split_1_sigmoid_cast_fp16")]; + tensor x_175_cast_fp16 = mul(x = x_175_split_cast_fp16_0, y = x_175_split_1_sigmoid_cast_fp16)[name = string("x_175_cast_fp16")]; + tensor input_365_cast_fp16 = select(a = var_44_to_fp16, b = x_175_cast_fp16, cond = var_575)[name = string("input_365_cast_fp16")]; + bool new_x_27_interleave_0 = const()[name = string("new_x_27_interleave_0"), val = bool(false)]; + tensor new_x_27_cast_fp16 = concat(axis = var_59, interleave = new_x_27_interleave_0, values = (cache_27_cast_fp16, input_365_cast_fp16))[name = string("new_x_27_cast_fp16")]; + tensor var_1866_begin_0 = const()[name = string("op_1866_begin_0"), val = tensor([0, 0, 28])]; + tensor var_1866_end_0 = const()[name = string("op_1866_end_0"), val = tensor([1, 1024, 36])]; + tensor var_1866_end_mask_0 = const()[name = string("op_1866_end_mask_0"), val = tensor([true, true, true])]; + tensor var_1866_cast_fp16 = slice_by_index(begin = var_1866_begin_0, end = var_1866_end_0, end_mask = var_1866_end_mask_0, x = new_x_27_cast_fp16)[name = string("op_1866_cast_fp16")]; + string x_177_pad_type_0 = const()[name = string("x_177_pad_type_0"), val = string("valid")]; + int32 x_177_groups_0 = const()[name = string("x_177_groups_0"), val = int32(1024)]; + tensor x_177_strides_0 = const()[name = string("x_177_strides_0"), val = tensor([1])]; + tensor x_177_pad_0 = const()[name = string("x_177_pad_0"), val = tensor([0, 0])]; + tensor x_177_dilations_0 = const()[name = string("x_177_dilations_0"), val = tensor([1])]; + tensor encoder_layers_6_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144341056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144350336))))[name = string("encoder_layers_6_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_177_cast_fp16 = conv(dilations = x_177_dilations_0, groups = x_177_groups_0, pad = x_177_pad_0, pad_type = x_177_pad_type_0, strides = x_177_strides_0, weight = encoder_layers_6_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_27_cast_fp16)[name = string("x_177_cast_fp16")]; + tensor input_367_perm_0 = const()[name = string("input_367_perm_0"), val = tensor([0, 2, 1])]; + tensor x_179_axes_0 = const()[name = string("x_179_axes_0"), val = tensor([-1])]; + tensor encoder_layers_6_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_6_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144352448)))]; + tensor encoder_layers_6_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_6_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144354560)))]; + tensor input_367_cast_fp16 = transpose(perm = input_367_perm_0, x = x_177_cast_fp16)[name = string("transpose_302")]; + tensor x_179_cast_fp16 = layer_norm(axes = x_179_axes_0, beta = encoder_layers_6_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_6_conv_batch_norm_weight_to_fp16, x = input_367_cast_fp16)[name = string("x_179_cast_fp16")]; + tensor input_369_perm_0 = const()[name = string("input_369_perm_0"), val = tensor([0, 2, 1])]; + tensor input_369_cast_fp16 = transpose(perm = input_369_perm_0, x = x_179_cast_fp16)[name = string("transpose_301")]; + tensor input_371_cast_fp16 = silu(x = input_369_cast_fp16)[name = string("input_371_cast_fp16")]; + string x_181_pad_type_0 = const()[name = string("x_181_pad_type_0"), val = string("valid")]; + tensor x_181_strides_0 = const()[name = string("x_181_strides_0"), val = tensor([1])]; + tensor x_181_pad_0 = const()[name = string("x_181_pad_0"), val = tensor([0, 0])]; + tensor x_181_dilations_0 = const()[name = string("x_181_dilations_0"), val = tensor([1])]; + int32 x_181_groups_0 = const()[name = string("x_181_groups_0"), val = int32(1)]; + tensor encoder_layers_6_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144356672))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145405312))))[name = string("encoder_layers_6_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_181_cast_fp16 = conv(dilations = x_181_dilations_0, groups = x_181_groups_0, pad = x_181_pad_0, pad_type = x_181_pad_type_0, strides = x_181_strides_0, weight = encoder_layers_6_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_371_cast_fp16)[name = string("x_181_cast_fp16")]; + tensor input_373_perm_0 = const()[name = string("input_373_perm_0"), val = tensor([0, 2, 1])]; + tensor input_373_cast_fp16 = transpose(perm = input_373_perm_0, x = x_181_cast_fp16)[name = string("transpose_300")]; + tensor input_375_cast_fp16 = add(x = input_359_cast_fp16, y = input_373_cast_fp16)[name = string("input_375_cast_fp16")]; + tensor input_377_axes_0 = const()[name = string("input_377_axes_0"), val = tensor([-1])]; + tensor encoder_layers_6_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_6_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145407424)))]; + tensor encoder_layers_6_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_6_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145409536)))]; + tensor input_377_cast_fp16 = layer_norm(axes = input_377_axes_0, beta = encoder_layers_6_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_6_norm_feed_forward2_weight_to_fp16, x = input_375_cast_fp16)[name = string("input_377_cast_fp16")]; + tensor encoder_layers_6_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145411648))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(148557440))))[name = string("encoder_layers_6_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_6_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_6_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(148557632)))]; + tensor linear_62_cast_fp16 = linear(bias = encoder_layers_6_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_6_feed_forward2_linear1_weight_to_fp16_palettized, x = input_377_cast_fp16)[name = string("linear_62_cast_fp16")]; + tensor input_381_cast_fp16 = silu(x = linear_62_cast_fp16)[name = string("input_381_cast_fp16")]; + tensor encoder_layers_6_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(148565888))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(151711680))))[name = string("encoder_layers_6_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_6_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_6_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(151711872)))]; + tensor linear_63_cast_fp16 = linear(bias = encoder_layers_6_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_6_feed_forward2_linear2_weight_to_fp16_palettized, x = input_381_cast_fp16)[name = string("linear_63_cast_fp16")]; + fp16 var_1909_to_fp16 = const()[name = string("op_1909_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1910_cast_fp16 = mul(x = linear_63_cast_fp16, y = var_1909_to_fp16)[name = string("op_1910_cast_fp16")]; + tensor input_387_cast_fp16 = add(x = input_375_cast_fp16, y = var_1910_cast_fp16)[name = string("input_387_cast_fp16")]; + tensor input_389_axes_0 = const()[name = string("input_389_axes_0"), val = tensor([-1])]; + tensor encoder_layers_6_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_6_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(151713984)))]; + tensor encoder_layers_6_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_6_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(151716096)))]; + tensor input_389_cast_fp16 = layer_norm(axes = input_389_axes_0, beta = encoder_layers_6_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_6_norm_out_weight_to_fp16, x = input_387_cast_fp16)[name = string("input_389_cast_fp16")]; + tensor cache_29_begin_0 = const()[name = string("cache_29_begin_0"), val = tensor([7, 0, 0, 0])]; + tensor cache_29_end_0 = const()[name = string("cache_29_end_0"), val = tensor([8, 1, 42, 1024])]; + tensor cache_29_end_mask_0 = const()[name = string("cache_29_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_29_squeeze_mask_0 = const()[name = string("cache_29_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_29_cast_fp16 = slice_by_index(begin = cache_29_begin_0, end = cache_29_end_0, end_mask = cache_29_end_mask_0, squeeze_mask = cache_29_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_29_cast_fp16")]; + tensor cache_31_begin_0 = const()[name = string("cache_31_begin_0"), val = tensor([7, 0, 0, 0])]; + tensor cache_31_end_0 = const()[name = string("cache_31_end_0"), val = tensor([8, 1, 1024, 8])]; + tensor cache_31_end_mask_0 = const()[name = string("cache_31_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_31_squeeze_mask_0 = const()[name = string("cache_31_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_31_cast_fp16 = slice_by_index(begin = cache_31_begin_0, end = cache_31_end_0, end_mask = cache_31_end_mask_0, squeeze_mask = cache_31_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_31_cast_fp16")]; + tensor input_391_axes_0 = const()[name = string("input_391_axes_0"), val = tensor([-1])]; + tensor encoder_layers_7_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_7_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(151718208)))]; + tensor encoder_layers_7_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_7_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(151720320)))]; + tensor input_391_cast_fp16 = layer_norm(axes = input_391_axes_0, beta = encoder_layers_7_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_7_norm_feed_forward1_weight_to_fp16, x = input_389_cast_fp16)[name = string("input_391_cast_fp16")]; + tensor encoder_layers_7_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(151722432))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154868224))))[name = string("encoder_layers_7_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_7_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_7_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154868416)))]; + tensor linear_64_cast_fp16 = linear(bias = encoder_layers_7_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_7_feed_forward1_linear1_weight_to_fp16_palettized, x = input_391_cast_fp16)[name = string("linear_64_cast_fp16")]; + tensor input_395_cast_fp16 = silu(x = linear_64_cast_fp16)[name = string("input_395_cast_fp16")]; + tensor encoder_layers_7_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154876672))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158022464))))[name = string("encoder_layers_7_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_7_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_7_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158022656)))]; + tensor linear_65_cast_fp16 = linear(bias = encoder_layers_7_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_7_feed_forward1_linear2_weight_to_fp16_palettized, x = input_395_cast_fp16)[name = string("linear_65_cast_fp16")]; + fp16 var_1946_to_fp16 = const()[name = string("op_1946_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1947_cast_fp16 = mul(x = linear_65_cast_fp16, y = var_1946_to_fp16)[name = string("op_1947_cast_fp16")]; + tensor input_401_cast_fp16 = add(x = input_389_cast_fp16, y = var_1947_cast_fp16)[name = string("input_401_cast_fp16")]; + tensor key_15_axes_0 = const()[name = string("key_15_axes_0"), val = tensor([-1])]; + tensor encoder_layers_7_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_7_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158024768)))]; + tensor encoder_layers_7_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_7_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158026880)))]; + tensor key_15_cast_fp16 = layer_norm(axes = key_15_axes_0, beta = encoder_layers_7_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_7_norm_self_att_weight_to_fp16, x = input_401_cast_fp16)[name = string("key_15_cast_fp16")]; + bool input_403_interleave_0 = const()[name = string("input_403_interleave_0"), val = bool(false)]; + tensor input_403_cast_fp16 = concat(axis = var_68, interleave = input_403_interleave_0, values = (cache_29_cast_fp16, key_15_cast_fp16))[name = string("input_403_cast_fp16")]; + tensor var_1969_begin_0 = const()[name = string("op_1969_begin_0"), val = tensor([0, 28, 0])]; + tensor var_1969_end_0 = const()[name = string("op_1969_end_0"), val = tensor([1, 42, 1024])]; + tensor var_1969_end_mask_0 = const()[name = string("op_1969_end_mask_0"), val = tensor([true, true, true])]; + tensor var_1969_cast_fp16 = slice_by_index(begin = var_1969_begin_0, end = var_1969_end_0, end_mask = var_1969_end_mask_0, x = cache_29_cast_fp16)[name = string("op_1969_cast_fp16")]; + bool var_1975_interleave_0 = const()[name = string("op_1975_interleave_0"), val = bool(false)]; + tensor var_1975_cast_fp16 = concat(axis = var_68, interleave = var_1975_interleave_0, values = (var_1969_cast_fp16, key_15_cast_fp16))[name = string("op_1975_cast_fp16")]; + tensor encoder_layers_7_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158028992))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158815488))))[name = string("encoder_layers_7_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_7_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_7_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158815680)))]; + tensor linear_66_cast_fp16 = linear(bias = encoder_layers_7_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_7_self_attn_linear_q_weight_to_fp16_palettized, x = key_15_cast_fp16)[name = string("linear_66_cast_fp16")]; + tensor var_1980 = const()[name = string("op_1980"), val = tensor([1, -1, 8, 128])]; + tensor q_43_cast_fp16 = reshape(shape = var_1980, x = linear_66_cast_fp16)[name = string("q_43_cast_fp16")]; + tensor encoder_layers_7_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158817792))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(159604288))))[name = string("encoder_layers_7_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_7_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_7_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(159604480)))]; + tensor linear_67_cast_fp16 = linear(bias = encoder_layers_7_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_7_self_attn_linear_k_weight_to_fp16_palettized, x = input_403_cast_fp16)[name = string("linear_67_cast_fp16")]; + tensor var_1985 = const()[name = string("op_1985"), val = tensor([1, -1, 8, 128])]; + tensor k_29_cast_fp16 = reshape(shape = var_1985, x = linear_67_cast_fp16)[name = string("k_29_cast_fp16")]; + tensor encoder_layers_7_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(159606592))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160393088))))[name = string("encoder_layers_7_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_7_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_7_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160393280)))]; + tensor linear_68_cast_fp16 = linear(bias = encoder_layers_7_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_7_self_attn_linear_v_weight_to_fp16_palettized, x = input_403_cast_fp16)[name = string("linear_68_cast_fp16")]; + tensor var_1990 = const()[name = string("op_1990"), val = tensor([1, -1, 8, 128])]; + tensor v_15_cast_fp16 = reshape(shape = var_1990, x = linear_68_cast_fp16)[name = string("v_15_cast_fp16")]; + tensor value_23_perm_0 = const()[name = string("value_23_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_7_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_7_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160395392)))]; + tensor var_2003_cast_fp16 = add(x = q_43_cast_fp16, y = encoder_layers_7_self_attn_pos_bias_u_to_fp16)[name = string("op_2003_cast_fp16")]; + tensor encoder_layers_7_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_7_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160397504)))]; + tensor var_2005_cast_fp16 = add(x = q_43_cast_fp16, y = encoder_layers_7_self_attn_pos_bias_v_to_fp16)[name = string("op_2005_cast_fp16")]; + tensor q_with_bias_v_15_perm_0 = const()[name = string("q_with_bias_v_15_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_189_transpose_x_0 = const()[name = string("x_189_transpose_x_0"), val = bool(false)]; + bool x_189_transpose_y_0 = const()[name = string("x_189_transpose_y_0"), val = bool(false)]; + tensor op_2007_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160399616))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160542016))))[name = string("op_2007_to_fp16_quantized")]; + tensor q_with_bias_v_15_cast_fp16 = transpose(perm = q_with_bias_v_15_perm_0, x = var_2005_cast_fp16)[name = string("transpose_299")]; + tensor x_189_cast_fp16 = matmul(transpose_x = x_189_transpose_x_0, transpose_y = x_189_transpose_y_0, x = q_with_bias_v_15_cast_fp16, y = op_2007_to_fp16_quantized)[name = string("x_189_cast_fp16")]; + tensor x_191_pad_0 = const()[name = string("x_191_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_191_mode_0 = const()[name = string("x_191_mode_0"), val = string("constant")]; + fp16 const_170_to_fp16 = const()[name = string("const_170_to_fp16"), val = fp16(0x0p+0)]; + tensor x_191_cast_fp16 = pad(constant_val = const_170_to_fp16, mode = x_191_mode_0, pad = x_191_pad_0, x = x_189_cast_fp16)[name = string("x_191_cast_fp16")]; + tensor var_2015 = const()[name = string("op_2015"), val = tensor([1, 8, -1, 28])]; + tensor x_193_cast_fp16 = reshape(shape = var_2015, x = x_191_cast_fp16)[name = string("x_193_cast_fp16")]; + tensor var_2019_begin_0 = const()[name = string("op_2019_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_2019_end_0 = const()[name = string("op_2019_end_0"), val = tensor([1, 8, 140, 28])]; + tensor var_2019_end_mask_0 = const()[name = string("op_2019_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_2019_cast_fp16 = slice_by_index(begin = var_2019_begin_0, end = var_2019_end_0, end_mask = var_2019_end_mask_0, x = x_193_cast_fp16)[name = string("op_2019_cast_fp16")]; + tensor var_2020 = const()[name = string("op_2020"), val = tensor([1, 8, 28, 139])]; + tensor matrix_bd_29_cast_fp16 = reshape(shape = var_2020, x = var_2019_cast_fp16)[name = string("matrix_bd_29_cast_fp16")]; + bool matrix_ac_15_transpose_x_0 = const()[name = string("matrix_ac_15_transpose_x_0"), val = bool(false)]; + bool matrix_ac_15_transpose_y_0 = const()[name = string("matrix_ac_15_transpose_y_0"), val = bool(false)]; + tensor transpose_110_perm_0 = const()[name = string("transpose_110_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_111_perm_0 = const()[name = string("transpose_111_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_111 = transpose(perm = transpose_111_perm_0, x = k_29_cast_fp16)[name = string("transpose_297")]; + tensor transpose_110 = transpose(perm = transpose_110_perm_0, x = var_2003_cast_fp16)[name = string("transpose_298")]; + tensor matrix_ac_15_cast_fp16 = matmul(transpose_x = matrix_ac_15_transpose_x_0, transpose_y = matrix_ac_15_transpose_y_0, x = transpose_110, y = transpose_111)[name = string("matrix_ac_15_cast_fp16")]; + tensor matrix_bd_31_begin_0 = const()[name = string("matrix_bd_31_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_31_end_0 = const()[name = string("matrix_bd_31_end_0"), val = tensor([1, 8, 28, 70])]; + tensor matrix_bd_31_end_mask_0 = const()[name = string("matrix_bd_31_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_31_cast_fp16 = slice_by_index(begin = matrix_bd_31_begin_0, end = matrix_bd_31_end_0, end_mask = matrix_bd_31_end_mask_0, x = matrix_bd_29_cast_fp16)[name = string("matrix_bd_31_cast_fp16")]; + tensor var_2029_cast_fp16 = add(x = matrix_ac_15_cast_fp16, y = matrix_bd_31_cast_fp16)[name = string("op_2029_cast_fp16")]; + fp16 _inversed_scores_29_y_0_to_fp16 = const()[name = string("_inversed_scores_29_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_29_cast_fp16 = mul(x = var_2029_cast_fp16, y = _inversed_scores_29_y_0_to_fp16)[name = string("_inversed_scores_29_cast_fp16")]; + tensor scores_31_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_29_cast_fp16, cond = mask_11)[name = string("scores_31_cast_fp16")]; + tensor var_2035_cast_fp16 = softmax(axis = var_59, x = scores_31_cast_fp16)[name = string("op_2035_cast_fp16")]; + tensor input_405_cast_fp16 = select(a = var_44_to_fp16, b = var_2035_cast_fp16, cond = mask_11)[name = string("input_405_cast_fp16")]; + bool x_195_transpose_x_0 = const()[name = string("x_195_transpose_x_0"), val = bool(false)]; + bool x_195_transpose_y_0 = const()[name = string("x_195_transpose_y_0"), val = bool(false)]; + tensor value_23_cast_fp16 = transpose(perm = value_23_perm_0, x = v_15_cast_fp16)[name = string("transpose_296")]; + tensor x_195_cast_fp16 = matmul(transpose_x = x_195_transpose_x_0, transpose_y = x_195_transpose_y_0, x = input_405_cast_fp16, y = value_23_cast_fp16)[name = string("x_195_cast_fp16")]; + tensor var_2039_perm_0 = const()[name = string("op_2039_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2040 = const()[name = string("op_2040"), val = tensor([1, -1, 1024])]; + tensor var_2039_cast_fp16 = transpose(perm = var_2039_perm_0, x = x_195_cast_fp16)[name = string("transpose_295")]; + tensor input_407_cast_fp16 = reshape(shape = var_2040, x = var_2039_cast_fp16)[name = string("input_407_cast_fp16")]; + tensor encoder_layers_7_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160542400))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(161328896))))[name = string("encoder_layers_7_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_7_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_7_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(161329088)))]; + tensor linear_70_cast_fp16 = linear(bias = encoder_layers_7_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_7_self_attn_linear_out_weight_to_fp16_palettized, x = input_407_cast_fp16)[name = string("linear_70_cast_fp16")]; + tensor input_411_cast_fp16 = add(x = input_401_cast_fp16, y = linear_70_cast_fp16)[name = string("input_411_cast_fp16")]; + tensor x_199_axes_0 = const()[name = string("x_199_axes_0"), val = tensor([-1])]; + tensor encoder_layers_7_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_7_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(161331200)))]; + tensor encoder_layers_7_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_7_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(161333312)))]; + tensor x_199_cast_fp16 = layer_norm(axes = x_199_axes_0, beta = encoder_layers_7_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_7_norm_conv_weight_to_fp16, x = input_411_cast_fp16)[name = string("x_199_cast_fp16")]; + tensor input_413_perm_0 = const()[name = string("input_413_perm_0"), val = tensor([0, 2, 1])]; + string input_415_pad_type_0 = const()[name = string("input_415_pad_type_0"), val = string("valid")]; + tensor input_415_strides_0 = const()[name = string("input_415_strides_0"), val = tensor([1])]; + tensor input_415_pad_0 = const()[name = string("input_415_pad_0"), val = tensor([0, 0])]; + tensor input_415_dilations_0 = const()[name = string("input_415_dilations_0"), val = tensor([1])]; + int32 input_415_groups_0 = const()[name = string("input_415_groups_0"), val = int32(1)]; + tensor encoder_layers_7_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(161335424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163432640))))[name = string("encoder_layers_7_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_413_cast_fp16 = transpose(perm = input_413_perm_0, x = x_199_cast_fp16)[name = string("transpose_294")]; + tensor input_415_cast_fp16 = conv(dilations = input_415_dilations_0, groups = input_415_groups_0, pad = input_415_pad_0, pad_type = input_415_pad_type_0, strides = input_415_strides_0, weight = encoder_layers_7_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_413_cast_fp16)[name = string("input_415_cast_fp16")]; + int32 x_201_split_num_splits_0 = const()[name = string("x_201_split_num_splits_0"), val = int32(2)]; + int32 x_201_split_axis_0 = const()[name = string("x_201_split_axis_0"), val = int32(1)]; + tensor x_201_split_cast_fp16_0, tensor x_201_split_cast_fp16_1 = split(axis = x_201_split_axis_0, num_splits = x_201_split_num_splits_0, x = input_415_cast_fp16)[name = string("x_201_split_cast_fp16")]; + tensor x_201_split_1_sigmoid_cast_fp16 = sigmoid(x = x_201_split_cast_fp16_1)[name = string("x_201_split_1_sigmoid_cast_fp16")]; + tensor x_201_cast_fp16 = mul(x = x_201_split_cast_fp16_0, y = x_201_split_1_sigmoid_cast_fp16)[name = string("x_201_cast_fp16")]; + tensor input_417_cast_fp16 = select(a = var_44_to_fp16, b = x_201_cast_fp16, cond = var_575)[name = string("input_417_cast_fp16")]; + bool new_x_31_interleave_0 = const()[name = string("new_x_31_interleave_0"), val = bool(false)]; + tensor new_x_31_cast_fp16 = concat(axis = var_59, interleave = new_x_31_interleave_0, values = (cache_31_cast_fp16, input_417_cast_fp16))[name = string("new_x_31_cast_fp16")]; + tensor var_2079_begin_0 = const()[name = string("op_2079_begin_0"), val = tensor([0, 0, 28])]; + tensor var_2079_end_0 = const()[name = string("op_2079_end_0"), val = tensor([1, 1024, 36])]; + tensor var_2079_end_mask_0 = const()[name = string("op_2079_end_mask_0"), val = tensor([true, true, true])]; + tensor var_2079_cast_fp16 = slice_by_index(begin = var_2079_begin_0, end = var_2079_end_0, end_mask = var_2079_end_mask_0, x = new_x_31_cast_fp16)[name = string("op_2079_cast_fp16")]; + string x_203_pad_type_0 = const()[name = string("x_203_pad_type_0"), val = string("valid")]; + int32 x_203_groups_0 = const()[name = string("x_203_groups_0"), val = int32(1024)]; + tensor x_203_strides_0 = const()[name = string("x_203_strides_0"), val = tensor([1])]; + tensor x_203_pad_0 = const()[name = string("x_203_pad_0"), val = tensor([0, 0])]; + tensor x_203_dilations_0 = const()[name = string("x_203_dilations_0"), val = tensor([1])]; + tensor encoder_layers_7_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163436800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163446080))))[name = string("encoder_layers_7_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_203_cast_fp16 = conv(dilations = x_203_dilations_0, groups = x_203_groups_0, pad = x_203_pad_0, pad_type = x_203_pad_type_0, strides = x_203_strides_0, weight = encoder_layers_7_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_31_cast_fp16)[name = string("x_203_cast_fp16")]; + tensor input_419_perm_0 = const()[name = string("input_419_perm_0"), val = tensor([0, 2, 1])]; + tensor x_205_axes_0 = const()[name = string("x_205_axes_0"), val = tensor([-1])]; + tensor encoder_layers_7_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_7_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163448192)))]; + tensor encoder_layers_7_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_7_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163450304)))]; + tensor input_419_cast_fp16 = transpose(perm = input_419_perm_0, x = x_203_cast_fp16)[name = string("transpose_293")]; + tensor x_205_cast_fp16 = layer_norm(axes = x_205_axes_0, beta = encoder_layers_7_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_7_conv_batch_norm_weight_to_fp16, x = input_419_cast_fp16)[name = string("x_205_cast_fp16")]; + tensor input_421_perm_0 = const()[name = string("input_421_perm_0"), val = tensor([0, 2, 1])]; + tensor input_421_cast_fp16 = transpose(perm = input_421_perm_0, x = x_205_cast_fp16)[name = string("transpose_292")]; + tensor input_423_cast_fp16 = silu(x = input_421_cast_fp16)[name = string("input_423_cast_fp16")]; + string x_207_pad_type_0 = const()[name = string("x_207_pad_type_0"), val = string("valid")]; + tensor x_207_strides_0 = const()[name = string("x_207_strides_0"), val = tensor([1])]; + tensor x_207_pad_0 = const()[name = string("x_207_pad_0"), val = tensor([0, 0])]; + tensor x_207_dilations_0 = const()[name = string("x_207_dilations_0"), val = tensor([1])]; + int32 x_207_groups_0 = const()[name = string("x_207_groups_0"), val = int32(1)]; + tensor encoder_layers_7_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163452416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164501056))))[name = string("encoder_layers_7_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_207_cast_fp16 = conv(dilations = x_207_dilations_0, groups = x_207_groups_0, pad = x_207_pad_0, pad_type = x_207_pad_type_0, strides = x_207_strides_0, weight = encoder_layers_7_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_423_cast_fp16)[name = string("x_207_cast_fp16")]; + tensor input_425_perm_0 = const()[name = string("input_425_perm_0"), val = tensor([0, 2, 1])]; + tensor input_425_cast_fp16 = transpose(perm = input_425_perm_0, x = x_207_cast_fp16)[name = string("transpose_291")]; + tensor input_427_cast_fp16 = add(x = input_411_cast_fp16, y = input_425_cast_fp16)[name = string("input_427_cast_fp16")]; + tensor input_429_axes_0 = const()[name = string("input_429_axes_0"), val = tensor([-1])]; + tensor encoder_layers_7_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_7_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164503168)))]; + tensor encoder_layers_7_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_7_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164505280)))]; + tensor input_429_cast_fp16 = layer_norm(axes = input_429_axes_0, beta = encoder_layers_7_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_7_norm_feed_forward2_weight_to_fp16, x = input_427_cast_fp16)[name = string("input_429_cast_fp16")]; + tensor encoder_layers_7_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164507392))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167653184))))[name = string("encoder_layers_7_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_7_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_7_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167653376)))]; + tensor linear_71_cast_fp16 = linear(bias = encoder_layers_7_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_7_feed_forward2_linear1_weight_to_fp16_palettized, x = input_429_cast_fp16)[name = string("linear_71_cast_fp16")]; + tensor input_433_cast_fp16 = silu(x = linear_71_cast_fp16)[name = string("input_433_cast_fp16")]; + tensor encoder_layers_7_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167661632))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170807424))))[name = string("encoder_layers_7_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_7_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_7_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170807616)))]; + tensor linear_72_cast_fp16 = linear(bias = encoder_layers_7_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_7_feed_forward2_linear2_weight_to_fp16_palettized, x = input_433_cast_fp16)[name = string("linear_72_cast_fp16")]; + fp16 var_2122_to_fp16 = const()[name = string("op_2122_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2123_cast_fp16 = mul(x = linear_72_cast_fp16, y = var_2122_to_fp16)[name = string("op_2123_cast_fp16")]; + tensor input_439_cast_fp16 = add(x = input_427_cast_fp16, y = var_2123_cast_fp16)[name = string("input_439_cast_fp16")]; + tensor input_441_axes_0 = const()[name = string("input_441_axes_0"), val = tensor([-1])]; + tensor encoder_layers_7_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_7_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170809728)))]; + tensor encoder_layers_7_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_7_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170811840)))]; + tensor input_441_cast_fp16 = layer_norm(axes = input_441_axes_0, beta = encoder_layers_7_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_7_norm_out_weight_to_fp16, x = input_439_cast_fp16)[name = string("input_441_cast_fp16")]; + tensor cache_33_begin_0 = const()[name = string("cache_33_begin_0"), val = tensor([8, 0, 0, 0])]; + tensor cache_33_end_0 = const()[name = string("cache_33_end_0"), val = tensor([9, 1, 42, 1024])]; + tensor cache_33_end_mask_0 = const()[name = string("cache_33_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_33_squeeze_mask_0 = const()[name = string("cache_33_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_33_cast_fp16 = slice_by_index(begin = cache_33_begin_0, end = cache_33_end_0, end_mask = cache_33_end_mask_0, squeeze_mask = cache_33_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_33_cast_fp16")]; + tensor cache_35_begin_0 = const()[name = string("cache_35_begin_0"), val = tensor([8, 0, 0, 0])]; + tensor cache_35_end_0 = const()[name = string("cache_35_end_0"), val = tensor([9, 1, 1024, 8])]; + tensor cache_35_end_mask_0 = const()[name = string("cache_35_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_35_squeeze_mask_0 = const()[name = string("cache_35_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_35_cast_fp16 = slice_by_index(begin = cache_35_begin_0, end = cache_35_end_0, end_mask = cache_35_end_mask_0, squeeze_mask = cache_35_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_35_cast_fp16")]; + tensor input_443_axes_0 = const()[name = string("input_443_axes_0"), val = tensor([-1])]; + tensor encoder_layers_8_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_8_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170813952)))]; + tensor encoder_layers_8_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_8_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170816064)))]; + tensor input_443_cast_fp16 = layer_norm(axes = input_443_axes_0, beta = encoder_layers_8_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_8_norm_feed_forward1_weight_to_fp16, x = input_441_cast_fp16)[name = string("input_443_cast_fp16")]; + tensor encoder_layers_8_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170818176))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(173963968))))[name = string("encoder_layers_8_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_8_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_8_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(173964160)))]; + tensor linear_73_cast_fp16 = linear(bias = encoder_layers_8_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_8_feed_forward1_linear1_weight_to_fp16_palettized, x = input_443_cast_fp16)[name = string("linear_73_cast_fp16")]; + tensor input_447_cast_fp16 = silu(x = linear_73_cast_fp16)[name = string("input_447_cast_fp16")]; + tensor encoder_layers_8_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(173972416))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177118208))))[name = string("encoder_layers_8_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_8_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_8_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177118400)))]; + tensor linear_74_cast_fp16 = linear(bias = encoder_layers_8_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_8_feed_forward1_linear2_weight_to_fp16_palettized, x = input_447_cast_fp16)[name = string("linear_74_cast_fp16")]; + fp16 var_2159_to_fp16 = const()[name = string("op_2159_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2160_cast_fp16 = mul(x = linear_74_cast_fp16, y = var_2159_to_fp16)[name = string("op_2160_cast_fp16")]; + tensor input_453_cast_fp16 = add(x = input_441_cast_fp16, y = var_2160_cast_fp16)[name = string("input_453_cast_fp16")]; + tensor key_17_axes_0 = const()[name = string("key_17_axes_0"), val = tensor([-1])]; + tensor encoder_layers_8_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_8_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177120512)))]; + tensor encoder_layers_8_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_8_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177122624)))]; + tensor key_17_cast_fp16 = layer_norm(axes = key_17_axes_0, beta = encoder_layers_8_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_8_norm_self_att_weight_to_fp16, x = input_453_cast_fp16)[name = string("key_17_cast_fp16")]; + bool input_455_interleave_0 = const()[name = string("input_455_interleave_0"), val = bool(false)]; + tensor input_455_cast_fp16 = concat(axis = var_68, interleave = input_455_interleave_0, values = (cache_33_cast_fp16, key_17_cast_fp16))[name = string("input_455_cast_fp16")]; + tensor var_2182_begin_0 = const()[name = string("op_2182_begin_0"), val = tensor([0, 28, 0])]; + tensor var_2182_end_0 = const()[name = string("op_2182_end_0"), val = tensor([1, 42, 1024])]; + tensor var_2182_end_mask_0 = const()[name = string("op_2182_end_mask_0"), val = tensor([true, true, true])]; + tensor var_2182_cast_fp16 = slice_by_index(begin = var_2182_begin_0, end = var_2182_end_0, end_mask = var_2182_end_mask_0, x = cache_33_cast_fp16)[name = string("op_2182_cast_fp16")]; + bool var_2188_interleave_0 = const()[name = string("op_2188_interleave_0"), val = bool(false)]; + tensor var_2188_cast_fp16 = concat(axis = var_68, interleave = var_2188_interleave_0, values = (var_2182_cast_fp16, key_17_cast_fp16))[name = string("op_2188_cast_fp16")]; + tensor encoder_layers_8_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177124736))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177911232))))[name = string("encoder_layers_8_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_8_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_8_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177911424)))]; + tensor linear_75_cast_fp16 = linear(bias = encoder_layers_8_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_8_self_attn_linear_q_weight_to_fp16_palettized, x = key_17_cast_fp16)[name = string("linear_75_cast_fp16")]; + tensor var_2193 = const()[name = string("op_2193"), val = tensor([1, -1, 8, 128])]; + tensor q_49_cast_fp16 = reshape(shape = var_2193, x = linear_75_cast_fp16)[name = string("q_49_cast_fp16")]; + tensor encoder_layers_8_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177913536))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(178700032))))[name = string("encoder_layers_8_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_8_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_8_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(178700224)))]; + tensor linear_76_cast_fp16 = linear(bias = encoder_layers_8_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_8_self_attn_linear_k_weight_to_fp16_palettized, x = input_455_cast_fp16)[name = string("linear_76_cast_fp16")]; + tensor var_2198 = const()[name = string("op_2198"), val = tensor([1, -1, 8, 128])]; + tensor k_33_cast_fp16 = reshape(shape = var_2198, x = linear_76_cast_fp16)[name = string("k_33_cast_fp16")]; + tensor encoder_layers_8_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(178702336))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179488832))))[name = string("encoder_layers_8_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_8_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_8_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179489024)))]; + tensor linear_77_cast_fp16 = linear(bias = encoder_layers_8_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_8_self_attn_linear_v_weight_to_fp16_palettized, x = input_455_cast_fp16)[name = string("linear_77_cast_fp16")]; + tensor var_2203 = const()[name = string("op_2203"), val = tensor([1, -1, 8, 128])]; + tensor v_17_cast_fp16 = reshape(shape = var_2203, x = linear_77_cast_fp16)[name = string("v_17_cast_fp16")]; + tensor value_25_perm_0 = const()[name = string("value_25_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_8_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_8_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179491136)))]; + tensor var_2216_cast_fp16 = add(x = q_49_cast_fp16, y = encoder_layers_8_self_attn_pos_bias_u_to_fp16)[name = string("op_2216_cast_fp16")]; + tensor encoder_layers_8_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_8_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179493248)))]; + tensor var_2218_cast_fp16 = add(x = q_49_cast_fp16, y = encoder_layers_8_self_attn_pos_bias_v_to_fp16)[name = string("op_2218_cast_fp16")]; + tensor q_with_bias_v_17_perm_0 = const()[name = string("q_with_bias_v_17_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_215_transpose_x_0 = const()[name = string("x_215_transpose_x_0"), val = bool(false)]; + bool x_215_transpose_y_0 = const()[name = string("x_215_transpose_y_0"), val = bool(false)]; + tensor op_2220_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179495360))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179637760))))[name = string("op_2220_to_fp16_quantized")]; + tensor q_with_bias_v_17_cast_fp16 = transpose(perm = q_with_bias_v_17_perm_0, x = var_2218_cast_fp16)[name = string("transpose_290")]; + tensor x_215_cast_fp16 = matmul(transpose_x = x_215_transpose_x_0, transpose_y = x_215_transpose_y_0, x = q_with_bias_v_17_cast_fp16, y = op_2220_to_fp16_quantized)[name = string("x_215_cast_fp16")]; + tensor x_217_pad_0 = const()[name = string("x_217_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_217_mode_0 = const()[name = string("x_217_mode_0"), val = string("constant")]; + fp16 const_183_to_fp16 = const()[name = string("const_183_to_fp16"), val = fp16(0x0p+0)]; + tensor x_217_cast_fp16 = pad(constant_val = const_183_to_fp16, mode = x_217_mode_0, pad = x_217_pad_0, x = x_215_cast_fp16)[name = string("x_217_cast_fp16")]; + tensor var_2228 = const()[name = string("op_2228"), val = tensor([1, 8, -1, 28])]; + tensor x_219_cast_fp16 = reshape(shape = var_2228, x = x_217_cast_fp16)[name = string("x_219_cast_fp16")]; + tensor var_2232_begin_0 = const()[name = string("op_2232_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_2232_end_0 = const()[name = string("op_2232_end_0"), val = tensor([1, 8, 140, 28])]; + tensor var_2232_end_mask_0 = const()[name = string("op_2232_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_2232_cast_fp16 = slice_by_index(begin = var_2232_begin_0, end = var_2232_end_0, end_mask = var_2232_end_mask_0, x = x_219_cast_fp16)[name = string("op_2232_cast_fp16")]; + tensor var_2233 = const()[name = string("op_2233"), val = tensor([1, 8, 28, 139])]; + tensor matrix_bd_33_cast_fp16 = reshape(shape = var_2233, x = var_2232_cast_fp16)[name = string("matrix_bd_33_cast_fp16")]; + bool matrix_ac_17_transpose_x_0 = const()[name = string("matrix_ac_17_transpose_x_0"), val = bool(false)]; + bool matrix_ac_17_transpose_y_0 = const()[name = string("matrix_ac_17_transpose_y_0"), val = bool(false)]; + tensor transpose_112_perm_0 = const()[name = string("transpose_112_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_113_perm_0 = const()[name = string("transpose_113_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_113 = transpose(perm = transpose_113_perm_0, x = k_33_cast_fp16)[name = string("transpose_288")]; + tensor transpose_112 = transpose(perm = transpose_112_perm_0, x = var_2216_cast_fp16)[name = string("transpose_289")]; + tensor matrix_ac_17_cast_fp16 = matmul(transpose_x = matrix_ac_17_transpose_x_0, transpose_y = matrix_ac_17_transpose_y_0, x = transpose_112, y = transpose_113)[name = string("matrix_ac_17_cast_fp16")]; + tensor matrix_bd_35_begin_0 = const()[name = string("matrix_bd_35_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_35_end_0 = const()[name = string("matrix_bd_35_end_0"), val = tensor([1, 8, 28, 70])]; + tensor matrix_bd_35_end_mask_0 = const()[name = string("matrix_bd_35_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_35_cast_fp16 = slice_by_index(begin = matrix_bd_35_begin_0, end = matrix_bd_35_end_0, end_mask = matrix_bd_35_end_mask_0, x = matrix_bd_33_cast_fp16)[name = string("matrix_bd_35_cast_fp16")]; + tensor var_2242_cast_fp16 = add(x = matrix_ac_17_cast_fp16, y = matrix_bd_35_cast_fp16)[name = string("op_2242_cast_fp16")]; + fp16 _inversed_scores_33_y_0_to_fp16 = const()[name = string("_inversed_scores_33_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_33_cast_fp16 = mul(x = var_2242_cast_fp16, y = _inversed_scores_33_y_0_to_fp16)[name = string("_inversed_scores_33_cast_fp16")]; + tensor scores_35_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_33_cast_fp16, cond = mask_11)[name = string("scores_35_cast_fp16")]; + tensor var_2248_cast_fp16 = softmax(axis = var_59, x = scores_35_cast_fp16)[name = string("op_2248_cast_fp16")]; + tensor input_457_cast_fp16 = select(a = var_44_to_fp16, b = var_2248_cast_fp16, cond = mask_11)[name = string("input_457_cast_fp16")]; + bool x_221_transpose_x_0 = const()[name = string("x_221_transpose_x_0"), val = bool(false)]; + bool x_221_transpose_y_0 = const()[name = string("x_221_transpose_y_0"), val = bool(false)]; + tensor value_25_cast_fp16 = transpose(perm = value_25_perm_0, x = v_17_cast_fp16)[name = string("transpose_287")]; + tensor x_221_cast_fp16 = matmul(transpose_x = x_221_transpose_x_0, transpose_y = x_221_transpose_y_0, x = input_457_cast_fp16, y = value_25_cast_fp16)[name = string("x_221_cast_fp16")]; + tensor var_2252_perm_0 = const()[name = string("op_2252_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2253 = const()[name = string("op_2253"), val = tensor([1, -1, 1024])]; + tensor var_2252_cast_fp16 = transpose(perm = var_2252_perm_0, x = x_221_cast_fp16)[name = string("transpose_286")]; + tensor input_459_cast_fp16 = reshape(shape = var_2253, x = var_2252_cast_fp16)[name = string("input_459_cast_fp16")]; + tensor encoder_layers_8_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179638144))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180424640))))[name = string("encoder_layers_8_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_8_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_8_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180424832)))]; + tensor linear_79_cast_fp16 = linear(bias = encoder_layers_8_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_8_self_attn_linear_out_weight_to_fp16_palettized, x = input_459_cast_fp16)[name = string("linear_79_cast_fp16")]; + tensor input_463_cast_fp16 = add(x = input_453_cast_fp16, y = linear_79_cast_fp16)[name = string("input_463_cast_fp16")]; + tensor x_225_axes_0 = const()[name = string("x_225_axes_0"), val = tensor([-1])]; + tensor encoder_layers_8_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_8_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180426944)))]; + tensor encoder_layers_8_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_8_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180429056)))]; + tensor x_225_cast_fp16 = layer_norm(axes = x_225_axes_0, beta = encoder_layers_8_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_8_norm_conv_weight_to_fp16, x = input_463_cast_fp16)[name = string("x_225_cast_fp16")]; + tensor input_465_perm_0 = const()[name = string("input_465_perm_0"), val = tensor([0, 2, 1])]; + string input_467_pad_type_0 = const()[name = string("input_467_pad_type_0"), val = string("valid")]; + tensor input_467_strides_0 = const()[name = string("input_467_strides_0"), val = tensor([1])]; + tensor input_467_pad_0 = const()[name = string("input_467_pad_0"), val = tensor([0, 0])]; + tensor input_467_dilations_0 = const()[name = string("input_467_dilations_0"), val = tensor([1])]; + int32 input_467_groups_0 = const()[name = string("input_467_groups_0"), val = int32(1)]; + tensor encoder_layers_8_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180431168))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(182528384))))[name = string("encoder_layers_8_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_465_cast_fp16 = transpose(perm = input_465_perm_0, x = x_225_cast_fp16)[name = string("transpose_285")]; + tensor input_467_cast_fp16 = conv(dilations = input_467_dilations_0, groups = input_467_groups_0, pad = input_467_pad_0, pad_type = input_467_pad_type_0, strides = input_467_strides_0, weight = encoder_layers_8_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_465_cast_fp16)[name = string("input_467_cast_fp16")]; + int32 x_227_split_num_splits_0 = const()[name = string("x_227_split_num_splits_0"), val = int32(2)]; + int32 x_227_split_axis_0 = const()[name = string("x_227_split_axis_0"), val = int32(1)]; + tensor x_227_split_cast_fp16_0, tensor x_227_split_cast_fp16_1 = split(axis = x_227_split_axis_0, num_splits = x_227_split_num_splits_0, x = input_467_cast_fp16)[name = string("x_227_split_cast_fp16")]; + tensor x_227_split_1_sigmoid_cast_fp16 = sigmoid(x = x_227_split_cast_fp16_1)[name = string("x_227_split_1_sigmoid_cast_fp16")]; + tensor x_227_cast_fp16 = mul(x = x_227_split_cast_fp16_0, y = x_227_split_1_sigmoid_cast_fp16)[name = string("x_227_cast_fp16")]; + tensor input_469_cast_fp16 = select(a = var_44_to_fp16, b = x_227_cast_fp16, cond = var_575)[name = string("input_469_cast_fp16")]; + bool new_x_35_interleave_0 = const()[name = string("new_x_35_interleave_0"), val = bool(false)]; + tensor new_x_35_cast_fp16 = concat(axis = var_59, interleave = new_x_35_interleave_0, values = (cache_35_cast_fp16, input_469_cast_fp16))[name = string("new_x_35_cast_fp16")]; + tensor var_2292_begin_0 = const()[name = string("op_2292_begin_0"), val = tensor([0, 0, 28])]; + tensor var_2292_end_0 = const()[name = string("op_2292_end_0"), val = tensor([1, 1024, 36])]; + tensor var_2292_end_mask_0 = const()[name = string("op_2292_end_mask_0"), val = tensor([true, true, true])]; + tensor var_2292_cast_fp16 = slice_by_index(begin = var_2292_begin_0, end = var_2292_end_0, end_mask = var_2292_end_mask_0, x = new_x_35_cast_fp16)[name = string("op_2292_cast_fp16")]; + string x_229_pad_type_0 = const()[name = string("x_229_pad_type_0"), val = string("valid")]; + int32 x_229_groups_0 = const()[name = string("x_229_groups_0"), val = int32(1024)]; + tensor x_229_strides_0 = const()[name = string("x_229_strides_0"), val = tensor([1])]; + tensor x_229_pad_0 = const()[name = string("x_229_pad_0"), val = tensor([0, 0])]; + tensor x_229_dilations_0 = const()[name = string("x_229_dilations_0"), val = tensor([1])]; + tensor encoder_layers_8_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(182532544))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(182541824))))[name = string("encoder_layers_8_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_229_cast_fp16 = conv(dilations = x_229_dilations_0, groups = x_229_groups_0, pad = x_229_pad_0, pad_type = x_229_pad_type_0, strides = x_229_strides_0, weight = encoder_layers_8_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_35_cast_fp16)[name = string("x_229_cast_fp16")]; + tensor input_471_perm_0 = const()[name = string("input_471_perm_0"), val = tensor([0, 2, 1])]; + tensor x_231_axes_0 = const()[name = string("x_231_axes_0"), val = tensor([-1])]; + tensor encoder_layers_8_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_8_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(182543936)))]; + tensor encoder_layers_8_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_8_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(182546048)))]; + tensor input_471_cast_fp16 = transpose(perm = input_471_perm_0, x = x_229_cast_fp16)[name = string("transpose_284")]; + tensor x_231_cast_fp16 = layer_norm(axes = x_231_axes_0, beta = encoder_layers_8_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_8_conv_batch_norm_weight_to_fp16, x = input_471_cast_fp16)[name = string("x_231_cast_fp16")]; + tensor input_473_perm_0 = const()[name = string("input_473_perm_0"), val = tensor([0, 2, 1])]; + tensor input_473_cast_fp16 = transpose(perm = input_473_perm_0, x = x_231_cast_fp16)[name = string("transpose_283")]; + tensor input_475_cast_fp16 = silu(x = input_473_cast_fp16)[name = string("input_475_cast_fp16")]; + string x_233_pad_type_0 = const()[name = string("x_233_pad_type_0"), val = string("valid")]; + tensor x_233_strides_0 = const()[name = string("x_233_strides_0"), val = tensor([1])]; + tensor x_233_pad_0 = const()[name = string("x_233_pad_0"), val = tensor([0, 0])]; + tensor x_233_dilations_0 = const()[name = string("x_233_dilations_0"), val = tensor([1])]; + int32 x_233_groups_0 = const()[name = string("x_233_groups_0"), val = int32(1)]; + tensor encoder_layers_8_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(182548160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(183596800))))[name = string("encoder_layers_8_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_233_cast_fp16 = conv(dilations = x_233_dilations_0, groups = x_233_groups_0, pad = x_233_pad_0, pad_type = x_233_pad_type_0, strides = x_233_strides_0, weight = encoder_layers_8_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_475_cast_fp16)[name = string("x_233_cast_fp16")]; + tensor input_477_perm_0 = const()[name = string("input_477_perm_0"), val = tensor([0, 2, 1])]; + tensor input_477_cast_fp16 = transpose(perm = input_477_perm_0, x = x_233_cast_fp16)[name = string("transpose_282")]; + tensor input_479_cast_fp16 = add(x = input_463_cast_fp16, y = input_477_cast_fp16)[name = string("input_479_cast_fp16")]; + tensor input_481_axes_0 = const()[name = string("input_481_axes_0"), val = tensor([-1])]; + tensor encoder_layers_8_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_8_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(183598912)))]; + tensor encoder_layers_8_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_8_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(183601024)))]; + tensor input_481_cast_fp16 = layer_norm(axes = input_481_axes_0, beta = encoder_layers_8_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_8_norm_feed_forward2_weight_to_fp16, x = input_479_cast_fp16)[name = string("input_481_cast_fp16")]; + tensor encoder_layers_8_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(183603136))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(186748928))))[name = string("encoder_layers_8_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_8_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_8_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(186749120)))]; + tensor linear_80_cast_fp16 = linear(bias = encoder_layers_8_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_8_feed_forward2_linear1_weight_to_fp16_palettized, x = input_481_cast_fp16)[name = string("linear_80_cast_fp16")]; + tensor input_485_cast_fp16 = silu(x = linear_80_cast_fp16)[name = string("input_485_cast_fp16")]; + tensor encoder_layers_8_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(186757376))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189903168))))[name = string("encoder_layers_8_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_8_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_8_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189903360)))]; + tensor linear_81_cast_fp16 = linear(bias = encoder_layers_8_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_8_feed_forward2_linear2_weight_to_fp16_palettized, x = input_485_cast_fp16)[name = string("linear_81_cast_fp16")]; + fp16 var_2335_to_fp16 = const()[name = string("op_2335_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2336_cast_fp16 = mul(x = linear_81_cast_fp16, y = var_2335_to_fp16)[name = string("op_2336_cast_fp16")]; + tensor input_491_cast_fp16 = add(x = input_479_cast_fp16, y = var_2336_cast_fp16)[name = string("input_491_cast_fp16")]; + tensor input_493_axes_0 = const()[name = string("input_493_axes_0"), val = tensor([-1])]; + tensor encoder_layers_8_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_8_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189905472)))]; + tensor encoder_layers_8_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_8_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189907584)))]; + tensor input_493_cast_fp16 = layer_norm(axes = input_493_axes_0, beta = encoder_layers_8_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_8_norm_out_weight_to_fp16, x = input_491_cast_fp16)[name = string("input_493_cast_fp16")]; + tensor cache_37_begin_0 = const()[name = string("cache_37_begin_0"), val = tensor([9, 0, 0, 0])]; + tensor cache_37_end_0 = const()[name = string("cache_37_end_0"), val = tensor([10, 1, 42, 1024])]; + tensor cache_37_end_mask_0 = const()[name = string("cache_37_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_37_squeeze_mask_0 = const()[name = string("cache_37_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_37_cast_fp16 = slice_by_index(begin = cache_37_begin_0, end = cache_37_end_0, end_mask = cache_37_end_mask_0, squeeze_mask = cache_37_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_37_cast_fp16")]; + tensor cache_39_begin_0 = const()[name = string("cache_39_begin_0"), val = tensor([9, 0, 0, 0])]; + tensor cache_39_end_0 = const()[name = string("cache_39_end_0"), val = tensor([10, 1, 1024, 8])]; + tensor cache_39_end_mask_0 = const()[name = string("cache_39_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_39_squeeze_mask_0 = const()[name = string("cache_39_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_39_cast_fp16 = slice_by_index(begin = cache_39_begin_0, end = cache_39_end_0, end_mask = cache_39_end_mask_0, squeeze_mask = cache_39_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_39_cast_fp16")]; + tensor input_495_axes_0 = const()[name = string("input_495_axes_0"), val = tensor([-1])]; + tensor encoder_layers_9_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_9_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189909696)))]; + tensor encoder_layers_9_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_9_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189911808)))]; + tensor input_495_cast_fp16 = layer_norm(axes = input_495_axes_0, beta = encoder_layers_9_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_9_norm_feed_forward1_weight_to_fp16, x = input_493_cast_fp16)[name = string("input_495_cast_fp16")]; + tensor encoder_layers_9_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189913920))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193059712))))[name = string("encoder_layers_9_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_9_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_9_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193059904)))]; + tensor linear_82_cast_fp16 = linear(bias = encoder_layers_9_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_9_feed_forward1_linear1_weight_to_fp16_palettized, x = input_495_cast_fp16)[name = string("linear_82_cast_fp16")]; + tensor input_499_cast_fp16 = silu(x = linear_82_cast_fp16)[name = string("input_499_cast_fp16")]; + tensor encoder_layers_9_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193068160))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196213952))))[name = string("encoder_layers_9_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_9_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_9_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196214144)))]; + tensor linear_83_cast_fp16 = linear(bias = encoder_layers_9_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_9_feed_forward1_linear2_weight_to_fp16_palettized, x = input_499_cast_fp16)[name = string("linear_83_cast_fp16")]; + fp16 var_2372_to_fp16 = const()[name = string("op_2372_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2373_cast_fp16 = mul(x = linear_83_cast_fp16, y = var_2372_to_fp16)[name = string("op_2373_cast_fp16")]; + tensor input_505_cast_fp16 = add(x = input_493_cast_fp16, y = var_2373_cast_fp16)[name = string("input_505_cast_fp16")]; + tensor key_19_axes_0 = const()[name = string("key_19_axes_0"), val = tensor([-1])]; + tensor encoder_layers_9_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_9_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196216256)))]; + tensor encoder_layers_9_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_9_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196218368)))]; + tensor key_19_cast_fp16 = layer_norm(axes = key_19_axes_0, beta = encoder_layers_9_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_9_norm_self_att_weight_to_fp16, x = input_505_cast_fp16)[name = string("key_19_cast_fp16")]; + bool input_507_interleave_0 = const()[name = string("input_507_interleave_0"), val = bool(false)]; + tensor input_507_cast_fp16 = concat(axis = var_68, interleave = input_507_interleave_0, values = (cache_37_cast_fp16, key_19_cast_fp16))[name = string("input_507_cast_fp16")]; + tensor var_2395_begin_0 = const()[name = string("op_2395_begin_0"), val = tensor([0, 28, 0])]; + tensor var_2395_end_0 = const()[name = string("op_2395_end_0"), val = tensor([1, 42, 1024])]; + tensor var_2395_end_mask_0 = const()[name = string("op_2395_end_mask_0"), val = tensor([true, true, true])]; + tensor var_2395_cast_fp16 = slice_by_index(begin = var_2395_begin_0, end = var_2395_end_0, end_mask = var_2395_end_mask_0, x = cache_37_cast_fp16)[name = string("op_2395_cast_fp16")]; + bool var_2401_interleave_0 = const()[name = string("op_2401_interleave_0"), val = bool(false)]; + tensor var_2401_cast_fp16 = concat(axis = var_68, interleave = var_2401_interleave_0, values = (var_2395_cast_fp16, key_19_cast_fp16))[name = string("op_2401_cast_fp16")]; + tensor encoder_layers_9_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196220480))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197006976))))[name = string("encoder_layers_9_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_9_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_9_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197007168)))]; + tensor linear_84_cast_fp16 = linear(bias = encoder_layers_9_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_9_self_attn_linear_q_weight_to_fp16_palettized, x = key_19_cast_fp16)[name = string("linear_84_cast_fp16")]; + tensor var_2406 = const()[name = string("op_2406"), val = tensor([1, -1, 8, 128])]; + tensor q_55_cast_fp16 = reshape(shape = var_2406, x = linear_84_cast_fp16)[name = string("q_55_cast_fp16")]; + tensor encoder_layers_9_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197009280))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197795776))))[name = string("encoder_layers_9_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_9_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_9_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197795968)))]; + tensor linear_85_cast_fp16 = linear(bias = encoder_layers_9_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_9_self_attn_linear_k_weight_to_fp16_palettized, x = input_507_cast_fp16)[name = string("linear_85_cast_fp16")]; + tensor var_2411 = const()[name = string("op_2411"), val = tensor([1, -1, 8, 128])]; + tensor k_37_cast_fp16 = reshape(shape = var_2411, x = linear_85_cast_fp16)[name = string("k_37_cast_fp16")]; + tensor encoder_layers_9_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197798080))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198584576))))[name = string("encoder_layers_9_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_9_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_9_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198584768)))]; + tensor linear_86_cast_fp16 = linear(bias = encoder_layers_9_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_9_self_attn_linear_v_weight_to_fp16_palettized, x = input_507_cast_fp16)[name = string("linear_86_cast_fp16")]; + tensor var_2416 = const()[name = string("op_2416"), val = tensor([1, -1, 8, 128])]; + tensor v_19_cast_fp16 = reshape(shape = var_2416, x = linear_86_cast_fp16)[name = string("v_19_cast_fp16")]; + tensor value_27_perm_0 = const()[name = string("value_27_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_9_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_9_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198586880)))]; + tensor var_2429_cast_fp16 = add(x = q_55_cast_fp16, y = encoder_layers_9_self_attn_pos_bias_u_to_fp16)[name = string("op_2429_cast_fp16")]; + tensor encoder_layers_9_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_9_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198588992)))]; + tensor var_2431_cast_fp16 = add(x = q_55_cast_fp16, y = encoder_layers_9_self_attn_pos_bias_v_to_fp16)[name = string("op_2431_cast_fp16")]; + tensor q_with_bias_v_19_perm_0 = const()[name = string("q_with_bias_v_19_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_241_transpose_x_0 = const()[name = string("x_241_transpose_x_0"), val = bool(false)]; + bool x_241_transpose_y_0 = const()[name = string("x_241_transpose_y_0"), val = bool(false)]; + tensor op_2433_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198591104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198733504))))[name = string("op_2433_to_fp16_quantized")]; + tensor q_with_bias_v_19_cast_fp16 = transpose(perm = q_with_bias_v_19_perm_0, x = var_2431_cast_fp16)[name = string("transpose_281")]; + tensor x_241_cast_fp16 = matmul(transpose_x = x_241_transpose_x_0, transpose_y = x_241_transpose_y_0, x = q_with_bias_v_19_cast_fp16, y = op_2433_to_fp16_quantized)[name = string("x_241_cast_fp16")]; + tensor x_243_pad_0 = const()[name = string("x_243_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_243_mode_0 = const()[name = string("x_243_mode_0"), val = string("constant")]; + fp16 const_196_to_fp16 = const()[name = string("const_196_to_fp16"), val = fp16(0x0p+0)]; + tensor x_243_cast_fp16 = pad(constant_val = const_196_to_fp16, mode = x_243_mode_0, pad = x_243_pad_0, x = x_241_cast_fp16)[name = string("x_243_cast_fp16")]; + tensor var_2441 = const()[name = string("op_2441"), val = tensor([1, 8, -1, 28])]; + tensor x_245_cast_fp16 = reshape(shape = var_2441, x = x_243_cast_fp16)[name = string("x_245_cast_fp16")]; + tensor var_2445_begin_0 = const()[name = string("op_2445_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_2445_end_0 = const()[name = string("op_2445_end_0"), val = tensor([1, 8, 140, 28])]; + tensor var_2445_end_mask_0 = const()[name = string("op_2445_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_2445_cast_fp16 = slice_by_index(begin = var_2445_begin_0, end = var_2445_end_0, end_mask = var_2445_end_mask_0, x = x_245_cast_fp16)[name = string("op_2445_cast_fp16")]; + tensor var_2446 = const()[name = string("op_2446"), val = tensor([1, 8, 28, 139])]; + tensor matrix_bd_37_cast_fp16 = reshape(shape = var_2446, x = var_2445_cast_fp16)[name = string("matrix_bd_37_cast_fp16")]; + bool matrix_ac_19_transpose_x_0 = const()[name = string("matrix_ac_19_transpose_x_0"), val = bool(false)]; + bool matrix_ac_19_transpose_y_0 = const()[name = string("matrix_ac_19_transpose_y_0"), val = bool(false)]; + tensor transpose_114_perm_0 = const()[name = string("transpose_114_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_115_perm_0 = const()[name = string("transpose_115_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_115 = transpose(perm = transpose_115_perm_0, x = k_37_cast_fp16)[name = string("transpose_279")]; + tensor transpose_114 = transpose(perm = transpose_114_perm_0, x = var_2429_cast_fp16)[name = string("transpose_280")]; + tensor matrix_ac_19_cast_fp16 = matmul(transpose_x = matrix_ac_19_transpose_x_0, transpose_y = matrix_ac_19_transpose_y_0, x = transpose_114, y = transpose_115)[name = string("matrix_ac_19_cast_fp16")]; + tensor matrix_bd_39_begin_0 = const()[name = string("matrix_bd_39_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_39_end_0 = const()[name = string("matrix_bd_39_end_0"), val = tensor([1, 8, 28, 70])]; + tensor matrix_bd_39_end_mask_0 = const()[name = string("matrix_bd_39_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_39_cast_fp16 = slice_by_index(begin = matrix_bd_39_begin_0, end = matrix_bd_39_end_0, end_mask = matrix_bd_39_end_mask_0, x = matrix_bd_37_cast_fp16)[name = string("matrix_bd_39_cast_fp16")]; + tensor var_2455_cast_fp16 = add(x = matrix_ac_19_cast_fp16, y = matrix_bd_39_cast_fp16)[name = string("op_2455_cast_fp16")]; + fp16 _inversed_scores_37_y_0_to_fp16 = const()[name = string("_inversed_scores_37_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_37_cast_fp16 = mul(x = var_2455_cast_fp16, y = _inversed_scores_37_y_0_to_fp16)[name = string("_inversed_scores_37_cast_fp16")]; + tensor scores_39_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_37_cast_fp16, cond = mask_11)[name = string("scores_39_cast_fp16")]; + tensor var_2461_cast_fp16 = softmax(axis = var_59, x = scores_39_cast_fp16)[name = string("op_2461_cast_fp16")]; + tensor input_509_cast_fp16 = select(a = var_44_to_fp16, b = var_2461_cast_fp16, cond = mask_11)[name = string("input_509_cast_fp16")]; + bool x_247_transpose_x_0 = const()[name = string("x_247_transpose_x_0"), val = bool(false)]; + bool x_247_transpose_y_0 = const()[name = string("x_247_transpose_y_0"), val = bool(false)]; + tensor value_27_cast_fp16 = transpose(perm = value_27_perm_0, x = v_19_cast_fp16)[name = string("transpose_278")]; + tensor x_247_cast_fp16 = matmul(transpose_x = x_247_transpose_x_0, transpose_y = x_247_transpose_y_0, x = input_509_cast_fp16, y = value_27_cast_fp16)[name = string("x_247_cast_fp16")]; + tensor var_2465_perm_0 = const()[name = string("op_2465_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2466 = const()[name = string("op_2466"), val = tensor([1, -1, 1024])]; + tensor var_2465_cast_fp16 = transpose(perm = var_2465_perm_0, x = x_247_cast_fp16)[name = string("transpose_277")]; + tensor input_511_cast_fp16 = reshape(shape = var_2466, x = var_2465_cast_fp16)[name = string("input_511_cast_fp16")]; + tensor encoder_layers_9_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198733888))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199520384))))[name = string("encoder_layers_9_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_9_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_9_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199520576)))]; + tensor linear_88_cast_fp16 = linear(bias = encoder_layers_9_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_9_self_attn_linear_out_weight_to_fp16_palettized, x = input_511_cast_fp16)[name = string("linear_88_cast_fp16")]; + tensor input_515_cast_fp16 = add(x = input_505_cast_fp16, y = linear_88_cast_fp16)[name = string("input_515_cast_fp16")]; + tensor x_251_axes_0 = const()[name = string("x_251_axes_0"), val = tensor([-1])]; + tensor encoder_layers_9_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_9_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199522688)))]; + tensor encoder_layers_9_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_9_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199524800)))]; + tensor x_251_cast_fp16 = layer_norm(axes = x_251_axes_0, beta = encoder_layers_9_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_9_norm_conv_weight_to_fp16, x = input_515_cast_fp16)[name = string("x_251_cast_fp16")]; + tensor input_517_perm_0 = const()[name = string("input_517_perm_0"), val = tensor([0, 2, 1])]; + string input_519_pad_type_0 = const()[name = string("input_519_pad_type_0"), val = string("valid")]; + tensor input_519_strides_0 = const()[name = string("input_519_strides_0"), val = tensor([1])]; + tensor input_519_pad_0 = const()[name = string("input_519_pad_0"), val = tensor([0, 0])]; + tensor input_519_dilations_0 = const()[name = string("input_519_dilations_0"), val = tensor([1])]; + int32 input_519_groups_0 = const()[name = string("input_519_groups_0"), val = int32(1)]; + tensor encoder_layers_9_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199526912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201624128))))[name = string("encoder_layers_9_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_517_cast_fp16 = transpose(perm = input_517_perm_0, x = x_251_cast_fp16)[name = string("transpose_276")]; + tensor input_519_cast_fp16 = conv(dilations = input_519_dilations_0, groups = input_519_groups_0, pad = input_519_pad_0, pad_type = input_519_pad_type_0, strides = input_519_strides_0, weight = encoder_layers_9_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_517_cast_fp16)[name = string("input_519_cast_fp16")]; + int32 x_253_split_num_splits_0 = const()[name = string("x_253_split_num_splits_0"), val = int32(2)]; + int32 x_253_split_axis_0 = const()[name = string("x_253_split_axis_0"), val = int32(1)]; + tensor x_253_split_cast_fp16_0, tensor x_253_split_cast_fp16_1 = split(axis = x_253_split_axis_0, num_splits = x_253_split_num_splits_0, x = input_519_cast_fp16)[name = string("x_253_split_cast_fp16")]; + tensor x_253_split_1_sigmoid_cast_fp16 = sigmoid(x = x_253_split_cast_fp16_1)[name = string("x_253_split_1_sigmoid_cast_fp16")]; + tensor x_253_cast_fp16 = mul(x = x_253_split_cast_fp16_0, y = x_253_split_1_sigmoid_cast_fp16)[name = string("x_253_cast_fp16")]; + tensor input_521_cast_fp16 = select(a = var_44_to_fp16, b = x_253_cast_fp16, cond = var_575)[name = string("input_521_cast_fp16")]; + bool new_x_39_interleave_0 = const()[name = string("new_x_39_interleave_0"), val = bool(false)]; + tensor new_x_39_cast_fp16 = concat(axis = var_59, interleave = new_x_39_interleave_0, values = (cache_39_cast_fp16, input_521_cast_fp16))[name = string("new_x_39_cast_fp16")]; + tensor var_2505_begin_0 = const()[name = string("op_2505_begin_0"), val = tensor([0, 0, 28])]; + tensor var_2505_end_0 = const()[name = string("op_2505_end_0"), val = tensor([1, 1024, 36])]; + tensor var_2505_end_mask_0 = const()[name = string("op_2505_end_mask_0"), val = tensor([true, true, true])]; + tensor var_2505_cast_fp16 = slice_by_index(begin = var_2505_begin_0, end = var_2505_end_0, end_mask = var_2505_end_mask_0, x = new_x_39_cast_fp16)[name = string("op_2505_cast_fp16")]; + string x_255_pad_type_0 = const()[name = string("x_255_pad_type_0"), val = string("valid")]; + int32 x_255_groups_0 = const()[name = string("x_255_groups_0"), val = int32(1024)]; + tensor x_255_strides_0 = const()[name = string("x_255_strides_0"), val = tensor([1])]; + tensor x_255_pad_0 = const()[name = string("x_255_pad_0"), val = tensor([0, 0])]; + tensor x_255_dilations_0 = const()[name = string("x_255_dilations_0"), val = tensor([1])]; + tensor encoder_layers_9_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201628288))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201637568))))[name = string("encoder_layers_9_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_255_cast_fp16 = conv(dilations = x_255_dilations_0, groups = x_255_groups_0, pad = x_255_pad_0, pad_type = x_255_pad_type_0, strides = x_255_strides_0, weight = encoder_layers_9_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_39_cast_fp16)[name = string("x_255_cast_fp16")]; + tensor input_523_perm_0 = const()[name = string("input_523_perm_0"), val = tensor([0, 2, 1])]; + tensor x_257_axes_0 = const()[name = string("x_257_axes_0"), val = tensor([-1])]; + tensor encoder_layers_9_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_9_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201639680)))]; + tensor encoder_layers_9_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_9_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201641792)))]; + tensor input_523_cast_fp16 = transpose(perm = input_523_perm_0, x = x_255_cast_fp16)[name = string("transpose_275")]; + tensor x_257_cast_fp16 = layer_norm(axes = x_257_axes_0, beta = encoder_layers_9_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_9_conv_batch_norm_weight_to_fp16, x = input_523_cast_fp16)[name = string("x_257_cast_fp16")]; + tensor input_525_perm_0 = const()[name = string("input_525_perm_0"), val = tensor([0, 2, 1])]; + tensor input_525_cast_fp16 = transpose(perm = input_525_perm_0, x = x_257_cast_fp16)[name = string("transpose_274")]; + tensor input_527_cast_fp16 = silu(x = input_525_cast_fp16)[name = string("input_527_cast_fp16")]; + string x_259_pad_type_0 = const()[name = string("x_259_pad_type_0"), val = string("valid")]; + tensor x_259_strides_0 = const()[name = string("x_259_strides_0"), val = tensor([1])]; + tensor x_259_pad_0 = const()[name = string("x_259_pad_0"), val = tensor([0, 0])]; + tensor x_259_dilations_0 = const()[name = string("x_259_dilations_0"), val = tensor([1])]; + int32 x_259_groups_0 = const()[name = string("x_259_groups_0"), val = int32(1)]; + tensor encoder_layers_9_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201643904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(202692544))))[name = string("encoder_layers_9_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_259_cast_fp16 = conv(dilations = x_259_dilations_0, groups = x_259_groups_0, pad = x_259_pad_0, pad_type = x_259_pad_type_0, strides = x_259_strides_0, weight = encoder_layers_9_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_527_cast_fp16)[name = string("x_259_cast_fp16")]; + tensor input_529_perm_0 = const()[name = string("input_529_perm_0"), val = tensor([0, 2, 1])]; + tensor input_529_cast_fp16 = transpose(perm = input_529_perm_0, x = x_259_cast_fp16)[name = string("transpose_273")]; + tensor input_531_cast_fp16 = add(x = input_515_cast_fp16, y = input_529_cast_fp16)[name = string("input_531_cast_fp16")]; + tensor input_533_axes_0 = const()[name = string("input_533_axes_0"), val = tensor([-1])]; + tensor encoder_layers_9_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_9_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(202694656)))]; + tensor encoder_layers_9_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_9_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(202696768)))]; + tensor input_533_cast_fp16 = layer_norm(axes = input_533_axes_0, beta = encoder_layers_9_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_9_norm_feed_forward2_weight_to_fp16, x = input_531_cast_fp16)[name = string("input_533_cast_fp16")]; + tensor encoder_layers_9_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(202698880))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205844672))))[name = string("encoder_layers_9_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_9_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_9_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205844864)))]; + tensor linear_89_cast_fp16 = linear(bias = encoder_layers_9_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_9_feed_forward2_linear1_weight_to_fp16_palettized, x = input_533_cast_fp16)[name = string("linear_89_cast_fp16")]; + tensor input_537_cast_fp16 = silu(x = linear_89_cast_fp16)[name = string("input_537_cast_fp16")]; + tensor encoder_layers_9_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205853120))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(208998912))))[name = string("encoder_layers_9_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_9_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_9_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(208999104)))]; + tensor linear_90_cast_fp16 = linear(bias = encoder_layers_9_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_9_feed_forward2_linear2_weight_to_fp16_palettized, x = input_537_cast_fp16)[name = string("linear_90_cast_fp16")]; + fp16 var_2548_to_fp16 = const()[name = string("op_2548_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2549_cast_fp16 = mul(x = linear_90_cast_fp16, y = var_2548_to_fp16)[name = string("op_2549_cast_fp16")]; + tensor input_543_cast_fp16 = add(x = input_531_cast_fp16, y = var_2549_cast_fp16)[name = string("input_543_cast_fp16")]; + tensor input_545_axes_0 = const()[name = string("input_545_axes_0"), val = tensor([-1])]; + tensor encoder_layers_9_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_9_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209001216)))]; + tensor encoder_layers_9_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_9_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209003328)))]; + tensor input_545_cast_fp16 = layer_norm(axes = input_545_axes_0, beta = encoder_layers_9_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_9_norm_out_weight_to_fp16, x = input_543_cast_fp16)[name = string("input_545_cast_fp16")]; + tensor cache_41_begin_0 = const()[name = string("cache_41_begin_0"), val = tensor([10, 0, 0, 0])]; + tensor cache_41_end_0 = const()[name = string("cache_41_end_0"), val = tensor([11, 1, 42, 1024])]; + tensor cache_41_end_mask_0 = const()[name = string("cache_41_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_41_squeeze_mask_0 = const()[name = string("cache_41_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_41_cast_fp16 = slice_by_index(begin = cache_41_begin_0, end = cache_41_end_0, end_mask = cache_41_end_mask_0, squeeze_mask = cache_41_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_41_cast_fp16")]; + tensor cache_43_begin_0 = const()[name = string("cache_43_begin_0"), val = tensor([10, 0, 0, 0])]; + tensor cache_43_end_0 = const()[name = string("cache_43_end_0"), val = tensor([11, 1, 1024, 8])]; + tensor cache_43_end_mask_0 = const()[name = string("cache_43_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_43_squeeze_mask_0 = const()[name = string("cache_43_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_43_cast_fp16 = slice_by_index(begin = cache_43_begin_0, end = cache_43_end_0, end_mask = cache_43_end_mask_0, squeeze_mask = cache_43_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_43_cast_fp16")]; + tensor input_547_axes_0 = const()[name = string("input_547_axes_0"), val = tensor([-1])]; + tensor encoder_layers_10_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_10_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209005440)))]; + tensor encoder_layers_10_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_10_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209007552)))]; + tensor input_547_cast_fp16 = layer_norm(axes = input_547_axes_0, beta = encoder_layers_10_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_10_norm_feed_forward1_weight_to_fp16, x = input_545_cast_fp16)[name = string("input_547_cast_fp16")]; + tensor encoder_layers_10_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209009664))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(212155456))))[name = string("encoder_layers_10_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_10_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_10_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(212155648)))]; + tensor linear_91_cast_fp16 = linear(bias = encoder_layers_10_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_10_feed_forward1_linear1_weight_to_fp16_palettized, x = input_547_cast_fp16)[name = string("linear_91_cast_fp16")]; + tensor input_551_cast_fp16 = silu(x = linear_91_cast_fp16)[name = string("input_551_cast_fp16")]; + tensor encoder_layers_10_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(212163904))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215309696))))[name = string("encoder_layers_10_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_10_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_10_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215309888)))]; + tensor linear_92_cast_fp16 = linear(bias = encoder_layers_10_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_10_feed_forward1_linear2_weight_to_fp16_palettized, x = input_551_cast_fp16)[name = string("linear_92_cast_fp16")]; + fp16 var_2585_to_fp16 = const()[name = string("op_2585_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2586_cast_fp16 = mul(x = linear_92_cast_fp16, y = var_2585_to_fp16)[name = string("op_2586_cast_fp16")]; + tensor input_557_cast_fp16 = add(x = input_545_cast_fp16, y = var_2586_cast_fp16)[name = string("input_557_cast_fp16")]; + tensor key_21_axes_0 = const()[name = string("key_21_axes_0"), val = tensor([-1])]; + tensor encoder_layers_10_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_10_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215312000)))]; + tensor encoder_layers_10_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_10_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215314112)))]; + tensor key_21_cast_fp16 = layer_norm(axes = key_21_axes_0, beta = encoder_layers_10_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_10_norm_self_att_weight_to_fp16, x = input_557_cast_fp16)[name = string("key_21_cast_fp16")]; + bool input_559_interleave_0 = const()[name = string("input_559_interleave_0"), val = bool(false)]; + tensor input_559_cast_fp16 = concat(axis = var_68, interleave = input_559_interleave_0, values = (cache_41_cast_fp16, key_21_cast_fp16))[name = string("input_559_cast_fp16")]; + tensor var_2608_begin_0 = const()[name = string("op_2608_begin_0"), val = tensor([0, 28, 0])]; + tensor var_2608_end_0 = const()[name = string("op_2608_end_0"), val = tensor([1, 42, 1024])]; + tensor var_2608_end_mask_0 = const()[name = string("op_2608_end_mask_0"), val = tensor([true, true, true])]; + tensor var_2608_cast_fp16 = slice_by_index(begin = var_2608_begin_0, end = var_2608_end_0, end_mask = var_2608_end_mask_0, x = cache_41_cast_fp16)[name = string("op_2608_cast_fp16")]; + bool var_2614_interleave_0 = const()[name = string("op_2614_interleave_0"), val = bool(false)]; + tensor var_2614_cast_fp16 = concat(axis = var_68, interleave = var_2614_interleave_0, values = (var_2608_cast_fp16, key_21_cast_fp16))[name = string("op_2614_cast_fp16")]; + tensor encoder_layers_10_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215316224))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216102720))))[name = string("encoder_layers_10_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_10_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_10_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216102912)))]; + tensor linear_93_cast_fp16 = linear(bias = encoder_layers_10_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_10_self_attn_linear_q_weight_to_fp16_palettized, x = key_21_cast_fp16)[name = string("linear_93_cast_fp16")]; + tensor var_2619 = const()[name = string("op_2619"), val = tensor([1, -1, 8, 128])]; + tensor q_61_cast_fp16 = reshape(shape = var_2619, x = linear_93_cast_fp16)[name = string("q_61_cast_fp16")]; + tensor encoder_layers_10_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216105024))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216891520))))[name = string("encoder_layers_10_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_10_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_10_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216891712)))]; + tensor linear_94_cast_fp16 = linear(bias = encoder_layers_10_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_10_self_attn_linear_k_weight_to_fp16_palettized, x = input_559_cast_fp16)[name = string("linear_94_cast_fp16")]; + tensor var_2624 = const()[name = string("op_2624"), val = tensor([1, -1, 8, 128])]; + tensor k_41_cast_fp16 = reshape(shape = var_2624, x = linear_94_cast_fp16)[name = string("k_41_cast_fp16")]; + tensor encoder_layers_10_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216893824))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217680320))))[name = string("encoder_layers_10_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_10_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_10_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217680512)))]; + tensor linear_95_cast_fp16 = linear(bias = encoder_layers_10_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_10_self_attn_linear_v_weight_to_fp16_palettized, x = input_559_cast_fp16)[name = string("linear_95_cast_fp16")]; + tensor var_2629 = const()[name = string("op_2629"), val = tensor([1, -1, 8, 128])]; + tensor v_21_cast_fp16 = reshape(shape = var_2629, x = linear_95_cast_fp16)[name = string("v_21_cast_fp16")]; + tensor value_29_perm_0 = const()[name = string("value_29_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_10_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_10_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217682624)))]; + tensor var_2642_cast_fp16 = add(x = q_61_cast_fp16, y = encoder_layers_10_self_attn_pos_bias_u_to_fp16)[name = string("op_2642_cast_fp16")]; + tensor encoder_layers_10_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_10_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217684736)))]; + tensor var_2644_cast_fp16 = add(x = q_61_cast_fp16, y = encoder_layers_10_self_attn_pos_bias_v_to_fp16)[name = string("op_2644_cast_fp16")]; + tensor q_with_bias_v_21_perm_0 = const()[name = string("q_with_bias_v_21_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_267_transpose_x_0 = const()[name = string("x_267_transpose_x_0"), val = bool(false)]; + bool x_267_transpose_y_0 = const()[name = string("x_267_transpose_y_0"), val = bool(false)]; + tensor op_2646_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217686848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217829248))))[name = string("op_2646_to_fp16_quantized")]; + tensor q_with_bias_v_21_cast_fp16 = transpose(perm = q_with_bias_v_21_perm_0, x = var_2644_cast_fp16)[name = string("transpose_272")]; + tensor x_267_cast_fp16 = matmul(transpose_x = x_267_transpose_x_0, transpose_y = x_267_transpose_y_0, x = q_with_bias_v_21_cast_fp16, y = op_2646_to_fp16_quantized)[name = string("x_267_cast_fp16")]; + tensor x_269_pad_0 = const()[name = string("x_269_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_269_mode_0 = const()[name = string("x_269_mode_0"), val = string("constant")]; + fp16 const_209_to_fp16 = const()[name = string("const_209_to_fp16"), val = fp16(0x0p+0)]; + tensor x_269_cast_fp16 = pad(constant_val = const_209_to_fp16, mode = x_269_mode_0, pad = x_269_pad_0, x = x_267_cast_fp16)[name = string("x_269_cast_fp16")]; + tensor var_2654 = const()[name = string("op_2654"), val = tensor([1, 8, -1, 28])]; + tensor x_271_cast_fp16 = reshape(shape = var_2654, x = x_269_cast_fp16)[name = string("x_271_cast_fp16")]; + tensor var_2658_begin_0 = const()[name = string("op_2658_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_2658_end_0 = const()[name = string("op_2658_end_0"), val = tensor([1, 8, 140, 28])]; + tensor var_2658_end_mask_0 = const()[name = string("op_2658_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_2658_cast_fp16 = slice_by_index(begin = var_2658_begin_0, end = var_2658_end_0, end_mask = var_2658_end_mask_0, x = x_271_cast_fp16)[name = string("op_2658_cast_fp16")]; + tensor var_2659 = const()[name = string("op_2659"), val = tensor([1, 8, 28, 139])]; + tensor matrix_bd_41_cast_fp16 = reshape(shape = var_2659, x = var_2658_cast_fp16)[name = string("matrix_bd_41_cast_fp16")]; + bool matrix_ac_21_transpose_x_0 = const()[name = string("matrix_ac_21_transpose_x_0"), val = bool(false)]; + bool matrix_ac_21_transpose_y_0 = const()[name = string("matrix_ac_21_transpose_y_0"), val = bool(false)]; + tensor transpose_116_perm_0 = const()[name = string("transpose_116_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_117_perm_0 = const()[name = string("transpose_117_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_117 = transpose(perm = transpose_117_perm_0, x = k_41_cast_fp16)[name = string("transpose_270")]; + tensor transpose_116 = transpose(perm = transpose_116_perm_0, x = var_2642_cast_fp16)[name = string("transpose_271")]; + tensor matrix_ac_21_cast_fp16 = matmul(transpose_x = matrix_ac_21_transpose_x_0, transpose_y = matrix_ac_21_transpose_y_0, x = transpose_116, y = transpose_117)[name = string("matrix_ac_21_cast_fp16")]; + tensor matrix_bd_43_begin_0 = const()[name = string("matrix_bd_43_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_43_end_0 = const()[name = string("matrix_bd_43_end_0"), val = tensor([1, 8, 28, 70])]; + tensor matrix_bd_43_end_mask_0 = const()[name = string("matrix_bd_43_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_43_cast_fp16 = slice_by_index(begin = matrix_bd_43_begin_0, end = matrix_bd_43_end_0, end_mask = matrix_bd_43_end_mask_0, x = matrix_bd_41_cast_fp16)[name = string("matrix_bd_43_cast_fp16")]; + tensor var_2668_cast_fp16 = add(x = matrix_ac_21_cast_fp16, y = matrix_bd_43_cast_fp16)[name = string("op_2668_cast_fp16")]; + fp16 _inversed_scores_41_y_0_to_fp16 = const()[name = string("_inversed_scores_41_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_41_cast_fp16 = mul(x = var_2668_cast_fp16, y = _inversed_scores_41_y_0_to_fp16)[name = string("_inversed_scores_41_cast_fp16")]; + tensor scores_43_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_41_cast_fp16, cond = mask_11)[name = string("scores_43_cast_fp16")]; + tensor var_2674_cast_fp16 = softmax(axis = var_59, x = scores_43_cast_fp16)[name = string("op_2674_cast_fp16")]; + tensor input_561_cast_fp16 = select(a = var_44_to_fp16, b = var_2674_cast_fp16, cond = mask_11)[name = string("input_561_cast_fp16")]; + bool x_273_transpose_x_0 = const()[name = string("x_273_transpose_x_0"), val = bool(false)]; + bool x_273_transpose_y_0 = const()[name = string("x_273_transpose_y_0"), val = bool(false)]; + tensor value_29_cast_fp16 = transpose(perm = value_29_perm_0, x = v_21_cast_fp16)[name = string("transpose_269")]; + tensor x_273_cast_fp16 = matmul(transpose_x = x_273_transpose_x_0, transpose_y = x_273_transpose_y_0, x = input_561_cast_fp16, y = value_29_cast_fp16)[name = string("x_273_cast_fp16")]; + tensor var_2678_perm_0 = const()[name = string("op_2678_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2679 = const()[name = string("op_2679"), val = tensor([1, -1, 1024])]; + tensor var_2678_cast_fp16 = transpose(perm = var_2678_perm_0, x = x_273_cast_fp16)[name = string("transpose_268")]; + tensor input_563_cast_fp16 = reshape(shape = var_2679, x = var_2678_cast_fp16)[name = string("input_563_cast_fp16")]; + tensor encoder_layers_10_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217829632))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218616128))))[name = string("encoder_layers_10_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_10_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_10_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218616320)))]; + tensor linear_97_cast_fp16 = linear(bias = encoder_layers_10_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_10_self_attn_linear_out_weight_to_fp16_palettized, x = input_563_cast_fp16)[name = string("linear_97_cast_fp16")]; + tensor input_567_cast_fp16 = add(x = input_557_cast_fp16, y = linear_97_cast_fp16)[name = string("input_567_cast_fp16")]; + tensor x_277_axes_0 = const()[name = string("x_277_axes_0"), val = tensor([-1])]; + tensor encoder_layers_10_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_10_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218618432)))]; + tensor encoder_layers_10_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_10_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218620544)))]; + tensor x_277_cast_fp16 = layer_norm(axes = x_277_axes_0, beta = encoder_layers_10_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_10_norm_conv_weight_to_fp16, x = input_567_cast_fp16)[name = string("x_277_cast_fp16")]; + tensor input_569_perm_0 = const()[name = string("input_569_perm_0"), val = tensor([0, 2, 1])]; + string input_571_pad_type_0 = const()[name = string("input_571_pad_type_0"), val = string("valid")]; + tensor input_571_strides_0 = const()[name = string("input_571_strides_0"), val = tensor([1])]; + tensor input_571_pad_0 = const()[name = string("input_571_pad_0"), val = tensor([0, 0])]; + tensor input_571_dilations_0 = const()[name = string("input_571_dilations_0"), val = tensor([1])]; + int32 input_571_groups_0 = const()[name = string("input_571_groups_0"), val = int32(1)]; + tensor encoder_layers_10_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218622656))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220719872))))[name = string("encoder_layers_10_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_569_cast_fp16 = transpose(perm = input_569_perm_0, x = x_277_cast_fp16)[name = string("transpose_267")]; + tensor input_571_cast_fp16 = conv(dilations = input_571_dilations_0, groups = input_571_groups_0, pad = input_571_pad_0, pad_type = input_571_pad_type_0, strides = input_571_strides_0, weight = encoder_layers_10_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_569_cast_fp16)[name = string("input_571_cast_fp16")]; + int32 x_279_split_num_splits_0 = const()[name = string("x_279_split_num_splits_0"), val = int32(2)]; + int32 x_279_split_axis_0 = const()[name = string("x_279_split_axis_0"), val = int32(1)]; + tensor x_279_split_cast_fp16_0, tensor x_279_split_cast_fp16_1 = split(axis = x_279_split_axis_0, num_splits = x_279_split_num_splits_0, x = input_571_cast_fp16)[name = string("x_279_split_cast_fp16")]; + tensor x_279_split_1_sigmoid_cast_fp16 = sigmoid(x = x_279_split_cast_fp16_1)[name = string("x_279_split_1_sigmoid_cast_fp16")]; + tensor x_279_cast_fp16 = mul(x = x_279_split_cast_fp16_0, y = x_279_split_1_sigmoid_cast_fp16)[name = string("x_279_cast_fp16")]; + tensor input_573_cast_fp16 = select(a = var_44_to_fp16, b = x_279_cast_fp16, cond = var_575)[name = string("input_573_cast_fp16")]; + bool new_x_43_interleave_0 = const()[name = string("new_x_43_interleave_0"), val = bool(false)]; + tensor new_x_43_cast_fp16 = concat(axis = var_59, interleave = new_x_43_interleave_0, values = (cache_43_cast_fp16, input_573_cast_fp16))[name = string("new_x_43_cast_fp16")]; + tensor var_2718_begin_0 = const()[name = string("op_2718_begin_0"), val = tensor([0, 0, 28])]; + tensor var_2718_end_0 = const()[name = string("op_2718_end_0"), val = tensor([1, 1024, 36])]; + tensor var_2718_end_mask_0 = const()[name = string("op_2718_end_mask_0"), val = tensor([true, true, true])]; + tensor var_2718_cast_fp16 = slice_by_index(begin = var_2718_begin_0, end = var_2718_end_0, end_mask = var_2718_end_mask_0, x = new_x_43_cast_fp16)[name = string("op_2718_cast_fp16")]; + string x_281_pad_type_0 = const()[name = string("x_281_pad_type_0"), val = string("valid")]; + int32 x_281_groups_0 = const()[name = string("x_281_groups_0"), val = int32(1024)]; + tensor x_281_strides_0 = const()[name = string("x_281_strides_0"), val = tensor([1])]; + tensor x_281_pad_0 = const()[name = string("x_281_pad_0"), val = tensor([0, 0])]; + tensor x_281_dilations_0 = const()[name = string("x_281_dilations_0"), val = tensor([1])]; + tensor encoder_layers_10_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220724032))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220733312))))[name = string("encoder_layers_10_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_281_cast_fp16 = conv(dilations = x_281_dilations_0, groups = x_281_groups_0, pad = x_281_pad_0, pad_type = x_281_pad_type_0, strides = x_281_strides_0, weight = encoder_layers_10_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_43_cast_fp16)[name = string("x_281_cast_fp16")]; + tensor input_575_perm_0 = const()[name = string("input_575_perm_0"), val = tensor([0, 2, 1])]; + tensor x_283_axes_0 = const()[name = string("x_283_axes_0"), val = tensor([-1])]; + tensor encoder_layers_10_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_10_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220735424)))]; + tensor encoder_layers_10_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_10_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220737536)))]; + tensor input_575_cast_fp16 = transpose(perm = input_575_perm_0, x = x_281_cast_fp16)[name = string("transpose_266")]; + tensor x_283_cast_fp16 = layer_norm(axes = x_283_axes_0, beta = encoder_layers_10_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_10_conv_batch_norm_weight_to_fp16, x = input_575_cast_fp16)[name = string("x_283_cast_fp16")]; + tensor input_577_perm_0 = const()[name = string("input_577_perm_0"), val = tensor([0, 2, 1])]; + tensor input_577_cast_fp16 = transpose(perm = input_577_perm_0, x = x_283_cast_fp16)[name = string("transpose_265")]; + tensor input_579_cast_fp16 = silu(x = input_577_cast_fp16)[name = string("input_579_cast_fp16")]; + string x_285_pad_type_0 = const()[name = string("x_285_pad_type_0"), val = string("valid")]; + tensor x_285_strides_0 = const()[name = string("x_285_strides_0"), val = tensor([1])]; + tensor x_285_pad_0 = const()[name = string("x_285_pad_0"), val = tensor([0, 0])]; + tensor x_285_dilations_0 = const()[name = string("x_285_dilations_0"), val = tensor([1])]; + int32 x_285_groups_0 = const()[name = string("x_285_groups_0"), val = int32(1)]; + tensor encoder_layers_10_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220739648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(221788288))))[name = string("encoder_layers_10_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_285_cast_fp16 = conv(dilations = x_285_dilations_0, groups = x_285_groups_0, pad = x_285_pad_0, pad_type = x_285_pad_type_0, strides = x_285_strides_0, weight = encoder_layers_10_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_579_cast_fp16)[name = string("x_285_cast_fp16")]; + tensor input_581_perm_0 = const()[name = string("input_581_perm_0"), val = tensor([0, 2, 1])]; + tensor input_581_cast_fp16 = transpose(perm = input_581_perm_0, x = x_285_cast_fp16)[name = string("transpose_264")]; + tensor input_583_cast_fp16 = add(x = input_567_cast_fp16, y = input_581_cast_fp16)[name = string("input_583_cast_fp16")]; + tensor input_585_axes_0 = const()[name = string("input_585_axes_0"), val = tensor([-1])]; + tensor encoder_layers_10_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_10_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(221790400)))]; + tensor encoder_layers_10_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_10_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(221792512)))]; + tensor input_585_cast_fp16 = layer_norm(axes = input_585_axes_0, beta = encoder_layers_10_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_10_norm_feed_forward2_weight_to_fp16, x = input_583_cast_fp16)[name = string("input_585_cast_fp16")]; + tensor encoder_layers_10_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(221794624))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224940416))))[name = string("encoder_layers_10_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_10_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_10_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224940608)))]; + tensor linear_98_cast_fp16 = linear(bias = encoder_layers_10_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_10_feed_forward2_linear1_weight_to_fp16_palettized, x = input_585_cast_fp16)[name = string("linear_98_cast_fp16")]; + tensor input_589_cast_fp16 = silu(x = linear_98_cast_fp16)[name = string("input_589_cast_fp16")]; + tensor encoder_layers_10_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224948864))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(228094656))))[name = string("encoder_layers_10_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_10_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_10_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(228094848)))]; + tensor linear_99_cast_fp16 = linear(bias = encoder_layers_10_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_10_feed_forward2_linear2_weight_to_fp16_palettized, x = input_589_cast_fp16)[name = string("linear_99_cast_fp16")]; + fp16 var_2761_to_fp16 = const()[name = string("op_2761_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2762_cast_fp16 = mul(x = linear_99_cast_fp16, y = var_2761_to_fp16)[name = string("op_2762_cast_fp16")]; + tensor input_595_cast_fp16 = add(x = input_583_cast_fp16, y = var_2762_cast_fp16)[name = string("input_595_cast_fp16")]; + tensor input_597_axes_0 = const()[name = string("input_597_axes_0"), val = tensor([-1])]; + tensor encoder_layers_10_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_10_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(228096960)))]; + tensor encoder_layers_10_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_10_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(228099072)))]; + tensor input_597_cast_fp16 = layer_norm(axes = input_597_axes_0, beta = encoder_layers_10_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_10_norm_out_weight_to_fp16, x = input_595_cast_fp16)[name = string("input_597_cast_fp16")]; + tensor cache_45_begin_0 = const()[name = string("cache_45_begin_0"), val = tensor([11, 0, 0, 0])]; + tensor cache_45_end_0 = const()[name = string("cache_45_end_0"), val = tensor([12, 1, 42, 1024])]; + tensor cache_45_end_mask_0 = const()[name = string("cache_45_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_45_squeeze_mask_0 = const()[name = string("cache_45_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_45_cast_fp16 = slice_by_index(begin = cache_45_begin_0, end = cache_45_end_0, end_mask = cache_45_end_mask_0, squeeze_mask = cache_45_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_45_cast_fp16")]; + tensor cache_47_begin_0 = const()[name = string("cache_47_begin_0"), val = tensor([11, 0, 0, 0])]; + tensor cache_47_end_0 = const()[name = string("cache_47_end_0"), val = tensor([12, 1, 1024, 8])]; + tensor cache_47_end_mask_0 = const()[name = string("cache_47_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_47_squeeze_mask_0 = const()[name = string("cache_47_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_47_cast_fp16 = slice_by_index(begin = cache_47_begin_0, end = cache_47_end_0, end_mask = cache_47_end_mask_0, squeeze_mask = cache_47_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_47_cast_fp16")]; + tensor input_599_axes_0 = const()[name = string("input_599_axes_0"), val = tensor([-1])]; + tensor encoder_layers_11_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_11_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(228101184)))]; + tensor encoder_layers_11_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_11_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(228103296)))]; + tensor input_599_cast_fp16 = layer_norm(axes = input_599_axes_0, beta = encoder_layers_11_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_11_norm_feed_forward1_weight_to_fp16, x = input_597_cast_fp16)[name = string("input_599_cast_fp16")]; + tensor encoder_layers_11_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(228105408))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231251200))))[name = string("encoder_layers_11_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_11_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_11_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231251392)))]; + tensor linear_100_cast_fp16 = linear(bias = encoder_layers_11_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_11_feed_forward1_linear1_weight_to_fp16_palettized, x = input_599_cast_fp16)[name = string("linear_100_cast_fp16")]; + tensor input_603_cast_fp16 = silu(x = linear_100_cast_fp16)[name = string("input_603_cast_fp16")]; + tensor encoder_layers_11_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231259648))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(234405440))))[name = string("encoder_layers_11_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_11_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_11_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(234405632)))]; + tensor linear_101_cast_fp16 = linear(bias = encoder_layers_11_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_11_feed_forward1_linear2_weight_to_fp16_palettized, x = input_603_cast_fp16)[name = string("linear_101_cast_fp16")]; + fp16 var_2798_to_fp16 = const()[name = string("op_2798_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2799_cast_fp16 = mul(x = linear_101_cast_fp16, y = var_2798_to_fp16)[name = string("op_2799_cast_fp16")]; + tensor input_609_cast_fp16 = add(x = input_597_cast_fp16, y = var_2799_cast_fp16)[name = string("input_609_cast_fp16")]; + tensor key_23_axes_0 = const()[name = string("key_23_axes_0"), val = tensor([-1])]; + tensor encoder_layers_11_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_11_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(234407744)))]; + tensor encoder_layers_11_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_11_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(234409856)))]; + tensor key_23_cast_fp16 = layer_norm(axes = key_23_axes_0, beta = encoder_layers_11_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_11_norm_self_att_weight_to_fp16, x = input_609_cast_fp16)[name = string("key_23_cast_fp16")]; + bool input_611_interleave_0 = const()[name = string("input_611_interleave_0"), val = bool(false)]; + tensor input_611_cast_fp16 = concat(axis = var_68, interleave = input_611_interleave_0, values = (cache_45_cast_fp16, key_23_cast_fp16))[name = string("input_611_cast_fp16")]; + tensor var_2821_begin_0 = const()[name = string("op_2821_begin_0"), val = tensor([0, 28, 0])]; + tensor var_2821_end_0 = const()[name = string("op_2821_end_0"), val = tensor([1, 42, 1024])]; + tensor var_2821_end_mask_0 = const()[name = string("op_2821_end_mask_0"), val = tensor([true, true, true])]; + tensor var_2821_cast_fp16 = slice_by_index(begin = var_2821_begin_0, end = var_2821_end_0, end_mask = var_2821_end_mask_0, x = cache_45_cast_fp16)[name = string("op_2821_cast_fp16")]; + bool var_2827_interleave_0 = const()[name = string("op_2827_interleave_0"), val = bool(false)]; + tensor var_2827_cast_fp16 = concat(axis = var_68, interleave = var_2827_interleave_0, values = (var_2821_cast_fp16, key_23_cast_fp16))[name = string("op_2827_cast_fp16")]; + tensor encoder_layers_11_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(234411968))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235198464))))[name = string("encoder_layers_11_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_11_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_11_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235198656)))]; + tensor linear_102_cast_fp16 = linear(bias = encoder_layers_11_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_11_self_attn_linear_q_weight_to_fp16_palettized, x = key_23_cast_fp16)[name = string("linear_102_cast_fp16")]; + tensor var_2832 = const()[name = string("op_2832"), val = tensor([1, -1, 8, 128])]; + tensor q_67_cast_fp16 = reshape(shape = var_2832, x = linear_102_cast_fp16)[name = string("q_67_cast_fp16")]; + tensor encoder_layers_11_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235200768))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235987264))))[name = string("encoder_layers_11_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_11_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_11_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235987456)))]; + tensor linear_103_cast_fp16 = linear(bias = encoder_layers_11_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_11_self_attn_linear_k_weight_to_fp16_palettized, x = input_611_cast_fp16)[name = string("linear_103_cast_fp16")]; + tensor var_2837 = const()[name = string("op_2837"), val = tensor([1, -1, 8, 128])]; + tensor k_45_cast_fp16 = reshape(shape = var_2837, x = linear_103_cast_fp16)[name = string("k_45_cast_fp16")]; + tensor encoder_layers_11_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235989568))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236776064))))[name = string("encoder_layers_11_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_11_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_11_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236776256)))]; + tensor linear_104_cast_fp16 = linear(bias = encoder_layers_11_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_11_self_attn_linear_v_weight_to_fp16_palettized, x = input_611_cast_fp16)[name = string("linear_104_cast_fp16")]; + tensor var_2842 = const()[name = string("op_2842"), val = tensor([1, -1, 8, 128])]; + tensor v_23_cast_fp16 = reshape(shape = var_2842, x = linear_104_cast_fp16)[name = string("v_23_cast_fp16")]; + tensor value_31_perm_0 = const()[name = string("value_31_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_11_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_11_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236778368)))]; + tensor var_2855_cast_fp16 = add(x = q_67_cast_fp16, y = encoder_layers_11_self_attn_pos_bias_u_to_fp16)[name = string("op_2855_cast_fp16")]; + tensor encoder_layers_11_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_11_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236780480)))]; + tensor var_2857_cast_fp16 = add(x = q_67_cast_fp16, y = encoder_layers_11_self_attn_pos_bias_v_to_fp16)[name = string("op_2857_cast_fp16")]; + tensor q_with_bias_v_23_perm_0 = const()[name = string("q_with_bias_v_23_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_293_transpose_x_0 = const()[name = string("x_293_transpose_x_0"), val = bool(false)]; + bool x_293_transpose_y_0 = const()[name = string("x_293_transpose_y_0"), val = bool(false)]; + tensor op_2859_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236782592))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236924992))))[name = string("op_2859_to_fp16_quantized")]; + tensor q_with_bias_v_23_cast_fp16 = transpose(perm = q_with_bias_v_23_perm_0, x = var_2857_cast_fp16)[name = string("transpose_263")]; + tensor x_293_cast_fp16 = matmul(transpose_x = x_293_transpose_x_0, transpose_y = x_293_transpose_y_0, x = q_with_bias_v_23_cast_fp16, y = op_2859_to_fp16_quantized)[name = string("x_293_cast_fp16")]; + tensor x_295_pad_0 = const()[name = string("x_295_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_295_mode_0 = const()[name = string("x_295_mode_0"), val = string("constant")]; + fp16 const_222_to_fp16 = const()[name = string("const_222_to_fp16"), val = fp16(0x0p+0)]; + tensor x_295_cast_fp16 = pad(constant_val = const_222_to_fp16, mode = x_295_mode_0, pad = x_295_pad_0, x = x_293_cast_fp16)[name = string("x_295_cast_fp16")]; + tensor var_2867 = const()[name = string("op_2867"), val = tensor([1, 8, -1, 28])]; + tensor x_297_cast_fp16 = reshape(shape = var_2867, x = x_295_cast_fp16)[name = string("x_297_cast_fp16")]; + tensor var_2871_begin_0 = const()[name = string("op_2871_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_2871_end_0 = const()[name = string("op_2871_end_0"), val = tensor([1, 8, 140, 28])]; + tensor var_2871_end_mask_0 = const()[name = string("op_2871_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_2871_cast_fp16 = slice_by_index(begin = var_2871_begin_0, end = var_2871_end_0, end_mask = var_2871_end_mask_0, x = x_297_cast_fp16)[name = string("op_2871_cast_fp16")]; + tensor var_2872 = const()[name = string("op_2872"), val = tensor([1, 8, 28, 139])]; + tensor matrix_bd_45_cast_fp16 = reshape(shape = var_2872, x = var_2871_cast_fp16)[name = string("matrix_bd_45_cast_fp16")]; + bool matrix_ac_23_transpose_x_0 = const()[name = string("matrix_ac_23_transpose_x_0"), val = bool(false)]; + bool matrix_ac_23_transpose_y_0 = const()[name = string("matrix_ac_23_transpose_y_0"), val = bool(false)]; + tensor transpose_118_perm_0 = const()[name = string("transpose_118_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_119_perm_0 = const()[name = string("transpose_119_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_119 = transpose(perm = transpose_119_perm_0, x = k_45_cast_fp16)[name = string("transpose_261")]; + tensor transpose_118 = transpose(perm = transpose_118_perm_0, x = var_2855_cast_fp16)[name = string("transpose_262")]; + tensor matrix_ac_23_cast_fp16 = matmul(transpose_x = matrix_ac_23_transpose_x_0, transpose_y = matrix_ac_23_transpose_y_0, x = transpose_118, y = transpose_119)[name = string("matrix_ac_23_cast_fp16")]; + tensor matrix_bd_47_begin_0 = const()[name = string("matrix_bd_47_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_47_end_0 = const()[name = string("matrix_bd_47_end_0"), val = tensor([1, 8, 28, 70])]; + tensor matrix_bd_47_end_mask_0 = const()[name = string("matrix_bd_47_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_47_cast_fp16 = slice_by_index(begin = matrix_bd_47_begin_0, end = matrix_bd_47_end_0, end_mask = matrix_bd_47_end_mask_0, x = matrix_bd_45_cast_fp16)[name = string("matrix_bd_47_cast_fp16")]; + tensor var_2881_cast_fp16 = add(x = matrix_ac_23_cast_fp16, y = matrix_bd_47_cast_fp16)[name = string("op_2881_cast_fp16")]; + fp16 _inversed_scores_45_y_0_to_fp16 = const()[name = string("_inversed_scores_45_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_45_cast_fp16 = mul(x = var_2881_cast_fp16, y = _inversed_scores_45_y_0_to_fp16)[name = string("_inversed_scores_45_cast_fp16")]; + tensor scores_47_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_45_cast_fp16, cond = mask_11)[name = string("scores_47_cast_fp16")]; + tensor var_2887_cast_fp16 = softmax(axis = var_59, x = scores_47_cast_fp16)[name = string("op_2887_cast_fp16")]; + tensor input_613_cast_fp16 = select(a = var_44_to_fp16, b = var_2887_cast_fp16, cond = mask_11)[name = string("input_613_cast_fp16")]; + bool x_299_transpose_x_0 = const()[name = string("x_299_transpose_x_0"), val = bool(false)]; + bool x_299_transpose_y_0 = const()[name = string("x_299_transpose_y_0"), val = bool(false)]; + tensor value_31_cast_fp16 = transpose(perm = value_31_perm_0, x = v_23_cast_fp16)[name = string("transpose_260")]; + tensor x_299_cast_fp16 = matmul(transpose_x = x_299_transpose_x_0, transpose_y = x_299_transpose_y_0, x = input_613_cast_fp16, y = value_31_cast_fp16)[name = string("x_299_cast_fp16")]; + tensor var_2891_perm_0 = const()[name = string("op_2891_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2892 = const()[name = string("op_2892"), val = tensor([1, -1, 1024])]; + tensor var_2891_cast_fp16 = transpose(perm = var_2891_perm_0, x = x_299_cast_fp16)[name = string("transpose_259")]; + tensor input_615_cast_fp16 = reshape(shape = var_2892, x = var_2891_cast_fp16)[name = string("input_615_cast_fp16")]; + tensor encoder_layers_11_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236925376))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(237711872))))[name = string("encoder_layers_11_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_11_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_11_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(237712064)))]; + tensor linear_106_cast_fp16 = linear(bias = encoder_layers_11_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_11_self_attn_linear_out_weight_to_fp16_palettized, x = input_615_cast_fp16)[name = string("linear_106_cast_fp16")]; + tensor input_619_cast_fp16 = add(x = input_609_cast_fp16, y = linear_106_cast_fp16)[name = string("input_619_cast_fp16")]; + tensor x_303_axes_0 = const()[name = string("x_303_axes_0"), val = tensor([-1])]; + tensor encoder_layers_11_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_11_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(237714176)))]; + tensor encoder_layers_11_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_11_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(237716288)))]; + tensor x_303_cast_fp16 = layer_norm(axes = x_303_axes_0, beta = encoder_layers_11_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_11_norm_conv_weight_to_fp16, x = input_619_cast_fp16)[name = string("x_303_cast_fp16")]; + tensor input_621_perm_0 = const()[name = string("input_621_perm_0"), val = tensor([0, 2, 1])]; + string input_623_pad_type_0 = const()[name = string("input_623_pad_type_0"), val = string("valid")]; + tensor input_623_strides_0 = const()[name = string("input_623_strides_0"), val = tensor([1])]; + tensor input_623_pad_0 = const()[name = string("input_623_pad_0"), val = tensor([0, 0])]; + tensor input_623_dilations_0 = const()[name = string("input_623_dilations_0"), val = tensor([1])]; + int32 input_623_groups_0 = const()[name = string("input_623_groups_0"), val = int32(1)]; + tensor encoder_layers_11_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(237718400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(239815616))))[name = string("encoder_layers_11_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_621_cast_fp16 = transpose(perm = input_621_perm_0, x = x_303_cast_fp16)[name = string("transpose_258")]; + tensor input_623_cast_fp16 = conv(dilations = input_623_dilations_0, groups = input_623_groups_0, pad = input_623_pad_0, pad_type = input_623_pad_type_0, strides = input_623_strides_0, weight = encoder_layers_11_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_621_cast_fp16)[name = string("input_623_cast_fp16")]; + int32 x_305_split_num_splits_0 = const()[name = string("x_305_split_num_splits_0"), val = int32(2)]; + int32 x_305_split_axis_0 = const()[name = string("x_305_split_axis_0"), val = int32(1)]; + tensor x_305_split_cast_fp16_0, tensor x_305_split_cast_fp16_1 = split(axis = x_305_split_axis_0, num_splits = x_305_split_num_splits_0, x = input_623_cast_fp16)[name = string("x_305_split_cast_fp16")]; + tensor x_305_split_1_sigmoid_cast_fp16 = sigmoid(x = x_305_split_cast_fp16_1)[name = string("x_305_split_1_sigmoid_cast_fp16")]; + tensor x_305_cast_fp16 = mul(x = x_305_split_cast_fp16_0, y = x_305_split_1_sigmoid_cast_fp16)[name = string("x_305_cast_fp16")]; + tensor input_625_cast_fp16 = select(a = var_44_to_fp16, b = x_305_cast_fp16, cond = var_575)[name = string("input_625_cast_fp16")]; + bool new_x_47_interleave_0 = const()[name = string("new_x_47_interleave_0"), val = bool(false)]; + tensor new_x_47_cast_fp16 = concat(axis = var_59, interleave = new_x_47_interleave_0, values = (cache_47_cast_fp16, input_625_cast_fp16))[name = string("new_x_47_cast_fp16")]; + tensor var_2931_begin_0 = const()[name = string("op_2931_begin_0"), val = tensor([0, 0, 28])]; + tensor var_2931_end_0 = const()[name = string("op_2931_end_0"), val = tensor([1, 1024, 36])]; + tensor var_2931_end_mask_0 = const()[name = string("op_2931_end_mask_0"), val = tensor([true, true, true])]; + tensor var_2931_cast_fp16 = slice_by_index(begin = var_2931_begin_0, end = var_2931_end_0, end_mask = var_2931_end_mask_0, x = new_x_47_cast_fp16)[name = string("op_2931_cast_fp16")]; + string x_307_pad_type_0 = const()[name = string("x_307_pad_type_0"), val = string("valid")]; + int32 x_307_groups_0 = const()[name = string("x_307_groups_0"), val = int32(1024)]; + tensor x_307_strides_0 = const()[name = string("x_307_strides_0"), val = tensor([1])]; + tensor x_307_pad_0 = const()[name = string("x_307_pad_0"), val = tensor([0, 0])]; + tensor x_307_dilations_0 = const()[name = string("x_307_dilations_0"), val = tensor([1])]; + tensor encoder_layers_11_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(239819776))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(239829056))))[name = string("encoder_layers_11_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_307_cast_fp16 = conv(dilations = x_307_dilations_0, groups = x_307_groups_0, pad = x_307_pad_0, pad_type = x_307_pad_type_0, strides = x_307_strides_0, weight = encoder_layers_11_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_47_cast_fp16)[name = string("x_307_cast_fp16")]; + tensor input_627_perm_0 = const()[name = string("input_627_perm_0"), val = tensor([0, 2, 1])]; + tensor x_309_axes_0 = const()[name = string("x_309_axes_0"), val = tensor([-1])]; + tensor encoder_layers_11_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_11_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(239831168)))]; + tensor encoder_layers_11_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_11_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(239833280)))]; + tensor input_627_cast_fp16 = transpose(perm = input_627_perm_0, x = x_307_cast_fp16)[name = string("transpose_257")]; + tensor x_309_cast_fp16 = layer_norm(axes = x_309_axes_0, beta = encoder_layers_11_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_11_conv_batch_norm_weight_to_fp16, x = input_627_cast_fp16)[name = string("x_309_cast_fp16")]; + tensor input_629_perm_0 = const()[name = string("input_629_perm_0"), val = tensor([0, 2, 1])]; + tensor input_629_cast_fp16 = transpose(perm = input_629_perm_0, x = x_309_cast_fp16)[name = string("transpose_256")]; + tensor input_631_cast_fp16 = silu(x = input_629_cast_fp16)[name = string("input_631_cast_fp16")]; + string x_311_pad_type_0 = const()[name = string("x_311_pad_type_0"), val = string("valid")]; + tensor x_311_strides_0 = const()[name = string("x_311_strides_0"), val = tensor([1])]; + tensor x_311_pad_0 = const()[name = string("x_311_pad_0"), val = tensor([0, 0])]; + tensor x_311_dilations_0 = const()[name = string("x_311_dilations_0"), val = tensor([1])]; + int32 x_311_groups_0 = const()[name = string("x_311_groups_0"), val = int32(1)]; + tensor encoder_layers_11_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(239835392))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240884032))))[name = string("encoder_layers_11_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_311_cast_fp16 = conv(dilations = x_311_dilations_0, groups = x_311_groups_0, pad = x_311_pad_0, pad_type = x_311_pad_type_0, strides = x_311_strides_0, weight = encoder_layers_11_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_631_cast_fp16)[name = string("x_311_cast_fp16")]; + tensor input_633_perm_0 = const()[name = string("input_633_perm_0"), val = tensor([0, 2, 1])]; + tensor input_633_cast_fp16 = transpose(perm = input_633_perm_0, x = x_311_cast_fp16)[name = string("transpose_255")]; + tensor input_635_cast_fp16 = add(x = input_619_cast_fp16, y = input_633_cast_fp16)[name = string("input_635_cast_fp16")]; + tensor input_637_axes_0 = const()[name = string("input_637_axes_0"), val = tensor([-1])]; + tensor encoder_layers_11_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_11_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240886144)))]; + tensor encoder_layers_11_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_11_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240888256)))]; + tensor input_637_cast_fp16 = layer_norm(axes = input_637_axes_0, beta = encoder_layers_11_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_11_norm_feed_forward2_weight_to_fp16, x = input_635_cast_fp16)[name = string("input_637_cast_fp16")]; + tensor encoder_layers_11_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240890368))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(244036160))))[name = string("encoder_layers_11_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_11_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_11_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(244036352)))]; + tensor linear_107_cast_fp16 = linear(bias = encoder_layers_11_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_11_feed_forward2_linear1_weight_to_fp16_palettized, x = input_637_cast_fp16)[name = string("linear_107_cast_fp16")]; + tensor input_641_cast_fp16 = silu(x = linear_107_cast_fp16)[name = string("input_641_cast_fp16")]; + tensor encoder_layers_11_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(244044608))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247190400))))[name = string("encoder_layers_11_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_11_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_11_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247190592)))]; + tensor linear_108_cast_fp16 = linear(bias = encoder_layers_11_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_11_feed_forward2_linear2_weight_to_fp16_palettized, x = input_641_cast_fp16)[name = string("linear_108_cast_fp16")]; + fp16 var_2974_to_fp16 = const()[name = string("op_2974_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2975_cast_fp16 = mul(x = linear_108_cast_fp16, y = var_2974_to_fp16)[name = string("op_2975_cast_fp16")]; + tensor input_647_cast_fp16 = add(x = input_635_cast_fp16, y = var_2975_cast_fp16)[name = string("input_647_cast_fp16")]; + tensor input_649_axes_0 = const()[name = string("input_649_axes_0"), val = tensor([-1])]; + tensor encoder_layers_11_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_11_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247192704)))]; + tensor encoder_layers_11_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_11_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247194816)))]; + tensor input_649_cast_fp16 = layer_norm(axes = input_649_axes_0, beta = encoder_layers_11_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_11_norm_out_weight_to_fp16, x = input_647_cast_fp16)[name = string("input_649_cast_fp16")]; + tensor cache_49_begin_0 = const()[name = string("cache_49_begin_0"), val = tensor([12, 0, 0, 0])]; + tensor cache_49_end_0 = const()[name = string("cache_49_end_0"), val = tensor([13, 1, 42, 1024])]; + tensor cache_49_end_mask_0 = const()[name = string("cache_49_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_49_squeeze_mask_0 = const()[name = string("cache_49_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_49_cast_fp16 = slice_by_index(begin = cache_49_begin_0, end = cache_49_end_0, end_mask = cache_49_end_mask_0, squeeze_mask = cache_49_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_49_cast_fp16")]; + tensor cache_51_begin_0 = const()[name = string("cache_51_begin_0"), val = tensor([12, 0, 0, 0])]; + tensor cache_51_end_0 = const()[name = string("cache_51_end_0"), val = tensor([13, 1, 1024, 8])]; + tensor cache_51_end_mask_0 = const()[name = string("cache_51_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_51_squeeze_mask_0 = const()[name = string("cache_51_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_51_cast_fp16 = slice_by_index(begin = cache_51_begin_0, end = cache_51_end_0, end_mask = cache_51_end_mask_0, squeeze_mask = cache_51_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_51_cast_fp16")]; + tensor input_651_axes_0 = const()[name = string("input_651_axes_0"), val = tensor([-1])]; + tensor encoder_layers_12_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_12_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247196928)))]; + tensor encoder_layers_12_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_12_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247199040)))]; + tensor input_651_cast_fp16 = layer_norm(axes = input_651_axes_0, beta = encoder_layers_12_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_12_norm_feed_forward1_weight_to_fp16, x = input_649_cast_fp16)[name = string("input_651_cast_fp16")]; + tensor encoder_layers_12_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247201152))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(250346944))))[name = string("encoder_layers_12_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_12_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_12_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(250347136)))]; + tensor linear_109_cast_fp16 = linear(bias = encoder_layers_12_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_12_feed_forward1_linear1_weight_to_fp16_palettized, x = input_651_cast_fp16)[name = string("linear_109_cast_fp16")]; + tensor input_655_cast_fp16 = silu(x = linear_109_cast_fp16)[name = string("input_655_cast_fp16")]; + tensor encoder_layers_12_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(250355392))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(253501184))))[name = string("encoder_layers_12_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_12_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_12_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(253501376)))]; + tensor linear_110_cast_fp16 = linear(bias = encoder_layers_12_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_12_feed_forward1_linear2_weight_to_fp16_palettized, x = input_655_cast_fp16)[name = string("linear_110_cast_fp16")]; + fp16 var_3011_to_fp16 = const()[name = string("op_3011_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3012_cast_fp16 = mul(x = linear_110_cast_fp16, y = var_3011_to_fp16)[name = string("op_3012_cast_fp16")]; + tensor input_661_cast_fp16 = add(x = input_649_cast_fp16, y = var_3012_cast_fp16)[name = string("input_661_cast_fp16")]; + tensor key_25_axes_0 = const()[name = string("key_25_axes_0"), val = tensor([-1])]; + tensor encoder_layers_12_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_12_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(253503488)))]; + tensor encoder_layers_12_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_12_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(253505600)))]; + tensor key_25_cast_fp16 = layer_norm(axes = key_25_axes_0, beta = encoder_layers_12_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_12_norm_self_att_weight_to_fp16, x = input_661_cast_fp16)[name = string("key_25_cast_fp16")]; + bool input_663_interleave_0 = const()[name = string("input_663_interleave_0"), val = bool(false)]; + tensor input_663_cast_fp16 = concat(axis = var_68, interleave = input_663_interleave_0, values = (cache_49_cast_fp16, key_25_cast_fp16))[name = string("input_663_cast_fp16")]; + tensor var_3034_begin_0 = const()[name = string("op_3034_begin_0"), val = tensor([0, 28, 0])]; + tensor var_3034_end_0 = const()[name = string("op_3034_end_0"), val = tensor([1, 42, 1024])]; + tensor var_3034_end_mask_0 = const()[name = string("op_3034_end_mask_0"), val = tensor([true, true, true])]; + tensor var_3034_cast_fp16 = slice_by_index(begin = var_3034_begin_0, end = var_3034_end_0, end_mask = var_3034_end_mask_0, x = cache_49_cast_fp16)[name = string("op_3034_cast_fp16")]; + bool var_3040_interleave_0 = const()[name = string("op_3040_interleave_0"), val = bool(false)]; + tensor var_3040_cast_fp16 = concat(axis = var_68, interleave = var_3040_interleave_0, values = (var_3034_cast_fp16, key_25_cast_fp16))[name = string("op_3040_cast_fp16")]; + tensor encoder_layers_12_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(253507712))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(254294208))))[name = string("encoder_layers_12_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_12_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_12_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(254294400)))]; + tensor linear_111_cast_fp16 = linear(bias = encoder_layers_12_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_12_self_attn_linear_q_weight_to_fp16_palettized, x = key_25_cast_fp16)[name = string("linear_111_cast_fp16")]; + tensor var_3045 = const()[name = string("op_3045"), val = tensor([1, -1, 8, 128])]; + tensor q_73_cast_fp16 = reshape(shape = var_3045, x = linear_111_cast_fp16)[name = string("q_73_cast_fp16")]; + tensor encoder_layers_12_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(254296512))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(255083008))))[name = string("encoder_layers_12_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_12_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_12_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(255083200)))]; + tensor linear_112_cast_fp16 = linear(bias = encoder_layers_12_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_12_self_attn_linear_k_weight_to_fp16_palettized, x = input_663_cast_fp16)[name = string("linear_112_cast_fp16")]; + tensor var_3050 = const()[name = string("op_3050"), val = tensor([1, -1, 8, 128])]; + tensor k_49_cast_fp16 = reshape(shape = var_3050, x = linear_112_cast_fp16)[name = string("k_49_cast_fp16")]; + tensor encoder_layers_12_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(255085312))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(255871808))))[name = string("encoder_layers_12_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_12_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_12_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(255872000)))]; + tensor linear_113_cast_fp16 = linear(bias = encoder_layers_12_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_12_self_attn_linear_v_weight_to_fp16_palettized, x = input_663_cast_fp16)[name = string("linear_113_cast_fp16")]; + tensor var_3055 = const()[name = string("op_3055"), val = tensor([1, -1, 8, 128])]; + tensor v_25_cast_fp16 = reshape(shape = var_3055, x = linear_113_cast_fp16)[name = string("v_25_cast_fp16")]; + tensor value_33_perm_0 = const()[name = string("value_33_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_12_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_12_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(255874112)))]; + tensor var_3068_cast_fp16 = add(x = q_73_cast_fp16, y = encoder_layers_12_self_attn_pos_bias_u_to_fp16)[name = string("op_3068_cast_fp16")]; + tensor encoder_layers_12_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_12_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(255876224)))]; + tensor var_3070_cast_fp16 = add(x = q_73_cast_fp16, y = encoder_layers_12_self_attn_pos_bias_v_to_fp16)[name = string("op_3070_cast_fp16")]; + tensor q_with_bias_v_25_perm_0 = const()[name = string("q_with_bias_v_25_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_319_transpose_x_0 = const()[name = string("x_319_transpose_x_0"), val = bool(false)]; + bool x_319_transpose_y_0 = const()[name = string("x_319_transpose_y_0"), val = bool(false)]; + tensor op_3072_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(255878336))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(256020736))))[name = string("op_3072_to_fp16_quantized")]; + tensor q_with_bias_v_25_cast_fp16 = transpose(perm = q_with_bias_v_25_perm_0, x = var_3070_cast_fp16)[name = string("transpose_254")]; + tensor x_319_cast_fp16 = matmul(transpose_x = x_319_transpose_x_0, transpose_y = x_319_transpose_y_0, x = q_with_bias_v_25_cast_fp16, y = op_3072_to_fp16_quantized)[name = string("x_319_cast_fp16")]; + tensor x_321_pad_0 = const()[name = string("x_321_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_321_mode_0 = const()[name = string("x_321_mode_0"), val = string("constant")]; + fp16 const_235_to_fp16 = const()[name = string("const_235_to_fp16"), val = fp16(0x0p+0)]; + tensor x_321_cast_fp16 = pad(constant_val = const_235_to_fp16, mode = x_321_mode_0, pad = x_321_pad_0, x = x_319_cast_fp16)[name = string("x_321_cast_fp16")]; + tensor var_3080 = const()[name = string("op_3080"), val = tensor([1, 8, -1, 28])]; + tensor x_323_cast_fp16 = reshape(shape = var_3080, x = x_321_cast_fp16)[name = string("x_323_cast_fp16")]; + tensor var_3084_begin_0 = const()[name = string("op_3084_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_3084_end_0 = const()[name = string("op_3084_end_0"), val = tensor([1, 8, 140, 28])]; + tensor var_3084_end_mask_0 = const()[name = string("op_3084_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_3084_cast_fp16 = slice_by_index(begin = var_3084_begin_0, end = var_3084_end_0, end_mask = var_3084_end_mask_0, x = x_323_cast_fp16)[name = string("op_3084_cast_fp16")]; + tensor var_3085 = const()[name = string("op_3085"), val = tensor([1, 8, 28, 139])]; + tensor matrix_bd_49_cast_fp16 = reshape(shape = var_3085, x = var_3084_cast_fp16)[name = string("matrix_bd_49_cast_fp16")]; + bool matrix_ac_25_transpose_x_0 = const()[name = string("matrix_ac_25_transpose_x_0"), val = bool(false)]; + bool matrix_ac_25_transpose_y_0 = const()[name = string("matrix_ac_25_transpose_y_0"), val = bool(false)]; + tensor transpose_120_perm_0 = const()[name = string("transpose_120_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_121_perm_0 = const()[name = string("transpose_121_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_121 = transpose(perm = transpose_121_perm_0, x = k_49_cast_fp16)[name = string("transpose_252")]; + tensor transpose_120 = transpose(perm = transpose_120_perm_0, x = var_3068_cast_fp16)[name = string("transpose_253")]; + tensor matrix_ac_25_cast_fp16 = matmul(transpose_x = matrix_ac_25_transpose_x_0, transpose_y = matrix_ac_25_transpose_y_0, x = transpose_120, y = transpose_121)[name = string("matrix_ac_25_cast_fp16")]; + tensor matrix_bd_51_begin_0 = const()[name = string("matrix_bd_51_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_51_end_0 = const()[name = string("matrix_bd_51_end_0"), val = tensor([1, 8, 28, 70])]; + tensor matrix_bd_51_end_mask_0 = const()[name = string("matrix_bd_51_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_51_cast_fp16 = slice_by_index(begin = matrix_bd_51_begin_0, end = matrix_bd_51_end_0, end_mask = matrix_bd_51_end_mask_0, x = matrix_bd_49_cast_fp16)[name = string("matrix_bd_51_cast_fp16")]; + tensor var_3094_cast_fp16 = add(x = matrix_ac_25_cast_fp16, y = matrix_bd_51_cast_fp16)[name = string("op_3094_cast_fp16")]; + fp16 _inversed_scores_49_y_0_to_fp16 = const()[name = string("_inversed_scores_49_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_49_cast_fp16 = mul(x = var_3094_cast_fp16, y = _inversed_scores_49_y_0_to_fp16)[name = string("_inversed_scores_49_cast_fp16")]; + tensor scores_51_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_49_cast_fp16, cond = mask_11)[name = string("scores_51_cast_fp16")]; + tensor var_3100_cast_fp16 = softmax(axis = var_59, x = scores_51_cast_fp16)[name = string("op_3100_cast_fp16")]; + tensor input_665_cast_fp16 = select(a = var_44_to_fp16, b = var_3100_cast_fp16, cond = mask_11)[name = string("input_665_cast_fp16")]; + bool x_325_transpose_x_0 = const()[name = string("x_325_transpose_x_0"), val = bool(false)]; + bool x_325_transpose_y_0 = const()[name = string("x_325_transpose_y_0"), val = bool(false)]; + tensor value_33_cast_fp16 = transpose(perm = value_33_perm_0, x = v_25_cast_fp16)[name = string("transpose_251")]; + tensor x_325_cast_fp16 = matmul(transpose_x = x_325_transpose_x_0, transpose_y = x_325_transpose_y_0, x = input_665_cast_fp16, y = value_33_cast_fp16)[name = string("x_325_cast_fp16")]; + tensor var_3104_perm_0 = const()[name = string("op_3104_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3105 = const()[name = string("op_3105"), val = tensor([1, -1, 1024])]; + tensor var_3104_cast_fp16 = transpose(perm = var_3104_perm_0, x = x_325_cast_fp16)[name = string("transpose_250")]; + tensor input_667_cast_fp16 = reshape(shape = var_3105, x = var_3104_cast_fp16)[name = string("input_667_cast_fp16")]; + tensor encoder_layers_12_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(256021120))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(256807616))))[name = string("encoder_layers_12_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_12_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_12_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(256807808)))]; + tensor linear_115_cast_fp16 = linear(bias = encoder_layers_12_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_12_self_attn_linear_out_weight_to_fp16_palettized, x = input_667_cast_fp16)[name = string("linear_115_cast_fp16")]; + tensor input_671_cast_fp16 = add(x = input_661_cast_fp16, y = linear_115_cast_fp16)[name = string("input_671_cast_fp16")]; + tensor x_329_axes_0 = const()[name = string("x_329_axes_0"), val = tensor([-1])]; + tensor encoder_layers_12_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_12_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(256809920)))]; + tensor encoder_layers_12_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_12_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(256812032)))]; + tensor x_329_cast_fp16 = layer_norm(axes = x_329_axes_0, beta = encoder_layers_12_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_12_norm_conv_weight_to_fp16, x = input_671_cast_fp16)[name = string("x_329_cast_fp16")]; + tensor input_673_perm_0 = const()[name = string("input_673_perm_0"), val = tensor([0, 2, 1])]; + string input_675_pad_type_0 = const()[name = string("input_675_pad_type_0"), val = string("valid")]; + tensor input_675_strides_0 = const()[name = string("input_675_strides_0"), val = tensor([1])]; + tensor input_675_pad_0 = const()[name = string("input_675_pad_0"), val = tensor([0, 0])]; + tensor input_675_dilations_0 = const()[name = string("input_675_dilations_0"), val = tensor([1])]; + int32 input_675_groups_0 = const()[name = string("input_675_groups_0"), val = int32(1)]; + tensor encoder_layers_12_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(256814144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(258911360))))[name = string("encoder_layers_12_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_673_cast_fp16 = transpose(perm = input_673_perm_0, x = x_329_cast_fp16)[name = string("transpose_249")]; + tensor input_675_cast_fp16 = conv(dilations = input_675_dilations_0, groups = input_675_groups_0, pad = input_675_pad_0, pad_type = input_675_pad_type_0, strides = input_675_strides_0, weight = encoder_layers_12_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_673_cast_fp16)[name = string("input_675_cast_fp16")]; + int32 x_331_split_num_splits_0 = const()[name = string("x_331_split_num_splits_0"), val = int32(2)]; + int32 x_331_split_axis_0 = const()[name = string("x_331_split_axis_0"), val = int32(1)]; + tensor x_331_split_cast_fp16_0, tensor x_331_split_cast_fp16_1 = split(axis = x_331_split_axis_0, num_splits = x_331_split_num_splits_0, x = input_675_cast_fp16)[name = string("x_331_split_cast_fp16")]; + tensor x_331_split_1_sigmoid_cast_fp16 = sigmoid(x = x_331_split_cast_fp16_1)[name = string("x_331_split_1_sigmoid_cast_fp16")]; + tensor x_331_cast_fp16 = mul(x = x_331_split_cast_fp16_0, y = x_331_split_1_sigmoid_cast_fp16)[name = string("x_331_cast_fp16")]; + tensor input_677_cast_fp16 = select(a = var_44_to_fp16, b = x_331_cast_fp16, cond = var_575)[name = string("input_677_cast_fp16")]; + bool new_x_51_interleave_0 = const()[name = string("new_x_51_interleave_0"), val = bool(false)]; + tensor new_x_51_cast_fp16 = concat(axis = var_59, interleave = new_x_51_interleave_0, values = (cache_51_cast_fp16, input_677_cast_fp16))[name = string("new_x_51_cast_fp16")]; + tensor var_3144_begin_0 = const()[name = string("op_3144_begin_0"), val = tensor([0, 0, 28])]; + tensor var_3144_end_0 = const()[name = string("op_3144_end_0"), val = tensor([1, 1024, 36])]; + tensor var_3144_end_mask_0 = const()[name = string("op_3144_end_mask_0"), val = tensor([true, true, true])]; + tensor var_3144_cast_fp16 = slice_by_index(begin = var_3144_begin_0, end = var_3144_end_0, end_mask = var_3144_end_mask_0, x = new_x_51_cast_fp16)[name = string("op_3144_cast_fp16")]; + string x_333_pad_type_0 = const()[name = string("x_333_pad_type_0"), val = string("valid")]; + int32 x_333_groups_0 = const()[name = string("x_333_groups_0"), val = int32(1024)]; + tensor x_333_strides_0 = const()[name = string("x_333_strides_0"), val = tensor([1])]; + tensor x_333_pad_0 = const()[name = string("x_333_pad_0"), val = tensor([0, 0])]; + tensor x_333_dilations_0 = const()[name = string("x_333_dilations_0"), val = tensor([1])]; + tensor encoder_layers_12_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(258915520))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(258924800))))[name = string("encoder_layers_12_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_333_cast_fp16 = conv(dilations = x_333_dilations_0, groups = x_333_groups_0, pad = x_333_pad_0, pad_type = x_333_pad_type_0, strides = x_333_strides_0, weight = encoder_layers_12_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_51_cast_fp16)[name = string("x_333_cast_fp16")]; + tensor input_679_perm_0 = const()[name = string("input_679_perm_0"), val = tensor([0, 2, 1])]; + tensor x_335_axes_0 = const()[name = string("x_335_axes_0"), val = tensor([-1])]; + tensor encoder_layers_12_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_12_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(258926912)))]; + tensor encoder_layers_12_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_12_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(258929024)))]; + tensor input_679_cast_fp16 = transpose(perm = input_679_perm_0, x = x_333_cast_fp16)[name = string("transpose_248")]; + tensor x_335_cast_fp16 = layer_norm(axes = x_335_axes_0, beta = encoder_layers_12_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_12_conv_batch_norm_weight_to_fp16, x = input_679_cast_fp16)[name = string("x_335_cast_fp16")]; + tensor input_681_perm_0 = const()[name = string("input_681_perm_0"), val = tensor([0, 2, 1])]; + tensor input_681_cast_fp16 = transpose(perm = input_681_perm_0, x = x_335_cast_fp16)[name = string("transpose_247")]; + tensor input_683_cast_fp16 = silu(x = input_681_cast_fp16)[name = string("input_683_cast_fp16")]; + string x_337_pad_type_0 = const()[name = string("x_337_pad_type_0"), val = string("valid")]; + tensor x_337_strides_0 = const()[name = string("x_337_strides_0"), val = tensor([1])]; + tensor x_337_pad_0 = const()[name = string("x_337_pad_0"), val = tensor([0, 0])]; + tensor x_337_dilations_0 = const()[name = string("x_337_dilations_0"), val = tensor([1])]; + int32 x_337_groups_0 = const()[name = string("x_337_groups_0"), val = int32(1)]; + tensor encoder_layers_12_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(258931136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259979776))))[name = string("encoder_layers_12_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_337_cast_fp16 = conv(dilations = x_337_dilations_0, groups = x_337_groups_0, pad = x_337_pad_0, pad_type = x_337_pad_type_0, strides = x_337_strides_0, weight = encoder_layers_12_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_683_cast_fp16)[name = string("x_337_cast_fp16")]; + tensor input_685_perm_0 = const()[name = string("input_685_perm_0"), val = tensor([0, 2, 1])]; + tensor input_685_cast_fp16 = transpose(perm = input_685_perm_0, x = x_337_cast_fp16)[name = string("transpose_246")]; + tensor input_687_cast_fp16 = add(x = input_671_cast_fp16, y = input_685_cast_fp16)[name = string("input_687_cast_fp16")]; + tensor input_689_axes_0 = const()[name = string("input_689_axes_0"), val = tensor([-1])]; + tensor encoder_layers_12_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_12_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259981888)))]; + tensor encoder_layers_12_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_12_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259984000)))]; + tensor input_689_cast_fp16 = layer_norm(axes = input_689_axes_0, beta = encoder_layers_12_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_12_norm_feed_forward2_weight_to_fp16, x = input_687_cast_fp16)[name = string("input_689_cast_fp16")]; + tensor encoder_layers_12_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259986112))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263131904))))[name = string("encoder_layers_12_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_12_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_12_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263132096)))]; + tensor linear_116_cast_fp16 = linear(bias = encoder_layers_12_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_12_feed_forward2_linear1_weight_to_fp16_palettized, x = input_689_cast_fp16)[name = string("linear_116_cast_fp16")]; + tensor input_693_cast_fp16 = silu(x = linear_116_cast_fp16)[name = string("input_693_cast_fp16")]; + tensor encoder_layers_12_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263140352))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(266286144))))[name = string("encoder_layers_12_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_12_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_12_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(266286336)))]; + tensor linear_117_cast_fp16 = linear(bias = encoder_layers_12_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_12_feed_forward2_linear2_weight_to_fp16_palettized, x = input_693_cast_fp16)[name = string("linear_117_cast_fp16")]; + fp16 var_3187_to_fp16 = const()[name = string("op_3187_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3188_cast_fp16 = mul(x = linear_117_cast_fp16, y = var_3187_to_fp16)[name = string("op_3188_cast_fp16")]; + tensor input_699_cast_fp16 = add(x = input_687_cast_fp16, y = var_3188_cast_fp16)[name = string("input_699_cast_fp16")]; + tensor input_701_axes_0 = const()[name = string("input_701_axes_0"), val = tensor([-1])]; + tensor encoder_layers_12_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_12_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(266288448)))]; + tensor encoder_layers_12_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_12_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(266290560)))]; + tensor input_701_cast_fp16 = layer_norm(axes = input_701_axes_0, beta = encoder_layers_12_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_12_norm_out_weight_to_fp16, x = input_699_cast_fp16)[name = string("input_701_cast_fp16")]; + tensor cache_53_begin_0 = const()[name = string("cache_53_begin_0"), val = tensor([13, 0, 0, 0])]; + tensor cache_53_end_0 = const()[name = string("cache_53_end_0"), val = tensor([14, 1, 42, 1024])]; + tensor cache_53_end_mask_0 = const()[name = string("cache_53_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_53_squeeze_mask_0 = const()[name = string("cache_53_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_53_cast_fp16 = slice_by_index(begin = cache_53_begin_0, end = cache_53_end_0, end_mask = cache_53_end_mask_0, squeeze_mask = cache_53_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_53_cast_fp16")]; + tensor cache_55_begin_0 = const()[name = string("cache_55_begin_0"), val = tensor([13, 0, 0, 0])]; + tensor cache_55_end_0 = const()[name = string("cache_55_end_0"), val = tensor([14, 1, 1024, 8])]; + tensor cache_55_end_mask_0 = const()[name = string("cache_55_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_55_squeeze_mask_0 = const()[name = string("cache_55_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_55_cast_fp16 = slice_by_index(begin = cache_55_begin_0, end = cache_55_end_0, end_mask = cache_55_end_mask_0, squeeze_mask = cache_55_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_55_cast_fp16")]; + tensor input_703_axes_0 = const()[name = string("input_703_axes_0"), val = tensor([-1])]; + tensor encoder_layers_13_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_13_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(266292672)))]; + tensor encoder_layers_13_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_13_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(266294784)))]; + tensor input_703_cast_fp16 = layer_norm(axes = input_703_axes_0, beta = encoder_layers_13_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_13_norm_feed_forward1_weight_to_fp16, x = input_701_cast_fp16)[name = string("input_703_cast_fp16")]; + tensor encoder_layers_13_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(266296896))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(269442688))))[name = string("encoder_layers_13_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_13_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_13_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(269442880)))]; + tensor linear_118_cast_fp16 = linear(bias = encoder_layers_13_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_13_feed_forward1_linear1_weight_to_fp16_palettized, x = input_703_cast_fp16)[name = string("linear_118_cast_fp16")]; + tensor input_707_cast_fp16 = silu(x = linear_118_cast_fp16)[name = string("input_707_cast_fp16")]; + tensor encoder_layers_13_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(269451136))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(272596928))))[name = string("encoder_layers_13_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_13_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_13_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(272597120)))]; + tensor linear_119_cast_fp16 = linear(bias = encoder_layers_13_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_13_feed_forward1_linear2_weight_to_fp16_palettized, x = input_707_cast_fp16)[name = string("linear_119_cast_fp16")]; + fp16 var_3224_to_fp16 = const()[name = string("op_3224_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3225_cast_fp16 = mul(x = linear_119_cast_fp16, y = var_3224_to_fp16)[name = string("op_3225_cast_fp16")]; + tensor input_713_cast_fp16 = add(x = input_701_cast_fp16, y = var_3225_cast_fp16)[name = string("input_713_cast_fp16")]; + tensor key_27_axes_0 = const()[name = string("key_27_axes_0"), val = tensor([-1])]; + tensor encoder_layers_13_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_13_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(272599232)))]; + tensor encoder_layers_13_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_13_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(272601344)))]; + tensor key_27_cast_fp16 = layer_norm(axes = key_27_axes_0, beta = encoder_layers_13_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_13_norm_self_att_weight_to_fp16, x = input_713_cast_fp16)[name = string("key_27_cast_fp16")]; + bool input_715_interleave_0 = const()[name = string("input_715_interleave_0"), val = bool(false)]; + tensor input_715_cast_fp16 = concat(axis = var_68, interleave = input_715_interleave_0, values = (cache_53_cast_fp16, key_27_cast_fp16))[name = string("input_715_cast_fp16")]; + tensor var_3247_begin_0 = const()[name = string("op_3247_begin_0"), val = tensor([0, 28, 0])]; + tensor var_3247_end_0 = const()[name = string("op_3247_end_0"), val = tensor([1, 42, 1024])]; + tensor var_3247_end_mask_0 = const()[name = string("op_3247_end_mask_0"), val = tensor([true, true, true])]; + tensor var_3247_cast_fp16 = slice_by_index(begin = var_3247_begin_0, end = var_3247_end_0, end_mask = var_3247_end_mask_0, x = cache_53_cast_fp16)[name = string("op_3247_cast_fp16")]; + bool var_3253_interleave_0 = const()[name = string("op_3253_interleave_0"), val = bool(false)]; + tensor var_3253_cast_fp16 = concat(axis = var_68, interleave = var_3253_interleave_0, values = (var_3247_cast_fp16, key_27_cast_fp16))[name = string("op_3253_cast_fp16")]; + tensor encoder_layers_13_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(272603456))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(273389952))))[name = string("encoder_layers_13_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_13_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_13_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(273390144)))]; + tensor linear_120_cast_fp16 = linear(bias = encoder_layers_13_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_13_self_attn_linear_q_weight_to_fp16_palettized, x = key_27_cast_fp16)[name = string("linear_120_cast_fp16")]; + tensor var_3258 = const()[name = string("op_3258"), val = tensor([1, -1, 8, 128])]; + tensor q_79_cast_fp16 = reshape(shape = var_3258, x = linear_120_cast_fp16)[name = string("q_79_cast_fp16")]; + tensor encoder_layers_13_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(273392256))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(274178752))))[name = string("encoder_layers_13_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_13_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_13_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(274178944)))]; + tensor linear_121_cast_fp16 = linear(bias = encoder_layers_13_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_13_self_attn_linear_k_weight_to_fp16_palettized, x = input_715_cast_fp16)[name = string("linear_121_cast_fp16")]; + tensor var_3263 = const()[name = string("op_3263"), val = tensor([1, -1, 8, 128])]; + tensor k_53_cast_fp16 = reshape(shape = var_3263, x = linear_121_cast_fp16)[name = string("k_53_cast_fp16")]; + tensor encoder_layers_13_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(274181056))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(274967552))))[name = string("encoder_layers_13_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_13_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_13_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(274967744)))]; + tensor linear_122_cast_fp16 = linear(bias = encoder_layers_13_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_13_self_attn_linear_v_weight_to_fp16_palettized, x = input_715_cast_fp16)[name = string("linear_122_cast_fp16")]; + tensor var_3268 = const()[name = string("op_3268"), val = tensor([1, -1, 8, 128])]; + tensor v_27_cast_fp16 = reshape(shape = var_3268, x = linear_122_cast_fp16)[name = string("v_27_cast_fp16")]; + tensor value_35_perm_0 = const()[name = string("value_35_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_13_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_13_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(274969856)))]; + tensor var_3281_cast_fp16 = add(x = q_79_cast_fp16, y = encoder_layers_13_self_attn_pos_bias_u_to_fp16)[name = string("op_3281_cast_fp16")]; + tensor encoder_layers_13_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_13_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(274971968)))]; + tensor var_3283_cast_fp16 = add(x = q_79_cast_fp16, y = encoder_layers_13_self_attn_pos_bias_v_to_fp16)[name = string("op_3283_cast_fp16")]; + tensor q_with_bias_v_27_perm_0 = const()[name = string("q_with_bias_v_27_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_345_transpose_x_0 = const()[name = string("x_345_transpose_x_0"), val = bool(false)]; + bool x_345_transpose_y_0 = const()[name = string("x_345_transpose_y_0"), val = bool(false)]; + tensor op_3285_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(274974080))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275116480))))[name = string("op_3285_to_fp16_quantized")]; + tensor q_with_bias_v_27_cast_fp16 = transpose(perm = q_with_bias_v_27_perm_0, x = var_3283_cast_fp16)[name = string("transpose_245")]; + tensor x_345_cast_fp16 = matmul(transpose_x = x_345_transpose_x_0, transpose_y = x_345_transpose_y_0, x = q_with_bias_v_27_cast_fp16, y = op_3285_to_fp16_quantized)[name = string("x_345_cast_fp16")]; + tensor x_347_pad_0 = const()[name = string("x_347_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_347_mode_0 = const()[name = string("x_347_mode_0"), val = string("constant")]; + fp16 const_248_to_fp16 = const()[name = string("const_248_to_fp16"), val = fp16(0x0p+0)]; + tensor x_347_cast_fp16 = pad(constant_val = const_248_to_fp16, mode = x_347_mode_0, pad = x_347_pad_0, x = x_345_cast_fp16)[name = string("x_347_cast_fp16")]; + tensor var_3293 = const()[name = string("op_3293"), val = tensor([1, 8, -1, 28])]; + tensor x_349_cast_fp16 = reshape(shape = var_3293, x = x_347_cast_fp16)[name = string("x_349_cast_fp16")]; + tensor var_3297_begin_0 = const()[name = string("op_3297_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_3297_end_0 = const()[name = string("op_3297_end_0"), val = tensor([1, 8, 140, 28])]; + tensor var_3297_end_mask_0 = const()[name = string("op_3297_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_3297_cast_fp16 = slice_by_index(begin = var_3297_begin_0, end = var_3297_end_0, end_mask = var_3297_end_mask_0, x = x_349_cast_fp16)[name = string("op_3297_cast_fp16")]; + tensor var_3298 = const()[name = string("op_3298"), val = tensor([1, 8, 28, 139])]; + tensor matrix_bd_53_cast_fp16 = reshape(shape = var_3298, x = var_3297_cast_fp16)[name = string("matrix_bd_53_cast_fp16")]; + bool matrix_ac_27_transpose_x_0 = const()[name = string("matrix_ac_27_transpose_x_0"), val = bool(false)]; + bool matrix_ac_27_transpose_y_0 = const()[name = string("matrix_ac_27_transpose_y_0"), val = bool(false)]; + tensor transpose_122_perm_0 = const()[name = string("transpose_122_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_123_perm_0 = const()[name = string("transpose_123_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_123 = transpose(perm = transpose_123_perm_0, x = k_53_cast_fp16)[name = string("transpose_243")]; + tensor transpose_122 = transpose(perm = transpose_122_perm_0, x = var_3281_cast_fp16)[name = string("transpose_244")]; + tensor matrix_ac_27_cast_fp16 = matmul(transpose_x = matrix_ac_27_transpose_x_0, transpose_y = matrix_ac_27_transpose_y_0, x = transpose_122, y = transpose_123)[name = string("matrix_ac_27_cast_fp16")]; + tensor matrix_bd_55_begin_0 = const()[name = string("matrix_bd_55_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_55_end_0 = const()[name = string("matrix_bd_55_end_0"), val = tensor([1, 8, 28, 70])]; + tensor matrix_bd_55_end_mask_0 = const()[name = string("matrix_bd_55_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_55_cast_fp16 = slice_by_index(begin = matrix_bd_55_begin_0, end = matrix_bd_55_end_0, end_mask = matrix_bd_55_end_mask_0, x = matrix_bd_53_cast_fp16)[name = string("matrix_bd_55_cast_fp16")]; + tensor var_3307_cast_fp16 = add(x = matrix_ac_27_cast_fp16, y = matrix_bd_55_cast_fp16)[name = string("op_3307_cast_fp16")]; + fp16 _inversed_scores_53_y_0_to_fp16 = const()[name = string("_inversed_scores_53_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_53_cast_fp16 = mul(x = var_3307_cast_fp16, y = _inversed_scores_53_y_0_to_fp16)[name = string("_inversed_scores_53_cast_fp16")]; + tensor scores_55_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_53_cast_fp16, cond = mask_11)[name = string("scores_55_cast_fp16")]; + tensor var_3313_cast_fp16 = softmax(axis = var_59, x = scores_55_cast_fp16)[name = string("op_3313_cast_fp16")]; + tensor input_717_cast_fp16 = select(a = var_44_to_fp16, b = var_3313_cast_fp16, cond = mask_11)[name = string("input_717_cast_fp16")]; + bool x_351_transpose_x_0 = const()[name = string("x_351_transpose_x_0"), val = bool(false)]; + bool x_351_transpose_y_0 = const()[name = string("x_351_transpose_y_0"), val = bool(false)]; + tensor value_35_cast_fp16 = transpose(perm = value_35_perm_0, x = v_27_cast_fp16)[name = string("transpose_242")]; + tensor x_351_cast_fp16 = matmul(transpose_x = x_351_transpose_x_0, transpose_y = x_351_transpose_y_0, x = input_717_cast_fp16, y = value_35_cast_fp16)[name = string("x_351_cast_fp16")]; + tensor var_3317_perm_0 = const()[name = string("op_3317_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3318 = const()[name = string("op_3318"), val = tensor([1, -1, 1024])]; + tensor var_3317_cast_fp16 = transpose(perm = var_3317_perm_0, x = x_351_cast_fp16)[name = string("transpose_241")]; + tensor input_719_cast_fp16 = reshape(shape = var_3318, x = var_3317_cast_fp16)[name = string("input_719_cast_fp16")]; + tensor encoder_layers_13_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275116864))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275903360))))[name = string("encoder_layers_13_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_13_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_13_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275903552)))]; + tensor linear_124_cast_fp16 = linear(bias = encoder_layers_13_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_13_self_attn_linear_out_weight_to_fp16_palettized, x = input_719_cast_fp16)[name = string("linear_124_cast_fp16")]; + tensor input_723_cast_fp16 = add(x = input_713_cast_fp16, y = linear_124_cast_fp16)[name = string("input_723_cast_fp16")]; + tensor x_355_axes_0 = const()[name = string("x_355_axes_0"), val = tensor([-1])]; + tensor encoder_layers_13_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_13_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275905664)))]; + tensor encoder_layers_13_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_13_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275907776)))]; + tensor x_355_cast_fp16 = layer_norm(axes = x_355_axes_0, beta = encoder_layers_13_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_13_norm_conv_weight_to_fp16, x = input_723_cast_fp16)[name = string("x_355_cast_fp16")]; + tensor input_725_perm_0 = const()[name = string("input_725_perm_0"), val = tensor([0, 2, 1])]; + string input_727_pad_type_0 = const()[name = string("input_727_pad_type_0"), val = string("valid")]; + tensor input_727_strides_0 = const()[name = string("input_727_strides_0"), val = tensor([1])]; + tensor input_727_pad_0 = const()[name = string("input_727_pad_0"), val = tensor([0, 0])]; + tensor input_727_dilations_0 = const()[name = string("input_727_dilations_0"), val = tensor([1])]; + int32 input_727_groups_0 = const()[name = string("input_727_groups_0"), val = int32(1)]; + tensor encoder_layers_13_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275909888))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278007104))))[name = string("encoder_layers_13_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_725_cast_fp16 = transpose(perm = input_725_perm_0, x = x_355_cast_fp16)[name = string("transpose_240")]; + tensor input_727_cast_fp16 = conv(dilations = input_727_dilations_0, groups = input_727_groups_0, pad = input_727_pad_0, pad_type = input_727_pad_type_0, strides = input_727_strides_0, weight = encoder_layers_13_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_725_cast_fp16)[name = string("input_727_cast_fp16")]; + int32 x_357_split_num_splits_0 = const()[name = string("x_357_split_num_splits_0"), val = int32(2)]; + int32 x_357_split_axis_0 = const()[name = string("x_357_split_axis_0"), val = int32(1)]; + tensor x_357_split_cast_fp16_0, tensor x_357_split_cast_fp16_1 = split(axis = x_357_split_axis_0, num_splits = x_357_split_num_splits_0, x = input_727_cast_fp16)[name = string("x_357_split_cast_fp16")]; + tensor x_357_split_1_sigmoid_cast_fp16 = sigmoid(x = x_357_split_cast_fp16_1)[name = string("x_357_split_1_sigmoid_cast_fp16")]; + tensor x_357_cast_fp16 = mul(x = x_357_split_cast_fp16_0, y = x_357_split_1_sigmoid_cast_fp16)[name = string("x_357_cast_fp16")]; + tensor input_729_cast_fp16 = select(a = var_44_to_fp16, b = x_357_cast_fp16, cond = var_575)[name = string("input_729_cast_fp16")]; + bool new_x_55_interleave_0 = const()[name = string("new_x_55_interleave_0"), val = bool(false)]; + tensor new_x_55_cast_fp16 = concat(axis = var_59, interleave = new_x_55_interleave_0, values = (cache_55_cast_fp16, input_729_cast_fp16))[name = string("new_x_55_cast_fp16")]; + tensor var_3357_begin_0 = const()[name = string("op_3357_begin_0"), val = tensor([0, 0, 28])]; + tensor var_3357_end_0 = const()[name = string("op_3357_end_0"), val = tensor([1, 1024, 36])]; + tensor var_3357_end_mask_0 = const()[name = string("op_3357_end_mask_0"), val = tensor([true, true, true])]; + tensor var_3357_cast_fp16 = slice_by_index(begin = var_3357_begin_0, end = var_3357_end_0, end_mask = var_3357_end_mask_0, x = new_x_55_cast_fp16)[name = string("op_3357_cast_fp16")]; + string x_359_pad_type_0 = const()[name = string("x_359_pad_type_0"), val = string("valid")]; + int32 x_359_groups_0 = const()[name = string("x_359_groups_0"), val = int32(1024)]; + tensor x_359_strides_0 = const()[name = string("x_359_strides_0"), val = tensor([1])]; + tensor x_359_pad_0 = const()[name = string("x_359_pad_0"), val = tensor([0, 0])]; + tensor x_359_dilations_0 = const()[name = string("x_359_dilations_0"), val = tensor([1])]; + tensor encoder_layers_13_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278011264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278020544))))[name = string("encoder_layers_13_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_359_cast_fp16 = conv(dilations = x_359_dilations_0, groups = x_359_groups_0, pad = x_359_pad_0, pad_type = x_359_pad_type_0, strides = x_359_strides_0, weight = encoder_layers_13_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_55_cast_fp16)[name = string("x_359_cast_fp16")]; + tensor input_731_perm_0 = const()[name = string("input_731_perm_0"), val = tensor([0, 2, 1])]; + tensor x_361_axes_0 = const()[name = string("x_361_axes_0"), val = tensor([-1])]; + tensor encoder_layers_13_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_13_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278022656)))]; + tensor encoder_layers_13_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_13_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278024768)))]; + tensor input_731_cast_fp16 = transpose(perm = input_731_perm_0, x = x_359_cast_fp16)[name = string("transpose_239")]; + tensor x_361_cast_fp16 = layer_norm(axes = x_361_axes_0, beta = encoder_layers_13_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_13_conv_batch_norm_weight_to_fp16, x = input_731_cast_fp16)[name = string("x_361_cast_fp16")]; + tensor input_733_perm_0 = const()[name = string("input_733_perm_0"), val = tensor([0, 2, 1])]; + tensor input_733_cast_fp16 = transpose(perm = input_733_perm_0, x = x_361_cast_fp16)[name = string("transpose_238")]; + tensor input_735_cast_fp16 = silu(x = input_733_cast_fp16)[name = string("input_735_cast_fp16")]; + string x_363_pad_type_0 = const()[name = string("x_363_pad_type_0"), val = string("valid")]; + tensor x_363_strides_0 = const()[name = string("x_363_strides_0"), val = tensor([1])]; + tensor x_363_pad_0 = const()[name = string("x_363_pad_0"), val = tensor([0, 0])]; + tensor x_363_dilations_0 = const()[name = string("x_363_dilations_0"), val = tensor([1])]; + int32 x_363_groups_0 = const()[name = string("x_363_groups_0"), val = int32(1)]; + tensor encoder_layers_13_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278026880))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(279075520))))[name = string("encoder_layers_13_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_363_cast_fp16 = conv(dilations = x_363_dilations_0, groups = x_363_groups_0, pad = x_363_pad_0, pad_type = x_363_pad_type_0, strides = x_363_strides_0, weight = encoder_layers_13_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_735_cast_fp16)[name = string("x_363_cast_fp16")]; + tensor input_737_perm_0 = const()[name = string("input_737_perm_0"), val = tensor([0, 2, 1])]; + tensor input_737_cast_fp16 = transpose(perm = input_737_perm_0, x = x_363_cast_fp16)[name = string("transpose_237")]; + tensor input_739_cast_fp16 = add(x = input_723_cast_fp16, y = input_737_cast_fp16)[name = string("input_739_cast_fp16")]; + tensor input_741_axes_0 = const()[name = string("input_741_axes_0"), val = tensor([-1])]; + tensor encoder_layers_13_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_13_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(279077632)))]; + tensor encoder_layers_13_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_13_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(279079744)))]; + tensor input_741_cast_fp16 = layer_norm(axes = input_741_axes_0, beta = encoder_layers_13_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_13_norm_feed_forward2_weight_to_fp16, x = input_739_cast_fp16)[name = string("input_741_cast_fp16")]; + tensor encoder_layers_13_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(279081856))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(282227648))))[name = string("encoder_layers_13_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_13_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_13_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(282227840)))]; + tensor linear_125_cast_fp16 = linear(bias = encoder_layers_13_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_13_feed_forward2_linear1_weight_to_fp16_palettized, x = input_741_cast_fp16)[name = string("linear_125_cast_fp16")]; + tensor input_745_cast_fp16 = silu(x = linear_125_cast_fp16)[name = string("input_745_cast_fp16")]; + tensor encoder_layers_13_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(282236096))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(285381888))))[name = string("encoder_layers_13_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_13_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_13_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(285382080)))]; + tensor linear_126_cast_fp16 = linear(bias = encoder_layers_13_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_13_feed_forward2_linear2_weight_to_fp16_palettized, x = input_745_cast_fp16)[name = string("linear_126_cast_fp16")]; + fp16 var_3400_to_fp16 = const()[name = string("op_3400_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3401_cast_fp16 = mul(x = linear_126_cast_fp16, y = var_3400_to_fp16)[name = string("op_3401_cast_fp16")]; + tensor input_751_cast_fp16 = add(x = input_739_cast_fp16, y = var_3401_cast_fp16)[name = string("input_751_cast_fp16")]; + tensor input_753_axes_0 = const()[name = string("input_753_axes_0"), val = tensor([-1])]; + tensor encoder_layers_13_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_13_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(285384192)))]; + tensor encoder_layers_13_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_13_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(285386304)))]; + tensor input_753_cast_fp16 = layer_norm(axes = input_753_axes_0, beta = encoder_layers_13_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_13_norm_out_weight_to_fp16, x = input_751_cast_fp16)[name = string("input_753_cast_fp16")]; + tensor cache_57_begin_0 = const()[name = string("cache_57_begin_0"), val = tensor([14, 0, 0, 0])]; + tensor cache_57_end_0 = const()[name = string("cache_57_end_0"), val = tensor([15, 1, 42, 1024])]; + tensor cache_57_end_mask_0 = const()[name = string("cache_57_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_57_squeeze_mask_0 = const()[name = string("cache_57_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_57_cast_fp16 = slice_by_index(begin = cache_57_begin_0, end = cache_57_end_0, end_mask = cache_57_end_mask_0, squeeze_mask = cache_57_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_57_cast_fp16")]; + tensor cache_59_begin_0 = const()[name = string("cache_59_begin_0"), val = tensor([14, 0, 0, 0])]; + tensor cache_59_end_0 = const()[name = string("cache_59_end_0"), val = tensor([15, 1, 1024, 8])]; + tensor cache_59_end_mask_0 = const()[name = string("cache_59_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_59_squeeze_mask_0 = const()[name = string("cache_59_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_59_cast_fp16 = slice_by_index(begin = cache_59_begin_0, end = cache_59_end_0, end_mask = cache_59_end_mask_0, squeeze_mask = cache_59_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_59_cast_fp16")]; + tensor input_755_axes_0 = const()[name = string("input_755_axes_0"), val = tensor([-1])]; + tensor encoder_layers_14_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_14_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(285388416)))]; + tensor encoder_layers_14_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_14_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(285390528)))]; + tensor input_755_cast_fp16 = layer_norm(axes = input_755_axes_0, beta = encoder_layers_14_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_14_norm_feed_forward1_weight_to_fp16, x = input_753_cast_fp16)[name = string("input_755_cast_fp16")]; + tensor encoder_layers_14_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(285392640))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(288538432))))[name = string("encoder_layers_14_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_14_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_14_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(288538624)))]; + tensor linear_127_cast_fp16 = linear(bias = encoder_layers_14_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_14_feed_forward1_linear1_weight_to_fp16_palettized, x = input_755_cast_fp16)[name = string("linear_127_cast_fp16")]; + tensor input_759_cast_fp16 = silu(x = linear_127_cast_fp16)[name = string("input_759_cast_fp16")]; + tensor encoder_layers_14_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(288546880))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291692672))))[name = string("encoder_layers_14_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_14_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_14_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291692864)))]; + tensor linear_128_cast_fp16 = linear(bias = encoder_layers_14_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_14_feed_forward1_linear2_weight_to_fp16_palettized, x = input_759_cast_fp16)[name = string("linear_128_cast_fp16")]; + fp16 var_3437_to_fp16 = const()[name = string("op_3437_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3438_cast_fp16 = mul(x = linear_128_cast_fp16, y = var_3437_to_fp16)[name = string("op_3438_cast_fp16")]; + tensor input_765_cast_fp16 = add(x = input_753_cast_fp16, y = var_3438_cast_fp16)[name = string("input_765_cast_fp16")]; + tensor key_29_axes_0 = const()[name = string("key_29_axes_0"), val = tensor([-1])]; + tensor encoder_layers_14_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_14_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291694976)))]; + tensor encoder_layers_14_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_14_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291697088)))]; + tensor key_29_cast_fp16 = layer_norm(axes = key_29_axes_0, beta = encoder_layers_14_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_14_norm_self_att_weight_to_fp16, x = input_765_cast_fp16)[name = string("key_29_cast_fp16")]; + bool input_767_interleave_0 = const()[name = string("input_767_interleave_0"), val = bool(false)]; + tensor input_767_cast_fp16 = concat(axis = var_68, interleave = input_767_interleave_0, values = (cache_57_cast_fp16, key_29_cast_fp16))[name = string("input_767_cast_fp16")]; + tensor var_3460_begin_0 = const()[name = string("op_3460_begin_0"), val = tensor([0, 28, 0])]; + tensor var_3460_end_0 = const()[name = string("op_3460_end_0"), val = tensor([1, 42, 1024])]; + tensor var_3460_end_mask_0 = const()[name = string("op_3460_end_mask_0"), val = tensor([true, true, true])]; + tensor var_3460_cast_fp16 = slice_by_index(begin = var_3460_begin_0, end = var_3460_end_0, end_mask = var_3460_end_mask_0, x = cache_57_cast_fp16)[name = string("op_3460_cast_fp16")]; + bool var_3466_interleave_0 = const()[name = string("op_3466_interleave_0"), val = bool(false)]; + tensor var_3466_cast_fp16 = concat(axis = var_68, interleave = var_3466_interleave_0, values = (var_3460_cast_fp16, key_29_cast_fp16))[name = string("op_3466_cast_fp16")]; + tensor encoder_layers_14_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291699200))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292485696))))[name = string("encoder_layers_14_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_14_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_14_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292485888)))]; + tensor linear_129_cast_fp16 = linear(bias = encoder_layers_14_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_14_self_attn_linear_q_weight_to_fp16_palettized, x = key_29_cast_fp16)[name = string("linear_129_cast_fp16")]; + tensor var_3471 = const()[name = string("op_3471"), val = tensor([1, -1, 8, 128])]; + tensor q_85_cast_fp16 = reshape(shape = var_3471, x = linear_129_cast_fp16)[name = string("q_85_cast_fp16")]; + tensor encoder_layers_14_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292488000))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(293274496))))[name = string("encoder_layers_14_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_14_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_14_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(293274688)))]; + tensor linear_130_cast_fp16 = linear(bias = encoder_layers_14_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_14_self_attn_linear_k_weight_to_fp16_palettized, x = input_767_cast_fp16)[name = string("linear_130_cast_fp16")]; + tensor var_3476 = const()[name = string("op_3476"), val = tensor([1, -1, 8, 128])]; + tensor k_57_cast_fp16 = reshape(shape = var_3476, x = linear_130_cast_fp16)[name = string("k_57_cast_fp16")]; + tensor encoder_layers_14_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(293276800))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(294063296))))[name = string("encoder_layers_14_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_14_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_14_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(294063488)))]; + tensor linear_131_cast_fp16 = linear(bias = encoder_layers_14_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_14_self_attn_linear_v_weight_to_fp16_palettized, x = input_767_cast_fp16)[name = string("linear_131_cast_fp16")]; + tensor var_3481 = const()[name = string("op_3481"), val = tensor([1, -1, 8, 128])]; + tensor v_29_cast_fp16 = reshape(shape = var_3481, x = linear_131_cast_fp16)[name = string("v_29_cast_fp16")]; + tensor value_37_perm_0 = const()[name = string("value_37_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_14_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_14_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(294065600)))]; + tensor var_3494_cast_fp16 = add(x = q_85_cast_fp16, y = encoder_layers_14_self_attn_pos_bias_u_to_fp16)[name = string("op_3494_cast_fp16")]; + tensor encoder_layers_14_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_14_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(294067712)))]; + tensor var_3496_cast_fp16 = add(x = q_85_cast_fp16, y = encoder_layers_14_self_attn_pos_bias_v_to_fp16)[name = string("op_3496_cast_fp16")]; + tensor q_with_bias_v_29_perm_0 = const()[name = string("q_with_bias_v_29_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_371_transpose_x_0 = const()[name = string("x_371_transpose_x_0"), val = bool(false)]; + bool x_371_transpose_y_0 = const()[name = string("x_371_transpose_y_0"), val = bool(false)]; + tensor op_3498_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(294069824))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(294212224))))[name = string("op_3498_to_fp16_quantized")]; + tensor q_with_bias_v_29_cast_fp16 = transpose(perm = q_with_bias_v_29_perm_0, x = var_3496_cast_fp16)[name = string("transpose_236")]; + tensor x_371_cast_fp16 = matmul(transpose_x = x_371_transpose_x_0, transpose_y = x_371_transpose_y_0, x = q_with_bias_v_29_cast_fp16, y = op_3498_to_fp16_quantized)[name = string("x_371_cast_fp16")]; + tensor x_373_pad_0 = const()[name = string("x_373_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_373_mode_0 = const()[name = string("x_373_mode_0"), val = string("constant")]; + fp16 const_261_to_fp16 = const()[name = string("const_261_to_fp16"), val = fp16(0x0p+0)]; + tensor x_373_cast_fp16 = pad(constant_val = const_261_to_fp16, mode = x_373_mode_0, pad = x_373_pad_0, x = x_371_cast_fp16)[name = string("x_373_cast_fp16")]; + tensor var_3506 = const()[name = string("op_3506"), val = tensor([1, 8, -1, 28])]; + tensor x_375_cast_fp16 = reshape(shape = var_3506, x = x_373_cast_fp16)[name = string("x_375_cast_fp16")]; + tensor var_3510_begin_0 = const()[name = string("op_3510_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_3510_end_0 = const()[name = string("op_3510_end_0"), val = tensor([1, 8, 140, 28])]; + tensor var_3510_end_mask_0 = const()[name = string("op_3510_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_3510_cast_fp16 = slice_by_index(begin = var_3510_begin_0, end = var_3510_end_0, end_mask = var_3510_end_mask_0, x = x_375_cast_fp16)[name = string("op_3510_cast_fp16")]; + tensor var_3511 = const()[name = string("op_3511"), val = tensor([1, 8, 28, 139])]; + tensor matrix_bd_57_cast_fp16 = reshape(shape = var_3511, x = var_3510_cast_fp16)[name = string("matrix_bd_57_cast_fp16")]; + bool matrix_ac_29_transpose_x_0 = const()[name = string("matrix_ac_29_transpose_x_0"), val = bool(false)]; + bool matrix_ac_29_transpose_y_0 = const()[name = string("matrix_ac_29_transpose_y_0"), val = bool(false)]; + tensor transpose_124_perm_0 = const()[name = string("transpose_124_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_125_perm_0 = const()[name = string("transpose_125_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_125 = transpose(perm = transpose_125_perm_0, x = k_57_cast_fp16)[name = string("transpose_234")]; + tensor transpose_124 = transpose(perm = transpose_124_perm_0, x = var_3494_cast_fp16)[name = string("transpose_235")]; + tensor matrix_ac_29_cast_fp16 = matmul(transpose_x = matrix_ac_29_transpose_x_0, transpose_y = matrix_ac_29_transpose_y_0, x = transpose_124, y = transpose_125)[name = string("matrix_ac_29_cast_fp16")]; + tensor matrix_bd_59_begin_0 = const()[name = string("matrix_bd_59_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_59_end_0 = const()[name = string("matrix_bd_59_end_0"), val = tensor([1, 8, 28, 70])]; + tensor matrix_bd_59_end_mask_0 = const()[name = string("matrix_bd_59_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_59_cast_fp16 = slice_by_index(begin = matrix_bd_59_begin_0, end = matrix_bd_59_end_0, end_mask = matrix_bd_59_end_mask_0, x = matrix_bd_57_cast_fp16)[name = string("matrix_bd_59_cast_fp16")]; + tensor var_3520_cast_fp16 = add(x = matrix_ac_29_cast_fp16, y = matrix_bd_59_cast_fp16)[name = string("op_3520_cast_fp16")]; + fp16 _inversed_scores_57_y_0_to_fp16 = const()[name = string("_inversed_scores_57_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_57_cast_fp16 = mul(x = var_3520_cast_fp16, y = _inversed_scores_57_y_0_to_fp16)[name = string("_inversed_scores_57_cast_fp16")]; + tensor scores_59_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_57_cast_fp16, cond = mask_11)[name = string("scores_59_cast_fp16")]; + tensor var_3526_cast_fp16 = softmax(axis = var_59, x = scores_59_cast_fp16)[name = string("op_3526_cast_fp16")]; + tensor input_769_cast_fp16 = select(a = var_44_to_fp16, b = var_3526_cast_fp16, cond = mask_11)[name = string("input_769_cast_fp16")]; + bool x_377_transpose_x_0 = const()[name = string("x_377_transpose_x_0"), val = bool(false)]; + bool x_377_transpose_y_0 = const()[name = string("x_377_transpose_y_0"), val = bool(false)]; + tensor value_37_cast_fp16 = transpose(perm = value_37_perm_0, x = v_29_cast_fp16)[name = string("transpose_233")]; + tensor x_377_cast_fp16 = matmul(transpose_x = x_377_transpose_x_0, transpose_y = x_377_transpose_y_0, x = input_769_cast_fp16, y = value_37_cast_fp16)[name = string("x_377_cast_fp16")]; + tensor var_3530_perm_0 = const()[name = string("op_3530_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3531 = const()[name = string("op_3531"), val = tensor([1, -1, 1024])]; + tensor var_3530_cast_fp16 = transpose(perm = var_3530_perm_0, x = x_377_cast_fp16)[name = string("transpose_232")]; + tensor input_771_cast_fp16 = reshape(shape = var_3531, x = var_3530_cast_fp16)[name = string("input_771_cast_fp16")]; + tensor encoder_layers_14_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(294212608))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(294999104))))[name = string("encoder_layers_14_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_14_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_14_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(294999296)))]; + tensor linear_133_cast_fp16 = linear(bias = encoder_layers_14_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_14_self_attn_linear_out_weight_to_fp16_palettized, x = input_771_cast_fp16)[name = string("linear_133_cast_fp16")]; + tensor input_775_cast_fp16 = add(x = input_765_cast_fp16, y = linear_133_cast_fp16)[name = string("input_775_cast_fp16")]; + tensor x_381_axes_0 = const()[name = string("x_381_axes_0"), val = tensor([-1])]; + tensor encoder_layers_14_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_14_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(295001408)))]; + tensor encoder_layers_14_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_14_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(295003520)))]; + tensor x_381_cast_fp16 = layer_norm(axes = x_381_axes_0, beta = encoder_layers_14_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_14_norm_conv_weight_to_fp16, x = input_775_cast_fp16)[name = string("x_381_cast_fp16")]; + tensor input_777_perm_0 = const()[name = string("input_777_perm_0"), val = tensor([0, 2, 1])]; + string input_779_pad_type_0 = const()[name = string("input_779_pad_type_0"), val = string("valid")]; + tensor input_779_strides_0 = const()[name = string("input_779_strides_0"), val = tensor([1])]; + tensor input_779_pad_0 = const()[name = string("input_779_pad_0"), val = tensor([0, 0])]; + tensor input_779_dilations_0 = const()[name = string("input_779_dilations_0"), val = tensor([1])]; + int32 input_779_groups_0 = const()[name = string("input_779_groups_0"), val = int32(1)]; + tensor encoder_layers_14_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(295005632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(297102848))))[name = string("encoder_layers_14_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_777_cast_fp16 = transpose(perm = input_777_perm_0, x = x_381_cast_fp16)[name = string("transpose_231")]; + tensor input_779_cast_fp16 = conv(dilations = input_779_dilations_0, groups = input_779_groups_0, pad = input_779_pad_0, pad_type = input_779_pad_type_0, strides = input_779_strides_0, weight = encoder_layers_14_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_777_cast_fp16)[name = string("input_779_cast_fp16")]; + int32 x_383_split_num_splits_0 = const()[name = string("x_383_split_num_splits_0"), val = int32(2)]; + int32 x_383_split_axis_0 = const()[name = string("x_383_split_axis_0"), val = int32(1)]; + tensor x_383_split_cast_fp16_0, tensor x_383_split_cast_fp16_1 = split(axis = x_383_split_axis_0, num_splits = x_383_split_num_splits_0, x = input_779_cast_fp16)[name = string("x_383_split_cast_fp16")]; + tensor x_383_split_1_sigmoid_cast_fp16 = sigmoid(x = x_383_split_cast_fp16_1)[name = string("x_383_split_1_sigmoid_cast_fp16")]; + tensor x_383_cast_fp16 = mul(x = x_383_split_cast_fp16_0, y = x_383_split_1_sigmoid_cast_fp16)[name = string("x_383_cast_fp16")]; + tensor input_781_cast_fp16 = select(a = var_44_to_fp16, b = x_383_cast_fp16, cond = var_575)[name = string("input_781_cast_fp16")]; + bool new_x_59_interleave_0 = const()[name = string("new_x_59_interleave_0"), val = bool(false)]; + tensor new_x_59_cast_fp16 = concat(axis = var_59, interleave = new_x_59_interleave_0, values = (cache_59_cast_fp16, input_781_cast_fp16))[name = string("new_x_59_cast_fp16")]; + tensor var_3570_begin_0 = const()[name = string("op_3570_begin_0"), val = tensor([0, 0, 28])]; + tensor var_3570_end_0 = const()[name = string("op_3570_end_0"), val = tensor([1, 1024, 36])]; + tensor var_3570_end_mask_0 = const()[name = string("op_3570_end_mask_0"), val = tensor([true, true, true])]; + tensor var_3570_cast_fp16 = slice_by_index(begin = var_3570_begin_0, end = var_3570_end_0, end_mask = var_3570_end_mask_0, x = new_x_59_cast_fp16)[name = string("op_3570_cast_fp16")]; + string x_385_pad_type_0 = const()[name = string("x_385_pad_type_0"), val = string("valid")]; + int32 x_385_groups_0 = const()[name = string("x_385_groups_0"), val = int32(1024)]; + tensor x_385_strides_0 = const()[name = string("x_385_strides_0"), val = tensor([1])]; + tensor x_385_pad_0 = const()[name = string("x_385_pad_0"), val = tensor([0, 0])]; + tensor x_385_dilations_0 = const()[name = string("x_385_dilations_0"), val = tensor([1])]; + tensor encoder_layers_14_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(297107008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(297116288))))[name = string("encoder_layers_14_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_385_cast_fp16 = conv(dilations = x_385_dilations_0, groups = x_385_groups_0, pad = x_385_pad_0, pad_type = x_385_pad_type_0, strides = x_385_strides_0, weight = encoder_layers_14_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_59_cast_fp16)[name = string("x_385_cast_fp16")]; + tensor input_783_perm_0 = const()[name = string("input_783_perm_0"), val = tensor([0, 2, 1])]; + tensor x_387_axes_0 = const()[name = string("x_387_axes_0"), val = tensor([-1])]; + tensor encoder_layers_14_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_14_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(297118400)))]; + tensor encoder_layers_14_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_14_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(297120512)))]; + tensor input_783_cast_fp16 = transpose(perm = input_783_perm_0, x = x_385_cast_fp16)[name = string("transpose_230")]; + tensor x_387_cast_fp16 = layer_norm(axes = x_387_axes_0, beta = encoder_layers_14_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_14_conv_batch_norm_weight_to_fp16, x = input_783_cast_fp16)[name = string("x_387_cast_fp16")]; + tensor input_785_perm_0 = const()[name = string("input_785_perm_0"), val = tensor([0, 2, 1])]; + tensor input_785_cast_fp16 = transpose(perm = input_785_perm_0, x = x_387_cast_fp16)[name = string("transpose_229")]; + tensor input_787_cast_fp16 = silu(x = input_785_cast_fp16)[name = string("input_787_cast_fp16")]; + string x_389_pad_type_0 = const()[name = string("x_389_pad_type_0"), val = string("valid")]; + tensor x_389_strides_0 = const()[name = string("x_389_strides_0"), val = tensor([1])]; + tensor x_389_pad_0 = const()[name = string("x_389_pad_0"), val = tensor([0, 0])]; + tensor x_389_dilations_0 = const()[name = string("x_389_dilations_0"), val = tensor([1])]; + int32 x_389_groups_0 = const()[name = string("x_389_groups_0"), val = int32(1)]; + tensor encoder_layers_14_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(297122624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(298171264))))[name = string("encoder_layers_14_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_389_cast_fp16 = conv(dilations = x_389_dilations_0, groups = x_389_groups_0, pad = x_389_pad_0, pad_type = x_389_pad_type_0, strides = x_389_strides_0, weight = encoder_layers_14_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_787_cast_fp16)[name = string("x_389_cast_fp16")]; + tensor input_789_perm_0 = const()[name = string("input_789_perm_0"), val = tensor([0, 2, 1])]; + tensor input_789_cast_fp16 = transpose(perm = input_789_perm_0, x = x_389_cast_fp16)[name = string("transpose_228")]; + tensor input_791_cast_fp16 = add(x = input_775_cast_fp16, y = input_789_cast_fp16)[name = string("input_791_cast_fp16")]; + tensor input_793_axes_0 = const()[name = string("input_793_axes_0"), val = tensor([-1])]; + tensor encoder_layers_14_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_14_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(298173376)))]; + tensor encoder_layers_14_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_14_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(298175488)))]; + tensor input_793_cast_fp16 = layer_norm(axes = input_793_axes_0, beta = encoder_layers_14_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_14_norm_feed_forward2_weight_to_fp16, x = input_791_cast_fp16)[name = string("input_793_cast_fp16")]; + tensor encoder_layers_14_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(298177600))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(301323392))))[name = string("encoder_layers_14_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_14_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_14_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(301323584)))]; + tensor linear_134_cast_fp16 = linear(bias = encoder_layers_14_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_14_feed_forward2_linear1_weight_to_fp16_palettized, x = input_793_cast_fp16)[name = string("linear_134_cast_fp16")]; + tensor input_797_cast_fp16 = silu(x = linear_134_cast_fp16)[name = string("input_797_cast_fp16")]; + tensor encoder_layers_14_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(301331840))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(304477632))))[name = string("encoder_layers_14_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_14_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_14_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(304477824)))]; + tensor linear_135_cast_fp16 = linear(bias = encoder_layers_14_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_14_feed_forward2_linear2_weight_to_fp16_palettized, x = input_797_cast_fp16)[name = string("linear_135_cast_fp16")]; + fp16 var_3613_to_fp16 = const()[name = string("op_3613_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3614_cast_fp16 = mul(x = linear_135_cast_fp16, y = var_3613_to_fp16)[name = string("op_3614_cast_fp16")]; + tensor input_803_cast_fp16 = add(x = input_791_cast_fp16, y = var_3614_cast_fp16)[name = string("input_803_cast_fp16")]; + tensor input_805_axes_0 = const()[name = string("input_805_axes_0"), val = tensor([-1])]; + tensor encoder_layers_14_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_14_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(304479936)))]; + tensor encoder_layers_14_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_14_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(304482048)))]; + tensor input_805_cast_fp16 = layer_norm(axes = input_805_axes_0, beta = encoder_layers_14_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_14_norm_out_weight_to_fp16, x = input_803_cast_fp16)[name = string("input_805_cast_fp16")]; + tensor cache_61_begin_0 = const()[name = string("cache_61_begin_0"), val = tensor([15, 0, 0, 0])]; + tensor cache_61_end_0 = const()[name = string("cache_61_end_0"), val = tensor([16, 1, 42, 1024])]; + tensor cache_61_end_mask_0 = const()[name = string("cache_61_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_61_squeeze_mask_0 = const()[name = string("cache_61_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_61_cast_fp16 = slice_by_index(begin = cache_61_begin_0, end = cache_61_end_0, end_mask = cache_61_end_mask_0, squeeze_mask = cache_61_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_61_cast_fp16")]; + tensor cache_63_begin_0 = const()[name = string("cache_63_begin_0"), val = tensor([15, 0, 0, 0])]; + tensor cache_63_end_0 = const()[name = string("cache_63_end_0"), val = tensor([16, 1, 1024, 8])]; + tensor cache_63_end_mask_0 = const()[name = string("cache_63_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_63_squeeze_mask_0 = const()[name = string("cache_63_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_63_cast_fp16 = slice_by_index(begin = cache_63_begin_0, end = cache_63_end_0, end_mask = cache_63_end_mask_0, squeeze_mask = cache_63_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_63_cast_fp16")]; + tensor input_807_axes_0 = const()[name = string("input_807_axes_0"), val = tensor([-1])]; + tensor encoder_layers_15_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_15_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(304484160)))]; + tensor encoder_layers_15_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_15_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(304486272)))]; + tensor input_807_cast_fp16 = layer_norm(axes = input_807_axes_0, beta = encoder_layers_15_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_15_norm_feed_forward1_weight_to_fp16, x = input_805_cast_fp16)[name = string("input_807_cast_fp16")]; + tensor encoder_layers_15_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(304488384))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307634176))))[name = string("encoder_layers_15_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_15_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_15_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307634368)))]; + tensor linear_136_cast_fp16 = linear(bias = encoder_layers_15_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_15_feed_forward1_linear1_weight_to_fp16_palettized, x = input_807_cast_fp16)[name = string("linear_136_cast_fp16")]; + tensor input_811_cast_fp16 = silu(x = linear_136_cast_fp16)[name = string("input_811_cast_fp16")]; + tensor encoder_layers_15_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307642624))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(310788416))))[name = string("encoder_layers_15_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_15_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_15_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(310788608)))]; + tensor linear_137_cast_fp16 = linear(bias = encoder_layers_15_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_15_feed_forward1_linear2_weight_to_fp16_palettized, x = input_811_cast_fp16)[name = string("linear_137_cast_fp16")]; + fp16 var_3650_to_fp16 = const()[name = string("op_3650_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3651_cast_fp16 = mul(x = linear_137_cast_fp16, y = var_3650_to_fp16)[name = string("op_3651_cast_fp16")]; + tensor input_817_cast_fp16 = add(x = input_805_cast_fp16, y = var_3651_cast_fp16)[name = string("input_817_cast_fp16")]; + tensor key_31_axes_0 = const()[name = string("key_31_axes_0"), val = tensor([-1])]; + tensor encoder_layers_15_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_15_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(310790720)))]; + tensor encoder_layers_15_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_15_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(310792832)))]; + tensor key_31_cast_fp16 = layer_norm(axes = key_31_axes_0, beta = encoder_layers_15_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_15_norm_self_att_weight_to_fp16, x = input_817_cast_fp16)[name = string("key_31_cast_fp16")]; + bool input_819_interleave_0 = const()[name = string("input_819_interleave_0"), val = bool(false)]; + tensor input_819_cast_fp16 = concat(axis = var_68, interleave = input_819_interleave_0, values = (cache_61_cast_fp16, key_31_cast_fp16))[name = string("input_819_cast_fp16")]; + tensor var_3673_begin_0 = const()[name = string("op_3673_begin_0"), val = tensor([0, 28, 0])]; + tensor var_3673_end_0 = const()[name = string("op_3673_end_0"), val = tensor([1, 42, 1024])]; + tensor var_3673_end_mask_0 = const()[name = string("op_3673_end_mask_0"), val = tensor([true, true, true])]; + tensor var_3673_cast_fp16 = slice_by_index(begin = var_3673_begin_0, end = var_3673_end_0, end_mask = var_3673_end_mask_0, x = cache_61_cast_fp16)[name = string("op_3673_cast_fp16")]; + bool var_3679_interleave_0 = const()[name = string("op_3679_interleave_0"), val = bool(false)]; + tensor var_3679_cast_fp16 = concat(axis = var_68, interleave = var_3679_interleave_0, values = (var_3673_cast_fp16, key_31_cast_fp16))[name = string("op_3679_cast_fp16")]; + tensor encoder_layers_15_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(310794944))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(311581440))))[name = string("encoder_layers_15_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_15_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_15_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(311581632)))]; + tensor linear_138_cast_fp16 = linear(bias = encoder_layers_15_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_15_self_attn_linear_q_weight_to_fp16_palettized, x = key_31_cast_fp16)[name = string("linear_138_cast_fp16")]; + tensor var_3684 = const()[name = string("op_3684"), val = tensor([1, -1, 8, 128])]; + tensor q_91_cast_fp16 = reshape(shape = var_3684, x = linear_138_cast_fp16)[name = string("q_91_cast_fp16")]; + tensor encoder_layers_15_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(311583744))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312370240))))[name = string("encoder_layers_15_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_15_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_15_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312370432)))]; + tensor linear_139_cast_fp16 = linear(bias = encoder_layers_15_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_15_self_attn_linear_k_weight_to_fp16_palettized, x = input_819_cast_fp16)[name = string("linear_139_cast_fp16")]; + tensor var_3689 = const()[name = string("op_3689"), val = tensor([1, -1, 8, 128])]; + tensor k_61_cast_fp16 = reshape(shape = var_3689, x = linear_139_cast_fp16)[name = string("k_61_cast_fp16")]; + tensor encoder_layers_15_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312372544))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(313159040))))[name = string("encoder_layers_15_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_15_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_15_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(313159232)))]; + tensor linear_140_cast_fp16 = linear(bias = encoder_layers_15_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_15_self_attn_linear_v_weight_to_fp16_palettized, x = input_819_cast_fp16)[name = string("linear_140_cast_fp16")]; + tensor var_3694 = const()[name = string("op_3694"), val = tensor([1, -1, 8, 128])]; + tensor v_31_cast_fp16 = reshape(shape = var_3694, x = linear_140_cast_fp16)[name = string("v_31_cast_fp16")]; + tensor value_39_perm_0 = const()[name = string("value_39_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_15_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_15_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(313161344)))]; + tensor var_3707_cast_fp16 = add(x = q_91_cast_fp16, y = encoder_layers_15_self_attn_pos_bias_u_to_fp16)[name = string("op_3707_cast_fp16")]; + tensor encoder_layers_15_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_15_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(313163456)))]; + tensor var_3709_cast_fp16 = add(x = q_91_cast_fp16, y = encoder_layers_15_self_attn_pos_bias_v_to_fp16)[name = string("op_3709_cast_fp16")]; + tensor q_with_bias_v_31_perm_0 = const()[name = string("q_with_bias_v_31_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_397_transpose_x_0 = const()[name = string("x_397_transpose_x_0"), val = bool(false)]; + bool x_397_transpose_y_0 = const()[name = string("x_397_transpose_y_0"), val = bool(false)]; + tensor op_3711_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(313165568))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(313307968))))[name = string("op_3711_to_fp16_quantized")]; + tensor q_with_bias_v_31_cast_fp16 = transpose(perm = q_with_bias_v_31_perm_0, x = var_3709_cast_fp16)[name = string("transpose_227")]; + tensor x_397_cast_fp16 = matmul(transpose_x = x_397_transpose_x_0, transpose_y = x_397_transpose_y_0, x = q_with_bias_v_31_cast_fp16, y = op_3711_to_fp16_quantized)[name = string("x_397_cast_fp16")]; + tensor x_399_pad_0 = const()[name = string("x_399_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_399_mode_0 = const()[name = string("x_399_mode_0"), val = string("constant")]; + fp16 const_274_to_fp16 = const()[name = string("const_274_to_fp16"), val = fp16(0x0p+0)]; + tensor x_399_cast_fp16 = pad(constant_val = const_274_to_fp16, mode = x_399_mode_0, pad = x_399_pad_0, x = x_397_cast_fp16)[name = string("x_399_cast_fp16")]; + tensor var_3719 = const()[name = string("op_3719"), val = tensor([1, 8, -1, 28])]; + tensor x_401_cast_fp16 = reshape(shape = var_3719, x = x_399_cast_fp16)[name = string("x_401_cast_fp16")]; + tensor var_3723_begin_0 = const()[name = string("op_3723_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_3723_end_0 = const()[name = string("op_3723_end_0"), val = tensor([1, 8, 140, 28])]; + tensor var_3723_end_mask_0 = const()[name = string("op_3723_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_3723_cast_fp16 = slice_by_index(begin = var_3723_begin_0, end = var_3723_end_0, end_mask = var_3723_end_mask_0, x = x_401_cast_fp16)[name = string("op_3723_cast_fp16")]; + tensor var_3724 = const()[name = string("op_3724"), val = tensor([1, 8, 28, 139])]; + tensor matrix_bd_61_cast_fp16 = reshape(shape = var_3724, x = var_3723_cast_fp16)[name = string("matrix_bd_61_cast_fp16")]; + bool matrix_ac_31_transpose_x_0 = const()[name = string("matrix_ac_31_transpose_x_0"), val = bool(false)]; + bool matrix_ac_31_transpose_y_0 = const()[name = string("matrix_ac_31_transpose_y_0"), val = bool(false)]; + tensor transpose_126_perm_0 = const()[name = string("transpose_126_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_127_perm_0 = const()[name = string("transpose_127_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_127 = transpose(perm = transpose_127_perm_0, x = k_61_cast_fp16)[name = string("transpose_225")]; + tensor transpose_126 = transpose(perm = transpose_126_perm_0, x = var_3707_cast_fp16)[name = string("transpose_226")]; + tensor matrix_ac_31_cast_fp16 = matmul(transpose_x = matrix_ac_31_transpose_x_0, transpose_y = matrix_ac_31_transpose_y_0, x = transpose_126, y = transpose_127)[name = string("matrix_ac_31_cast_fp16")]; + tensor matrix_bd_63_begin_0 = const()[name = string("matrix_bd_63_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_63_end_0 = const()[name = string("matrix_bd_63_end_0"), val = tensor([1, 8, 28, 70])]; + tensor matrix_bd_63_end_mask_0 = const()[name = string("matrix_bd_63_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_63_cast_fp16 = slice_by_index(begin = matrix_bd_63_begin_0, end = matrix_bd_63_end_0, end_mask = matrix_bd_63_end_mask_0, x = matrix_bd_61_cast_fp16)[name = string("matrix_bd_63_cast_fp16")]; + tensor var_3733_cast_fp16 = add(x = matrix_ac_31_cast_fp16, y = matrix_bd_63_cast_fp16)[name = string("op_3733_cast_fp16")]; + fp16 _inversed_scores_61_y_0_to_fp16 = const()[name = string("_inversed_scores_61_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_61_cast_fp16 = mul(x = var_3733_cast_fp16, y = _inversed_scores_61_y_0_to_fp16)[name = string("_inversed_scores_61_cast_fp16")]; + tensor scores_63_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_61_cast_fp16, cond = mask_11)[name = string("scores_63_cast_fp16")]; + tensor var_3739_cast_fp16 = softmax(axis = var_59, x = scores_63_cast_fp16)[name = string("op_3739_cast_fp16")]; + tensor input_821_cast_fp16 = select(a = var_44_to_fp16, b = var_3739_cast_fp16, cond = mask_11)[name = string("input_821_cast_fp16")]; + bool x_403_transpose_x_0 = const()[name = string("x_403_transpose_x_0"), val = bool(false)]; + bool x_403_transpose_y_0 = const()[name = string("x_403_transpose_y_0"), val = bool(false)]; + tensor value_39_cast_fp16 = transpose(perm = value_39_perm_0, x = v_31_cast_fp16)[name = string("transpose_224")]; + tensor x_403_cast_fp16 = matmul(transpose_x = x_403_transpose_x_0, transpose_y = x_403_transpose_y_0, x = input_821_cast_fp16, y = value_39_cast_fp16)[name = string("x_403_cast_fp16")]; + tensor var_3743_perm_0 = const()[name = string("op_3743_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3744 = const()[name = string("op_3744"), val = tensor([1, -1, 1024])]; + tensor var_3743_cast_fp16 = transpose(perm = var_3743_perm_0, x = x_403_cast_fp16)[name = string("transpose_223")]; + tensor input_823_cast_fp16 = reshape(shape = var_3744, x = var_3743_cast_fp16)[name = string("input_823_cast_fp16")]; + tensor encoder_layers_15_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(313308352))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(314094848))))[name = string("encoder_layers_15_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_15_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_15_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(314095040)))]; + tensor linear_142_cast_fp16 = linear(bias = encoder_layers_15_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_15_self_attn_linear_out_weight_to_fp16_palettized, x = input_823_cast_fp16)[name = string("linear_142_cast_fp16")]; + tensor input_827_cast_fp16 = add(x = input_817_cast_fp16, y = linear_142_cast_fp16)[name = string("input_827_cast_fp16")]; + tensor x_407_axes_0 = const()[name = string("x_407_axes_0"), val = tensor([-1])]; + tensor encoder_layers_15_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_15_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(314097152)))]; + tensor encoder_layers_15_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_15_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(314099264)))]; + tensor x_407_cast_fp16 = layer_norm(axes = x_407_axes_0, beta = encoder_layers_15_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_15_norm_conv_weight_to_fp16, x = input_827_cast_fp16)[name = string("x_407_cast_fp16")]; + tensor input_829_perm_0 = const()[name = string("input_829_perm_0"), val = tensor([0, 2, 1])]; + string input_831_pad_type_0 = const()[name = string("input_831_pad_type_0"), val = string("valid")]; + tensor input_831_strides_0 = const()[name = string("input_831_strides_0"), val = tensor([1])]; + tensor input_831_pad_0 = const()[name = string("input_831_pad_0"), val = tensor([0, 0])]; + tensor input_831_dilations_0 = const()[name = string("input_831_dilations_0"), val = tensor([1])]; + int32 input_831_groups_0 = const()[name = string("input_831_groups_0"), val = int32(1)]; + tensor encoder_layers_15_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(314101376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(316198592))))[name = string("encoder_layers_15_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_829_cast_fp16 = transpose(perm = input_829_perm_0, x = x_407_cast_fp16)[name = string("transpose_222")]; + tensor input_831_cast_fp16 = conv(dilations = input_831_dilations_0, groups = input_831_groups_0, pad = input_831_pad_0, pad_type = input_831_pad_type_0, strides = input_831_strides_0, weight = encoder_layers_15_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_829_cast_fp16)[name = string("input_831_cast_fp16")]; + int32 x_409_split_num_splits_0 = const()[name = string("x_409_split_num_splits_0"), val = int32(2)]; + int32 x_409_split_axis_0 = const()[name = string("x_409_split_axis_0"), val = int32(1)]; + tensor x_409_split_cast_fp16_0, tensor x_409_split_cast_fp16_1 = split(axis = x_409_split_axis_0, num_splits = x_409_split_num_splits_0, x = input_831_cast_fp16)[name = string("x_409_split_cast_fp16")]; + tensor x_409_split_1_sigmoid_cast_fp16 = sigmoid(x = x_409_split_cast_fp16_1)[name = string("x_409_split_1_sigmoid_cast_fp16")]; + tensor x_409_cast_fp16 = mul(x = x_409_split_cast_fp16_0, y = x_409_split_1_sigmoid_cast_fp16)[name = string("x_409_cast_fp16")]; + tensor input_833_cast_fp16 = select(a = var_44_to_fp16, b = x_409_cast_fp16, cond = var_575)[name = string("input_833_cast_fp16")]; + bool new_x_63_interleave_0 = const()[name = string("new_x_63_interleave_0"), val = bool(false)]; + tensor new_x_63_cast_fp16 = concat(axis = var_59, interleave = new_x_63_interleave_0, values = (cache_63_cast_fp16, input_833_cast_fp16))[name = string("new_x_63_cast_fp16")]; + tensor var_3783_begin_0 = const()[name = string("op_3783_begin_0"), val = tensor([0, 0, 28])]; + tensor var_3783_end_0 = const()[name = string("op_3783_end_0"), val = tensor([1, 1024, 36])]; + tensor var_3783_end_mask_0 = const()[name = string("op_3783_end_mask_0"), val = tensor([true, true, true])]; + tensor var_3783_cast_fp16 = slice_by_index(begin = var_3783_begin_0, end = var_3783_end_0, end_mask = var_3783_end_mask_0, x = new_x_63_cast_fp16)[name = string("op_3783_cast_fp16")]; + string x_411_pad_type_0 = const()[name = string("x_411_pad_type_0"), val = string("valid")]; + int32 x_411_groups_0 = const()[name = string("x_411_groups_0"), val = int32(1024)]; + tensor x_411_strides_0 = const()[name = string("x_411_strides_0"), val = tensor([1])]; + tensor x_411_pad_0 = const()[name = string("x_411_pad_0"), val = tensor([0, 0])]; + tensor x_411_dilations_0 = const()[name = string("x_411_dilations_0"), val = tensor([1])]; + tensor encoder_layers_15_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(316202752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(316212032))))[name = string("encoder_layers_15_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_411_cast_fp16 = conv(dilations = x_411_dilations_0, groups = x_411_groups_0, pad = x_411_pad_0, pad_type = x_411_pad_type_0, strides = x_411_strides_0, weight = encoder_layers_15_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_63_cast_fp16)[name = string("x_411_cast_fp16")]; + tensor input_835_perm_0 = const()[name = string("input_835_perm_0"), val = tensor([0, 2, 1])]; + tensor x_413_axes_0 = const()[name = string("x_413_axes_0"), val = tensor([-1])]; + tensor encoder_layers_15_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_15_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(316214144)))]; + tensor encoder_layers_15_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_15_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(316216256)))]; + tensor input_835_cast_fp16 = transpose(perm = input_835_perm_0, x = x_411_cast_fp16)[name = string("transpose_221")]; + tensor x_413_cast_fp16 = layer_norm(axes = x_413_axes_0, beta = encoder_layers_15_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_15_conv_batch_norm_weight_to_fp16, x = input_835_cast_fp16)[name = string("x_413_cast_fp16")]; + tensor input_837_perm_0 = const()[name = string("input_837_perm_0"), val = tensor([0, 2, 1])]; + tensor input_837_cast_fp16 = transpose(perm = input_837_perm_0, x = x_413_cast_fp16)[name = string("transpose_220")]; + tensor input_839_cast_fp16 = silu(x = input_837_cast_fp16)[name = string("input_839_cast_fp16")]; + string x_415_pad_type_0 = const()[name = string("x_415_pad_type_0"), val = string("valid")]; + tensor x_415_strides_0 = const()[name = string("x_415_strides_0"), val = tensor([1])]; + tensor x_415_pad_0 = const()[name = string("x_415_pad_0"), val = tensor([0, 0])]; + tensor x_415_dilations_0 = const()[name = string("x_415_dilations_0"), val = tensor([1])]; + int32 x_415_groups_0 = const()[name = string("x_415_groups_0"), val = int32(1)]; + tensor encoder_layers_15_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(316218368))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(317267008))))[name = string("encoder_layers_15_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_415_cast_fp16 = conv(dilations = x_415_dilations_0, groups = x_415_groups_0, pad = x_415_pad_0, pad_type = x_415_pad_type_0, strides = x_415_strides_0, weight = encoder_layers_15_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_839_cast_fp16)[name = string("x_415_cast_fp16")]; + tensor input_841_perm_0 = const()[name = string("input_841_perm_0"), val = tensor([0, 2, 1])]; + tensor input_841_cast_fp16 = transpose(perm = input_841_perm_0, x = x_415_cast_fp16)[name = string("transpose_219")]; + tensor input_843_cast_fp16 = add(x = input_827_cast_fp16, y = input_841_cast_fp16)[name = string("input_843_cast_fp16")]; + tensor input_845_axes_0 = const()[name = string("input_845_axes_0"), val = tensor([-1])]; + tensor encoder_layers_15_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_15_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(317269120)))]; + tensor encoder_layers_15_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_15_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(317271232)))]; + tensor input_845_cast_fp16 = layer_norm(axes = input_845_axes_0, beta = encoder_layers_15_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_15_norm_feed_forward2_weight_to_fp16, x = input_843_cast_fp16)[name = string("input_845_cast_fp16")]; + tensor encoder_layers_15_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(317273344))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(320419136))))[name = string("encoder_layers_15_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_15_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_15_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(320419328)))]; + tensor linear_143_cast_fp16 = linear(bias = encoder_layers_15_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_15_feed_forward2_linear1_weight_to_fp16_palettized, x = input_845_cast_fp16)[name = string("linear_143_cast_fp16")]; + tensor input_849_cast_fp16 = silu(x = linear_143_cast_fp16)[name = string("input_849_cast_fp16")]; + tensor encoder_layers_15_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(320427584))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(323573376))))[name = string("encoder_layers_15_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_15_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_15_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(323573568)))]; + tensor linear_144_cast_fp16 = linear(bias = encoder_layers_15_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_15_feed_forward2_linear2_weight_to_fp16_palettized, x = input_849_cast_fp16)[name = string("linear_144_cast_fp16")]; + fp16 var_3826_to_fp16 = const()[name = string("op_3826_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3827_cast_fp16 = mul(x = linear_144_cast_fp16, y = var_3826_to_fp16)[name = string("op_3827_cast_fp16")]; + tensor input_855_cast_fp16 = add(x = input_843_cast_fp16, y = var_3827_cast_fp16)[name = string("input_855_cast_fp16")]; + tensor input_857_axes_0 = const()[name = string("input_857_axes_0"), val = tensor([-1])]; + tensor encoder_layers_15_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_15_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(323575680)))]; + tensor encoder_layers_15_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_15_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(323577792)))]; + tensor input_857_cast_fp16 = layer_norm(axes = input_857_axes_0, beta = encoder_layers_15_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_15_norm_out_weight_to_fp16, x = input_855_cast_fp16)[name = string("input_857_cast_fp16")]; + tensor cache_65_begin_0 = const()[name = string("cache_65_begin_0"), val = tensor([16, 0, 0, 0])]; + tensor cache_65_end_0 = const()[name = string("cache_65_end_0"), val = tensor([17, 1, 42, 1024])]; + tensor cache_65_end_mask_0 = const()[name = string("cache_65_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_65_squeeze_mask_0 = const()[name = string("cache_65_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_65_cast_fp16 = slice_by_index(begin = cache_65_begin_0, end = cache_65_end_0, end_mask = cache_65_end_mask_0, squeeze_mask = cache_65_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_65_cast_fp16")]; + tensor cache_67_begin_0 = const()[name = string("cache_67_begin_0"), val = tensor([16, 0, 0, 0])]; + tensor cache_67_end_0 = const()[name = string("cache_67_end_0"), val = tensor([17, 1, 1024, 8])]; + tensor cache_67_end_mask_0 = const()[name = string("cache_67_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_67_squeeze_mask_0 = const()[name = string("cache_67_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_67_cast_fp16 = slice_by_index(begin = cache_67_begin_0, end = cache_67_end_0, end_mask = cache_67_end_mask_0, squeeze_mask = cache_67_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_67_cast_fp16")]; + tensor input_859_axes_0 = const()[name = string("input_859_axes_0"), val = tensor([-1])]; + tensor encoder_layers_16_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_16_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(323579904)))]; + tensor encoder_layers_16_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_16_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(323582016)))]; + tensor input_859_cast_fp16 = layer_norm(axes = input_859_axes_0, beta = encoder_layers_16_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_16_norm_feed_forward1_weight_to_fp16, x = input_857_cast_fp16)[name = string("input_859_cast_fp16")]; + tensor encoder_layers_16_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(323584128))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(326729920))))[name = string("encoder_layers_16_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_16_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_16_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(326730112)))]; + tensor linear_145_cast_fp16 = linear(bias = encoder_layers_16_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_16_feed_forward1_linear1_weight_to_fp16_palettized, x = input_859_cast_fp16)[name = string("linear_145_cast_fp16")]; + tensor input_863_cast_fp16 = silu(x = linear_145_cast_fp16)[name = string("input_863_cast_fp16")]; + tensor encoder_layers_16_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(326738368))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(329884160))))[name = string("encoder_layers_16_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_16_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_16_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(329884352)))]; + tensor linear_146_cast_fp16 = linear(bias = encoder_layers_16_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_16_feed_forward1_linear2_weight_to_fp16_palettized, x = input_863_cast_fp16)[name = string("linear_146_cast_fp16")]; + fp16 var_3863_to_fp16 = const()[name = string("op_3863_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3864_cast_fp16 = mul(x = linear_146_cast_fp16, y = var_3863_to_fp16)[name = string("op_3864_cast_fp16")]; + tensor input_869_cast_fp16 = add(x = input_857_cast_fp16, y = var_3864_cast_fp16)[name = string("input_869_cast_fp16")]; + tensor key_33_axes_0 = const()[name = string("key_33_axes_0"), val = tensor([-1])]; + tensor encoder_layers_16_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_16_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(329886464)))]; + tensor encoder_layers_16_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_16_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(329888576)))]; + tensor key_33_cast_fp16 = layer_norm(axes = key_33_axes_0, beta = encoder_layers_16_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_16_norm_self_att_weight_to_fp16, x = input_869_cast_fp16)[name = string("key_33_cast_fp16")]; + bool input_871_interleave_0 = const()[name = string("input_871_interleave_0"), val = bool(false)]; + tensor input_871_cast_fp16 = concat(axis = var_68, interleave = input_871_interleave_0, values = (cache_65_cast_fp16, key_33_cast_fp16))[name = string("input_871_cast_fp16")]; + tensor var_3886_begin_0 = const()[name = string("op_3886_begin_0"), val = tensor([0, 28, 0])]; + tensor var_3886_end_0 = const()[name = string("op_3886_end_0"), val = tensor([1, 42, 1024])]; + tensor var_3886_end_mask_0 = const()[name = string("op_3886_end_mask_0"), val = tensor([true, true, true])]; + tensor var_3886_cast_fp16 = slice_by_index(begin = var_3886_begin_0, end = var_3886_end_0, end_mask = var_3886_end_mask_0, x = cache_65_cast_fp16)[name = string("op_3886_cast_fp16")]; + bool var_3892_interleave_0 = const()[name = string("op_3892_interleave_0"), val = bool(false)]; + tensor var_3892_cast_fp16 = concat(axis = var_68, interleave = var_3892_interleave_0, values = (var_3886_cast_fp16, key_33_cast_fp16))[name = string("op_3892_cast_fp16")]; + tensor encoder_layers_16_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(329890688))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(330677184))))[name = string("encoder_layers_16_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_16_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_16_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(330677376)))]; + tensor linear_147_cast_fp16 = linear(bias = encoder_layers_16_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_16_self_attn_linear_q_weight_to_fp16_palettized, x = key_33_cast_fp16)[name = string("linear_147_cast_fp16")]; + tensor var_3897 = const()[name = string("op_3897"), val = tensor([1, -1, 8, 128])]; + tensor q_97_cast_fp16 = reshape(shape = var_3897, x = linear_147_cast_fp16)[name = string("q_97_cast_fp16")]; + tensor encoder_layers_16_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(330679488))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(331465984))))[name = string("encoder_layers_16_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_16_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_16_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(331466176)))]; + tensor linear_148_cast_fp16 = linear(bias = encoder_layers_16_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_16_self_attn_linear_k_weight_to_fp16_palettized, x = input_871_cast_fp16)[name = string("linear_148_cast_fp16")]; + tensor var_3902 = const()[name = string("op_3902"), val = tensor([1, -1, 8, 128])]; + tensor k_65_cast_fp16 = reshape(shape = var_3902, x = linear_148_cast_fp16)[name = string("k_65_cast_fp16")]; + tensor encoder_layers_16_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(331468288))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(332254784))))[name = string("encoder_layers_16_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_16_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_16_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(332254976)))]; + tensor linear_149_cast_fp16 = linear(bias = encoder_layers_16_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_16_self_attn_linear_v_weight_to_fp16_palettized, x = input_871_cast_fp16)[name = string("linear_149_cast_fp16")]; + tensor var_3907 = const()[name = string("op_3907"), val = tensor([1, -1, 8, 128])]; + tensor v_33_cast_fp16 = reshape(shape = var_3907, x = linear_149_cast_fp16)[name = string("v_33_cast_fp16")]; + tensor value_41_perm_0 = const()[name = string("value_41_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_16_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_16_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(332257088)))]; + tensor var_3920_cast_fp16 = add(x = q_97_cast_fp16, y = encoder_layers_16_self_attn_pos_bias_u_to_fp16)[name = string("op_3920_cast_fp16")]; + tensor encoder_layers_16_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_16_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(332259200)))]; + tensor var_3922_cast_fp16 = add(x = q_97_cast_fp16, y = encoder_layers_16_self_attn_pos_bias_v_to_fp16)[name = string("op_3922_cast_fp16")]; + tensor q_with_bias_v_33_perm_0 = const()[name = string("q_with_bias_v_33_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_423_transpose_x_0 = const()[name = string("x_423_transpose_x_0"), val = bool(false)]; + bool x_423_transpose_y_0 = const()[name = string("x_423_transpose_y_0"), val = bool(false)]; + tensor op_3924_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(332261312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(332403712))))[name = string("op_3924_to_fp16_quantized")]; + tensor q_with_bias_v_33_cast_fp16 = transpose(perm = q_with_bias_v_33_perm_0, x = var_3922_cast_fp16)[name = string("transpose_218")]; + tensor x_423_cast_fp16 = matmul(transpose_x = x_423_transpose_x_0, transpose_y = x_423_transpose_y_0, x = q_with_bias_v_33_cast_fp16, y = op_3924_to_fp16_quantized)[name = string("x_423_cast_fp16")]; + tensor x_425_pad_0 = const()[name = string("x_425_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_425_mode_0 = const()[name = string("x_425_mode_0"), val = string("constant")]; + fp16 const_287_to_fp16 = const()[name = string("const_287_to_fp16"), val = fp16(0x0p+0)]; + tensor x_425_cast_fp16 = pad(constant_val = const_287_to_fp16, mode = x_425_mode_0, pad = x_425_pad_0, x = x_423_cast_fp16)[name = string("x_425_cast_fp16")]; + tensor var_3932 = const()[name = string("op_3932"), val = tensor([1, 8, -1, 28])]; + tensor x_427_cast_fp16 = reshape(shape = var_3932, x = x_425_cast_fp16)[name = string("x_427_cast_fp16")]; + tensor var_3936_begin_0 = const()[name = string("op_3936_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_3936_end_0 = const()[name = string("op_3936_end_0"), val = tensor([1, 8, 140, 28])]; + tensor var_3936_end_mask_0 = const()[name = string("op_3936_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_3936_cast_fp16 = slice_by_index(begin = var_3936_begin_0, end = var_3936_end_0, end_mask = var_3936_end_mask_0, x = x_427_cast_fp16)[name = string("op_3936_cast_fp16")]; + tensor var_3937 = const()[name = string("op_3937"), val = tensor([1, 8, 28, 139])]; + tensor matrix_bd_65_cast_fp16 = reshape(shape = var_3937, x = var_3936_cast_fp16)[name = string("matrix_bd_65_cast_fp16")]; + bool matrix_ac_33_transpose_x_0 = const()[name = string("matrix_ac_33_transpose_x_0"), val = bool(false)]; + bool matrix_ac_33_transpose_y_0 = const()[name = string("matrix_ac_33_transpose_y_0"), val = bool(false)]; + tensor transpose_128_perm_0 = const()[name = string("transpose_128_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_129_perm_0 = const()[name = string("transpose_129_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_129 = transpose(perm = transpose_129_perm_0, x = k_65_cast_fp16)[name = string("transpose_216")]; + tensor transpose_128 = transpose(perm = transpose_128_perm_0, x = var_3920_cast_fp16)[name = string("transpose_217")]; + tensor matrix_ac_33_cast_fp16 = matmul(transpose_x = matrix_ac_33_transpose_x_0, transpose_y = matrix_ac_33_transpose_y_0, x = transpose_128, y = transpose_129)[name = string("matrix_ac_33_cast_fp16")]; + tensor matrix_bd_67_begin_0 = const()[name = string("matrix_bd_67_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_67_end_0 = const()[name = string("matrix_bd_67_end_0"), val = tensor([1, 8, 28, 70])]; + tensor matrix_bd_67_end_mask_0 = const()[name = string("matrix_bd_67_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_67_cast_fp16 = slice_by_index(begin = matrix_bd_67_begin_0, end = matrix_bd_67_end_0, end_mask = matrix_bd_67_end_mask_0, x = matrix_bd_65_cast_fp16)[name = string("matrix_bd_67_cast_fp16")]; + tensor var_3946_cast_fp16 = add(x = matrix_ac_33_cast_fp16, y = matrix_bd_67_cast_fp16)[name = string("op_3946_cast_fp16")]; + fp16 _inversed_scores_65_y_0_to_fp16 = const()[name = string("_inversed_scores_65_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_65_cast_fp16 = mul(x = var_3946_cast_fp16, y = _inversed_scores_65_y_0_to_fp16)[name = string("_inversed_scores_65_cast_fp16")]; + tensor scores_67_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_65_cast_fp16, cond = mask_11)[name = string("scores_67_cast_fp16")]; + tensor var_3952_cast_fp16 = softmax(axis = var_59, x = scores_67_cast_fp16)[name = string("op_3952_cast_fp16")]; + tensor input_873_cast_fp16 = select(a = var_44_to_fp16, b = var_3952_cast_fp16, cond = mask_11)[name = string("input_873_cast_fp16")]; + bool x_429_transpose_x_0 = const()[name = string("x_429_transpose_x_0"), val = bool(false)]; + bool x_429_transpose_y_0 = const()[name = string("x_429_transpose_y_0"), val = bool(false)]; + tensor value_41_cast_fp16 = transpose(perm = value_41_perm_0, x = v_33_cast_fp16)[name = string("transpose_215")]; + tensor x_429_cast_fp16 = matmul(transpose_x = x_429_transpose_x_0, transpose_y = x_429_transpose_y_0, x = input_873_cast_fp16, y = value_41_cast_fp16)[name = string("x_429_cast_fp16")]; + tensor var_3956_perm_0 = const()[name = string("op_3956_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3957 = const()[name = string("op_3957"), val = tensor([1, -1, 1024])]; + tensor var_3956_cast_fp16 = transpose(perm = var_3956_perm_0, x = x_429_cast_fp16)[name = string("transpose_214")]; + tensor input_875_cast_fp16 = reshape(shape = var_3957, x = var_3956_cast_fp16)[name = string("input_875_cast_fp16")]; + tensor encoder_layers_16_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(332404096))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(333190592))))[name = string("encoder_layers_16_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_16_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_16_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(333190784)))]; + tensor linear_151_cast_fp16 = linear(bias = encoder_layers_16_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_16_self_attn_linear_out_weight_to_fp16_palettized, x = input_875_cast_fp16)[name = string("linear_151_cast_fp16")]; + tensor input_879_cast_fp16 = add(x = input_869_cast_fp16, y = linear_151_cast_fp16)[name = string("input_879_cast_fp16")]; + tensor x_433_axes_0 = const()[name = string("x_433_axes_0"), val = tensor([-1])]; + tensor encoder_layers_16_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_16_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(333192896)))]; + tensor encoder_layers_16_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_16_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(333195008)))]; + tensor x_433_cast_fp16 = layer_norm(axes = x_433_axes_0, beta = encoder_layers_16_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_16_norm_conv_weight_to_fp16, x = input_879_cast_fp16)[name = string("x_433_cast_fp16")]; + tensor input_881_perm_0 = const()[name = string("input_881_perm_0"), val = tensor([0, 2, 1])]; + string input_883_pad_type_0 = const()[name = string("input_883_pad_type_0"), val = string("valid")]; + tensor input_883_strides_0 = const()[name = string("input_883_strides_0"), val = tensor([1])]; + tensor input_883_pad_0 = const()[name = string("input_883_pad_0"), val = tensor([0, 0])]; + tensor input_883_dilations_0 = const()[name = string("input_883_dilations_0"), val = tensor([1])]; + int32 input_883_groups_0 = const()[name = string("input_883_groups_0"), val = int32(1)]; + tensor encoder_layers_16_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(333197120))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335294336))))[name = string("encoder_layers_16_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_881_cast_fp16 = transpose(perm = input_881_perm_0, x = x_433_cast_fp16)[name = string("transpose_213")]; + tensor input_883_cast_fp16 = conv(dilations = input_883_dilations_0, groups = input_883_groups_0, pad = input_883_pad_0, pad_type = input_883_pad_type_0, strides = input_883_strides_0, weight = encoder_layers_16_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_881_cast_fp16)[name = string("input_883_cast_fp16")]; + int32 x_435_split_num_splits_0 = const()[name = string("x_435_split_num_splits_0"), val = int32(2)]; + int32 x_435_split_axis_0 = const()[name = string("x_435_split_axis_0"), val = int32(1)]; + tensor x_435_split_cast_fp16_0, tensor x_435_split_cast_fp16_1 = split(axis = x_435_split_axis_0, num_splits = x_435_split_num_splits_0, x = input_883_cast_fp16)[name = string("x_435_split_cast_fp16")]; + tensor x_435_split_1_sigmoid_cast_fp16 = sigmoid(x = x_435_split_cast_fp16_1)[name = string("x_435_split_1_sigmoid_cast_fp16")]; + tensor x_435_cast_fp16 = mul(x = x_435_split_cast_fp16_0, y = x_435_split_1_sigmoid_cast_fp16)[name = string("x_435_cast_fp16")]; + tensor input_885_cast_fp16 = select(a = var_44_to_fp16, b = x_435_cast_fp16, cond = var_575)[name = string("input_885_cast_fp16")]; + bool new_x_67_interleave_0 = const()[name = string("new_x_67_interleave_0"), val = bool(false)]; + tensor new_x_67_cast_fp16 = concat(axis = var_59, interleave = new_x_67_interleave_0, values = (cache_67_cast_fp16, input_885_cast_fp16))[name = string("new_x_67_cast_fp16")]; + tensor var_3996_begin_0 = const()[name = string("op_3996_begin_0"), val = tensor([0, 0, 28])]; + tensor var_3996_end_0 = const()[name = string("op_3996_end_0"), val = tensor([1, 1024, 36])]; + tensor var_3996_end_mask_0 = const()[name = string("op_3996_end_mask_0"), val = tensor([true, true, true])]; + tensor var_3996_cast_fp16 = slice_by_index(begin = var_3996_begin_0, end = var_3996_end_0, end_mask = var_3996_end_mask_0, x = new_x_67_cast_fp16)[name = string("op_3996_cast_fp16")]; + string x_437_pad_type_0 = const()[name = string("x_437_pad_type_0"), val = string("valid")]; + int32 x_437_groups_0 = const()[name = string("x_437_groups_0"), val = int32(1024)]; + tensor x_437_strides_0 = const()[name = string("x_437_strides_0"), val = tensor([1])]; + tensor x_437_pad_0 = const()[name = string("x_437_pad_0"), val = tensor([0, 0])]; + tensor x_437_dilations_0 = const()[name = string("x_437_dilations_0"), val = tensor([1])]; + tensor encoder_layers_16_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335298496))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335307776))))[name = string("encoder_layers_16_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_437_cast_fp16 = conv(dilations = x_437_dilations_0, groups = x_437_groups_0, pad = x_437_pad_0, pad_type = x_437_pad_type_0, strides = x_437_strides_0, weight = encoder_layers_16_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_67_cast_fp16)[name = string("x_437_cast_fp16")]; + tensor input_887_perm_0 = const()[name = string("input_887_perm_0"), val = tensor([0, 2, 1])]; + tensor x_439_axes_0 = const()[name = string("x_439_axes_0"), val = tensor([-1])]; + tensor encoder_layers_16_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_16_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335309888)))]; + tensor encoder_layers_16_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_16_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335312000)))]; + tensor input_887_cast_fp16 = transpose(perm = input_887_perm_0, x = x_437_cast_fp16)[name = string("transpose_212")]; + tensor x_439_cast_fp16 = layer_norm(axes = x_439_axes_0, beta = encoder_layers_16_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_16_conv_batch_norm_weight_to_fp16, x = input_887_cast_fp16)[name = string("x_439_cast_fp16")]; + tensor input_889_perm_0 = const()[name = string("input_889_perm_0"), val = tensor([0, 2, 1])]; + tensor input_889_cast_fp16 = transpose(perm = input_889_perm_0, x = x_439_cast_fp16)[name = string("transpose_211")]; + tensor input_891_cast_fp16 = silu(x = input_889_cast_fp16)[name = string("input_891_cast_fp16")]; + string x_441_pad_type_0 = const()[name = string("x_441_pad_type_0"), val = string("valid")]; + tensor x_441_strides_0 = const()[name = string("x_441_strides_0"), val = tensor([1])]; + tensor x_441_pad_0 = const()[name = string("x_441_pad_0"), val = tensor([0, 0])]; + tensor x_441_dilations_0 = const()[name = string("x_441_dilations_0"), val = tensor([1])]; + int32 x_441_groups_0 = const()[name = string("x_441_groups_0"), val = int32(1)]; + tensor encoder_layers_16_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335314112))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(336362752))))[name = string("encoder_layers_16_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_441_cast_fp16 = conv(dilations = x_441_dilations_0, groups = x_441_groups_0, pad = x_441_pad_0, pad_type = x_441_pad_type_0, strides = x_441_strides_0, weight = encoder_layers_16_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_891_cast_fp16)[name = string("x_441_cast_fp16")]; + tensor input_893_perm_0 = const()[name = string("input_893_perm_0"), val = tensor([0, 2, 1])]; + tensor input_893_cast_fp16 = transpose(perm = input_893_perm_0, x = x_441_cast_fp16)[name = string("transpose_210")]; + tensor input_895_cast_fp16 = add(x = input_879_cast_fp16, y = input_893_cast_fp16)[name = string("input_895_cast_fp16")]; + tensor input_897_axes_0 = const()[name = string("input_897_axes_0"), val = tensor([-1])]; + tensor encoder_layers_16_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_16_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(336364864)))]; + tensor encoder_layers_16_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_16_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(336366976)))]; + tensor input_897_cast_fp16 = layer_norm(axes = input_897_axes_0, beta = encoder_layers_16_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_16_norm_feed_forward2_weight_to_fp16, x = input_895_cast_fp16)[name = string("input_897_cast_fp16")]; + tensor encoder_layers_16_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(336369088))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(339514880))))[name = string("encoder_layers_16_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_16_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_16_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(339515072)))]; + tensor linear_152_cast_fp16 = linear(bias = encoder_layers_16_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_16_feed_forward2_linear1_weight_to_fp16_palettized, x = input_897_cast_fp16)[name = string("linear_152_cast_fp16")]; + tensor input_901_cast_fp16 = silu(x = linear_152_cast_fp16)[name = string("input_901_cast_fp16")]; + tensor encoder_layers_16_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(339523328))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(342669120))))[name = string("encoder_layers_16_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_16_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_16_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(342669312)))]; + tensor linear_153_cast_fp16 = linear(bias = encoder_layers_16_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_16_feed_forward2_linear2_weight_to_fp16_palettized, x = input_901_cast_fp16)[name = string("linear_153_cast_fp16")]; + fp16 var_4039_to_fp16 = const()[name = string("op_4039_to_fp16"), val = fp16(0x1p-1)]; + tensor var_4040_cast_fp16 = mul(x = linear_153_cast_fp16, y = var_4039_to_fp16)[name = string("op_4040_cast_fp16")]; + tensor input_907_cast_fp16 = add(x = input_895_cast_fp16, y = var_4040_cast_fp16)[name = string("input_907_cast_fp16")]; + tensor input_909_axes_0 = const()[name = string("input_909_axes_0"), val = tensor([-1])]; + tensor encoder_layers_16_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_16_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(342671424)))]; + tensor encoder_layers_16_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_16_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(342673536)))]; + tensor input_909_cast_fp16 = layer_norm(axes = input_909_axes_0, beta = encoder_layers_16_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_16_norm_out_weight_to_fp16, x = input_907_cast_fp16)[name = string("input_909_cast_fp16")]; + tensor cache_69_begin_0 = const()[name = string("cache_69_begin_0"), val = tensor([17, 0, 0, 0])]; + tensor cache_69_end_0 = const()[name = string("cache_69_end_0"), val = tensor([18, 1, 42, 1024])]; + tensor cache_69_end_mask_0 = const()[name = string("cache_69_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_69_squeeze_mask_0 = const()[name = string("cache_69_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_69_cast_fp16 = slice_by_index(begin = cache_69_begin_0, end = cache_69_end_0, end_mask = cache_69_end_mask_0, squeeze_mask = cache_69_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_69_cast_fp16")]; + tensor cache_71_begin_0 = const()[name = string("cache_71_begin_0"), val = tensor([17, 0, 0, 0])]; + tensor cache_71_end_0 = const()[name = string("cache_71_end_0"), val = tensor([18, 1, 1024, 8])]; + tensor cache_71_end_mask_0 = const()[name = string("cache_71_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_71_squeeze_mask_0 = const()[name = string("cache_71_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_71_cast_fp16 = slice_by_index(begin = cache_71_begin_0, end = cache_71_end_0, end_mask = cache_71_end_mask_0, squeeze_mask = cache_71_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_71_cast_fp16")]; + tensor input_911_axes_0 = const()[name = string("input_911_axes_0"), val = tensor([-1])]; + tensor encoder_layers_17_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_17_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(342675648)))]; + tensor encoder_layers_17_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_17_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(342677760)))]; + tensor input_911_cast_fp16 = layer_norm(axes = input_911_axes_0, beta = encoder_layers_17_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_17_norm_feed_forward1_weight_to_fp16, x = input_909_cast_fp16)[name = string("input_911_cast_fp16")]; + tensor encoder_layers_17_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(342679872))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(345825664))))[name = string("encoder_layers_17_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_17_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_17_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(345825856)))]; + tensor linear_154_cast_fp16 = linear(bias = encoder_layers_17_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_17_feed_forward1_linear1_weight_to_fp16_palettized, x = input_911_cast_fp16)[name = string("linear_154_cast_fp16")]; + tensor input_915_cast_fp16 = silu(x = linear_154_cast_fp16)[name = string("input_915_cast_fp16")]; + tensor encoder_layers_17_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(345834112))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(348979904))))[name = string("encoder_layers_17_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_17_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_17_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(348980096)))]; + tensor linear_155_cast_fp16 = linear(bias = encoder_layers_17_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_17_feed_forward1_linear2_weight_to_fp16_palettized, x = input_915_cast_fp16)[name = string("linear_155_cast_fp16")]; + fp16 var_4076_to_fp16 = const()[name = string("op_4076_to_fp16"), val = fp16(0x1p-1)]; + tensor var_4077_cast_fp16 = mul(x = linear_155_cast_fp16, y = var_4076_to_fp16)[name = string("op_4077_cast_fp16")]; + tensor input_921_cast_fp16 = add(x = input_909_cast_fp16, y = var_4077_cast_fp16)[name = string("input_921_cast_fp16")]; + tensor key_35_axes_0 = const()[name = string("key_35_axes_0"), val = tensor([-1])]; + tensor encoder_layers_17_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_17_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(348982208)))]; + tensor encoder_layers_17_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_17_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(348984320)))]; + tensor key_35_cast_fp16 = layer_norm(axes = key_35_axes_0, beta = encoder_layers_17_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_17_norm_self_att_weight_to_fp16, x = input_921_cast_fp16)[name = string("key_35_cast_fp16")]; + bool input_923_interleave_0 = const()[name = string("input_923_interleave_0"), val = bool(false)]; + tensor input_923_cast_fp16 = concat(axis = var_68, interleave = input_923_interleave_0, values = (cache_69_cast_fp16, key_35_cast_fp16))[name = string("input_923_cast_fp16")]; + tensor var_4099_begin_0 = const()[name = string("op_4099_begin_0"), val = tensor([0, 28, 0])]; + tensor var_4099_end_0 = const()[name = string("op_4099_end_0"), val = tensor([1, 42, 1024])]; + tensor var_4099_end_mask_0 = const()[name = string("op_4099_end_mask_0"), val = tensor([true, true, true])]; + tensor var_4099_cast_fp16 = slice_by_index(begin = var_4099_begin_0, end = var_4099_end_0, end_mask = var_4099_end_mask_0, x = cache_69_cast_fp16)[name = string("op_4099_cast_fp16")]; + bool var_4105_interleave_0 = const()[name = string("op_4105_interleave_0"), val = bool(false)]; + tensor var_4105_cast_fp16 = concat(axis = var_68, interleave = var_4105_interleave_0, values = (var_4099_cast_fp16, key_35_cast_fp16))[name = string("op_4105_cast_fp16")]; + tensor encoder_layers_17_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(348986432))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(349772928))))[name = string("encoder_layers_17_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_17_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_17_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(349773120)))]; + tensor linear_156_cast_fp16 = linear(bias = encoder_layers_17_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_17_self_attn_linear_q_weight_to_fp16_palettized, x = key_35_cast_fp16)[name = string("linear_156_cast_fp16")]; + tensor var_4110 = const()[name = string("op_4110"), val = tensor([1, -1, 8, 128])]; + tensor q_103_cast_fp16 = reshape(shape = var_4110, x = linear_156_cast_fp16)[name = string("q_103_cast_fp16")]; + tensor encoder_layers_17_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(349775232))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350561728))))[name = string("encoder_layers_17_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_17_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_17_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350561920)))]; + tensor linear_157_cast_fp16 = linear(bias = encoder_layers_17_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_17_self_attn_linear_k_weight_to_fp16_palettized, x = input_923_cast_fp16)[name = string("linear_157_cast_fp16")]; + tensor var_4115 = const()[name = string("op_4115"), val = tensor([1, -1, 8, 128])]; + tensor k_69_cast_fp16 = reshape(shape = var_4115, x = linear_157_cast_fp16)[name = string("k_69_cast_fp16")]; + tensor encoder_layers_17_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350564032))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(351350528))))[name = string("encoder_layers_17_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_17_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_17_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(351350720)))]; + tensor linear_158_cast_fp16 = linear(bias = encoder_layers_17_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_17_self_attn_linear_v_weight_to_fp16_palettized, x = input_923_cast_fp16)[name = string("linear_158_cast_fp16")]; + tensor var_4120 = const()[name = string("op_4120"), val = tensor([1, -1, 8, 128])]; + tensor v_35_cast_fp16 = reshape(shape = var_4120, x = linear_158_cast_fp16)[name = string("v_35_cast_fp16")]; + tensor value_43_perm_0 = const()[name = string("value_43_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_17_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_17_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(351352832)))]; + tensor var_4133_cast_fp16 = add(x = q_103_cast_fp16, y = encoder_layers_17_self_attn_pos_bias_u_to_fp16)[name = string("op_4133_cast_fp16")]; + tensor encoder_layers_17_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_17_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(351354944)))]; + tensor var_4135_cast_fp16 = add(x = q_103_cast_fp16, y = encoder_layers_17_self_attn_pos_bias_v_to_fp16)[name = string("op_4135_cast_fp16")]; + tensor q_with_bias_v_35_perm_0 = const()[name = string("q_with_bias_v_35_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_449_transpose_x_0 = const()[name = string("x_449_transpose_x_0"), val = bool(false)]; + bool x_449_transpose_y_0 = const()[name = string("x_449_transpose_y_0"), val = bool(false)]; + tensor op_4137_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(351357056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(351499456))))[name = string("op_4137_to_fp16_quantized")]; + tensor q_with_bias_v_35_cast_fp16 = transpose(perm = q_with_bias_v_35_perm_0, x = var_4135_cast_fp16)[name = string("transpose_209")]; + tensor x_449_cast_fp16 = matmul(transpose_x = x_449_transpose_x_0, transpose_y = x_449_transpose_y_0, x = q_with_bias_v_35_cast_fp16, y = op_4137_to_fp16_quantized)[name = string("x_449_cast_fp16")]; + tensor x_451_pad_0 = const()[name = string("x_451_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_451_mode_0 = const()[name = string("x_451_mode_0"), val = string("constant")]; + fp16 const_300_to_fp16 = const()[name = string("const_300_to_fp16"), val = fp16(0x0p+0)]; + tensor x_451_cast_fp16 = pad(constant_val = const_300_to_fp16, mode = x_451_mode_0, pad = x_451_pad_0, x = x_449_cast_fp16)[name = string("x_451_cast_fp16")]; + tensor var_4145 = const()[name = string("op_4145"), val = tensor([1, 8, -1, 28])]; + tensor x_453_cast_fp16 = reshape(shape = var_4145, x = x_451_cast_fp16)[name = string("x_453_cast_fp16")]; + tensor var_4149_begin_0 = const()[name = string("op_4149_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_4149_end_0 = const()[name = string("op_4149_end_0"), val = tensor([1, 8, 140, 28])]; + tensor var_4149_end_mask_0 = const()[name = string("op_4149_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_4149_cast_fp16 = slice_by_index(begin = var_4149_begin_0, end = var_4149_end_0, end_mask = var_4149_end_mask_0, x = x_453_cast_fp16)[name = string("op_4149_cast_fp16")]; + tensor var_4150 = const()[name = string("op_4150"), val = tensor([1, 8, 28, 139])]; + tensor matrix_bd_69_cast_fp16 = reshape(shape = var_4150, x = var_4149_cast_fp16)[name = string("matrix_bd_69_cast_fp16")]; + bool matrix_ac_35_transpose_x_0 = const()[name = string("matrix_ac_35_transpose_x_0"), val = bool(false)]; + bool matrix_ac_35_transpose_y_0 = const()[name = string("matrix_ac_35_transpose_y_0"), val = bool(false)]; + tensor transpose_130_perm_0 = const()[name = string("transpose_130_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_131_perm_0 = const()[name = string("transpose_131_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_131 = transpose(perm = transpose_131_perm_0, x = k_69_cast_fp16)[name = string("transpose_207")]; + tensor transpose_130 = transpose(perm = transpose_130_perm_0, x = var_4133_cast_fp16)[name = string("transpose_208")]; + tensor matrix_ac_35_cast_fp16 = matmul(transpose_x = matrix_ac_35_transpose_x_0, transpose_y = matrix_ac_35_transpose_y_0, x = transpose_130, y = transpose_131)[name = string("matrix_ac_35_cast_fp16")]; + tensor matrix_bd_71_begin_0 = const()[name = string("matrix_bd_71_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_71_end_0 = const()[name = string("matrix_bd_71_end_0"), val = tensor([1, 8, 28, 70])]; + tensor matrix_bd_71_end_mask_0 = const()[name = string("matrix_bd_71_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_71_cast_fp16 = slice_by_index(begin = matrix_bd_71_begin_0, end = matrix_bd_71_end_0, end_mask = matrix_bd_71_end_mask_0, x = matrix_bd_69_cast_fp16)[name = string("matrix_bd_71_cast_fp16")]; + tensor var_4159_cast_fp16 = add(x = matrix_ac_35_cast_fp16, y = matrix_bd_71_cast_fp16)[name = string("op_4159_cast_fp16")]; + fp16 _inversed_scores_69_y_0_to_fp16 = const()[name = string("_inversed_scores_69_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_69_cast_fp16 = mul(x = var_4159_cast_fp16, y = _inversed_scores_69_y_0_to_fp16)[name = string("_inversed_scores_69_cast_fp16")]; + tensor scores_71_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_69_cast_fp16, cond = mask_11)[name = string("scores_71_cast_fp16")]; + tensor var_4165_cast_fp16 = softmax(axis = var_59, x = scores_71_cast_fp16)[name = string("op_4165_cast_fp16")]; + tensor input_925_cast_fp16 = select(a = var_44_to_fp16, b = var_4165_cast_fp16, cond = mask_11)[name = string("input_925_cast_fp16")]; + bool x_455_transpose_x_0 = const()[name = string("x_455_transpose_x_0"), val = bool(false)]; + bool x_455_transpose_y_0 = const()[name = string("x_455_transpose_y_0"), val = bool(false)]; + tensor value_43_cast_fp16 = transpose(perm = value_43_perm_0, x = v_35_cast_fp16)[name = string("transpose_206")]; + tensor x_455_cast_fp16 = matmul(transpose_x = x_455_transpose_x_0, transpose_y = x_455_transpose_y_0, x = input_925_cast_fp16, y = value_43_cast_fp16)[name = string("x_455_cast_fp16")]; + tensor var_4169_perm_0 = const()[name = string("op_4169_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_4170 = const()[name = string("op_4170"), val = tensor([1, -1, 1024])]; + tensor var_4169_cast_fp16 = transpose(perm = var_4169_perm_0, x = x_455_cast_fp16)[name = string("transpose_205")]; + tensor input_927_cast_fp16 = reshape(shape = var_4170, x = var_4169_cast_fp16)[name = string("input_927_cast_fp16")]; + tensor encoder_layers_17_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(351499840))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(352286336))))[name = string("encoder_layers_17_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_17_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_17_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(352286528)))]; + tensor linear_160_cast_fp16 = linear(bias = encoder_layers_17_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_17_self_attn_linear_out_weight_to_fp16_palettized, x = input_927_cast_fp16)[name = string("linear_160_cast_fp16")]; + tensor input_931_cast_fp16 = add(x = input_921_cast_fp16, y = linear_160_cast_fp16)[name = string("input_931_cast_fp16")]; + tensor x_459_axes_0 = const()[name = string("x_459_axes_0"), val = tensor([-1])]; + tensor encoder_layers_17_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_17_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(352288640)))]; + tensor encoder_layers_17_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_17_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(352290752)))]; + tensor x_459_cast_fp16 = layer_norm(axes = x_459_axes_0, beta = encoder_layers_17_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_17_norm_conv_weight_to_fp16, x = input_931_cast_fp16)[name = string("x_459_cast_fp16")]; + tensor input_933_perm_0 = const()[name = string("input_933_perm_0"), val = tensor([0, 2, 1])]; + string input_935_pad_type_0 = const()[name = string("input_935_pad_type_0"), val = string("valid")]; + tensor input_935_strides_0 = const()[name = string("input_935_strides_0"), val = tensor([1])]; + tensor input_935_pad_0 = const()[name = string("input_935_pad_0"), val = tensor([0, 0])]; + tensor input_935_dilations_0 = const()[name = string("input_935_dilations_0"), val = tensor([1])]; + int32 input_935_groups_0 = const()[name = string("input_935_groups_0"), val = int32(1)]; + tensor encoder_layers_17_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(352292864))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(354390080))))[name = string("encoder_layers_17_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_933_cast_fp16 = transpose(perm = input_933_perm_0, x = x_459_cast_fp16)[name = string("transpose_204")]; + tensor input_935_cast_fp16 = conv(dilations = input_935_dilations_0, groups = input_935_groups_0, pad = input_935_pad_0, pad_type = input_935_pad_type_0, strides = input_935_strides_0, weight = encoder_layers_17_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_933_cast_fp16)[name = string("input_935_cast_fp16")]; + int32 x_461_split_num_splits_0 = const()[name = string("x_461_split_num_splits_0"), val = int32(2)]; + int32 x_461_split_axis_0 = const()[name = string("x_461_split_axis_0"), val = int32(1)]; + tensor x_461_split_cast_fp16_0, tensor x_461_split_cast_fp16_1 = split(axis = x_461_split_axis_0, num_splits = x_461_split_num_splits_0, x = input_935_cast_fp16)[name = string("x_461_split_cast_fp16")]; + tensor x_461_split_1_sigmoid_cast_fp16 = sigmoid(x = x_461_split_cast_fp16_1)[name = string("x_461_split_1_sigmoid_cast_fp16")]; + tensor x_461_cast_fp16 = mul(x = x_461_split_cast_fp16_0, y = x_461_split_1_sigmoid_cast_fp16)[name = string("x_461_cast_fp16")]; + tensor input_937_cast_fp16 = select(a = var_44_to_fp16, b = x_461_cast_fp16, cond = var_575)[name = string("input_937_cast_fp16")]; + bool new_x_71_interleave_0 = const()[name = string("new_x_71_interleave_0"), val = bool(false)]; + tensor new_x_71_cast_fp16 = concat(axis = var_59, interleave = new_x_71_interleave_0, values = (cache_71_cast_fp16, input_937_cast_fp16))[name = string("new_x_71_cast_fp16")]; + tensor var_4209_begin_0 = const()[name = string("op_4209_begin_0"), val = tensor([0, 0, 28])]; + tensor var_4209_end_0 = const()[name = string("op_4209_end_0"), val = tensor([1, 1024, 36])]; + tensor var_4209_end_mask_0 = const()[name = string("op_4209_end_mask_0"), val = tensor([true, true, true])]; + tensor var_4209_cast_fp16 = slice_by_index(begin = var_4209_begin_0, end = var_4209_end_0, end_mask = var_4209_end_mask_0, x = new_x_71_cast_fp16)[name = string("op_4209_cast_fp16")]; + string x_463_pad_type_0 = const()[name = string("x_463_pad_type_0"), val = string("valid")]; + int32 x_463_groups_0 = const()[name = string("x_463_groups_0"), val = int32(1024)]; + tensor x_463_strides_0 = const()[name = string("x_463_strides_0"), val = tensor([1])]; + tensor x_463_pad_0 = const()[name = string("x_463_pad_0"), val = tensor([0, 0])]; + tensor x_463_dilations_0 = const()[name = string("x_463_dilations_0"), val = tensor([1])]; + tensor encoder_layers_17_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(354394240))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(354403520))))[name = string("encoder_layers_17_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_463_cast_fp16 = conv(dilations = x_463_dilations_0, groups = x_463_groups_0, pad = x_463_pad_0, pad_type = x_463_pad_type_0, strides = x_463_strides_0, weight = encoder_layers_17_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_71_cast_fp16)[name = string("x_463_cast_fp16")]; + tensor input_939_perm_0 = const()[name = string("input_939_perm_0"), val = tensor([0, 2, 1])]; + tensor x_465_axes_0 = const()[name = string("x_465_axes_0"), val = tensor([-1])]; + tensor encoder_layers_17_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_17_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(354405632)))]; + tensor encoder_layers_17_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_17_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(354407744)))]; + tensor input_939_cast_fp16 = transpose(perm = input_939_perm_0, x = x_463_cast_fp16)[name = string("transpose_203")]; + tensor x_465_cast_fp16 = layer_norm(axes = x_465_axes_0, beta = encoder_layers_17_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_17_conv_batch_norm_weight_to_fp16, x = input_939_cast_fp16)[name = string("x_465_cast_fp16")]; + tensor input_941_perm_0 = const()[name = string("input_941_perm_0"), val = tensor([0, 2, 1])]; + tensor input_941_cast_fp16 = transpose(perm = input_941_perm_0, x = x_465_cast_fp16)[name = string("transpose_202")]; + tensor input_943_cast_fp16 = silu(x = input_941_cast_fp16)[name = string("input_943_cast_fp16")]; + string x_467_pad_type_0 = const()[name = string("x_467_pad_type_0"), val = string("valid")]; + tensor x_467_strides_0 = const()[name = string("x_467_strides_0"), val = tensor([1])]; + tensor x_467_pad_0 = const()[name = string("x_467_pad_0"), val = tensor([0, 0])]; + tensor x_467_dilations_0 = const()[name = string("x_467_dilations_0"), val = tensor([1])]; + int32 x_467_groups_0 = const()[name = string("x_467_groups_0"), val = int32(1)]; + tensor encoder_layers_17_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(354409856))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355458496))))[name = string("encoder_layers_17_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_467_cast_fp16 = conv(dilations = x_467_dilations_0, groups = x_467_groups_0, pad = x_467_pad_0, pad_type = x_467_pad_type_0, strides = x_467_strides_0, weight = encoder_layers_17_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_943_cast_fp16)[name = string("x_467_cast_fp16")]; + tensor input_945_perm_0 = const()[name = string("input_945_perm_0"), val = tensor([0, 2, 1])]; + tensor input_945_cast_fp16 = transpose(perm = input_945_perm_0, x = x_467_cast_fp16)[name = string("transpose_201")]; + tensor input_947_cast_fp16 = add(x = input_931_cast_fp16, y = input_945_cast_fp16)[name = string("input_947_cast_fp16")]; + tensor input_949_axes_0 = const()[name = string("input_949_axes_0"), val = tensor([-1])]; + tensor encoder_layers_17_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_17_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355460608)))]; + tensor encoder_layers_17_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_17_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355462720)))]; + tensor input_949_cast_fp16 = layer_norm(axes = input_949_axes_0, beta = encoder_layers_17_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_17_norm_feed_forward2_weight_to_fp16, x = input_947_cast_fp16)[name = string("input_949_cast_fp16")]; + tensor encoder_layers_17_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355464832))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(358610624))))[name = string("encoder_layers_17_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_17_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_17_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(358610816)))]; + tensor linear_161_cast_fp16 = linear(bias = encoder_layers_17_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_17_feed_forward2_linear1_weight_to_fp16_palettized, x = input_949_cast_fp16)[name = string("linear_161_cast_fp16")]; + tensor input_953_cast_fp16 = silu(x = linear_161_cast_fp16)[name = string("input_953_cast_fp16")]; + tensor encoder_layers_17_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(358619072))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(361764864))))[name = string("encoder_layers_17_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_17_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_17_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(361765056)))]; + tensor linear_162_cast_fp16 = linear(bias = encoder_layers_17_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_17_feed_forward2_linear2_weight_to_fp16_palettized, x = input_953_cast_fp16)[name = string("linear_162_cast_fp16")]; + fp16 var_4252_to_fp16 = const()[name = string("op_4252_to_fp16"), val = fp16(0x1p-1)]; + tensor var_4253_cast_fp16 = mul(x = linear_162_cast_fp16, y = var_4252_to_fp16)[name = string("op_4253_cast_fp16")]; + tensor input_959_cast_fp16 = add(x = input_947_cast_fp16, y = var_4253_cast_fp16)[name = string("input_959_cast_fp16")]; + tensor input_961_axes_0 = const()[name = string("input_961_axes_0"), val = tensor([-1])]; + tensor encoder_layers_17_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_17_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(361767168)))]; + tensor encoder_layers_17_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_17_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(361769280)))]; + tensor input_961_cast_fp16 = layer_norm(axes = input_961_axes_0, beta = encoder_layers_17_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_17_norm_out_weight_to_fp16, x = input_959_cast_fp16)[name = string("input_961_cast_fp16")]; + tensor cache_73_begin_0 = const()[name = string("cache_73_begin_0"), val = tensor([18, 0, 0, 0])]; + tensor cache_73_end_0 = const()[name = string("cache_73_end_0"), val = tensor([19, 1, 42, 1024])]; + tensor cache_73_end_mask_0 = const()[name = string("cache_73_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_73_squeeze_mask_0 = const()[name = string("cache_73_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_73_cast_fp16 = slice_by_index(begin = cache_73_begin_0, end = cache_73_end_0, end_mask = cache_73_end_mask_0, squeeze_mask = cache_73_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_73_cast_fp16")]; + tensor cache_75_begin_0 = const()[name = string("cache_75_begin_0"), val = tensor([18, 0, 0, 0])]; + tensor cache_75_end_0 = const()[name = string("cache_75_end_0"), val = tensor([19, 1, 1024, 8])]; + tensor cache_75_end_mask_0 = const()[name = string("cache_75_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_75_squeeze_mask_0 = const()[name = string("cache_75_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_75_cast_fp16 = slice_by_index(begin = cache_75_begin_0, end = cache_75_end_0, end_mask = cache_75_end_mask_0, squeeze_mask = cache_75_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_75_cast_fp16")]; + tensor input_963_axes_0 = const()[name = string("input_963_axes_0"), val = tensor([-1])]; + tensor encoder_layers_18_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_18_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(361771392)))]; + tensor encoder_layers_18_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_18_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(361773504)))]; + tensor input_963_cast_fp16 = layer_norm(axes = input_963_axes_0, beta = encoder_layers_18_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_18_norm_feed_forward1_weight_to_fp16, x = input_961_cast_fp16)[name = string("input_963_cast_fp16")]; + tensor encoder_layers_18_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(361775616))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(364921408))))[name = string("encoder_layers_18_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_18_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_18_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(364921600)))]; + tensor linear_163_cast_fp16 = linear(bias = encoder_layers_18_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_18_feed_forward1_linear1_weight_to_fp16_palettized, x = input_963_cast_fp16)[name = string("linear_163_cast_fp16")]; + tensor input_967_cast_fp16 = silu(x = linear_163_cast_fp16)[name = string("input_967_cast_fp16")]; + tensor encoder_layers_18_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(364929856))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(368075648))))[name = string("encoder_layers_18_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_18_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_18_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(368075840)))]; + tensor linear_164_cast_fp16 = linear(bias = encoder_layers_18_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_18_feed_forward1_linear2_weight_to_fp16_palettized, x = input_967_cast_fp16)[name = string("linear_164_cast_fp16")]; + fp16 var_4289_to_fp16 = const()[name = string("op_4289_to_fp16"), val = fp16(0x1p-1)]; + tensor var_4290_cast_fp16 = mul(x = linear_164_cast_fp16, y = var_4289_to_fp16)[name = string("op_4290_cast_fp16")]; + tensor input_973_cast_fp16 = add(x = input_961_cast_fp16, y = var_4290_cast_fp16)[name = string("input_973_cast_fp16")]; + tensor key_37_axes_0 = const()[name = string("key_37_axes_0"), val = tensor([-1])]; + tensor encoder_layers_18_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_18_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(368077952)))]; + tensor encoder_layers_18_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_18_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(368080064)))]; + tensor key_37_cast_fp16 = layer_norm(axes = key_37_axes_0, beta = encoder_layers_18_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_18_norm_self_att_weight_to_fp16, x = input_973_cast_fp16)[name = string("key_37_cast_fp16")]; + bool input_975_interleave_0 = const()[name = string("input_975_interleave_0"), val = bool(false)]; + tensor input_975_cast_fp16 = concat(axis = var_68, interleave = input_975_interleave_0, values = (cache_73_cast_fp16, key_37_cast_fp16))[name = string("input_975_cast_fp16")]; + tensor var_4312_begin_0 = const()[name = string("op_4312_begin_0"), val = tensor([0, 28, 0])]; + tensor var_4312_end_0 = const()[name = string("op_4312_end_0"), val = tensor([1, 42, 1024])]; + tensor var_4312_end_mask_0 = const()[name = string("op_4312_end_mask_0"), val = tensor([true, true, true])]; + tensor var_4312_cast_fp16 = slice_by_index(begin = var_4312_begin_0, end = var_4312_end_0, end_mask = var_4312_end_mask_0, x = cache_73_cast_fp16)[name = string("op_4312_cast_fp16")]; + bool var_4318_interleave_0 = const()[name = string("op_4318_interleave_0"), val = bool(false)]; + tensor var_4318_cast_fp16 = concat(axis = var_68, interleave = var_4318_interleave_0, values = (var_4312_cast_fp16, key_37_cast_fp16))[name = string("op_4318_cast_fp16")]; + tensor encoder_layers_18_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(368082176))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(368868672))))[name = string("encoder_layers_18_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_18_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_18_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(368868864)))]; + tensor linear_165_cast_fp16 = linear(bias = encoder_layers_18_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_18_self_attn_linear_q_weight_to_fp16_palettized, x = key_37_cast_fp16)[name = string("linear_165_cast_fp16")]; + tensor var_4323 = const()[name = string("op_4323"), val = tensor([1, -1, 8, 128])]; + tensor q_109_cast_fp16 = reshape(shape = var_4323, x = linear_165_cast_fp16)[name = string("q_109_cast_fp16")]; + tensor encoder_layers_18_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(368870976))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(369657472))))[name = string("encoder_layers_18_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_18_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_18_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(369657664)))]; + tensor linear_166_cast_fp16 = linear(bias = encoder_layers_18_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_18_self_attn_linear_k_weight_to_fp16_palettized, x = input_975_cast_fp16)[name = string("linear_166_cast_fp16")]; + tensor var_4328 = const()[name = string("op_4328"), val = tensor([1, -1, 8, 128])]; + tensor k_73_cast_fp16 = reshape(shape = var_4328, x = linear_166_cast_fp16)[name = string("k_73_cast_fp16")]; + tensor encoder_layers_18_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(369659776))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(370446272))))[name = string("encoder_layers_18_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_18_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_18_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(370446464)))]; + tensor linear_167_cast_fp16 = linear(bias = encoder_layers_18_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_18_self_attn_linear_v_weight_to_fp16_palettized, x = input_975_cast_fp16)[name = string("linear_167_cast_fp16")]; + tensor var_4333 = const()[name = string("op_4333"), val = tensor([1, -1, 8, 128])]; + tensor v_37_cast_fp16 = reshape(shape = var_4333, x = linear_167_cast_fp16)[name = string("v_37_cast_fp16")]; + tensor value_45_perm_0 = const()[name = string("value_45_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_18_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_18_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(370448576)))]; + tensor var_4346_cast_fp16 = add(x = q_109_cast_fp16, y = encoder_layers_18_self_attn_pos_bias_u_to_fp16)[name = string("op_4346_cast_fp16")]; + tensor encoder_layers_18_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_18_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(370450688)))]; + tensor var_4348_cast_fp16 = add(x = q_109_cast_fp16, y = encoder_layers_18_self_attn_pos_bias_v_to_fp16)[name = string("op_4348_cast_fp16")]; + tensor q_with_bias_v_37_perm_0 = const()[name = string("q_with_bias_v_37_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_475_transpose_x_0 = const()[name = string("x_475_transpose_x_0"), val = bool(false)]; + bool x_475_transpose_y_0 = const()[name = string("x_475_transpose_y_0"), val = bool(false)]; + tensor op_4350_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(370452800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(370595200))))[name = string("op_4350_to_fp16_quantized")]; + tensor q_with_bias_v_37_cast_fp16 = transpose(perm = q_with_bias_v_37_perm_0, x = var_4348_cast_fp16)[name = string("transpose_200")]; + tensor x_475_cast_fp16 = matmul(transpose_x = x_475_transpose_x_0, transpose_y = x_475_transpose_y_0, x = q_with_bias_v_37_cast_fp16, y = op_4350_to_fp16_quantized)[name = string("x_475_cast_fp16")]; + tensor x_477_pad_0 = const()[name = string("x_477_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_477_mode_0 = const()[name = string("x_477_mode_0"), val = string("constant")]; + fp16 const_313_to_fp16 = const()[name = string("const_313_to_fp16"), val = fp16(0x0p+0)]; + tensor x_477_cast_fp16 = pad(constant_val = const_313_to_fp16, mode = x_477_mode_0, pad = x_477_pad_0, x = x_475_cast_fp16)[name = string("x_477_cast_fp16")]; + tensor var_4358 = const()[name = string("op_4358"), val = tensor([1, 8, -1, 28])]; + tensor x_479_cast_fp16 = reshape(shape = var_4358, x = x_477_cast_fp16)[name = string("x_479_cast_fp16")]; + tensor var_4362_begin_0 = const()[name = string("op_4362_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_4362_end_0 = const()[name = string("op_4362_end_0"), val = tensor([1, 8, 140, 28])]; + tensor var_4362_end_mask_0 = const()[name = string("op_4362_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_4362_cast_fp16 = slice_by_index(begin = var_4362_begin_0, end = var_4362_end_0, end_mask = var_4362_end_mask_0, x = x_479_cast_fp16)[name = string("op_4362_cast_fp16")]; + tensor var_4363 = const()[name = string("op_4363"), val = tensor([1, 8, 28, 139])]; + tensor matrix_bd_73_cast_fp16 = reshape(shape = var_4363, x = var_4362_cast_fp16)[name = string("matrix_bd_73_cast_fp16")]; + bool matrix_ac_37_transpose_x_0 = const()[name = string("matrix_ac_37_transpose_x_0"), val = bool(false)]; + bool matrix_ac_37_transpose_y_0 = const()[name = string("matrix_ac_37_transpose_y_0"), val = bool(false)]; + tensor transpose_132_perm_0 = const()[name = string("transpose_132_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_133_perm_0 = const()[name = string("transpose_133_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_133 = transpose(perm = transpose_133_perm_0, x = k_73_cast_fp16)[name = string("transpose_198")]; + tensor transpose_132 = transpose(perm = transpose_132_perm_0, x = var_4346_cast_fp16)[name = string("transpose_199")]; + tensor matrix_ac_37_cast_fp16 = matmul(transpose_x = matrix_ac_37_transpose_x_0, transpose_y = matrix_ac_37_transpose_y_0, x = transpose_132, y = transpose_133)[name = string("matrix_ac_37_cast_fp16")]; + tensor matrix_bd_75_begin_0 = const()[name = string("matrix_bd_75_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_75_end_0 = const()[name = string("matrix_bd_75_end_0"), val = tensor([1, 8, 28, 70])]; + tensor matrix_bd_75_end_mask_0 = const()[name = string("matrix_bd_75_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_75_cast_fp16 = slice_by_index(begin = matrix_bd_75_begin_0, end = matrix_bd_75_end_0, end_mask = matrix_bd_75_end_mask_0, x = matrix_bd_73_cast_fp16)[name = string("matrix_bd_75_cast_fp16")]; + tensor var_4372_cast_fp16 = add(x = matrix_ac_37_cast_fp16, y = matrix_bd_75_cast_fp16)[name = string("op_4372_cast_fp16")]; + fp16 _inversed_scores_73_y_0_to_fp16 = const()[name = string("_inversed_scores_73_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_73_cast_fp16 = mul(x = var_4372_cast_fp16, y = _inversed_scores_73_y_0_to_fp16)[name = string("_inversed_scores_73_cast_fp16")]; + tensor scores_75_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_73_cast_fp16, cond = mask_11)[name = string("scores_75_cast_fp16")]; + tensor var_4378_cast_fp16 = softmax(axis = var_59, x = scores_75_cast_fp16)[name = string("op_4378_cast_fp16")]; + tensor input_977_cast_fp16 = select(a = var_44_to_fp16, b = var_4378_cast_fp16, cond = mask_11)[name = string("input_977_cast_fp16")]; + bool x_481_transpose_x_0 = const()[name = string("x_481_transpose_x_0"), val = bool(false)]; + bool x_481_transpose_y_0 = const()[name = string("x_481_transpose_y_0"), val = bool(false)]; + tensor value_45_cast_fp16 = transpose(perm = value_45_perm_0, x = v_37_cast_fp16)[name = string("transpose_197")]; + tensor x_481_cast_fp16 = matmul(transpose_x = x_481_transpose_x_0, transpose_y = x_481_transpose_y_0, x = input_977_cast_fp16, y = value_45_cast_fp16)[name = string("x_481_cast_fp16")]; + tensor var_4382_perm_0 = const()[name = string("op_4382_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_4383 = const()[name = string("op_4383"), val = tensor([1, -1, 1024])]; + tensor var_4382_cast_fp16 = transpose(perm = var_4382_perm_0, x = x_481_cast_fp16)[name = string("transpose_196")]; + tensor input_979_cast_fp16 = reshape(shape = var_4383, x = var_4382_cast_fp16)[name = string("input_979_cast_fp16")]; + tensor encoder_layers_18_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(370595584))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(371644224))))[name = string("encoder_layers_18_self_attn_linear_out_weight_to_fp16_quantized")]; + tensor encoder_layers_18_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_18_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(371646336)))]; + tensor linear_169_cast_fp16 = linear(bias = encoder_layers_18_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_18_self_attn_linear_out_weight_to_fp16_quantized, x = input_979_cast_fp16)[name = string("linear_169_cast_fp16")]; + tensor input_983_cast_fp16 = add(x = input_973_cast_fp16, y = linear_169_cast_fp16)[name = string("input_983_cast_fp16")]; + tensor x_485_axes_0 = const()[name = string("x_485_axes_0"), val = tensor([-1])]; + tensor encoder_layers_18_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_18_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(371648448)))]; + tensor encoder_layers_18_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_18_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(371650560)))]; + tensor x_485_cast_fp16 = layer_norm(axes = x_485_axes_0, beta = encoder_layers_18_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_18_norm_conv_weight_to_fp16, x = input_983_cast_fp16)[name = string("x_485_cast_fp16")]; + tensor input_985_perm_0 = const()[name = string("input_985_perm_0"), val = tensor([0, 2, 1])]; + string input_987_pad_type_0 = const()[name = string("input_987_pad_type_0"), val = string("valid")]; + tensor input_987_strides_0 = const()[name = string("input_987_strides_0"), val = tensor([1])]; + tensor input_987_pad_0 = const()[name = string("input_987_pad_0"), val = tensor([0, 0])]; + tensor input_987_dilations_0 = const()[name = string("input_987_dilations_0"), val = tensor([1])]; + int32 input_987_groups_0 = const()[name = string("input_987_groups_0"), val = int32(1)]; + tensor encoder_layers_18_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(371652672))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(373749888))))[name = string("encoder_layers_18_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_985_cast_fp16 = transpose(perm = input_985_perm_0, x = x_485_cast_fp16)[name = string("transpose_195")]; + tensor input_987_cast_fp16 = conv(dilations = input_987_dilations_0, groups = input_987_groups_0, pad = input_987_pad_0, pad_type = input_987_pad_type_0, strides = input_987_strides_0, weight = encoder_layers_18_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_985_cast_fp16)[name = string("input_987_cast_fp16")]; + int32 x_487_split_num_splits_0 = const()[name = string("x_487_split_num_splits_0"), val = int32(2)]; + int32 x_487_split_axis_0 = const()[name = string("x_487_split_axis_0"), val = int32(1)]; + tensor x_487_split_cast_fp16_0, tensor x_487_split_cast_fp16_1 = split(axis = x_487_split_axis_0, num_splits = x_487_split_num_splits_0, x = input_987_cast_fp16)[name = string("x_487_split_cast_fp16")]; + tensor x_487_split_1_sigmoid_cast_fp16 = sigmoid(x = x_487_split_cast_fp16_1)[name = string("x_487_split_1_sigmoid_cast_fp16")]; + tensor x_487_cast_fp16 = mul(x = x_487_split_cast_fp16_0, y = x_487_split_1_sigmoid_cast_fp16)[name = string("x_487_cast_fp16")]; + tensor input_989_cast_fp16 = select(a = var_44_to_fp16, b = x_487_cast_fp16, cond = var_575)[name = string("input_989_cast_fp16")]; + bool new_x_75_interleave_0 = const()[name = string("new_x_75_interleave_0"), val = bool(false)]; + tensor new_x_75_cast_fp16 = concat(axis = var_59, interleave = new_x_75_interleave_0, values = (cache_75_cast_fp16, input_989_cast_fp16))[name = string("new_x_75_cast_fp16")]; + tensor var_4422_begin_0 = const()[name = string("op_4422_begin_0"), val = tensor([0, 0, 28])]; + tensor var_4422_end_0 = const()[name = string("op_4422_end_0"), val = tensor([1, 1024, 36])]; + tensor var_4422_end_mask_0 = const()[name = string("op_4422_end_mask_0"), val = tensor([true, true, true])]; + tensor var_4422_cast_fp16 = slice_by_index(begin = var_4422_begin_0, end = var_4422_end_0, end_mask = var_4422_end_mask_0, x = new_x_75_cast_fp16)[name = string("op_4422_cast_fp16")]; + string x_489_pad_type_0 = const()[name = string("x_489_pad_type_0"), val = string("valid")]; + int32 x_489_groups_0 = const()[name = string("x_489_groups_0"), val = int32(1024)]; + tensor x_489_strides_0 = const()[name = string("x_489_strides_0"), val = tensor([1])]; + tensor x_489_pad_0 = const()[name = string("x_489_pad_0"), val = tensor([0, 0])]; + tensor x_489_dilations_0 = const()[name = string("x_489_dilations_0"), val = tensor([1])]; + tensor encoder_layers_18_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(373754048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(373763328))))[name = string("encoder_layers_18_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_489_cast_fp16 = conv(dilations = x_489_dilations_0, groups = x_489_groups_0, pad = x_489_pad_0, pad_type = x_489_pad_type_0, strides = x_489_strides_0, weight = encoder_layers_18_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_75_cast_fp16)[name = string("x_489_cast_fp16")]; + tensor input_991_perm_0 = const()[name = string("input_991_perm_0"), val = tensor([0, 2, 1])]; + tensor x_491_axes_0 = const()[name = string("x_491_axes_0"), val = tensor([-1])]; + tensor encoder_layers_18_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_18_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(373765440)))]; + tensor encoder_layers_18_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_18_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(373767552)))]; + tensor input_991_cast_fp16 = transpose(perm = input_991_perm_0, x = x_489_cast_fp16)[name = string("transpose_194")]; + tensor x_491_cast_fp16 = layer_norm(axes = x_491_axes_0, beta = encoder_layers_18_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_18_conv_batch_norm_weight_to_fp16, x = input_991_cast_fp16)[name = string("x_491_cast_fp16")]; + tensor input_993_perm_0 = const()[name = string("input_993_perm_0"), val = tensor([0, 2, 1])]; + tensor input_993_cast_fp16 = transpose(perm = input_993_perm_0, x = x_491_cast_fp16)[name = string("transpose_193")]; + tensor input_995_cast_fp16 = silu(x = input_993_cast_fp16)[name = string("input_995_cast_fp16")]; + string x_493_pad_type_0 = const()[name = string("x_493_pad_type_0"), val = string("valid")]; + tensor x_493_strides_0 = const()[name = string("x_493_strides_0"), val = tensor([1])]; + tensor x_493_pad_0 = const()[name = string("x_493_pad_0"), val = tensor([0, 0])]; + tensor x_493_dilations_0 = const()[name = string("x_493_dilations_0"), val = tensor([1])]; + int32 x_493_groups_0 = const()[name = string("x_493_groups_0"), val = int32(1)]; + tensor encoder_layers_18_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(373769664))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(374818304))))[name = string("encoder_layers_18_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_493_cast_fp16 = conv(dilations = x_493_dilations_0, groups = x_493_groups_0, pad = x_493_pad_0, pad_type = x_493_pad_type_0, strides = x_493_strides_0, weight = encoder_layers_18_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_995_cast_fp16)[name = string("x_493_cast_fp16")]; + tensor input_997_perm_0 = const()[name = string("input_997_perm_0"), val = tensor([0, 2, 1])]; + tensor input_997_cast_fp16 = transpose(perm = input_997_perm_0, x = x_493_cast_fp16)[name = string("transpose_192")]; + tensor input_999_cast_fp16 = add(x = input_983_cast_fp16, y = input_997_cast_fp16)[name = string("input_999_cast_fp16")]; + tensor input_1001_axes_0 = const()[name = string("input_1001_axes_0"), val = tensor([-1])]; + tensor encoder_layers_18_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_18_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(374820416)))]; + tensor encoder_layers_18_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_18_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(374822528)))]; + tensor input_1001_cast_fp16 = layer_norm(axes = input_1001_axes_0, beta = encoder_layers_18_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_18_norm_feed_forward2_weight_to_fp16, x = input_999_cast_fp16)[name = string("input_1001_cast_fp16")]; + tensor encoder_layers_18_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(374824640))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(379019008))))[name = string("encoder_layers_18_feed_forward2_linear1_weight_to_fp16_quantized")]; + tensor encoder_layers_18_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_18_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(379027264)))]; + tensor linear_170_cast_fp16 = linear(bias = encoder_layers_18_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_18_feed_forward2_linear1_weight_to_fp16_quantized, x = input_1001_cast_fp16)[name = string("linear_170_cast_fp16")]; + tensor input_1005_cast_fp16 = silu(x = linear_170_cast_fp16)[name = string("input_1005_cast_fp16")]; + tensor encoder_layers_18_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(379035520))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(383229888))))[name = string("encoder_layers_18_feed_forward2_linear2_weight_to_fp16_quantized")]; + tensor encoder_layers_18_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_18_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(383232000)))]; + tensor linear_171_cast_fp16 = linear(bias = encoder_layers_18_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_18_feed_forward2_linear2_weight_to_fp16_quantized, x = input_1005_cast_fp16)[name = string("linear_171_cast_fp16")]; + fp16 var_4465_to_fp16 = const()[name = string("op_4465_to_fp16"), val = fp16(0x1p-1)]; + tensor var_4466_cast_fp16 = mul(x = linear_171_cast_fp16, y = var_4465_to_fp16)[name = string("op_4466_cast_fp16")]; + tensor input_1011_cast_fp16 = add(x = input_999_cast_fp16, y = var_4466_cast_fp16)[name = string("input_1011_cast_fp16")]; + tensor input_1013_axes_0 = const()[name = string("input_1013_axes_0"), val = tensor([-1])]; + tensor encoder_layers_18_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_18_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(383234112)))]; + tensor encoder_layers_18_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_18_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(383236224)))]; + tensor input_1013_cast_fp16 = layer_norm(axes = input_1013_axes_0, beta = encoder_layers_18_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_18_norm_out_weight_to_fp16, x = input_1011_cast_fp16)[name = string("input_1013_cast_fp16")]; + tensor cache_77_begin_0 = const()[name = string("cache_77_begin_0"), val = tensor([19, 0, 0, 0])]; + tensor cache_77_end_0 = const()[name = string("cache_77_end_0"), val = tensor([20, 1, 42, 1024])]; + tensor cache_77_end_mask_0 = const()[name = string("cache_77_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_77_squeeze_mask_0 = const()[name = string("cache_77_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_77_cast_fp16 = slice_by_index(begin = cache_77_begin_0, end = cache_77_end_0, end_mask = cache_77_end_mask_0, squeeze_mask = cache_77_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_77_cast_fp16")]; + tensor cache_79_begin_0 = const()[name = string("cache_79_begin_0"), val = tensor([19, 0, 0, 0])]; + tensor cache_79_end_0 = const()[name = string("cache_79_end_0"), val = tensor([20, 1, 1024, 8])]; + tensor cache_79_end_mask_0 = const()[name = string("cache_79_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_79_squeeze_mask_0 = const()[name = string("cache_79_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_79_cast_fp16 = slice_by_index(begin = cache_79_begin_0, end = cache_79_end_0, end_mask = cache_79_end_mask_0, squeeze_mask = cache_79_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_79_cast_fp16")]; + tensor input_1015_axes_0 = const()[name = string("input_1015_axes_0"), val = tensor([-1])]; + tensor encoder_layers_19_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_19_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(383238336)))]; + tensor encoder_layers_19_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_19_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(383240448)))]; + tensor input_1015_cast_fp16 = layer_norm(axes = input_1015_axes_0, beta = encoder_layers_19_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_19_norm_feed_forward1_weight_to_fp16, x = input_1013_cast_fp16)[name = string("input_1015_cast_fp16")]; + tensor encoder_layers_19_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(383242560))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(387436928))))[name = string("encoder_layers_19_feed_forward1_linear1_weight_to_fp16_quantized")]; + tensor encoder_layers_19_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_19_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(387445184)))]; + tensor linear_172_cast_fp16 = linear(bias = encoder_layers_19_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_19_feed_forward1_linear1_weight_to_fp16_quantized, x = input_1015_cast_fp16)[name = string("linear_172_cast_fp16")]; + tensor input_1019_cast_fp16 = silu(x = linear_172_cast_fp16)[name = string("input_1019_cast_fp16")]; + tensor encoder_layers_19_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(387453440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(391647808))))[name = string("encoder_layers_19_feed_forward1_linear2_weight_to_fp16_quantized")]; + tensor encoder_layers_19_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_19_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(391649920)))]; + tensor linear_173_cast_fp16 = linear(bias = encoder_layers_19_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_19_feed_forward1_linear2_weight_to_fp16_quantized, x = input_1019_cast_fp16)[name = string("linear_173_cast_fp16")]; + fp16 var_4502_to_fp16 = const()[name = string("op_4502_to_fp16"), val = fp16(0x1p-1)]; + tensor var_4503_cast_fp16 = mul(x = linear_173_cast_fp16, y = var_4502_to_fp16)[name = string("op_4503_cast_fp16")]; + tensor input_1025_cast_fp16 = add(x = input_1013_cast_fp16, y = var_4503_cast_fp16)[name = string("input_1025_cast_fp16")]; + tensor key_39_axes_0 = const()[name = string("key_39_axes_0"), val = tensor([-1])]; + tensor encoder_layers_19_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_19_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(391652032)))]; + tensor encoder_layers_19_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_19_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(391654144)))]; + tensor key_39_cast_fp16 = layer_norm(axes = key_39_axes_0, beta = encoder_layers_19_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_19_norm_self_att_weight_to_fp16, x = input_1025_cast_fp16)[name = string("key_39_cast_fp16")]; + bool input_1027_interleave_0 = const()[name = string("input_1027_interleave_0"), val = bool(false)]; + tensor input_1027_cast_fp16 = concat(axis = var_68, interleave = input_1027_interleave_0, values = (cache_77_cast_fp16, key_39_cast_fp16))[name = string("input_1027_cast_fp16")]; + tensor var_4525_begin_0 = const()[name = string("op_4525_begin_0"), val = tensor([0, 28, 0])]; + tensor var_4525_end_0 = const()[name = string("op_4525_end_0"), val = tensor([1, 42, 1024])]; + tensor var_4525_end_mask_0 = const()[name = string("op_4525_end_mask_0"), val = tensor([true, true, true])]; + tensor var_4525_cast_fp16 = slice_by_index(begin = var_4525_begin_0, end = var_4525_end_0, end_mask = var_4525_end_mask_0, x = cache_77_cast_fp16)[name = string("op_4525_cast_fp16")]; + bool var_4531_interleave_0 = const()[name = string("op_4531_interleave_0"), val = bool(false)]; + tensor var_4531_cast_fp16 = concat(axis = var_68, interleave = var_4531_interleave_0, values = (var_4525_cast_fp16, key_39_cast_fp16))[name = string("op_4531_cast_fp16")]; + tensor encoder_layers_19_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(391656256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(392704896))))[name = string("encoder_layers_19_self_attn_linear_q_weight_to_fp16_quantized")]; + tensor encoder_layers_19_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_19_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(392707008)))]; + tensor linear_174_cast_fp16 = linear(bias = encoder_layers_19_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_19_self_attn_linear_q_weight_to_fp16_quantized, x = key_39_cast_fp16)[name = string("linear_174_cast_fp16")]; + tensor var_4536 = const()[name = string("op_4536"), val = tensor([1, -1, 8, 128])]; + tensor q_115_cast_fp16 = reshape(shape = var_4536, x = linear_174_cast_fp16)[name = string("q_115_cast_fp16")]; + tensor encoder_layers_19_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(392709120))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(393757760))))[name = string("encoder_layers_19_self_attn_linear_k_weight_to_fp16_quantized")]; + tensor encoder_layers_19_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_19_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(393759872)))]; + tensor linear_175_cast_fp16 = linear(bias = encoder_layers_19_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_19_self_attn_linear_k_weight_to_fp16_quantized, x = input_1027_cast_fp16)[name = string("linear_175_cast_fp16")]; + tensor var_4541 = const()[name = string("op_4541"), val = tensor([1, -1, 8, 128])]; + tensor k_77_cast_fp16 = reshape(shape = var_4541, x = linear_175_cast_fp16)[name = string("k_77_cast_fp16")]; + tensor encoder_layers_19_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(393761984))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(394810624))))[name = string("encoder_layers_19_self_attn_linear_v_weight_to_fp16_quantized")]; + tensor encoder_layers_19_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_19_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(394812736)))]; + tensor linear_176_cast_fp16 = linear(bias = encoder_layers_19_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_19_self_attn_linear_v_weight_to_fp16_quantized, x = input_1027_cast_fp16)[name = string("linear_176_cast_fp16")]; + tensor var_4546 = const()[name = string("op_4546"), val = tensor([1, -1, 8, 128])]; + tensor v_39_cast_fp16 = reshape(shape = var_4546, x = linear_176_cast_fp16)[name = string("v_39_cast_fp16")]; + tensor value_47_perm_0 = const()[name = string("value_47_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_19_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_19_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(394814848)))]; + tensor var_4559_cast_fp16 = add(x = q_115_cast_fp16, y = encoder_layers_19_self_attn_pos_bias_u_to_fp16)[name = string("op_4559_cast_fp16")]; + tensor encoder_layers_19_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_19_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(394816960)))]; + tensor var_4561_cast_fp16 = add(x = q_115_cast_fp16, y = encoder_layers_19_self_attn_pos_bias_v_to_fp16)[name = string("op_4561_cast_fp16")]; + tensor q_with_bias_v_39_perm_0 = const()[name = string("q_with_bias_v_39_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_501_transpose_x_0 = const()[name = string("x_501_transpose_x_0"), val = bool(false)]; + bool x_501_transpose_y_0 = const()[name = string("x_501_transpose_y_0"), val = bool(false)]; + tensor op_4563_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(394819072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(394961472))))[name = string("op_4563_to_fp16_quantized")]; + tensor q_with_bias_v_39_cast_fp16 = transpose(perm = q_with_bias_v_39_perm_0, x = var_4561_cast_fp16)[name = string("transpose_191")]; + tensor x_501_cast_fp16 = matmul(transpose_x = x_501_transpose_x_0, transpose_y = x_501_transpose_y_0, x = q_with_bias_v_39_cast_fp16, y = op_4563_to_fp16_quantized)[name = string("x_501_cast_fp16")]; + tensor x_503_pad_0 = const()[name = string("x_503_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_503_mode_0 = const()[name = string("x_503_mode_0"), val = string("constant")]; + fp16 const_326_to_fp16 = const()[name = string("const_326_to_fp16"), val = fp16(0x0p+0)]; + tensor x_503_cast_fp16 = pad(constant_val = const_326_to_fp16, mode = x_503_mode_0, pad = x_503_pad_0, x = x_501_cast_fp16)[name = string("x_503_cast_fp16")]; + tensor var_4571 = const()[name = string("op_4571"), val = tensor([1, 8, -1, 28])]; + tensor x_505_cast_fp16 = reshape(shape = var_4571, x = x_503_cast_fp16)[name = string("x_505_cast_fp16")]; + tensor var_4575_begin_0 = const()[name = string("op_4575_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_4575_end_0 = const()[name = string("op_4575_end_0"), val = tensor([1, 8, 140, 28])]; + tensor var_4575_end_mask_0 = const()[name = string("op_4575_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_4575_cast_fp16 = slice_by_index(begin = var_4575_begin_0, end = var_4575_end_0, end_mask = var_4575_end_mask_0, x = x_505_cast_fp16)[name = string("op_4575_cast_fp16")]; + tensor var_4576 = const()[name = string("op_4576"), val = tensor([1, 8, 28, 139])]; + tensor matrix_bd_77_cast_fp16 = reshape(shape = var_4576, x = var_4575_cast_fp16)[name = string("matrix_bd_77_cast_fp16")]; + bool matrix_ac_39_transpose_x_0 = const()[name = string("matrix_ac_39_transpose_x_0"), val = bool(false)]; + bool matrix_ac_39_transpose_y_0 = const()[name = string("matrix_ac_39_transpose_y_0"), val = bool(false)]; + tensor transpose_134_perm_0 = const()[name = string("transpose_134_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_135_perm_0 = const()[name = string("transpose_135_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_135 = transpose(perm = transpose_135_perm_0, x = k_77_cast_fp16)[name = string("transpose_189")]; + tensor transpose_134 = transpose(perm = transpose_134_perm_0, x = var_4559_cast_fp16)[name = string("transpose_190")]; + tensor matrix_ac_39_cast_fp16 = matmul(transpose_x = matrix_ac_39_transpose_x_0, transpose_y = matrix_ac_39_transpose_y_0, x = transpose_134, y = transpose_135)[name = string("matrix_ac_39_cast_fp16")]; + tensor matrix_bd_79_begin_0 = const()[name = string("matrix_bd_79_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_79_end_0 = const()[name = string("matrix_bd_79_end_0"), val = tensor([1, 8, 28, 70])]; + tensor matrix_bd_79_end_mask_0 = const()[name = string("matrix_bd_79_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_79_cast_fp16 = slice_by_index(begin = matrix_bd_79_begin_0, end = matrix_bd_79_end_0, end_mask = matrix_bd_79_end_mask_0, x = matrix_bd_77_cast_fp16)[name = string("matrix_bd_79_cast_fp16")]; + tensor var_4585_cast_fp16 = add(x = matrix_ac_39_cast_fp16, y = matrix_bd_79_cast_fp16)[name = string("op_4585_cast_fp16")]; + fp16 _inversed_scores_77_y_0_to_fp16 = const()[name = string("_inversed_scores_77_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_77_cast_fp16 = mul(x = var_4585_cast_fp16, y = _inversed_scores_77_y_0_to_fp16)[name = string("_inversed_scores_77_cast_fp16")]; + tensor scores_79_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_77_cast_fp16, cond = mask_11)[name = string("scores_79_cast_fp16")]; + tensor var_4591_cast_fp16 = softmax(axis = var_59, x = scores_79_cast_fp16)[name = string("op_4591_cast_fp16")]; + tensor input_1029_cast_fp16 = select(a = var_44_to_fp16, b = var_4591_cast_fp16, cond = mask_11)[name = string("input_1029_cast_fp16")]; + bool x_507_transpose_x_0 = const()[name = string("x_507_transpose_x_0"), val = bool(false)]; + bool x_507_transpose_y_0 = const()[name = string("x_507_transpose_y_0"), val = bool(false)]; + tensor value_47_cast_fp16 = transpose(perm = value_47_perm_0, x = v_39_cast_fp16)[name = string("transpose_188")]; + tensor x_507_cast_fp16 = matmul(transpose_x = x_507_transpose_x_0, transpose_y = x_507_transpose_y_0, x = input_1029_cast_fp16, y = value_47_cast_fp16)[name = string("x_507_cast_fp16")]; + tensor var_4595_perm_0 = const()[name = string("op_4595_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_4596 = const()[name = string("op_4596"), val = tensor([1, -1, 1024])]; + tensor var_4595_cast_fp16 = transpose(perm = var_4595_perm_0, x = x_507_cast_fp16)[name = string("transpose_187")]; + tensor input_1031_cast_fp16 = reshape(shape = var_4596, x = var_4595_cast_fp16)[name = string("input_1031_cast_fp16")]; + tensor encoder_layers_19_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(394961856))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(396010496))))[name = string("encoder_layers_19_self_attn_linear_out_weight_to_fp16_quantized")]; + tensor encoder_layers_19_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_19_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(396012608)))]; + tensor linear_178_cast_fp16 = linear(bias = encoder_layers_19_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_19_self_attn_linear_out_weight_to_fp16_quantized, x = input_1031_cast_fp16)[name = string("linear_178_cast_fp16")]; + tensor input_1035_cast_fp16 = add(x = input_1025_cast_fp16, y = linear_178_cast_fp16)[name = string("input_1035_cast_fp16")]; + tensor x_511_axes_0 = const()[name = string("x_511_axes_0"), val = tensor([-1])]; + tensor encoder_layers_19_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_19_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(396014720)))]; + tensor encoder_layers_19_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_19_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(396016832)))]; + tensor x_511_cast_fp16 = layer_norm(axes = x_511_axes_0, beta = encoder_layers_19_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_19_norm_conv_weight_to_fp16, x = input_1035_cast_fp16)[name = string("x_511_cast_fp16")]; + tensor input_1037_perm_0 = const()[name = string("input_1037_perm_0"), val = tensor([0, 2, 1])]; + string input_1039_pad_type_0 = const()[name = string("input_1039_pad_type_0"), val = string("valid")]; + tensor input_1039_strides_0 = const()[name = string("input_1039_strides_0"), val = tensor([1])]; + tensor input_1039_pad_0 = const()[name = string("input_1039_pad_0"), val = tensor([0, 0])]; + tensor input_1039_dilations_0 = const()[name = string("input_1039_dilations_0"), val = tensor([1])]; + int32 input_1039_groups_0 = const()[name = string("input_1039_groups_0"), val = int32(1)]; + tensor encoder_layers_19_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(396018944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(398116160))))[name = string("encoder_layers_19_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_1037_cast_fp16 = transpose(perm = input_1037_perm_0, x = x_511_cast_fp16)[name = string("transpose_186")]; + tensor input_1039_cast_fp16 = conv(dilations = input_1039_dilations_0, groups = input_1039_groups_0, pad = input_1039_pad_0, pad_type = input_1039_pad_type_0, strides = input_1039_strides_0, weight = encoder_layers_19_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1037_cast_fp16)[name = string("input_1039_cast_fp16")]; + int32 x_513_split_num_splits_0 = const()[name = string("x_513_split_num_splits_0"), val = int32(2)]; + int32 x_513_split_axis_0 = const()[name = string("x_513_split_axis_0"), val = int32(1)]; + tensor x_513_split_cast_fp16_0, tensor x_513_split_cast_fp16_1 = split(axis = x_513_split_axis_0, num_splits = x_513_split_num_splits_0, x = input_1039_cast_fp16)[name = string("x_513_split_cast_fp16")]; + tensor x_513_split_1_sigmoid_cast_fp16 = sigmoid(x = x_513_split_cast_fp16_1)[name = string("x_513_split_1_sigmoid_cast_fp16")]; + tensor x_513_cast_fp16 = mul(x = x_513_split_cast_fp16_0, y = x_513_split_1_sigmoid_cast_fp16)[name = string("x_513_cast_fp16")]; + tensor input_1041_cast_fp16 = select(a = var_44_to_fp16, b = x_513_cast_fp16, cond = var_575)[name = string("input_1041_cast_fp16")]; + bool new_x_79_interleave_0 = const()[name = string("new_x_79_interleave_0"), val = bool(false)]; + tensor new_x_79_cast_fp16 = concat(axis = var_59, interleave = new_x_79_interleave_0, values = (cache_79_cast_fp16, input_1041_cast_fp16))[name = string("new_x_79_cast_fp16")]; + tensor var_4635_begin_0 = const()[name = string("op_4635_begin_0"), val = tensor([0, 0, 28])]; + tensor var_4635_end_0 = const()[name = string("op_4635_end_0"), val = tensor([1, 1024, 36])]; + tensor var_4635_end_mask_0 = const()[name = string("op_4635_end_mask_0"), val = tensor([true, true, true])]; + tensor var_4635_cast_fp16 = slice_by_index(begin = var_4635_begin_0, end = var_4635_end_0, end_mask = var_4635_end_mask_0, x = new_x_79_cast_fp16)[name = string("op_4635_cast_fp16")]; + string x_515_pad_type_0 = const()[name = string("x_515_pad_type_0"), val = string("valid")]; + int32 x_515_groups_0 = const()[name = string("x_515_groups_0"), val = int32(1024)]; + tensor x_515_strides_0 = const()[name = string("x_515_strides_0"), val = tensor([1])]; + tensor x_515_pad_0 = const()[name = string("x_515_pad_0"), val = tensor([0, 0])]; + tensor x_515_dilations_0 = const()[name = string("x_515_dilations_0"), val = tensor([1])]; + tensor encoder_layers_19_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(398120320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(398129600))))[name = string("encoder_layers_19_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_515_cast_fp16 = conv(dilations = x_515_dilations_0, groups = x_515_groups_0, pad = x_515_pad_0, pad_type = x_515_pad_type_0, strides = x_515_strides_0, weight = encoder_layers_19_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_79_cast_fp16)[name = string("x_515_cast_fp16")]; + tensor input_1043_perm_0 = const()[name = string("input_1043_perm_0"), val = tensor([0, 2, 1])]; + tensor x_517_axes_0 = const()[name = string("x_517_axes_0"), val = tensor([-1])]; + tensor encoder_layers_19_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_19_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(398131712)))]; + tensor encoder_layers_19_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_19_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(398133824)))]; + tensor input_1043_cast_fp16 = transpose(perm = input_1043_perm_0, x = x_515_cast_fp16)[name = string("transpose_185")]; + tensor x_517_cast_fp16 = layer_norm(axes = x_517_axes_0, beta = encoder_layers_19_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_19_conv_batch_norm_weight_to_fp16, x = input_1043_cast_fp16)[name = string("x_517_cast_fp16")]; + tensor input_1045_perm_0 = const()[name = string("input_1045_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1045_cast_fp16 = transpose(perm = input_1045_perm_0, x = x_517_cast_fp16)[name = string("transpose_184")]; + tensor input_1047_cast_fp16 = silu(x = input_1045_cast_fp16)[name = string("input_1047_cast_fp16")]; + string x_519_pad_type_0 = const()[name = string("x_519_pad_type_0"), val = string("valid")]; + tensor x_519_strides_0 = const()[name = string("x_519_strides_0"), val = tensor([1])]; + tensor x_519_pad_0 = const()[name = string("x_519_pad_0"), val = tensor([0, 0])]; + tensor x_519_dilations_0 = const()[name = string("x_519_dilations_0"), val = tensor([1])]; + int32 x_519_groups_0 = const()[name = string("x_519_groups_0"), val = int32(1)]; + tensor encoder_layers_19_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(398135936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(399184576))))[name = string("encoder_layers_19_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_519_cast_fp16 = conv(dilations = x_519_dilations_0, groups = x_519_groups_0, pad = x_519_pad_0, pad_type = x_519_pad_type_0, strides = x_519_strides_0, weight = encoder_layers_19_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1047_cast_fp16)[name = string("x_519_cast_fp16")]; + tensor input_1049_perm_0 = const()[name = string("input_1049_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1049_cast_fp16 = transpose(perm = input_1049_perm_0, x = x_519_cast_fp16)[name = string("transpose_183")]; + tensor input_1051_cast_fp16 = add(x = input_1035_cast_fp16, y = input_1049_cast_fp16)[name = string("input_1051_cast_fp16")]; + tensor input_1053_axes_0 = const()[name = string("input_1053_axes_0"), val = tensor([-1])]; + tensor encoder_layers_19_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_19_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(399186688)))]; + tensor encoder_layers_19_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_19_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(399188800)))]; + tensor input_1053_cast_fp16 = layer_norm(axes = input_1053_axes_0, beta = encoder_layers_19_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_19_norm_feed_forward2_weight_to_fp16, x = input_1051_cast_fp16)[name = string("input_1053_cast_fp16")]; + tensor encoder_layers_19_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(399190912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(403385280))))[name = string("encoder_layers_19_feed_forward2_linear1_weight_to_fp16_quantized")]; + tensor encoder_layers_19_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_19_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(403393536)))]; + tensor linear_179_cast_fp16 = linear(bias = encoder_layers_19_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_19_feed_forward2_linear1_weight_to_fp16_quantized, x = input_1053_cast_fp16)[name = string("linear_179_cast_fp16")]; + tensor input_1057_cast_fp16 = silu(x = linear_179_cast_fp16)[name = string("input_1057_cast_fp16")]; + tensor encoder_layers_19_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(403401792))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(407596160))))[name = string("encoder_layers_19_feed_forward2_linear2_weight_to_fp16_quantized")]; + tensor encoder_layers_19_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_19_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(407598272)))]; + tensor linear_180_cast_fp16 = linear(bias = encoder_layers_19_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_19_feed_forward2_linear2_weight_to_fp16_quantized, x = input_1057_cast_fp16)[name = string("linear_180_cast_fp16")]; + fp16 var_4678_to_fp16 = const()[name = string("op_4678_to_fp16"), val = fp16(0x1p-1)]; + tensor var_4679_cast_fp16 = mul(x = linear_180_cast_fp16, y = var_4678_to_fp16)[name = string("op_4679_cast_fp16")]; + tensor input_1063_cast_fp16 = add(x = input_1051_cast_fp16, y = var_4679_cast_fp16)[name = string("input_1063_cast_fp16")]; + tensor input_1065_axes_0 = const()[name = string("input_1065_axes_0"), val = tensor([-1])]; + tensor encoder_layers_19_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_19_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(407600384)))]; + tensor encoder_layers_19_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_19_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(407602496)))]; + tensor input_1065_cast_fp16 = layer_norm(axes = input_1065_axes_0, beta = encoder_layers_19_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_19_norm_out_weight_to_fp16, x = input_1063_cast_fp16)[name = string("input_1065_cast_fp16")]; + tensor cache_81_begin_0 = const()[name = string("cache_81_begin_0"), val = tensor([20, 0, 0, 0])]; + tensor cache_81_end_0 = const()[name = string("cache_81_end_0"), val = tensor([21, 1, 42, 1024])]; + tensor cache_81_end_mask_0 = const()[name = string("cache_81_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_81_squeeze_mask_0 = const()[name = string("cache_81_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_81_cast_fp16 = slice_by_index(begin = cache_81_begin_0, end = cache_81_end_0, end_mask = cache_81_end_mask_0, squeeze_mask = cache_81_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_81_cast_fp16")]; + tensor cache_83_begin_0 = const()[name = string("cache_83_begin_0"), val = tensor([20, 0, 0, 0])]; + tensor cache_83_end_0 = const()[name = string("cache_83_end_0"), val = tensor([21, 1, 1024, 8])]; + tensor cache_83_end_mask_0 = const()[name = string("cache_83_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_83_squeeze_mask_0 = const()[name = string("cache_83_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_83_cast_fp16 = slice_by_index(begin = cache_83_begin_0, end = cache_83_end_0, end_mask = cache_83_end_mask_0, squeeze_mask = cache_83_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_83_cast_fp16")]; + tensor input_1067_axes_0 = const()[name = string("input_1067_axes_0"), val = tensor([-1])]; + tensor encoder_layers_20_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_20_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(407604608)))]; + tensor encoder_layers_20_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_20_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(407606720)))]; + tensor input_1067_cast_fp16 = layer_norm(axes = input_1067_axes_0, beta = encoder_layers_20_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_20_norm_feed_forward1_weight_to_fp16, x = input_1065_cast_fp16)[name = string("input_1067_cast_fp16")]; + tensor encoder_layers_20_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(407608832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(411803200))))[name = string("encoder_layers_20_feed_forward1_linear1_weight_to_fp16_quantized")]; + tensor encoder_layers_20_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_20_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(411811456)))]; + tensor linear_181_cast_fp16 = linear(bias = encoder_layers_20_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_20_feed_forward1_linear1_weight_to_fp16_quantized, x = input_1067_cast_fp16)[name = string("linear_181_cast_fp16")]; + tensor input_1071_cast_fp16 = silu(x = linear_181_cast_fp16)[name = string("input_1071_cast_fp16")]; + tensor encoder_layers_20_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(411819712))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(416014080))))[name = string("encoder_layers_20_feed_forward1_linear2_weight_to_fp16_quantized")]; + tensor encoder_layers_20_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_20_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(416016192)))]; + tensor linear_182_cast_fp16 = linear(bias = encoder_layers_20_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_20_feed_forward1_linear2_weight_to_fp16_quantized, x = input_1071_cast_fp16)[name = string("linear_182_cast_fp16")]; + fp16 var_4715_to_fp16 = const()[name = string("op_4715_to_fp16"), val = fp16(0x1p-1)]; + tensor var_4716_cast_fp16 = mul(x = linear_182_cast_fp16, y = var_4715_to_fp16)[name = string("op_4716_cast_fp16")]; + tensor input_1077_cast_fp16 = add(x = input_1065_cast_fp16, y = var_4716_cast_fp16)[name = string("input_1077_cast_fp16")]; + tensor key_41_axes_0 = const()[name = string("key_41_axes_0"), val = tensor([-1])]; + tensor encoder_layers_20_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_20_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(416018304)))]; + tensor encoder_layers_20_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_20_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(416020416)))]; + tensor key_41_cast_fp16 = layer_norm(axes = key_41_axes_0, beta = encoder_layers_20_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_20_norm_self_att_weight_to_fp16, x = input_1077_cast_fp16)[name = string("key_41_cast_fp16")]; + bool input_1079_interleave_0 = const()[name = string("input_1079_interleave_0"), val = bool(false)]; + tensor input_1079_cast_fp16 = concat(axis = var_68, interleave = input_1079_interleave_0, values = (cache_81_cast_fp16, key_41_cast_fp16))[name = string("input_1079_cast_fp16")]; + tensor var_4738_begin_0 = const()[name = string("op_4738_begin_0"), val = tensor([0, 28, 0])]; + tensor var_4738_end_0 = const()[name = string("op_4738_end_0"), val = tensor([1, 42, 1024])]; + tensor var_4738_end_mask_0 = const()[name = string("op_4738_end_mask_0"), val = tensor([true, true, true])]; + tensor var_4738_cast_fp16 = slice_by_index(begin = var_4738_begin_0, end = var_4738_end_0, end_mask = var_4738_end_mask_0, x = cache_81_cast_fp16)[name = string("op_4738_cast_fp16")]; + bool var_4744_interleave_0 = const()[name = string("op_4744_interleave_0"), val = bool(false)]; + tensor var_4744_cast_fp16 = concat(axis = var_68, interleave = var_4744_interleave_0, values = (var_4738_cast_fp16, key_41_cast_fp16))[name = string("op_4744_cast_fp16")]; + tensor encoder_layers_20_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(416022528))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(417071168))))[name = string("encoder_layers_20_self_attn_linear_q_weight_to_fp16_quantized")]; + tensor encoder_layers_20_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_20_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(417073280)))]; + tensor linear_183_cast_fp16 = linear(bias = encoder_layers_20_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_20_self_attn_linear_q_weight_to_fp16_quantized, x = key_41_cast_fp16)[name = string("linear_183_cast_fp16")]; + tensor var_4749 = const()[name = string("op_4749"), val = tensor([1, -1, 8, 128])]; + tensor q_121_cast_fp16 = reshape(shape = var_4749, x = linear_183_cast_fp16)[name = string("q_121_cast_fp16")]; + tensor encoder_layers_20_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(417075392))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(418124032))))[name = string("encoder_layers_20_self_attn_linear_k_weight_to_fp16_quantized")]; + tensor encoder_layers_20_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_20_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(418126144)))]; + tensor linear_184_cast_fp16 = linear(bias = encoder_layers_20_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_20_self_attn_linear_k_weight_to_fp16_quantized, x = input_1079_cast_fp16)[name = string("linear_184_cast_fp16")]; + tensor var_4754 = const()[name = string("op_4754"), val = tensor([1, -1, 8, 128])]; + tensor k_81_cast_fp16 = reshape(shape = var_4754, x = linear_184_cast_fp16)[name = string("k_81_cast_fp16")]; + tensor encoder_layers_20_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(418128256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(419176896))))[name = string("encoder_layers_20_self_attn_linear_v_weight_to_fp16_quantized")]; + tensor encoder_layers_20_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_20_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(419179008)))]; + tensor linear_185_cast_fp16 = linear(bias = encoder_layers_20_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_20_self_attn_linear_v_weight_to_fp16_quantized, x = input_1079_cast_fp16)[name = string("linear_185_cast_fp16")]; + tensor var_4759 = const()[name = string("op_4759"), val = tensor([1, -1, 8, 128])]; + tensor v_41_cast_fp16 = reshape(shape = var_4759, x = linear_185_cast_fp16)[name = string("v_41_cast_fp16")]; + tensor value_49_perm_0 = const()[name = string("value_49_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_20_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_20_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(419181120)))]; + tensor var_4772_cast_fp16 = add(x = q_121_cast_fp16, y = encoder_layers_20_self_attn_pos_bias_u_to_fp16)[name = string("op_4772_cast_fp16")]; + tensor encoder_layers_20_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_20_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(419183232)))]; + tensor var_4774_cast_fp16 = add(x = q_121_cast_fp16, y = encoder_layers_20_self_attn_pos_bias_v_to_fp16)[name = string("op_4774_cast_fp16")]; + tensor q_with_bias_v_41_perm_0 = const()[name = string("q_with_bias_v_41_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_527_transpose_x_0 = const()[name = string("x_527_transpose_x_0"), val = bool(false)]; + bool x_527_transpose_y_0 = const()[name = string("x_527_transpose_y_0"), val = bool(false)]; + tensor op_4776_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(419185344))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(419327744))))[name = string("op_4776_to_fp16_quantized")]; + tensor q_with_bias_v_41_cast_fp16 = transpose(perm = q_with_bias_v_41_perm_0, x = var_4774_cast_fp16)[name = string("transpose_182")]; + tensor x_527_cast_fp16 = matmul(transpose_x = x_527_transpose_x_0, transpose_y = x_527_transpose_y_0, x = q_with_bias_v_41_cast_fp16, y = op_4776_to_fp16_quantized)[name = string("x_527_cast_fp16")]; + tensor x_529_pad_0 = const()[name = string("x_529_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_529_mode_0 = const()[name = string("x_529_mode_0"), val = string("constant")]; + fp16 const_339_to_fp16 = const()[name = string("const_339_to_fp16"), val = fp16(0x0p+0)]; + tensor x_529_cast_fp16 = pad(constant_val = const_339_to_fp16, mode = x_529_mode_0, pad = x_529_pad_0, x = x_527_cast_fp16)[name = string("x_529_cast_fp16")]; + tensor var_4784 = const()[name = string("op_4784"), val = tensor([1, 8, -1, 28])]; + tensor x_531_cast_fp16 = reshape(shape = var_4784, x = x_529_cast_fp16)[name = string("x_531_cast_fp16")]; + tensor var_4788_begin_0 = const()[name = string("op_4788_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_4788_end_0 = const()[name = string("op_4788_end_0"), val = tensor([1, 8, 140, 28])]; + tensor var_4788_end_mask_0 = const()[name = string("op_4788_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_4788_cast_fp16 = slice_by_index(begin = var_4788_begin_0, end = var_4788_end_0, end_mask = var_4788_end_mask_0, x = x_531_cast_fp16)[name = string("op_4788_cast_fp16")]; + tensor var_4789 = const()[name = string("op_4789"), val = tensor([1, 8, 28, 139])]; + tensor matrix_bd_81_cast_fp16 = reshape(shape = var_4789, x = var_4788_cast_fp16)[name = string("matrix_bd_81_cast_fp16")]; + bool matrix_ac_41_transpose_x_0 = const()[name = string("matrix_ac_41_transpose_x_0"), val = bool(false)]; + bool matrix_ac_41_transpose_y_0 = const()[name = string("matrix_ac_41_transpose_y_0"), val = bool(false)]; + tensor transpose_136_perm_0 = const()[name = string("transpose_136_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_137_perm_0 = const()[name = string("transpose_137_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_137 = transpose(perm = transpose_137_perm_0, x = k_81_cast_fp16)[name = string("transpose_180")]; + tensor transpose_136 = transpose(perm = transpose_136_perm_0, x = var_4772_cast_fp16)[name = string("transpose_181")]; + tensor matrix_ac_41_cast_fp16 = matmul(transpose_x = matrix_ac_41_transpose_x_0, transpose_y = matrix_ac_41_transpose_y_0, x = transpose_136, y = transpose_137)[name = string("matrix_ac_41_cast_fp16")]; + tensor matrix_bd_83_begin_0 = const()[name = string("matrix_bd_83_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_83_end_0 = const()[name = string("matrix_bd_83_end_0"), val = tensor([1, 8, 28, 70])]; + tensor matrix_bd_83_end_mask_0 = const()[name = string("matrix_bd_83_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_83_cast_fp16 = slice_by_index(begin = matrix_bd_83_begin_0, end = matrix_bd_83_end_0, end_mask = matrix_bd_83_end_mask_0, x = matrix_bd_81_cast_fp16)[name = string("matrix_bd_83_cast_fp16")]; + tensor var_4798_cast_fp16 = add(x = matrix_ac_41_cast_fp16, y = matrix_bd_83_cast_fp16)[name = string("op_4798_cast_fp16")]; + fp16 _inversed_scores_81_y_0_to_fp16 = const()[name = string("_inversed_scores_81_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_81_cast_fp16 = mul(x = var_4798_cast_fp16, y = _inversed_scores_81_y_0_to_fp16)[name = string("_inversed_scores_81_cast_fp16")]; + tensor scores_83_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_81_cast_fp16, cond = mask_11)[name = string("scores_83_cast_fp16")]; + tensor var_4804_cast_fp16 = softmax(axis = var_59, x = scores_83_cast_fp16)[name = string("op_4804_cast_fp16")]; + tensor input_1081_cast_fp16 = select(a = var_44_to_fp16, b = var_4804_cast_fp16, cond = mask_11)[name = string("input_1081_cast_fp16")]; + bool x_533_transpose_x_0 = const()[name = string("x_533_transpose_x_0"), val = bool(false)]; + bool x_533_transpose_y_0 = const()[name = string("x_533_transpose_y_0"), val = bool(false)]; + tensor value_49_cast_fp16 = transpose(perm = value_49_perm_0, x = v_41_cast_fp16)[name = string("transpose_179")]; + tensor x_533_cast_fp16 = matmul(transpose_x = x_533_transpose_x_0, transpose_y = x_533_transpose_y_0, x = input_1081_cast_fp16, y = value_49_cast_fp16)[name = string("x_533_cast_fp16")]; + tensor var_4808_perm_0 = const()[name = string("op_4808_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_4809 = const()[name = string("op_4809"), val = tensor([1, -1, 1024])]; + tensor var_4808_cast_fp16 = transpose(perm = var_4808_perm_0, x = x_533_cast_fp16)[name = string("transpose_178")]; + tensor input_1083_cast_fp16 = reshape(shape = var_4809, x = var_4808_cast_fp16)[name = string("input_1083_cast_fp16")]; + tensor encoder_layers_20_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(419328128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(420376768))))[name = string("encoder_layers_20_self_attn_linear_out_weight_to_fp16_quantized")]; + tensor encoder_layers_20_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_20_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(420378880)))]; + tensor linear_187_cast_fp16 = linear(bias = encoder_layers_20_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_20_self_attn_linear_out_weight_to_fp16_quantized, x = input_1083_cast_fp16)[name = string("linear_187_cast_fp16")]; + tensor input_1087_cast_fp16 = add(x = input_1077_cast_fp16, y = linear_187_cast_fp16)[name = string("input_1087_cast_fp16")]; + tensor x_537_axes_0 = const()[name = string("x_537_axes_0"), val = tensor([-1])]; + tensor encoder_layers_20_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_20_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(420380992)))]; + tensor encoder_layers_20_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_20_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(420383104)))]; + tensor x_537_cast_fp16 = layer_norm(axes = x_537_axes_0, beta = encoder_layers_20_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_20_norm_conv_weight_to_fp16, x = input_1087_cast_fp16)[name = string("x_537_cast_fp16")]; + tensor input_1089_perm_0 = const()[name = string("input_1089_perm_0"), val = tensor([0, 2, 1])]; + string input_1091_pad_type_0 = const()[name = string("input_1091_pad_type_0"), val = string("valid")]; + tensor input_1091_strides_0 = const()[name = string("input_1091_strides_0"), val = tensor([1])]; + tensor input_1091_pad_0 = const()[name = string("input_1091_pad_0"), val = tensor([0, 0])]; + tensor input_1091_dilations_0 = const()[name = string("input_1091_dilations_0"), val = tensor([1])]; + int32 input_1091_groups_0 = const()[name = string("input_1091_groups_0"), val = int32(1)]; + tensor encoder_layers_20_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(420385216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(422482432))))[name = string("encoder_layers_20_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_1089_cast_fp16 = transpose(perm = input_1089_perm_0, x = x_537_cast_fp16)[name = string("transpose_177")]; + tensor input_1091_cast_fp16 = conv(dilations = input_1091_dilations_0, groups = input_1091_groups_0, pad = input_1091_pad_0, pad_type = input_1091_pad_type_0, strides = input_1091_strides_0, weight = encoder_layers_20_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1089_cast_fp16)[name = string("input_1091_cast_fp16")]; + int32 x_539_split_num_splits_0 = const()[name = string("x_539_split_num_splits_0"), val = int32(2)]; + int32 x_539_split_axis_0 = const()[name = string("x_539_split_axis_0"), val = int32(1)]; + tensor x_539_split_cast_fp16_0, tensor x_539_split_cast_fp16_1 = split(axis = x_539_split_axis_0, num_splits = x_539_split_num_splits_0, x = input_1091_cast_fp16)[name = string("x_539_split_cast_fp16")]; + tensor x_539_split_1_sigmoid_cast_fp16 = sigmoid(x = x_539_split_cast_fp16_1)[name = string("x_539_split_1_sigmoid_cast_fp16")]; + tensor x_539_cast_fp16 = mul(x = x_539_split_cast_fp16_0, y = x_539_split_1_sigmoid_cast_fp16)[name = string("x_539_cast_fp16")]; + tensor input_1093_cast_fp16 = select(a = var_44_to_fp16, b = x_539_cast_fp16, cond = var_575)[name = string("input_1093_cast_fp16")]; + bool new_x_83_interleave_0 = const()[name = string("new_x_83_interleave_0"), val = bool(false)]; + tensor new_x_83_cast_fp16 = concat(axis = var_59, interleave = new_x_83_interleave_0, values = (cache_83_cast_fp16, input_1093_cast_fp16))[name = string("new_x_83_cast_fp16")]; + tensor var_4848_begin_0 = const()[name = string("op_4848_begin_0"), val = tensor([0, 0, 28])]; + tensor var_4848_end_0 = const()[name = string("op_4848_end_0"), val = tensor([1, 1024, 36])]; + tensor var_4848_end_mask_0 = const()[name = string("op_4848_end_mask_0"), val = tensor([true, true, true])]; + tensor var_4848_cast_fp16 = slice_by_index(begin = var_4848_begin_0, end = var_4848_end_0, end_mask = var_4848_end_mask_0, x = new_x_83_cast_fp16)[name = string("op_4848_cast_fp16")]; + string x_541_pad_type_0 = const()[name = string("x_541_pad_type_0"), val = string("valid")]; + int32 x_541_groups_0 = const()[name = string("x_541_groups_0"), val = int32(1024)]; + tensor x_541_strides_0 = const()[name = string("x_541_strides_0"), val = tensor([1])]; + tensor x_541_pad_0 = const()[name = string("x_541_pad_0"), val = tensor([0, 0])]; + tensor x_541_dilations_0 = const()[name = string("x_541_dilations_0"), val = tensor([1])]; + tensor encoder_layers_20_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(422486592))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(422495872))))[name = string("encoder_layers_20_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_541_cast_fp16 = conv(dilations = x_541_dilations_0, groups = x_541_groups_0, pad = x_541_pad_0, pad_type = x_541_pad_type_0, strides = x_541_strides_0, weight = encoder_layers_20_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_83_cast_fp16)[name = string("x_541_cast_fp16")]; + tensor input_1095_perm_0 = const()[name = string("input_1095_perm_0"), val = tensor([0, 2, 1])]; + tensor x_543_axes_0 = const()[name = string("x_543_axes_0"), val = tensor([-1])]; + tensor encoder_layers_20_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_20_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(422497984)))]; + tensor encoder_layers_20_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_20_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(422500096)))]; + tensor input_1095_cast_fp16 = transpose(perm = input_1095_perm_0, x = x_541_cast_fp16)[name = string("transpose_176")]; + tensor x_543_cast_fp16 = layer_norm(axes = x_543_axes_0, beta = encoder_layers_20_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_20_conv_batch_norm_weight_to_fp16, x = input_1095_cast_fp16)[name = string("x_543_cast_fp16")]; + tensor input_1097_perm_0 = const()[name = string("input_1097_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1097_cast_fp16 = transpose(perm = input_1097_perm_0, x = x_543_cast_fp16)[name = string("transpose_175")]; + tensor input_1099_cast_fp16 = silu(x = input_1097_cast_fp16)[name = string("input_1099_cast_fp16")]; + string x_545_pad_type_0 = const()[name = string("x_545_pad_type_0"), val = string("valid")]; + tensor x_545_strides_0 = const()[name = string("x_545_strides_0"), val = tensor([1])]; + tensor x_545_pad_0 = const()[name = string("x_545_pad_0"), val = tensor([0, 0])]; + tensor x_545_dilations_0 = const()[name = string("x_545_dilations_0"), val = tensor([1])]; + int32 x_545_groups_0 = const()[name = string("x_545_groups_0"), val = int32(1)]; + tensor encoder_layers_20_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(422502208))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(423550848))))[name = string("encoder_layers_20_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_545_cast_fp16 = conv(dilations = x_545_dilations_0, groups = x_545_groups_0, pad = x_545_pad_0, pad_type = x_545_pad_type_0, strides = x_545_strides_0, weight = encoder_layers_20_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1099_cast_fp16)[name = string("x_545_cast_fp16")]; + tensor input_1101_perm_0 = const()[name = string("input_1101_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1101_cast_fp16 = transpose(perm = input_1101_perm_0, x = x_545_cast_fp16)[name = string("transpose_174")]; + tensor input_1103_cast_fp16 = add(x = input_1087_cast_fp16, y = input_1101_cast_fp16)[name = string("input_1103_cast_fp16")]; + tensor input_1105_axes_0 = const()[name = string("input_1105_axes_0"), val = tensor([-1])]; + tensor encoder_layers_20_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_20_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(423552960)))]; + tensor encoder_layers_20_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_20_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(423555072)))]; + tensor input_1105_cast_fp16 = layer_norm(axes = input_1105_axes_0, beta = encoder_layers_20_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_20_norm_feed_forward2_weight_to_fp16, x = input_1103_cast_fp16)[name = string("input_1105_cast_fp16")]; + tensor encoder_layers_20_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(423557184))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(427751552))))[name = string("encoder_layers_20_feed_forward2_linear1_weight_to_fp16_quantized")]; + tensor encoder_layers_20_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_20_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(427759808)))]; + tensor linear_188_cast_fp16 = linear(bias = encoder_layers_20_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_20_feed_forward2_linear1_weight_to_fp16_quantized, x = input_1105_cast_fp16)[name = string("linear_188_cast_fp16")]; + tensor input_1109_cast_fp16 = silu(x = linear_188_cast_fp16)[name = string("input_1109_cast_fp16")]; + tensor encoder_layers_20_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(427768064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(431962432))))[name = string("encoder_layers_20_feed_forward2_linear2_weight_to_fp16_quantized")]; + tensor encoder_layers_20_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_20_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(431964544)))]; + tensor linear_189_cast_fp16 = linear(bias = encoder_layers_20_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_20_feed_forward2_linear2_weight_to_fp16_quantized, x = input_1109_cast_fp16)[name = string("linear_189_cast_fp16")]; + fp16 var_4891_to_fp16 = const()[name = string("op_4891_to_fp16"), val = fp16(0x1p-1)]; + tensor var_4892_cast_fp16 = mul(x = linear_189_cast_fp16, y = var_4891_to_fp16)[name = string("op_4892_cast_fp16")]; + tensor input_1115_cast_fp16 = add(x = input_1103_cast_fp16, y = var_4892_cast_fp16)[name = string("input_1115_cast_fp16")]; + tensor input_1117_axes_0 = const()[name = string("input_1117_axes_0"), val = tensor([-1])]; + tensor encoder_layers_20_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_20_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(431966656)))]; + tensor encoder_layers_20_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_20_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(431968768)))]; + tensor input_1117_cast_fp16 = layer_norm(axes = input_1117_axes_0, beta = encoder_layers_20_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_20_norm_out_weight_to_fp16, x = input_1115_cast_fp16)[name = string("input_1117_cast_fp16")]; + tensor cache_85_begin_0 = const()[name = string("cache_85_begin_0"), val = tensor([21, 0, 0, 0])]; + tensor cache_85_end_0 = const()[name = string("cache_85_end_0"), val = tensor([22, 1, 42, 1024])]; + tensor cache_85_end_mask_0 = const()[name = string("cache_85_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_85_squeeze_mask_0 = const()[name = string("cache_85_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_85_cast_fp16 = slice_by_index(begin = cache_85_begin_0, end = cache_85_end_0, end_mask = cache_85_end_mask_0, squeeze_mask = cache_85_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_85_cast_fp16")]; + tensor cache_87_begin_0 = const()[name = string("cache_87_begin_0"), val = tensor([21, 0, 0, 0])]; + tensor cache_87_end_0 = const()[name = string("cache_87_end_0"), val = tensor([22, 1, 1024, 8])]; + tensor cache_87_end_mask_0 = const()[name = string("cache_87_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_87_squeeze_mask_0 = const()[name = string("cache_87_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_87_cast_fp16 = slice_by_index(begin = cache_87_begin_0, end = cache_87_end_0, end_mask = cache_87_end_mask_0, squeeze_mask = cache_87_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_87_cast_fp16")]; + tensor input_1119_axes_0 = const()[name = string("input_1119_axes_0"), val = tensor([-1])]; + tensor encoder_layers_21_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_21_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(431970880)))]; + tensor encoder_layers_21_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_21_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(431972992)))]; + tensor input_1119_cast_fp16 = layer_norm(axes = input_1119_axes_0, beta = encoder_layers_21_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_21_norm_feed_forward1_weight_to_fp16, x = input_1117_cast_fp16)[name = string("input_1119_cast_fp16")]; + tensor encoder_layers_21_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(431975104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(436169472))))[name = string("encoder_layers_21_feed_forward1_linear1_weight_to_fp16_quantized")]; + tensor encoder_layers_21_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_21_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(436177728)))]; + tensor linear_190_cast_fp16 = linear(bias = encoder_layers_21_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_21_feed_forward1_linear1_weight_to_fp16_quantized, x = input_1119_cast_fp16)[name = string("linear_190_cast_fp16")]; + tensor input_1123_cast_fp16 = silu(x = linear_190_cast_fp16)[name = string("input_1123_cast_fp16")]; + tensor encoder_layers_21_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(436185984))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(440380352))))[name = string("encoder_layers_21_feed_forward1_linear2_weight_to_fp16_quantized")]; + tensor encoder_layers_21_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_21_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(440382464)))]; + tensor linear_191_cast_fp16 = linear(bias = encoder_layers_21_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_21_feed_forward1_linear2_weight_to_fp16_quantized, x = input_1123_cast_fp16)[name = string("linear_191_cast_fp16")]; + fp16 var_4928_to_fp16 = const()[name = string("op_4928_to_fp16"), val = fp16(0x1p-1)]; + tensor var_4929_cast_fp16 = mul(x = linear_191_cast_fp16, y = var_4928_to_fp16)[name = string("op_4929_cast_fp16")]; + tensor input_1129_cast_fp16 = add(x = input_1117_cast_fp16, y = var_4929_cast_fp16)[name = string("input_1129_cast_fp16")]; + tensor key_43_axes_0 = const()[name = string("key_43_axes_0"), val = tensor([-1])]; + tensor encoder_layers_21_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_21_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(440384576)))]; + tensor encoder_layers_21_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_21_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(440386688)))]; + tensor key_43_cast_fp16 = layer_norm(axes = key_43_axes_0, beta = encoder_layers_21_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_21_norm_self_att_weight_to_fp16, x = input_1129_cast_fp16)[name = string("key_43_cast_fp16")]; + bool input_1131_interleave_0 = const()[name = string("input_1131_interleave_0"), val = bool(false)]; + tensor input_1131_cast_fp16 = concat(axis = var_68, interleave = input_1131_interleave_0, values = (cache_85_cast_fp16, key_43_cast_fp16))[name = string("input_1131_cast_fp16")]; + tensor var_4951_begin_0 = const()[name = string("op_4951_begin_0"), val = tensor([0, 28, 0])]; + tensor var_4951_end_0 = const()[name = string("op_4951_end_0"), val = tensor([1, 42, 1024])]; + tensor var_4951_end_mask_0 = const()[name = string("op_4951_end_mask_0"), val = tensor([true, true, true])]; + tensor var_4951_cast_fp16 = slice_by_index(begin = var_4951_begin_0, end = var_4951_end_0, end_mask = var_4951_end_mask_0, x = cache_85_cast_fp16)[name = string("op_4951_cast_fp16")]; + bool var_4957_interleave_0 = const()[name = string("op_4957_interleave_0"), val = bool(false)]; + tensor var_4957_cast_fp16 = concat(axis = var_68, interleave = var_4957_interleave_0, values = (var_4951_cast_fp16, key_43_cast_fp16))[name = string("op_4957_cast_fp16")]; + tensor encoder_layers_21_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(440388800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(441437440))))[name = string("encoder_layers_21_self_attn_linear_q_weight_to_fp16_quantized")]; + tensor encoder_layers_21_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_21_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(441439552)))]; + tensor linear_192_cast_fp16 = linear(bias = encoder_layers_21_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_21_self_attn_linear_q_weight_to_fp16_quantized, x = key_43_cast_fp16)[name = string("linear_192_cast_fp16")]; + tensor var_4962 = const()[name = string("op_4962"), val = tensor([1, -1, 8, 128])]; + tensor q_127_cast_fp16 = reshape(shape = var_4962, x = linear_192_cast_fp16)[name = string("q_127_cast_fp16")]; + tensor encoder_layers_21_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(441441664))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(442490304))))[name = string("encoder_layers_21_self_attn_linear_k_weight_to_fp16_quantized")]; + tensor encoder_layers_21_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_21_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(442492416)))]; + tensor linear_193_cast_fp16 = linear(bias = encoder_layers_21_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_21_self_attn_linear_k_weight_to_fp16_quantized, x = input_1131_cast_fp16)[name = string("linear_193_cast_fp16")]; + tensor var_4967 = const()[name = string("op_4967"), val = tensor([1, -1, 8, 128])]; + tensor k_85_cast_fp16 = reshape(shape = var_4967, x = linear_193_cast_fp16)[name = string("k_85_cast_fp16")]; + tensor encoder_layers_21_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(442494528))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(443543168))))[name = string("encoder_layers_21_self_attn_linear_v_weight_to_fp16_quantized")]; + tensor encoder_layers_21_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_21_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(443545280)))]; + tensor linear_194_cast_fp16 = linear(bias = encoder_layers_21_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_21_self_attn_linear_v_weight_to_fp16_quantized, x = input_1131_cast_fp16)[name = string("linear_194_cast_fp16")]; + tensor var_4972 = const()[name = string("op_4972"), val = tensor([1, -1, 8, 128])]; + tensor v_43_cast_fp16 = reshape(shape = var_4972, x = linear_194_cast_fp16)[name = string("v_43_cast_fp16")]; + tensor value_51_perm_0 = const()[name = string("value_51_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_21_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_21_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(443547392)))]; + tensor var_4985_cast_fp16 = add(x = q_127_cast_fp16, y = encoder_layers_21_self_attn_pos_bias_u_to_fp16)[name = string("op_4985_cast_fp16")]; + tensor encoder_layers_21_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_21_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(443549504)))]; + tensor var_4987_cast_fp16 = add(x = q_127_cast_fp16, y = encoder_layers_21_self_attn_pos_bias_v_to_fp16)[name = string("op_4987_cast_fp16")]; + tensor q_with_bias_v_43_perm_0 = const()[name = string("q_with_bias_v_43_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_553_transpose_x_0 = const()[name = string("x_553_transpose_x_0"), val = bool(false)]; + bool x_553_transpose_y_0 = const()[name = string("x_553_transpose_y_0"), val = bool(false)]; + tensor op_4989_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(443551616))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(443694016))))[name = string("op_4989_to_fp16_quantized")]; + tensor q_with_bias_v_43_cast_fp16 = transpose(perm = q_with_bias_v_43_perm_0, x = var_4987_cast_fp16)[name = string("transpose_173")]; + tensor x_553_cast_fp16 = matmul(transpose_x = x_553_transpose_x_0, transpose_y = x_553_transpose_y_0, x = q_with_bias_v_43_cast_fp16, y = op_4989_to_fp16_quantized)[name = string("x_553_cast_fp16")]; + tensor x_555_pad_0 = const()[name = string("x_555_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_555_mode_0 = const()[name = string("x_555_mode_0"), val = string("constant")]; + fp16 const_352_to_fp16 = const()[name = string("const_352_to_fp16"), val = fp16(0x0p+0)]; + tensor x_555_cast_fp16 = pad(constant_val = const_352_to_fp16, mode = x_555_mode_0, pad = x_555_pad_0, x = x_553_cast_fp16)[name = string("x_555_cast_fp16")]; + tensor var_4997 = const()[name = string("op_4997"), val = tensor([1, 8, -1, 28])]; + tensor x_557_cast_fp16 = reshape(shape = var_4997, x = x_555_cast_fp16)[name = string("x_557_cast_fp16")]; + tensor var_5001_begin_0 = const()[name = string("op_5001_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_5001_end_0 = const()[name = string("op_5001_end_0"), val = tensor([1, 8, 140, 28])]; + tensor var_5001_end_mask_0 = const()[name = string("op_5001_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_5001_cast_fp16 = slice_by_index(begin = var_5001_begin_0, end = var_5001_end_0, end_mask = var_5001_end_mask_0, x = x_557_cast_fp16)[name = string("op_5001_cast_fp16")]; + tensor var_5002 = const()[name = string("op_5002"), val = tensor([1, 8, 28, 139])]; + tensor matrix_bd_85_cast_fp16 = reshape(shape = var_5002, x = var_5001_cast_fp16)[name = string("matrix_bd_85_cast_fp16")]; + bool matrix_ac_43_transpose_x_0 = const()[name = string("matrix_ac_43_transpose_x_0"), val = bool(false)]; + bool matrix_ac_43_transpose_y_0 = const()[name = string("matrix_ac_43_transpose_y_0"), val = bool(false)]; + tensor transpose_138_perm_0 = const()[name = string("transpose_138_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_139_perm_0 = const()[name = string("transpose_139_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_139 = transpose(perm = transpose_139_perm_0, x = k_85_cast_fp16)[name = string("transpose_171")]; + tensor transpose_138 = transpose(perm = transpose_138_perm_0, x = var_4985_cast_fp16)[name = string("transpose_172")]; + tensor matrix_ac_43_cast_fp16 = matmul(transpose_x = matrix_ac_43_transpose_x_0, transpose_y = matrix_ac_43_transpose_y_0, x = transpose_138, y = transpose_139)[name = string("matrix_ac_43_cast_fp16")]; + tensor matrix_bd_87_begin_0 = const()[name = string("matrix_bd_87_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_87_end_0 = const()[name = string("matrix_bd_87_end_0"), val = tensor([1, 8, 28, 70])]; + tensor matrix_bd_87_end_mask_0 = const()[name = string("matrix_bd_87_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_87_cast_fp16 = slice_by_index(begin = matrix_bd_87_begin_0, end = matrix_bd_87_end_0, end_mask = matrix_bd_87_end_mask_0, x = matrix_bd_85_cast_fp16)[name = string("matrix_bd_87_cast_fp16")]; + tensor var_5011_cast_fp16 = add(x = matrix_ac_43_cast_fp16, y = matrix_bd_87_cast_fp16)[name = string("op_5011_cast_fp16")]; + fp16 _inversed_scores_85_y_0_to_fp16 = const()[name = string("_inversed_scores_85_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_85_cast_fp16 = mul(x = var_5011_cast_fp16, y = _inversed_scores_85_y_0_to_fp16)[name = string("_inversed_scores_85_cast_fp16")]; + tensor scores_87_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_85_cast_fp16, cond = mask_11)[name = string("scores_87_cast_fp16")]; + tensor var_5017_cast_fp16 = softmax(axis = var_59, x = scores_87_cast_fp16)[name = string("op_5017_cast_fp16")]; + tensor input_1133_cast_fp16 = select(a = var_44_to_fp16, b = var_5017_cast_fp16, cond = mask_11)[name = string("input_1133_cast_fp16")]; + bool x_559_transpose_x_0 = const()[name = string("x_559_transpose_x_0"), val = bool(false)]; + bool x_559_transpose_y_0 = const()[name = string("x_559_transpose_y_0"), val = bool(false)]; + tensor value_51_cast_fp16 = transpose(perm = value_51_perm_0, x = v_43_cast_fp16)[name = string("transpose_170")]; + tensor x_559_cast_fp16 = matmul(transpose_x = x_559_transpose_x_0, transpose_y = x_559_transpose_y_0, x = input_1133_cast_fp16, y = value_51_cast_fp16)[name = string("x_559_cast_fp16")]; + tensor var_5021_perm_0 = const()[name = string("op_5021_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_5022 = const()[name = string("op_5022"), val = tensor([1, -1, 1024])]; + tensor var_5021_cast_fp16 = transpose(perm = var_5021_perm_0, x = x_559_cast_fp16)[name = string("transpose_169")]; + tensor input_1135_cast_fp16 = reshape(shape = var_5022, x = var_5021_cast_fp16)[name = string("input_1135_cast_fp16")]; + tensor encoder_layers_21_self_attn_linear_out_weight_to_fp16 = const()[name = string("encoder_layers_21_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(443694400)))]; + tensor encoder_layers_21_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_21_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(445791616)))]; + tensor linear_196_cast_fp16 = linear(bias = encoder_layers_21_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_21_self_attn_linear_out_weight_to_fp16, x = input_1135_cast_fp16)[name = string("linear_196_cast_fp16")]; + tensor input_1139_cast_fp16 = add(x = input_1129_cast_fp16, y = linear_196_cast_fp16)[name = string("input_1139_cast_fp16")]; + tensor x_563_axes_0 = const()[name = string("x_563_axes_0"), val = tensor([-1])]; + tensor encoder_layers_21_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_21_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(445793728)))]; + tensor encoder_layers_21_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_21_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(445795840)))]; + tensor x_563_cast_fp16 = layer_norm(axes = x_563_axes_0, beta = encoder_layers_21_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_21_norm_conv_weight_to_fp16, x = input_1139_cast_fp16)[name = string("x_563_cast_fp16")]; + tensor input_1141_perm_0 = const()[name = string("input_1141_perm_0"), val = tensor([0, 2, 1])]; + string input_1143_pad_type_0 = const()[name = string("input_1143_pad_type_0"), val = string("valid")]; + tensor input_1143_strides_0 = const()[name = string("input_1143_strides_0"), val = tensor([1])]; + tensor input_1143_pad_0 = const()[name = string("input_1143_pad_0"), val = tensor([0, 0])]; + tensor input_1143_dilations_0 = const()[name = string("input_1143_dilations_0"), val = tensor([1])]; + int32 input_1143_groups_0 = const()[name = string("input_1143_groups_0"), val = int32(1)]; + tensor encoder_layers_21_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(445797952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(447895168))))[name = string("encoder_layers_21_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_1141_cast_fp16 = transpose(perm = input_1141_perm_0, x = x_563_cast_fp16)[name = string("transpose_168")]; + tensor input_1143_cast_fp16 = conv(dilations = input_1143_dilations_0, groups = input_1143_groups_0, pad = input_1143_pad_0, pad_type = input_1143_pad_type_0, strides = input_1143_strides_0, weight = encoder_layers_21_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1141_cast_fp16)[name = string("input_1143_cast_fp16")]; + int32 x_565_split_num_splits_0 = const()[name = string("x_565_split_num_splits_0"), val = int32(2)]; + int32 x_565_split_axis_0 = const()[name = string("x_565_split_axis_0"), val = int32(1)]; + tensor x_565_split_cast_fp16_0, tensor x_565_split_cast_fp16_1 = split(axis = x_565_split_axis_0, num_splits = x_565_split_num_splits_0, x = input_1143_cast_fp16)[name = string("x_565_split_cast_fp16")]; + tensor x_565_split_1_sigmoid_cast_fp16 = sigmoid(x = x_565_split_cast_fp16_1)[name = string("x_565_split_1_sigmoid_cast_fp16")]; + tensor x_565_cast_fp16 = mul(x = x_565_split_cast_fp16_0, y = x_565_split_1_sigmoid_cast_fp16)[name = string("x_565_cast_fp16")]; + tensor input_1145_cast_fp16 = select(a = var_44_to_fp16, b = x_565_cast_fp16, cond = var_575)[name = string("input_1145_cast_fp16")]; + bool new_x_87_interleave_0 = const()[name = string("new_x_87_interleave_0"), val = bool(false)]; + tensor new_x_87_cast_fp16 = concat(axis = var_59, interleave = new_x_87_interleave_0, values = (cache_87_cast_fp16, input_1145_cast_fp16))[name = string("new_x_87_cast_fp16")]; + tensor var_5061_begin_0 = const()[name = string("op_5061_begin_0"), val = tensor([0, 0, 28])]; + tensor var_5061_end_0 = const()[name = string("op_5061_end_0"), val = tensor([1, 1024, 36])]; + tensor var_5061_end_mask_0 = const()[name = string("op_5061_end_mask_0"), val = tensor([true, true, true])]; + tensor var_5061_cast_fp16 = slice_by_index(begin = var_5061_begin_0, end = var_5061_end_0, end_mask = var_5061_end_mask_0, x = new_x_87_cast_fp16)[name = string("op_5061_cast_fp16")]; + string x_567_pad_type_0 = const()[name = string("x_567_pad_type_0"), val = string("valid")]; + int32 x_567_groups_0 = const()[name = string("x_567_groups_0"), val = int32(1024)]; + tensor x_567_strides_0 = const()[name = string("x_567_strides_0"), val = tensor([1])]; + tensor x_567_pad_0 = const()[name = string("x_567_pad_0"), val = tensor([0, 0])]; + tensor x_567_dilations_0 = const()[name = string("x_567_dilations_0"), val = tensor([1])]; + tensor encoder_layers_21_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(447899328))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(447908608))))[name = string("encoder_layers_21_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_567_cast_fp16 = conv(dilations = x_567_dilations_0, groups = x_567_groups_0, pad = x_567_pad_0, pad_type = x_567_pad_type_0, strides = x_567_strides_0, weight = encoder_layers_21_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_87_cast_fp16)[name = string("x_567_cast_fp16")]; + tensor input_1147_perm_0 = const()[name = string("input_1147_perm_0"), val = tensor([0, 2, 1])]; + tensor x_569_axes_0 = const()[name = string("x_569_axes_0"), val = tensor([-1])]; + tensor encoder_layers_21_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_21_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(447910720)))]; + tensor encoder_layers_21_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_21_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(447912832)))]; + tensor input_1147_cast_fp16 = transpose(perm = input_1147_perm_0, x = x_567_cast_fp16)[name = string("transpose_167")]; + tensor x_569_cast_fp16 = layer_norm(axes = x_569_axes_0, beta = encoder_layers_21_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_21_conv_batch_norm_weight_to_fp16, x = input_1147_cast_fp16)[name = string("x_569_cast_fp16")]; + tensor input_1149_perm_0 = const()[name = string("input_1149_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1149_cast_fp16 = transpose(perm = input_1149_perm_0, x = x_569_cast_fp16)[name = string("transpose_166")]; + tensor input_1151_cast_fp16 = silu(x = input_1149_cast_fp16)[name = string("input_1151_cast_fp16")]; + string x_571_pad_type_0 = const()[name = string("x_571_pad_type_0"), val = string("valid")]; + tensor x_571_strides_0 = const()[name = string("x_571_strides_0"), val = tensor([1])]; + tensor x_571_pad_0 = const()[name = string("x_571_pad_0"), val = tensor([0, 0])]; + tensor x_571_dilations_0 = const()[name = string("x_571_dilations_0"), val = tensor([1])]; + int32 x_571_groups_0 = const()[name = string("x_571_groups_0"), val = int32(1)]; + tensor encoder_layers_21_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(447914944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(448963584))))[name = string("encoder_layers_21_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_571_cast_fp16 = conv(dilations = x_571_dilations_0, groups = x_571_groups_0, pad = x_571_pad_0, pad_type = x_571_pad_type_0, strides = x_571_strides_0, weight = encoder_layers_21_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1151_cast_fp16)[name = string("x_571_cast_fp16")]; + tensor input_1153_perm_0 = const()[name = string("input_1153_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1153_cast_fp16 = transpose(perm = input_1153_perm_0, x = x_571_cast_fp16)[name = string("transpose_165")]; + tensor input_1155_cast_fp16 = add(x = input_1139_cast_fp16, y = input_1153_cast_fp16)[name = string("input_1155_cast_fp16")]; + tensor input_1157_axes_0 = const()[name = string("input_1157_axes_0"), val = tensor([-1])]; + tensor encoder_layers_21_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_21_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(448965696)))]; + tensor encoder_layers_21_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_21_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(448967808)))]; + tensor input_1157_cast_fp16 = layer_norm(axes = input_1157_axes_0, beta = encoder_layers_21_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_21_norm_feed_forward2_weight_to_fp16, x = input_1155_cast_fp16)[name = string("input_1157_cast_fp16")]; + tensor encoder_layers_21_feed_forward2_linear1_weight_to_fp16 = const()[name = string("encoder_layers_21_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(448969920)))]; + tensor encoder_layers_21_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_21_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(457358592)))]; + tensor linear_197_cast_fp16 = linear(bias = encoder_layers_21_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_21_feed_forward2_linear1_weight_to_fp16, x = input_1157_cast_fp16)[name = string("linear_197_cast_fp16")]; + tensor input_1161_cast_fp16 = silu(x = linear_197_cast_fp16)[name = string("input_1161_cast_fp16")]; + tensor encoder_layers_21_feed_forward2_linear2_weight_to_fp16 = const()[name = string("encoder_layers_21_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(457366848)))]; + tensor encoder_layers_21_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_21_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(465755520)))]; + tensor linear_198_cast_fp16 = linear(bias = encoder_layers_21_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_21_feed_forward2_linear2_weight_to_fp16, x = input_1161_cast_fp16)[name = string("linear_198_cast_fp16")]; + fp16 var_5104_to_fp16 = const()[name = string("op_5104_to_fp16"), val = fp16(0x1p-1)]; + tensor var_5105_cast_fp16 = mul(x = linear_198_cast_fp16, y = var_5104_to_fp16)[name = string("op_5105_cast_fp16")]; + tensor input_1167_cast_fp16 = add(x = input_1155_cast_fp16, y = var_5105_cast_fp16)[name = string("input_1167_cast_fp16")]; + tensor input_1169_axes_0 = const()[name = string("input_1169_axes_0"), val = tensor([-1])]; + tensor encoder_layers_21_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_21_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(465757632)))]; + tensor encoder_layers_21_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_21_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(465759744)))]; + tensor input_1169_cast_fp16 = layer_norm(axes = input_1169_axes_0, beta = encoder_layers_21_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_21_norm_out_weight_to_fp16, x = input_1167_cast_fp16)[name = string("input_1169_cast_fp16")]; + tensor cache_89_begin_0 = const()[name = string("cache_89_begin_0"), val = tensor([22, 0, 0, 0])]; + tensor cache_89_end_0 = const()[name = string("cache_89_end_0"), val = tensor([23, 1, 42, 1024])]; + tensor cache_89_end_mask_0 = const()[name = string("cache_89_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_89_squeeze_mask_0 = const()[name = string("cache_89_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_89_cast_fp16 = slice_by_index(begin = cache_89_begin_0, end = cache_89_end_0, end_mask = cache_89_end_mask_0, squeeze_mask = cache_89_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_89_cast_fp16")]; + tensor cache_91_begin_0 = const()[name = string("cache_91_begin_0"), val = tensor([22, 0, 0, 0])]; + tensor cache_91_end_0 = const()[name = string("cache_91_end_0"), val = tensor([23, 1, 1024, 8])]; + tensor cache_91_end_mask_0 = const()[name = string("cache_91_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_91_squeeze_mask_0 = const()[name = string("cache_91_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_91_cast_fp16 = slice_by_index(begin = cache_91_begin_0, end = cache_91_end_0, end_mask = cache_91_end_mask_0, squeeze_mask = cache_91_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_91_cast_fp16")]; + tensor input_1171_axes_0 = const()[name = string("input_1171_axes_0"), val = tensor([-1])]; + tensor encoder_layers_22_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_22_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(465761856)))]; + tensor encoder_layers_22_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_22_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(465763968)))]; + tensor input_1171_cast_fp16 = layer_norm(axes = input_1171_axes_0, beta = encoder_layers_22_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_22_norm_feed_forward1_weight_to_fp16, x = input_1169_cast_fp16)[name = string("input_1171_cast_fp16")]; + tensor encoder_layers_22_feed_forward1_linear1_weight_to_fp16 = const()[name = string("encoder_layers_22_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(465766080)))]; + tensor encoder_layers_22_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_22_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(474154752)))]; + tensor linear_199_cast_fp16 = linear(bias = encoder_layers_22_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_22_feed_forward1_linear1_weight_to_fp16, x = input_1171_cast_fp16)[name = string("linear_199_cast_fp16")]; + tensor input_1175_cast_fp16 = silu(x = linear_199_cast_fp16)[name = string("input_1175_cast_fp16")]; + tensor encoder_layers_22_feed_forward1_linear2_weight_to_fp16 = const()[name = string("encoder_layers_22_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(474163008)))]; + tensor encoder_layers_22_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_22_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(482551680)))]; + tensor linear_200_cast_fp16 = linear(bias = encoder_layers_22_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_22_feed_forward1_linear2_weight_to_fp16, x = input_1175_cast_fp16)[name = string("linear_200_cast_fp16")]; + fp16 var_5141_to_fp16 = const()[name = string("op_5141_to_fp16"), val = fp16(0x1p-1)]; + tensor var_5142_cast_fp16 = mul(x = linear_200_cast_fp16, y = var_5141_to_fp16)[name = string("op_5142_cast_fp16")]; + tensor input_1181_cast_fp16 = add(x = input_1169_cast_fp16, y = var_5142_cast_fp16)[name = string("input_1181_cast_fp16")]; + tensor key_45_axes_0 = const()[name = string("key_45_axes_0"), val = tensor([-1])]; + tensor encoder_layers_22_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_22_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(482553792)))]; + tensor encoder_layers_22_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_22_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(482555904)))]; + tensor key_45_cast_fp16 = layer_norm(axes = key_45_axes_0, beta = encoder_layers_22_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_22_norm_self_att_weight_to_fp16, x = input_1181_cast_fp16)[name = string("key_45_cast_fp16")]; + bool input_1183_interleave_0 = const()[name = string("input_1183_interleave_0"), val = bool(false)]; + tensor input_1183_cast_fp16 = concat(axis = var_68, interleave = input_1183_interleave_0, values = (cache_89_cast_fp16, key_45_cast_fp16))[name = string("input_1183_cast_fp16")]; + tensor var_5164_begin_0 = const()[name = string("op_5164_begin_0"), val = tensor([0, 28, 0])]; + tensor var_5164_end_0 = const()[name = string("op_5164_end_0"), val = tensor([1, 42, 1024])]; + tensor var_5164_end_mask_0 = const()[name = string("op_5164_end_mask_0"), val = tensor([true, true, true])]; + tensor var_5164_cast_fp16 = slice_by_index(begin = var_5164_begin_0, end = var_5164_end_0, end_mask = var_5164_end_mask_0, x = cache_89_cast_fp16)[name = string("op_5164_cast_fp16")]; + bool var_5170_interleave_0 = const()[name = string("op_5170_interleave_0"), val = bool(false)]; + tensor var_5170_cast_fp16 = concat(axis = var_68, interleave = var_5170_interleave_0, values = (var_5164_cast_fp16, key_45_cast_fp16))[name = string("op_5170_cast_fp16")]; + tensor encoder_layers_22_self_attn_linear_q_weight_to_fp16 = const()[name = string("encoder_layers_22_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(482558016)))]; + tensor encoder_layers_22_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_22_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(484655232)))]; + tensor linear_201_cast_fp16 = linear(bias = encoder_layers_22_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_22_self_attn_linear_q_weight_to_fp16, x = key_45_cast_fp16)[name = string("linear_201_cast_fp16")]; + tensor var_5175 = const()[name = string("op_5175"), val = tensor([1, -1, 8, 128])]; + tensor q_133_cast_fp16 = reshape(shape = var_5175, x = linear_201_cast_fp16)[name = string("q_133_cast_fp16")]; + tensor encoder_layers_22_self_attn_linear_k_weight_to_fp16 = const()[name = string("encoder_layers_22_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(484657344)))]; + tensor encoder_layers_22_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_22_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(486754560)))]; + tensor linear_202_cast_fp16 = linear(bias = encoder_layers_22_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_22_self_attn_linear_k_weight_to_fp16, x = input_1183_cast_fp16)[name = string("linear_202_cast_fp16")]; + tensor var_5180 = const()[name = string("op_5180"), val = tensor([1, -1, 8, 128])]; + tensor k_89_cast_fp16 = reshape(shape = var_5180, x = linear_202_cast_fp16)[name = string("k_89_cast_fp16")]; + tensor encoder_layers_22_self_attn_linear_v_weight_to_fp16 = const()[name = string("encoder_layers_22_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(486756672)))]; + tensor encoder_layers_22_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_22_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488853888)))]; + tensor linear_203_cast_fp16 = linear(bias = encoder_layers_22_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_22_self_attn_linear_v_weight_to_fp16, x = input_1183_cast_fp16)[name = string("linear_203_cast_fp16")]; + tensor var_5185 = const()[name = string("op_5185"), val = tensor([1, -1, 8, 128])]; + tensor v_45_cast_fp16 = reshape(shape = var_5185, x = linear_203_cast_fp16)[name = string("v_45_cast_fp16")]; + tensor value_53_perm_0 = const()[name = string("value_53_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_22_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_22_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488856000)))]; + tensor var_5198_cast_fp16 = add(x = q_133_cast_fp16, y = encoder_layers_22_self_attn_pos_bias_u_to_fp16)[name = string("op_5198_cast_fp16")]; + tensor encoder_layers_22_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_22_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488858112)))]; + tensor var_5200_cast_fp16 = add(x = q_133_cast_fp16, y = encoder_layers_22_self_attn_pos_bias_v_to_fp16)[name = string("op_5200_cast_fp16")]; + tensor q_with_bias_v_45_perm_0 = const()[name = string("q_with_bias_v_45_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_579_transpose_x_0 = const()[name = string("x_579_transpose_x_0"), val = bool(false)]; + bool x_579_transpose_y_0 = const()[name = string("x_579_transpose_y_0"), val = bool(false)]; + tensor op_5202_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488860224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(489002624))))[name = string("op_5202_to_fp16_quantized")]; + tensor q_with_bias_v_45_cast_fp16 = transpose(perm = q_with_bias_v_45_perm_0, x = var_5200_cast_fp16)[name = string("transpose_164")]; + tensor x_579_cast_fp16 = matmul(transpose_x = x_579_transpose_x_0, transpose_y = x_579_transpose_y_0, x = q_with_bias_v_45_cast_fp16, y = op_5202_to_fp16_quantized)[name = string("x_579_cast_fp16")]; + tensor x_581_pad_0 = const()[name = string("x_581_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_581_mode_0 = const()[name = string("x_581_mode_0"), val = string("constant")]; + fp16 const_365_to_fp16 = const()[name = string("const_365_to_fp16"), val = fp16(0x0p+0)]; + tensor x_581_cast_fp16 = pad(constant_val = const_365_to_fp16, mode = x_581_mode_0, pad = x_581_pad_0, x = x_579_cast_fp16)[name = string("x_581_cast_fp16")]; + tensor var_5210 = const()[name = string("op_5210"), val = tensor([1, 8, -1, 28])]; + tensor x_583_cast_fp16 = reshape(shape = var_5210, x = x_581_cast_fp16)[name = string("x_583_cast_fp16")]; + tensor var_5214_begin_0 = const()[name = string("op_5214_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_5214_end_0 = const()[name = string("op_5214_end_0"), val = tensor([1, 8, 140, 28])]; + tensor var_5214_end_mask_0 = const()[name = string("op_5214_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_5214_cast_fp16 = slice_by_index(begin = var_5214_begin_0, end = var_5214_end_0, end_mask = var_5214_end_mask_0, x = x_583_cast_fp16)[name = string("op_5214_cast_fp16")]; + tensor var_5215 = const()[name = string("op_5215"), val = tensor([1, 8, 28, 139])]; + tensor matrix_bd_89_cast_fp16 = reshape(shape = var_5215, x = var_5214_cast_fp16)[name = string("matrix_bd_89_cast_fp16")]; + bool matrix_ac_45_transpose_x_0 = const()[name = string("matrix_ac_45_transpose_x_0"), val = bool(false)]; + bool matrix_ac_45_transpose_y_0 = const()[name = string("matrix_ac_45_transpose_y_0"), val = bool(false)]; + tensor transpose_140_perm_0 = const()[name = string("transpose_140_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_141_perm_0 = const()[name = string("transpose_141_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_141 = transpose(perm = transpose_141_perm_0, x = k_89_cast_fp16)[name = string("transpose_162")]; + tensor transpose_140 = transpose(perm = transpose_140_perm_0, x = var_5198_cast_fp16)[name = string("transpose_163")]; + tensor matrix_ac_45_cast_fp16 = matmul(transpose_x = matrix_ac_45_transpose_x_0, transpose_y = matrix_ac_45_transpose_y_0, x = transpose_140, y = transpose_141)[name = string("matrix_ac_45_cast_fp16")]; + tensor matrix_bd_91_begin_0 = const()[name = string("matrix_bd_91_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_91_end_0 = const()[name = string("matrix_bd_91_end_0"), val = tensor([1, 8, 28, 70])]; + tensor matrix_bd_91_end_mask_0 = const()[name = string("matrix_bd_91_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_91_cast_fp16 = slice_by_index(begin = matrix_bd_91_begin_0, end = matrix_bd_91_end_0, end_mask = matrix_bd_91_end_mask_0, x = matrix_bd_89_cast_fp16)[name = string("matrix_bd_91_cast_fp16")]; + tensor var_5224_cast_fp16 = add(x = matrix_ac_45_cast_fp16, y = matrix_bd_91_cast_fp16)[name = string("op_5224_cast_fp16")]; + fp16 _inversed_scores_89_y_0_to_fp16 = const()[name = string("_inversed_scores_89_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_89_cast_fp16 = mul(x = var_5224_cast_fp16, y = _inversed_scores_89_y_0_to_fp16)[name = string("_inversed_scores_89_cast_fp16")]; + tensor scores_91_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_89_cast_fp16, cond = mask_11)[name = string("scores_91_cast_fp16")]; + tensor var_5230_cast_fp16 = softmax(axis = var_59, x = scores_91_cast_fp16)[name = string("op_5230_cast_fp16")]; + tensor input_1185_cast_fp16 = select(a = var_44_to_fp16, b = var_5230_cast_fp16, cond = mask_11)[name = string("input_1185_cast_fp16")]; + bool x_585_transpose_x_0 = const()[name = string("x_585_transpose_x_0"), val = bool(false)]; + bool x_585_transpose_y_0 = const()[name = string("x_585_transpose_y_0"), val = bool(false)]; + tensor value_53_cast_fp16 = transpose(perm = value_53_perm_0, x = v_45_cast_fp16)[name = string("transpose_161")]; + tensor x_585_cast_fp16 = matmul(transpose_x = x_585_transpose_x_0, transpose_y = x_585_transpose_y_0, x = input_1185_cast_fp16, y = value_53_cast_fp16)[name = string("x_585_cast_fp16")]; + tensor var_5234_perm_0 = const()[name = string("op_5234_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_5235 = const()[name = string("op_5235"), val = tensor([1, -1, 1024])]; + tensor var_5234_cast_fp16 = transpose(perm = var_5234_perm_0, x = x_585_cast_fp16)[name = string("transpose_160")]; + tensor input_1187_cast_fp16 = reshape(shape = var_5235, x = var_5234_cast_fp16)[name = string("input_1187_cast_fp16")]; + tensor encoder_layers_22_self_attn_linear_out_weight_to_fp16 = const()[name = string("encoder_layers_22_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(489003008)))]; + tensor encoder_layers_22_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_22_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(491100224)))]; + tensor linear_205_cast_fp16 = linear(bias = encoder_layers_22_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_22_self_attn_linear_out_weight_to_fp16, x = input_1187_cast_fp16)[name = string("linear_205_cast_fp16")]; + tensor input_1191_cast_fp16 = add(x = input_1181_cast_fp16, y = linear_205_cast_fp16)[name = string("input_1191_cast_fp16")]; + tensor x_589_axes_0 = const()[name = string("x_589_axes_0"), val = tensor([-1])]; + tensor encoder_layers_22_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_22_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(491102336)))]; + tensor encoder_layers_22_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_22_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(491104448)))]; + tensor x_589_cast_fp16 = layer_norm(axes = x_589_axes_0, beta = encoder_layers_22_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_22_norm_conv_weight_to_fp16, x = input_1191_cast_fp16)[name = string("x_589_cast_fp16")]; + tensor input_1193_perm_0 = const()[name = string("input_1193_perm_0"), val = tensor([0, 2, 1])]; + string input_1195_pad_type_0 = const()[name = string("input_1195_pad_type_0"), val = string("valid")]; + tensor input_1195_strides_0 = const()[name = string("input_1195_strides_0"), val = tensor([1])]; + tensor input_1195_pad_0 = const()[name = string("input_1195_pad_0"), val = tensor([0, 0])]; + tensor input_1195_dilations_0 = const()[name = string("input_1195_dilations_0"), val = tensor([1])]; + int32 input_1195_groups_0 = const()[name = string("input_1195_groups_0"), val = int32(1)]; + tensor encoder_layers_22_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(491106560))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(493203776))))[name = string("encoder_layers_22_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_1193_cast_fp16 = transpose(perm = input_1193_perm_0, x = x_589_cast_fp16)[name = string("transpose_159")]; + tensor input_1195_cast_fp16 = conv(dilations = input_1195_dilations_0, groups = input_1195_groups_0, pad = input_1195_pad_0, pad_type = input_1195_pad_type_0, strides = input_1195_strides_0, weight = encoder_layers_22_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1193_cast_fp16)[name = string("input_1195_cast_fp16")]; + int32 x_591_split_num_splits_0 = const()[name = string("x_591_split_num_splits_0"), val = int32(2)]; + int32 x_591_split_axis_0 = const()[name = string("x_591_split_axis_0"), val = int32(1)]; + tensor x_591_split_cast_fp16_0, tensor x_591_split_cast_fp16_1 = split(axis = x_591_split_axis_0, num_splits = x_591_split_num_splits_0, x = input_1195_cast_fp16)[name = string("x_591_split_cast_fp16")]; + tensor x_591_split_1_sigmoid_cast_fp16 = sigmoid(x = x_591_split_cast_fp16_1)[name = string("x_591_split_1_sigmoid_cast_fp16")]; + tensor x_591_cast_fp16 = mul(x = x_591_split_cast_fp16_0, y = x_591_split_1_sigmoid_cast_fp16)[name = string("x_591_cast_fp16")]; + tensor input_1197_cast_fp16 = select(a = var_44_to_fp16, b = x_591_cast_fp16, cond = var_575)[name = string("input_1197_cast_fp16")]; + bool new_x_91_interleave_0 = const()[name = string("new_x_91_interleave_0"), val = bool(false)]; + tensor new_x_91_cast_fp16 = concat(axis = var_59, interleave = new_x_91_interleave_0, values = (cache_91_cast_fp16, input_1197_cast_fp16))[name = string("new_x_91_cast_fp16")]; + tensor var_5274_begin_0 = const()[name = string("op_5274_begin_0"), val = tensor([0, 0, 28])]; + tensor var_5274_end_0 = const()[name = string("op_5274_end_0"), val = tensor([1, 1024, 36])]; + tensor var_5274_end_mask_0 = const()[name = string("op_5274_end_mask_0"), val = tensor([true, true, true])]; + tensor var_5274_cast_fp16 = slice_by_index(begin = var_5274_begin_0, end = var_5274_end_0, end_mask = var_5274_end_mask_0, x = new_x_91_cast_fp16)[name = string("op_5274_cast_fp16")]; + string x_593_pad_type_0 = const()[name = string("x_593_pad_type_0"), val = string("valid")]; + int32 x_593_groups_0 = const()[name = string("x_593_groups_0"), val = int32(1024)]; + tensor x_593_strides_0 = const()[name = string("x_593_strides_0"), val = tensor([1])]; + tensor x_593_pad_0 = const()[name = string("x_593_pad_0"), val = tensor([0, 0])]; + tensor x_593_dilations_0 = const()[name = string("x_593_dilations_0"), val = tensor([1])]; + tensor encoder_layers_22_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(493207936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(493217216))))[name = string("encoder_layers_22_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_593_cast_fp16 = conv(dilations = x_593_dilations_0, groups = x_593_groups_0, pad = x_593_pad_0, pad_type = x_593_pad_type_0, strides = x_593_strides_0, weight = encoder_layers_22_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_91_cast_fp16)[name = string("x_593_cast_fp16")]; + tensor input_1199_perm_0 = const()[name = string("input_1199_perm_0"), val = tensor([0, 2, 1])]; + tensor x_595_axes_0 = const()[name = string("x_595_axes_0"), val = tensor([-1])]; + tensor encoder_layers_22_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_22_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(493219328)))]; + tensor encoder_layers_22_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_22_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(493221440)))]; + tensor input_1199_cast_fp16 = transpose(perm = input_1199_perm_0, x = x_593_cast_fp16)[name = string("transpose_158")]; + tensor x_595_cast_fp16 = layer_norm(axes = x_595_axes_0, beta = encoder_layers_22_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_22_conv_batch_norm_weight_to_fp16, x = input_1199_cast_fp16)[name = string("x_595_cast_fp16")]; + tensor input_1201_perm_0 = const()[name = string("input_1201_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1201_cast_fp16 = transpose(perm = input_1201_perm_0, x = x_595_cast_fp16)[name = string("transpose_157")]; + tensor input_1203_cast_fp16 = silu(x = input_1201_cast_fp16)[name = string("input_1203_cast_fp16")]; + string x_597_pad_type_0 = const()[name = string("x_597_pad_type_0"), val = string("valid")]; + tensor x_597_strides_0 = const()[name = string("x_597_strides_0"), val = tensor([1])]; + tensor x_597_pad_0 = const()[name = string("x_597_pad_0"), val = tensor([0, 0])]; + tensor x_597_dilations_0 = const()[name = string("x_597_dilations_0"), val = tensor([1])]; + int32 x_597_groups_0 = const()[name = string("x_597_groups_0"), val = int32(1)]; + tensor encoder_layers_22_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(493223552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(494272192))))[name = string("encoder_layers_22_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_597_cast_fp16 = conv(dilations = x_597_dilations_0, groups = x_597_groups_0, pad = x_597_pad_0, pad_type = x_597_pad_type_0, strides = x_597_strides_0, weight = encoder_layers_22_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1203_cast_fp16)[name = string("x_597_cast_fp16")]; + tensor input_1205_perm_0 = const()[name = string("input_1205_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1205_cast_fp16 = transpose(perm = input_1205_perm_0, x = x_597_cast_fp16)[name = string("transpose_156")]; + tensor input_1207_cast_fp16 = add(x = input_1191_cast_fp16, y = input_1205_cast_fp16)[name = string("input_1207_cast_fp16")]; + tensor input_1209_axes_0 = const()[name = string("input_1209_axes_0"), val = tensor([-1])]; + tensor encoder_layers_22_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_22_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(494274304)))]; + tensor encoder_layers_22_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_22_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(494276416)))]; + tensor input_1209_cast_fp16 = layer_norm(axes = input_1209_axes_0, beta = encoder_layers_22_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_22_norm_feed_forward2_weight_to_fp16, x = input_1207_cast_fp16)[name = string("input_1209_cast_fp16")]; + tensor encoder_layers_22_feed_forward2_linear1_weight_to_fp16 = const()[name = string("encoder_layers_22_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(494278528)))]; + tensor encoder_layers_22_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_22_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(502667200)))]; + tensor linear_206_cast_fp16 = linear(bias = encoder_layers_22_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_22_feed_forward2_linear1_weight_to_fp16, x = input_1209_cast_fp16)[name = string("linear_206_cast_fp16")]; + tensor input_1213_cast_fp16 = silu(x = linear_206_cast_fp16)[name = string("input_1213_cast_fp16")]; + tensor encoder_layers_22_feed_forward2_linear2_weight_to_fp16 = const()[name = string("encoder_layers_22_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(502675456)))]; + tensor encoder_layers_22_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_22_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(511064128)))]; + tensor linear_207_cast_fp16 = linear(bias = encoder_layers_22_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_22_feed_forward2_linear2_weight_to_fp16, x = input_1213_cast_fp16)[name = string("linear_207_cast_fp16")]; + fp16 var_5317_to_fp16 = const()[name = string("op_5317_to_fp16"), val = fp16(0x1p-1)]; + tensor var_5318_cast_fp16 = mul(x = linear_207_cast_fp16, y = var_5317_to_fp16)[name = string("op_5318_cast_fp16")]; + tensor input_1219_cast_fp16 = add(x = input_1207_cast_fp16, y = var_5318_cast_fp16)[name = string("input_1219_cast_fp16")]; + tensor input_1221_axes_0 = const()[name = string("input_1221_axes_0"), val = tensor([-1])]; + tensor encoder_layers_22_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_22_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(511066240)))]; + tensor encoder_layers_22_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_22_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(511068352)))]; + tensor input_1221_cast_fp16 = layer_norm(axes = input_1221_axes_0, beta = encoder_layers_22_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_22_norm_out_weight_to_fp16, x = input_1219_cast_fp16)[name = string("input_1221_cast_fp16")]; + tensor cache_93_begin_0 = const()[name = string("cache_93_begin_0"), val = tensor([23, 0, 0, 0])]; + tensor cache_93_end_0 = const()[name = string("cache_93_end_0"), val = tensor([24, 1, 42, 1024])]; + tensor cache_93_end_mask_0 = const()[name = string("cache_93_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_93_squeeze_mask_0 = const()[name = string("cache_93_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_93_cast_fp16 = slice_by_index(begin = cache_93_begin_0, end = cache_93_end_0, end_mask = cache_93_end_mask_0, squeeze_mask = cache_93_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_93_cast_fp16")]; + tensor cache_begin_0 = const()[name = string("cache_begin_0"), val = tensor([23, 0, 0, 0])]; + tensor cache_end_0 = const()[name = string("cache_end_0"), val = tensor([24, 1, 1024, 8])]; + tensor cache_end_mask_0 = const()[name = string("cache_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_squeeze_mask_0 = const()[name = string("cache_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_cast_fp16 = slice_by_index(begin = cache_begin_0, end = cache_end_0, end_mask = cache_end_mask_0, squeeze_mask = cache_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_cast_fp16")]; + tensor input_1223_axes_0 = const()[name = string("input_1223_axes_0"), val = tensor([-1])]; + tensor encoder_layers_23_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_23_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(511070464)))]; + tensor encoder_layers_23_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_23_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(511072576)))]; + tensor input_1223_cast_fp16 = layer_norm(axes = input_1223_axes_0, beta = encoder_layers_23_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_23_norm_feed_forward1_weight_to_fp16, x = input_1221_cast_fp16)[name = string("input_1223_cast_fp16")]; + tensor encoder_layers_23_feed_forward1_linear1_weight_to_fp16 = const()[name = string("encoder_layers_23_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(511074688)))]; + tensor encoder_layers_23_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_23_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(519463360)))]; + tensor linear_208_cast_fp16 = linear(bias = encoder_layers_23_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_23_feed_forward1_linear1_weight_to_fp16, x = input_1223_cast_fp16)[name = string("linear_208_cast_fp16")]; + tensor input_1227_cast_fp16 = silu(x = linear_208_cast_fp16)[name = string("input_1227_cast_fp16")]; + tensor encoder_layers_23_feed_forward1_linear2_weight_to_fp16 = const()[name = string("encoder_layers_23_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(519471616)))]; + tensor encoder_layers_23_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_23_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(527860288)))]; + tensor linear_209_cast_fp16 = linear(bias = encoder_layers_23_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_23_feed_forward1_linear2_weight_to_fp16, x = input_1227_cast_fp16)[name = string("linear_209_cast_fp16")]; + fp16 var_5354_to_fp16 = const()[name = string("op_5354_to_fp16"), val = fp16(0x1p-1)]; + tensor var_5355_cast_fp16 = mul(x = linear_209_cast_fp16, y = var_5354_to_fp16)[name = string("op_5355_cast_fp16")]; + tensor input_1233_cast_fp16 = add(x = input_1221_cast_fp16, y = var_5355_cast_fp16)[name = string("input_1233_cast_fp16")]; + tensor key_axes_0 = const()[name = string("key_axes_0"), val = tensor([-1])]; + tensor encoder_layers_23_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_23_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(527862400)))]; + tensor encoder_layers_23_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_23_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(527864512)))]; + tensor key_cast_fp16 = layer_norm(axes = key_axes_0, beta = encoder_layers_23_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_23_norm_self_att_weight_to_fp16, x = input_1233_cast_fp16)[name = string("key_cast_fp16")]; + bool input_1235_interleave_0 = const()[name = string("input_1235_interleave_0"), val = bool(false)]; + tensor input_1235_cast_fp16 = concat(axis = var_68, interleave = input_1235_interleave_0, values = (cache_93_cast_fp16, key_cast_fp16))[name = string("input_1235_cast_fp16")]; + tensor var_5377_begin_0 = const()[name = string("op_5377_begin_0"), val = tensor([0, 28, 0])]; + tensor var_5377_end_0 = const()[name = string("op_5377_end_0"), val = tensor([1, 42, 1024])]; + tensor var_5377_end_mask_0 = const()[name = string("op_5377_end_mask_0"), val = tensor([true, true, true])]; + tensor var_5377_cast_fp16 = slice_by_index(begin = var_5377_begin_0, end = var_5377_end_0, end_mask = var_5377_end_mask_0, x = cache_93_cast_fp16)[name = string("op_5377_cast_fp16")]; + bool cache_last_channel_cur_interleave_0 = const()[name = string("cache_last_channel_cur_interleave_0"), val = bool(false)]; + tensor cache_last_channel_cur_cast_fp16 = concat(axis = var_68, interleave = cache_last_channel_cur_interleave_0, values = (var_5377_cast_fp16, key_cast_fp16))[name = string("cache_last_channel_cur_cast_fp16")]; + tensor encoder_layers_23_self_attn_linear_q_weight_to_fp16 = const()[name = string("encoder_layers_23_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(527866624)))]; + tensor encoder_layers_23_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_23_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(529963840)))]; + tensor linear_210_cast_fp16 = linear(bias = encoder_layers_23_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_23_self_attn_linear_q_weight_to_fp16, x = key_cast_fp16)[name = string("linear_210_cast_fp16")]; + tensor var_5388 = const()[name = string("op_5388"), val = tensor([1, -1, 8, 128])]; + tensor q_139_cast_fp16 = reshape(shape = var_5388, x = linear_210_cast_fp16)[name = string("q_139_cast_fp16")]; + tensor encoder_layers_23_self_attn_linear_k_weight_to_fp16 = const()[name = string("encoder_layers_23_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(529965952)))]; + tensor encoder_layers_23_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_23_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(532063168)))]; + tensor linear_211_cast_fp16 = linear(bias = encoder_layers_23_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_23_self_attn_linear_k_weight_to_fp16, x = input_1235_cast_fp16)[name = string("linear_211_cast_fp16")]; + tensor var_5393 = const()[name = string("op_5393"), val = tensor([1, -1, 8, 128])]; + tensor k_93_cast_fp16 = reshape(shape = var_5393, x = linear_211_cast_fp16)[name = string("k_93_cast_fp16")]; + tensor encoder_layers_23_self_attn_linear_v_weight_to_fp16 = const()[name = string("encoder_layers_23_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(532065280)))]; + tensor encoder_layers_23_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_23_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(534162496)))]; + tensor linear_212_cast_fp16 = linear(bias = encoder_layers_23_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_23_self_attn_linear_v_weight_to_fp16, x = input_1235_cast_fp16)[name = string("linear_212_cast_fp16")]; + tensor var_5398 = const()[name = string("op_5398"), val = tensor([1, -1, 8, 128])]; + tensor v_cast_fp16 = reshape(shape = var_5398, x = linear_212_cast_fp16)[name = string("v_cast_fp16")]; + tensor value_perm_0 = const()[name = string("value_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_23_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_23_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(534164608)))]; + tensor var_5411_cast_fp16 = add(x = q_139_cast_fp16, y = encoder_layers_23_self_attn_pos_bias_u_to_fp16)[name = string("op_5411_cast_fp16")]; + tensor encoder_layers_23_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_23_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(534166720)))]; + tensor var_5413_cast_fp16 = add(x = q_139_cast_fp16, y = encoder_layers_23_self_attn_pos_bias_v_to_fp16)[name = string("op_5413_cast_fp16")]; + tensor q_with_bias_v_perm_0 = const()[name = string("q_with_bias_v_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_605_transpose_x_0 = const()[name = string("x_605_transpose_x_0"), val = bool(false)]; + bool x_605_transpose_y_0 = const()[name = string("x_605_transpose_y_0"), val = bool(false)]; + tensor op_5415_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(534168832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(534311232))))[name = string("op_5415_to_fp16_quantized")]; + tensor q_with_bias_v_cast_fp16 = transpose(perm = q_with_bias_v_perm_0, x = var_5413_cast_fp16)[name = string("transpose_155")]; + tensor x_605_cast_fp16 = matmul(transpose_x = x_605_transpose_x_0, transpose_y = x_605_transpose_y_0, x = q_with_bias_v_cast_fp16, y = op_5415_to_fp16_quantized)[name = string("x_605_cast_fp16")]; + tensor x_607_pad_0 = const()[name = string("x_607_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_607_mode_0 = const()[name = string("x_607_mode_0"), val = string("constant")]; + fp16 const_378_to_fp16 = const()[name = string("const_378_to_fp16"), val = fp16(0x0p+0)]; + tensor x_607_cast_fp16 = pad(constant_val = const_378_to_fp16, mode = x_607_mode_0, pad = x_607_pad_0, x = x_605_cast_fp16)[name = string("x_607_cast_fp16")]; + tensor var_5423 = const()[name = string("op_5423"), val = tensor([1, 8, -1, 28])]; + tensor x_609_cast_fp16 = reshape(shape = var_5423, x = x_607_cast_fp16)[name = string("x_609_cast_fp16")]; + tensor var_5427_begin_0 = const()[name = string("op_5427_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_5427_end_0 = const()[name = string("op_5427_end_0"), val = tensor([1, 8, 140, 28])]; + tensor var_5427_end_mask_0 = const()[name = string("op_5427_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_5427_cast_fp16 = slice_by_index(begin = var_5427_begin_0, end = var_5427_end_0, end_mask = var_5427_end_mask_0, x = x_609_cast_fp16)[name = string("op_5427_cast_fp16")]; + tensor var_5428 = const()[name = string("op_5428"), val = tensor([1, 8, 28, 139])]; + tensor matrix_bd_93_cast_fp16 = reshape(shape = var_5428, x = var_5427_cast_fp16)[name = string("matrix_bd_93_cast_fp16")]; + bool matrix_ac_transpose_x_0 = const()[name = string("matrix_ac_transpose_x_0"), val = bool(false)]; + bool matrix_ac_transpose_y_0 = const()[name = string("matrix_ac_transpose_y_0"), val = bool(false)]; + tensor transpose_142_perm_0 = const()[name = string("transpose_142_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_143_perm_0 = const()[name = string("transpose_143_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_143 = transpose(perm = transpose_143_perm_0, x = k_93_cast_fp16)[name = string("transpose_153")]; + tensor transpose_142 = transpose(perm = transpose_142_perm_0, x = var_5411_cast_fp16)[name = string("transpose_154")]; + tensor matrix_ac_cast_fp16 = matmul(transpose_x = matrix_ac_transpose_x_0, transpose_y = matrix_ac_transpose_y_0, x = transpose_142, y = transpose_143)[name = string("matrix_ac_cast_fp16")]; + tensor matrix_bd_begin_0 = const()[name = string("matrix_bd_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_end_0 = const()[name = string("matrix_bd_end_0"), val = tensor([1, 8, 28, 70])]; + tensor matrix_bd_end_mask_0 = const()[name = string("matrix_bd_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_cast_fp16 = slice_by_index(begin = matrix_bd_begin_0, end = matrix_bd_end_0, end_mask = matrix_bd_end_mask_0, x = matrix_bd_93_cast_fp16)[name = string("matrix_bd_cast_fp16")]; + tensor var_5437_cast_fp16 = add(x = matrix_ac_cast_fp16, y = matrix_bd_cast_fp16)[name = string("op_5437_cast_fp16")]; + fp16 _inversed_scores_93_y_0_to_fp16 = const()[name = string("_inversed_scores_93_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_93_cast_fp16 = mul(x = var_5437_cast_fp16, y = _inversed_scores_93_y_0_to_fp16)[name = string("_inversed_scores_93_cast_fp16")]; + tensor scores_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_93_cast_fp16, cond = mask_11)[name = string("scores_cast_fp16")]; + tensor var_5443_cast_fp16 = softmax(axis = var_59, x = scores_cast_fp16)[name = string("op_5443_cast_fp16")]; + tensor input_1237_cast_fp16 = select(a = var_44_to_fp16, b = var_5443_cast_fp16, cond = mask_11)[name = string("input_1237_cast_fp16")]; + bool x_611_transpose_x_0 = const()[name = string("x_611_transpose_x_0"), val = bool(false)]; + bool x_611_transpose_y_0 = const()[name = string("x_611_transpose_y_0"), val = bool(false)]; + tensor value_cast_fp16 = transpose(perm = value_perm_0, x = v_cast_fp16)[name = string("transpose_152")]; + tensor x_611_cast_fp16 = matmul(transpose_x = x_611_transpose_x_0, transpose_y = x_611_transpose_y_0, x = input_1237_cast_fp16, y = value_cast_fp16)[name = string("x_611_cast_fp16")]; + tensor var_5447_perm_0 = const()[name = string("op_5447_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_5448 = const()[name = string("op_5448"), val = tensor([1, -1, 1024])]; + tensor var_5447_cast_fp16 = transpose(perm = var_5447_perm_0, x = x_611_cast_fp16)[name = string("transpose_151")]; + tensor input_1239_cast_fp16 = reshape(shape = var_5448, x = var_5447_cast_fp16)[name = string("input_1239_cast_fp16")]; + tensor encoder_layers_23_self_attn_linear_out_weight_to_fp16 = const()[name = string("encoder_layers_23_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(534311616)))]; + tensor encoder_layers_23_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_23_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(536408832)))]; + tensor linear_214_cast_fp16 = linear(bias = encoder_layers_23_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_23_self_attn_linear_out_weight_to_fp16, x = input_1239_cast_fp16)[name = string("linear_214_cast_fp16")]; + tensor input_1243_cast_fp16 = add(x = input_1233_cast_fp16, y = linear_214_cast_fp16)[name = string("input_1243_cast_fp16")]; + tensor x_615_axes_0 = const()[name = string("x_615_axes_0"), val = tensor([-1])]; + tensor encoder_layers_23_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_23_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(536410944)))]; + tensor encoder_layers_23_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_23_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(536413056)))]; + tensor x_615_cast_fp16 = layer_norm(axes = x_615_axes_0, beta = encoder_layers_23_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_23_norm_conv_weight_to_fp16, x = input_1243_cast_fp16)[name = string("x_615_cast_fp16")]; + tensor input_1245_perm_0 = const()[name = string("input_1245_perm_0"), val = tensor([0, 2, 1])]; + string input_1247_pad_type_0 = const()[name = string("input_1247_pad_type_0"), val = string("valid")]; + tensor input_1247_strides_0 = const()[name = string("input_1247_strides_0"), val = tensor([1])]; + tensor input_1247_pad_0 = const()[name = string("input_1247_pad_0"), val = tensor([0, 0])]; + tensor input_1247_dilations_0 = const()[name = string("input_1247_dilations_0"), val = tensor([1])]; + int32 input_1247_groups_0 = const()[name = string("input_1247_groups_0"), val = int32(1)]; + tensor encoder_layers_23_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(536415168))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(538512384))))[name = string("encoder_layers_23_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_1245_cast_fp16 = transpose(perm = input_1245_perm_0, x = x_615_cast_fp16)[name = string("transpose_150")]; + tensor input_1247_cast_fp16 = conv(dilations = input_1247_dilations_0, groups = input_1247_groups_0, pad = input_1247_pad_0, pad_type = input_1247_pad_type_0, strides = input_1247_strides_0, weight = encoder_layers_23_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1245_cast_fp16)[name = string("input_1247_cast_fp16")]; + int32 x_617_split_num_splits_0 = const()[name = string("x_617_split_num_splits_0"), val = int32(2)]; + int32 x_617_split_axis_0 = const()[name = string("x_617_split_axis_0"), val = int32(1)]; + tensor x_617_split_cast_fp16_0, tensor x_617_split_cast_fp16_1 = split(axis = x_617_split_axis_0, num_splits = x_617_split_num_splits_0, x = input_1247_cast_fp16)[name = string("x_617_split_cast_fp16")]; + tensor x_617_split_1_sigmoid_cast_fp16 = sigmoid(x = x_617_split_cast_fp16_1)[name = string("x_617_split_1_sigmoid_cast_fp16")]; + tensor x_617_cast_fp16 = mul(x = x_617_split_cast_fp16_0, y = x_617_split_1_sigmoid_cast_fp16)[name = string("x_617_cast_fp16")]; + tensor input_1249_cast_fp16 = select(a = var_44_to_fp16, b = x_617_cast_fp16, cond = var_575)[name = string("input_1249_cast_fp16")]; + bool new_x_interleave_0 = const()[name = string("new_x_interleave_0"), val = bool(false)]; + tensor new_x_cast_fp16 = concat(axis = var_59, interleave = new_x_interleave_0, values = (cache_cast_fp16, input_1249_cast_fp16))[name = string("new_x_cast_fp16")]; + tensor cache_last_time_cur_begin_0 = const()[name = string("cache_last_time_cur_begin_0"), val = tensor([0, 0, 28])]; + tensor cache_last_time_cur_end_0 = const()[name = string("cache_last_time_cur_end_0"), val = tensor([1, 1024, 36])]; + tensor cache_last_time_cur_end_mask_0 = const()[name = string("cache_last_time_cur_end_mask_0"), val = tensor([true, true, true])]; + tensor cache_last_time_cur_cast_fp16 = slice_by_index(begin = cache_last_time_cur_begin_0, end = cache_last_time_cur_end_0, end_mask = cache_last_time_cur_end_mask_0, x = new_x_cast_fp16)[name = string("cache_last_time_cur_cast_fp16")]; + string x_619_pad_type_0 = const()[name = string("x_619_pad_type_0"), val = string("valid")]; + int32 x_619_groups_0 = const()[name = string("x_619_groups_0"), val = int32(1024)]; + tensor x_619_strides_0 = const()[name = string("x_619_strides_0"), val = tensor([1])]; + tensor x_619_pad_0 = const()[name = string("x_619_pad_0"), val = tensor([0, 0])]; + tensor x_619_dilations_0 = const()[name = string("x_619_dilations_0"), val = tensor([1])]; + tensor encoder_layers_23_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(538516544))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(538525824))))[name = string("encoder_layers_23_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_619_cast_fp16 = conv(dilations = x_619_dilations_0, groups = x_619_groups_0, pad = x_619_pad_0, pad_type = x_619_pad_type_0, strides = x_619_strides_0, weight = encoder_layers_23_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_cast_fp16)[name = string("x_619_cast_fp16")]; + tensor input_1251_perm_0 = const()[name = string("input_1251_perm_0"), val = tensor([0, 2, 1])]; + tensor x_621_axes_0 = const()[name = string("x_621_axes_0"), val = tensor([-1])]; + tensor encoder_layers_23_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_23_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(538527936)))]; + tensor encoder_layers_23_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_23_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(538530048)))]; + tensor input_1251_cast_fp16 = transpose(perm = input_1251_perm_0, x = x_619_cast_fp16)[name = string("transpose_149")]; + tensor x_621_cast_fp16 = layer_norm(axes = x_621_axes_0, beta = encoder_layers_23_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_23_conv_batch_norm_weight_to_fp16, x = input_1251_cast_fp16)[name = string("x_621_cast_fp16")]; + tensor input_1253_perm_0 = const()[name = string("input_1253_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1253_cast_fp16 = transpose(perm = input_1253_perm_0, x = x_621_cast_fp16)[name = string("transpose_148")]; + tensor input_1255_cast_fp16 = silu(x = input_1253_cast_fp16)[name = string("input_1255_cast_fp16")]; + string x_623_pad_type_0 = const()[name = string("x_623_pad_type_0"), val = string("valid")]; + tensor x_623_strides_0 = const()[name = string("x_623_strides_0"), val = tensor([1])]; + tensor x_623_pad_0 = const()[name = string("x_623_pad_0"), val = tensor([0, 0])]; + tensor x_623_dilations_0 = const()[name = string("x_623_dilations_0"), val = tensor([1])]; + int32 x_623_groups_0 = const()[name = string("x_623_groups_0"), val = int32(1)]; + tensor encoder_layers_23_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(538532160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(539580800))))[name = string("encoder_layers_23_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_623_cast_fp16 = conv(dilations = x_623_dilations_0, groups = x_623_groups_0, pad = x_623_pad_0, pad_type = x_623_pad_type_0, strides = x_623_strides_0, weight = encoder_layers_23_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1255_cast_fp16)[name = string("x_623_cast_fp16")]; + tensor input_1257_perm_0 = const()[name = string("input_1257_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1257_cast_fp16 = transpose(perm = input_1257_perm_0, x = x_623_cast_fp16)[name = string("transpose_147")]; + tensor input_1259_cast_fp16 = add(x = input_1243_cast_fp16, y = input_1257_cast_fp16)[name = string("input_1259_cast_fp16")]; + tensor input_1261_axes_0 = const()[name = string("input_1261_axes_0"), val = tensor([-1])]; + tensor encoder_layers_23_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_23_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(539582912)))]; + tensor encoder_layers_23_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_23_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(539585024)))]; + tensor input_1261_cast_fp16 = layer_norm(axes = input_1261_axes_0, beta = encoder_layers_23_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_23_norm_feed_forward2_weight_to_fp16, x = input_1259_cast_fp16)[name = string("input_1261_cast_fp16")]; + tensor encoder_layers_23_feed_forward2_linear1_weight_to_fp16 = const()[name = string("encoder_layers_23_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(539587136)))]; + tensor encoder_layers_23_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_23_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(547975808)))]; + tensor linear_215_cast_fp16 = linear(bias = encoder_layers_23_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_23_feed_forward2_linear1_weight_to_fp16, x = input_1261_cast_fp16)[name = string("linear_215_cast_fp16")]; + tensor input_1265_cast_fp16 = silu(x = linear_215_cast_fp16)[name = string("input_1265_cast_fp16")]; + tensor encoder_layers_23_feed_forward2_linear2_weight_to_fp16 = const()[name = string("encoder_layers_23_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(547984064)))]; + tensor encoder_layers_23_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_23_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(556372736)))]; + tensor linear_216_cast_fp16 = linear(bias = encoder_layers_23_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_23_feed_forward2_linear2_weight_to_fp16, x = input_1265_cast_fp16)[name = string("linear_216_cast_fp16")]; + fp16 var_5530_to_fp16 = const()[name = string("op_5530_to_fp16"), val = fp16(0x1p-1)]; + tensor var_5531_cast_fp16 = mul(x = linear_216_cast_fp16, y = var_5530_to_fp16)[name = string("op_5531_cast_fp16")]; + tensor input_1271_cast_fp16 = add(x = input_1259_cast_fp16, y = var_5531_cast_fp16)[name = string("input_1271_cast_fp16")]; + tensor audio_signal_axes_0 = const()[name = string("audio_signal_axes_0"), val = tensor([-1])]; + tensor encoder_layers_23_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_23_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(556374848)))]; + tensor encoder_layers_23_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_23_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(556376960)))]; + tensor audio_signal_cast_fp16 = layer_norm(axes = audio_signal_axes_0, beta = encoder_layers_23_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_23_norm_out_weight_to_fp16, x = input_1271_cast_fp16)[name = string("audio_signal_cast_fp16")]; + int32 obj_5_axis_0 = const()[name = string("obj_5_axis_0"), val = int32(0)]; + tensor obj_5_cast_fp16 = stack(axis = obj_5_axis_0, values = (var_484_cast_fp16, var_697_cast_fp16, var_910_cast_fp16, var_1123_cast_fp16, var_1336_cast_fp16, var_1549_cast_fp16, var_1762_cast_fp16, var_1975_cast_fp16, var_2188_cast_fp16, var_2401_cast_fp16, var_2614_cast_fp16, var_2827_cast_fp16, var_3040_cast_fp16, var_3253_cast_fp16, var_3466_cast_fp16, var_3679_cast_fp16, var_3892_cast_fp16, var_4105_cast_fp16, var_4318_cast_fp16, var_4531_cast_fp16, var_4744_cast_fp16, var_4957_cast_fp16, var_5170_cast_fp16, cache_last_channel_cur_cast_fp16))[name = string("obj_5_cast_fp16")]; + int32 obj_7_axis_0 = const()[name = string("obj_7_axis_0"), val = int32(0)]; + tensor obj_7_cast_fp16 = stack(axis = obj_7_axis_0, values = (var_588_cast_fp16, var_801_cast_fp16, var_1014_cast_fp16, var_1227_cast_fp16, var_1440_cast_fp16, var_1653_cast_fp16, var_1866_cast_fp16, var_2079_cast_fp16, var_2292_cast_fp16, var_2505_cast_fp16, var_2718_cast_fp16, var_2931_cast_fp16, var_3144_cast_fp16, var_3357_cast_fp16, var_3570_cast_fp16, var_3783_cast_fp16, var_3996_cast_fp16, var_4209_cast_fp16, var_4422_cast_fp16, var_4635_cast_fp16, var_4848_cast_fp16, var_5061_cast_fp16, var_5274_cast_fp16, cache_last_time_cur_cast_fp16))[name = string("obj_7_cast_fp16")]; + tensor var_5547 = add(x = cache_len, y = max_audio_length_1)[name = string("op_5547")]; + string var_5547_promoted_to_fp16_dtype_0 = const()[name = string("op_5547_promoted_to_fp16_dtype_0"), val = string("fp16")]; + fp16 const_384_to_fp16 = const()[name = string("const_384_to_fp16"), val = fp16(-inf)]; + fp16 var_49_promoted_to_fp16 = const()[name = string("op_49_promoted_to_fp16"), val = fp16(0x1.5p+5)]; + tensor var_5547_to_fp16 = cast(dtype = var_5547_promoted_to_fp16_dtype_0, x = var_5547)[name = string("cast_9")]; + tensor clip_1_cast_fp16 = clip(alpha = const_384_to_fp16, beta = var_49_promoted_to_fp16, x = var_5547_to_fp16)[name = string("clip_1_cast_fp16")]; + int32 one_hot_1_batch_dims_0 = const()[name = string("one_hot_1_batch_dims_0"), val = int32(0)]; + bool one_hot_1_validate_indices_0 = const()[name = string("one_hot_1_validate_indices_0"), val = bool(false)]; + tensor to_onehot_identity_table_to_fp16 = const()[name = string("to_onehot_identity_table_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(556379072)))]; + string prompt_id_to_int16_dtype_0 = const()[name = string("prompt_id_to_int16_dtype_0"), val = string("int16")]; + string cast_230_dtype_0 = const()[name = string("cast_230_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor prompt_id_to_int16 = cast(dtype = prompt_id_to_int16_dtype_0, x = prompt_id)[name = string("cast_8")]; + tensor cast_230 = cast(dtype = cast_230_dtype_0, x = prompt_id_to_int16)[name = string("cast_7")]; + tensor greater_equal_0 = greater_equal(x = cast_230, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_2 = const()[name = string("slice_by_index_2"), val = int32(128)]; + tensor add_0 = add(x = cast_230, y = slice_by_index_2)[name = string("add_0")]; + tensor select_0 = select(a = cast_230, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_6")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_5")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(128)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 greater_equal_0_y_0_2 = const()[name = string("greater_equal_0_y_0_2"), val = int32(0)]; + tensor greater_equal_0_2 = greater_equal(x = select_0_1, y = greater_equal_0_y_0_2)[name = string("greater_equal_0_2")]; + int32 slice_by_index_0_1 = const()[name = string("slice_by_index_0_1"), val = int32(128)]; + tensor add_0_2 = add(x = select_0_1, y = slice_by_index_0_1)[name = string("add_0_2")]; + tensor select_0_2 = select(a = select_0_1, b = add_0_2, cond = greater_equal_0_2)[name = string("select_0_2")]; + int32 one_hot_1_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("one_hot_1_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor one_hot_1_cast_fp16_cast_uint16_cast_uint16 = gather(axis = one_hot_1_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = one_hot_1_batch_dims_0, indices = select_0_2, validate_indices = one_hot_1_validate_indices_0, x = to_onehot_identity_table_to_fp16)[name = string("one_hot_1_cast_fp16_cast_uint16_cast_uint16")]; + tensor var_5593_axes_0 = const()[name = string("op_5593_axes_0"), val = tensor([1])]; + tensor var_5593_cast_fp16 = expand_dims(axes = var_5593_axes_0, x = one_hot_1_cast_fp16_cast_uint16_cast_uint16)[name = string("op_5593_cast_fp16")]; + tensor one_hot_reps_0 = const()[name = string("one_hot_reps_0"), val = tensor([1, 28, 1])]; + tensor one_hot_cast_fp16 = tile(reps = one_hot_reps_0, x = var_5593_cast_fp16)[name = string("one_hot_cast_fp16")]; + int32 var_5602 = const()[name = string("op_5602"), val = int32(-1)]; + bool input_1273_interleave_0 = const()[name = string("input_1273_interleave_0"), val = bool(false)]; + tensor input_1273_cast_fp16 = concat(axis = var_5602, interleave = input_1273_interleave_0, values = (audio_signal_cast_fp16, one_hot_cast_fp16))[name = string("input_1273_cast_fp16")]; + tensor prompt_kernel_0_weight_to_fp16 = const()[name = string("prompt_kernel_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(556411904)))]; + tensor prompt_kernel_0_bias_to_fp16 = const()[name = string("prompt_kernel_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(561130560)))]; + tensor linear_217_cast_fp16 = linear(bias = prompt_kernel_0_bias_to_fp16, weight = prompt_kernel_0_weight_to_fp16, x = input_1273_cast_fp16)[name = string("linear_217_cast_fp16")]; + tensor input_cast_fp16 = relu(x = linear_217_cast_fp16)[name = string("input_cast_fp16")]; + tensor prompt_kernel_2_weight_to_fp16 = const()[name = string("prompt_kernel_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(561134720)))]; + tensor prompt_kernel_2_bias_to_fp16 = const()[name = string("prompt_kernel_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(565329088)))]; + tensor linear_218_cast_fp16 = linear(bias = prompt_kernel_2_bias_to_fp16, weight = prompt_kernel_2_weight_to_fp16, x = input_cast_fp16)[name = string("linear_218_cast_fp16")]; + tensor var_5615_perm_0 = const()[name = string("op_5615_perm_0"), val = tensor([0, 2, 1])]; + string var_5615_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_5615_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + string var_5620_dtype_0 = const()[name = string("op_5620_dtype_0"), val = string("int32")]; + tensor var_5623_perm_0 = const()[name = string("op_5623_perm_0"), val = tensor([1, 0, 2, 3])]; + string var_5623_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_5623_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor var_5626_perm_0 = const()[name = string("op_5626_perm_0"), val = tensor([1, 0, 2, 3])]; + string var_5626_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_5626_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + string var_5631_dtype_0 = const()[name = string("op_5631_dtype_0"), val = string("int32")]; + tensor cache_len_out = cast(dtype = var_5631_dtype_0, x = clip_1_cast_fp16)[name = string("cast_0")]; + tensor var_5626_cast_fp16 = transpose(perm = var_5626_perm_0, x = obj_7_cast_fp16)[name = string("transpose_144")]; + tensor cache_time_out = cast(dtype = var_5626_cast_fp16_to_fp32_dtype_0, x = var_5626_cast_fp16)[name = string("cast_1")]; + tensor var_5623_cast_fp16 = transpose(perm = var_5623_perm_0, x = obj_5_cast_fp16)[name = string("transpose_145")]; + tensor cache_channel_out = cast(dtype = var_5623_cast_fp16_to_fp32_dtype_0, x = var_5623_cast_fp16)[name = string("cast_2")]; + tensor encoded_length = cast(dtype = var_5620_dtype_0, x = clip_0_cast_fp16)[name = string("cast_3")]; + tensor var_5615_cast_fp16 = transpose(perm = var_5615_perm_0, x = linear_218_cast_fp16)[name = string("transpose_146")]; + tensor encoded = cast(dtype = var_5615_cast_fp16_to_fp32_dtype_0, x = var_5615_cast_fp16)[name = string("cast_4")]; + } -> (encoded, encoded_length, cache_channel_out, cache_time_out, cache_len_out); +} \ No newline at end of file diff --git a/multilingual/2240ms/encoder.mlmodelc/weights/weight.bin b/multilingual/2240ms/encoder.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..7a12bc272fe95f2080e5cebe63d7189329748a69 --- /dev/null +++ b/multilingual/2240ms/encoder.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:728cf78b85d85c573a2a33a8287ea44bc7a846fc8462d9038d1ed1b24a2c9ac8 +size 565331200 diff --git a/multilingual/2240ms/encoder.mlpackage/Data/com.apple.CoreML/model.mlmodel b/multilingual/2240ms/encoder.mlpackage/Data/com.apple.CoreML/model.mlmodel new file mode 100644 index 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dict({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.5.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] +{ + func main(tensor decoder, tensor encoder) { + tensor input_1_perm_0 = const()[name = string("input_1_perm_0"), val = tensor([0, 2, 1])]; + string encoder_to_fp16_dtype_0 = const()[name = string("encoder_to_fp16_dtype_0"), val = string("fp16")]; + tensor input_3_perm_0 = const()[name = string("input_3_perm_0"), val = tensor([0, 2, 1])]; + string decoder_to_fp16_dtype_0 = const()[name = string("decoder_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_enc_weight_to_fp16 = const()[name = string("module_enc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor module_enc_bias_to_fp16 = const()[name = string("module_enc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1310848)))]; + tensor encoder_to_fp16 = cast(dtype = encoder_to_fp16_dtype_0, x = encoder)[name = string("cast_2")]; + tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = encoder_to_fp16)[name = string("transpose_1")]; + tensor linear_0_cast_fp16 = linear(bias = module_enc_bias_to_fp16, weight = module_enc_weight_to_fp16, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor module_pred_weight_to_fp16 = const()[name = string("module_pred_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1312192)))]; + tensor module_pred_bias_to_fp16 = const()[name = string("module_pred_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2131456)))]; + tensor decoder_to_fp16 = cast(dtype = decoder_to_fp16_dtype_0, x = decoder)[name = string("cast_1")]; + tensor input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = decoder_to_fp16)[name = string("transpose_0")]; + tensor linear_1_cast_fp16 = linear(bias = module_pred_bias_to_fp16, weight = module_pred_weight_to_fp16, x = input_3_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_23_axes_0 = const()[name = string("op_23_axes_0"), val = tensor([2])]; + tensor var_23_cast_fp16 = expand_dims(axes = var_23_axes_0, x = linear_0_cast_fp16)[name = string("op_23_cast_fp16")]; + tensor var_25_axes_0 = const()[name = string("op_25_axes_0"), val = tensor([1])]; + tensor var_25_cast_fp16 = expand_dims(axes = var_25_axes_0, x = linear_1_cast_fp16)[name = string("op_25_cast_fp16")]; + tensor input_5_cast_fp16 = add(x = var_23_cast_fp16, y = var_25_cast_fp16)[name = string("input_5_cast_fp16")]; + tensor input_7_cast_fp16 = relu(x = input_5_cast_fp16)[name = string("input_7_cast_fp16")]; + tensor module_joint_net_2_weight_to_fp16 = const()[name = string("module_joint_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2132800)))]; + tensor module_joint_net_2_bias_to_fp16 = const()[name = string("module_joint_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18885504)))]; + tensor linear_2_cast_fp16 = linear(bias = module_joint_net_2_bias_to_fp16, weight = module_joint_net_2_weight_to_fp16, x = input_7_cast_fp16)[name = string("linear_2_cast_fp16")]; + string linear_2_cast_fp16_to_fp32_dtype_0 = const()[name = string("linear_2_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor logits = cast(dtype = linear_2_cast_fp16_to_fp32_dtype_0, x = linear_2_cast_fp16)[name = string("cast_0")]; + } -> (logits); +} \ No newline at end of file diff --git a/multilingual/2240ms/joint.mlmodelc/weights/weight.bin b/multilingual/2240ms/joint.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..0d899ae2b3a3c9be8967b683e91cd8ca7252c8ec --- /dev/null +++ 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/dev/null +++ b/multilingual/2240ms/joint.mlpackage/Data/com.apple.CoreML/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f4c8ae93e304a187ebfa0b88c812b70e79b625a549727922e7f63d61c1c7b6dd +size 18911744 diff --git a/multilingual/2240ms/joint.mlpackage/Manifest.json b/multilingual/2240ms/joint.mlpackage/Manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..f97f8d4fd73506ae49bbd5bc163cd2079627721d --- /dev/null +++ b/multilingual/2240ms/joint.mlpackage/Manifest.json @@ -0,0 +1,18 @@ +{ + "fileFormatVersion": "1.0.0", + "itemInfoEntries": { + "3F33ED2D-8E5C-4BBD-B243-7D003571E7E2": { + "author": "com.apple.CoreML", + "description": "CoreML Model Specification", + "name": "model.mlmodel", + "path": "com.apple.CoreML/model.mlmodel" + }, + "7D35F675-3334-491B-8264-00E768D11202": { + "author": "com.apple.CoreML", + "description": "CoreML Model Weights", + "name": "weights", + "path": "com.apple.CoreML/weights" + } + }, + "rootModelIdentifier": "3F33ED2D-8E5C-4BBD-B243-7D003571E7E2" +} diff --git a/multilingual/2240ms/joint_noencproj_batched.mlmodelc/analytics/coremldata.bin b/multilingual/2240ms/joint_noencproj_batched.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..e018ec9de1fd95cbb225a25d41f7166cc2650ccd --- /dev/null +++ b/multilingual/2240ms/joint_noencproj_batched.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e5f4a9b0771be9af64fd93db7ceb42dbd305920b1260fe2219f1b046e84841cd +size 243 diff --git a/multilingual/2240ms/joint_noencproj_batched.mlmodelc/coremldata.bin b/multilingual/2240ms/joint_noencproj_batched.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..26d077246e6f610576835d6040df735a7222e4a5 --- /dev/null +++ b/multilingual/2240ms/joint_noencproj_batched.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fc07c4c2de2b13127f406ee70373b2c178702a03755bdc7a2bd57e623b5e65c5 +size 406 diff --git a/multilingual/2240ms/joint_noencproj_batched.mlmodelc/model.mil b/multilingual/2240ms/joint_noencproj_batched.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..89b37c4bfd82ec0f8905ac19299fbe7a5f1d7e73 --- /dev/null +++ b/multilingual/2240ms/joint_noencproj_batched.mlmodelc/model.mil @@ -0,0 +1,26 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.10.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] +{ + func main(tensor decoder, tensor encoder_proj) { + tensor input_1_perm_0 = const()[name = string("input_1_perm_0"), val = tensor([0, 2, 1])]; + string decoder_to_fp16_dtype_0 = const()[name = string("decoder_to_fp16_dtype_0"), val = string("fp16")]; + tensor joint_module_pred_weight_to_fp16 = const()[name = string("joint_module_pred_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor joint_module_pred_bias_to_fp16 = const()[name = string("joint_module_pred_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(819328)))]; + tensor decoder_to_fp16 = cast(dtype = decoder_to_fp16_dtype_0, x = decoder)[name = string("cast_2")]; + tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = decoder_to_fp16)[name = string("transpose_0")]; + tensor linear_0_cast_fp16 = linear(bias = joint_module_pred_bias_to_fp16, weight = joint_module_pred_weight_to_fp16, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_15_axes_0 = const()[name = string("op_15_axes_0"), val = tensor([2])]; + string encoder_proj_to_fp16_dtype_0 = const()[name = string("encoder_proj_to_fp16_dtype_0"), val = string("fp16")]; + tensor encoder_proj_to_fp16 = cast(dtype = encoder_proj_to_fp16_dtype_0, x = encoder_proj)[name = string("cast_1")]; + tensor var_15_cast_fp16 = expand_dims(axes = var_15_axes_0, x = encoder_proj_to_fp16)[name = string("op_15_cast_fp16")]; + tensor var_17_axes_0 = const()[name = string("op_17_axes_0"), val = tensor([1])]; + tensor var_17_cast_fp16 = expand_dims(axes = var_17_axes_0, x = linear_0_cast_fp16)[name = string("op_17_cast_fp16")]; + tensor input_3_cast_fp16 = add(x = var_15_cast_fp16, y = var_17_cast_fp16)[name = string("input_3_cast_fp16")]; + tensor input_5_cast_fp16 = relu(x = input_3_cast_fp16)[name = string("input_5_cast_fp16")]; + tensor joint_module_joint_net_2_weight_to_fp16 = const()[name = string("joint_module_joint_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(820672)))]; + tensor joint_module_joint_net_2_bias_to_fp16 = const()[name = string("joint_module_joint_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17573376)))]; + tensor linear_1_cast_fp16 = linear(bias = joint_module_joint_net_2_bias_to_fp16, weight = joint_module_joint_net_2_weight_to_fp16, x = input_5_cast_fp16)[name = string("linear_1_cast_fp16")]; + string linear_1_cast_fp16_to_fp32_dtype_0 = const()[name = string("linear_1_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor logits = cast(dtype = linear_1_cast_fp16_to_fp32_dtype_0, x = linear_1_cast_fp16)[name = string("cast_0")]; + } -> (logits); +} \ No newline at end of file diff --git a/multilingual/2240ms/joint_noencproj_batched.mlmodelc/weights/weight.bin b/multilingual/2240ms/joint_noencproj_batched.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..defecb9c76ab612924f900c8d498e0e5ff52cc43 --- /dev/null +++ b/multilingual/2240ms/joint_noencproj_batched.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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b/multilingual/2240ms/joint_noencproj_batched.mlpackage/Data/com.apple.CoreML/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8d6b104e9d6990c07d6cd41bafe27cae8d39cfe037ec701584c47af1094daeeb +size 17599616 diff --git a/multilingual/2240ms/joint_noencproj_batched.mlpackage/Manifest.json b/multilingual/2240ms/joint_noencproj_batched.mlpackage/Manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..f32ce8ce814e436e645a7f1306381c47fd849292 --- /dev/null +++ b/multilingual/2240ms/joint_noencproj_batched.mlpackage/Manifest.json @@ -0,0 +1,18 @@ +{ + "fileFormatVersion": "1.0.0", + "itemInfoEntries": { + "55B094CA-55E5-480A-8B14-30A24DC3EEF0": { + "author": "com.apple.CoreML", + "description": "CoreML Model Weights", + "name": "weights", + "path": "com.apple.CoreML/weights" + }, + "CA02FD13-87CE-4425-9B49-DE8265EC1B54": { + "author": "com.apple.CoreML", + "description": "CoreML Model Specification", + "name": "model.mlmodel", + "path": "com.apple.CoreML/model.mlmodel" + } + }, + "rootModelIdentifier": "CA02FD13-87CE-4425-9B49-DE8265EC1B54" +} diff --git a/multilingual/2240ms/metadata.json b/multilingual/2240ms/metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..733dec59932bd542421baa5caa6003835777aad9 --- /dev/null +++ b/multilingual/2240ms/metadata.json @@ -0,0 +1,197 @@ +{ + "model": "nvidia/nemotron-asr-streaming-multilingual-0.6b", + "model_class": "nemo.collections.asr.models.rnnt_bpe_models_prompt.EncDecRNNTBPEModelWithPrompt", + "sample_rate": 16000, + "mel_features": 128, + "chunk_mel_frames": 224, + "pre_encode_cache": 9, + "total_mel_frames": 233, + "att_context_size": [ + 42, + 13 + ], + "vocab_size": 13087, + "blank_idx": 13087, + "cache_channel_shape": [ + 1, + 24, + 42, + 1024 + ], + "cache_time_shape": [ + 1, + 24, + 1024, + 8 + ], + "decoder_hidden": 640, + "decoder_layers": 2, + "encoder_dim": 1024, + "num_prompts": 128, + "prompt_dictionary": 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sha256:f1a2d1fd7a5c11a17fec0fc4c1191f33e5a99f9b28965675ae0848ee255963d1 +size 431 diff --git a/multilingual/2240ms/preprocessor.mlmodelc/model.mil b/multilingual/2240ms/preprocessor.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..b1a0b2b9193c992de42e51245fc1ef433d345afc --- /dev/null +++ b/multilingual/2240ms/preprocessor.mlmodelc/model.mil @@ -0,0 +1,122 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.10.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] +{ + func main(tensor audio, tensor audio_length) [FlexibleShapeInformation = tuple>>, tuple, ?>>>>((("DefaultShapes", {{"audio", [1, 1]}}), ("RangeDims", {{"audio", [[1, 1], [1, 1280000]]}})))] { + int32 var_9 = const()[name = string("op_9"), val = int32(1)]; + int32 var_10 = const()[name = string("op_10"), val = int32(160)]; + int32 var_12 = const()[name = string("op_12"), val = int32(0)]; + int32 var_33 = const()[name = string("op_33"), val = int32(512)]; + tensor var_34 = add(x = audio_length, y = var_33)[name = string("op_34")]; + int32 var_35 = const()[name = string("op_35"), val = int32(512)]; + tensor var_36 = sub(x = var_34, y = var_35)[name = string("op_36")]; + tensor floor_div_0 = floor_div(x = var_36, y = var_10)[name = string("floor_div_0")]; + tensor var_39 = equal(x = audio_length, y = var_12)[name = string("op_39")]; + tensor var_40 = const()[name = string("op_40"), val = tensor([0])]; + tensor mel_length = select(a = var_40, b = floor_div_0, cond = var_39)[name = string("seq_len")]; + string audio_to_fp16_dtype_0 = const()[name = string("audio_to_fp16_dtype_0"), val = string("fp16")]; + tensor audio_to_fp16 = cast(dtype = audio_to_fp16_dtype_0, x = audio)[name = string("cast_10")]; + tensor var_42_shape_cast_fp16 = shape(x = audio_to_fp16)[name = string("op_42_shape_cast_fp16")]; + int32 gather_0_axis_0 = const()[name = string("gather_0_axis_0"), val = int32(0)]; + int32 gather_0_batch_dims_0 = const()[name = string("gather_0_batch_dims_0"), val = int32(0)]; + bool gather_0_validate_indices_0 = const()[name = string("gather_0_validate_indices_0"), val = bool(false)]; + string var_42_shape_cast_fp16_to_int16_dtype_0 = const()[name = string("op_42_shape_cast_fp16_to_int16_dtype_0"), val = string("int16")]; + uint16 gather_0_indices_0_to_uint16 = const()[name = string("gather_0_indices_0_to_uint16"), val = uint16(1)]; + tensor var_42_shape_cast_fp16_to_int16 = cast(dtype = var_42_shape_cast_fp16_to_int16_dtype_0, x = var_42_shape_cast_fp16)[name = string("cast_9")]; + int16 gather_0_cast_uint16 = gather(axis = gather_0_axis_0, batch_dims = gather_0_batch_dims_0, indices = gather_0_indices_0_to_uint16, validate_indices = gather_0_validate_indices_0, x = var_42_shape_cast_fp16_to_int16)[name = string("gather_0_cast_uint16")]; + string gather_0_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_0_cast_uint16_to_int32_dtype_0"), val = string("int32")]; + int32 const_0 = const()[name = string("const_0"), val = int32(0)]; + int32 const_1 = const()[name = string("const_1"), val = int32(1)]; + int32 gather_0_cast_uint16_to_int32 = cast(dtype = gather_0_cast_uint16_to_int32_dtype_0, x = gather_0_cast_uint16)[name = string("cast_8")]; + tensor var_43 = range_1d(end = gather_0_cast_uint16_to_int32, start = const_0, step = const_1)[name = string("op_43")]; + tensor var_44_axes_0 = const()[name = string("op_44_axes_0"), val = tensor([0])]; + tensor var_44 = expand_dims(axes = var_44_axes_0, x = var_43)[name = string("op_44")]; + tensor var_45_axes_0 = const()[name = string("op_45_axes_0"), val = tensor([1])]; + tensor var_45 = expand_dims(axes = var_45_axes_0, x = audio_length)[name = string("op_45")]; + tensor timemask = less(x = var_44, y = var_45)[name = string("timemask")]; + tensor var_48_begin_0 = const()[name = string("op_48_begin_0"), val = tensor([0, 0])]; + tensor var_48_end_0 = const()[name = string("op_48_end_0"), val = tensor([1, 1])]; + tensor var_48_end_mask_0 = const()[name = string("op_48_end_mask_0"), val = tensor([true, false])]; + tensor var_48_squeeze_mask_0 = const()[name = string("op_48_squeeze_mask_0"), val = tensor([false, true])]; + tensor var_48_cast_fp16 = slice_by_index(begin = var_48_begin_0, end = var_48_end_0, end_mask = var_48_end_mask_0, squeeze_mask = var_48_squeeze_mask_0, x = audio_to_fp16)[name = string("op_48_cast_fp16")]; + tensor var_49_axes_0 = const()[name = string("op_49_axes_0"), val = tensor([1])]; + tensor var_49_cast_fp16 = expand_dims(axes = var_49_axes_0, x = var_48_cast_fp16)[name = string("op_49_cast_fp16")]; + tensor var_51_begin_0 = const()[name = string("op_51_begin_0"), val = tensor([0, 1])]; + tensor var_51_end_0 = const()[name = string("op_51_end_0"), val = tensor([1, 0])]; + tensor var_51_end_mask_0 = const()[name = string("op_51_end_mask_0"), val = tensor([true, true])]; + tensor var_51_cast_fp16 = slice_by_index(begin = var_51_begin_0, end = var_51_end_0, end_mask = var_51_end_mask_0, x = audio_to_fp16)[name = string("op_51_cast_fp16")]; + tensor var_53_begin_0 = const()[name = string("op_53_begin_0"), val = tensor([0, 0])]; + tensor var_53_end_0 = const()[name = string("op_53_end_0"), val = tensor([1, -1])]; + tensor var_53_end_mask_0 = const()[name = string("op_53_end_mask_0"), val = tensor([true, false])]; + tensor var_53_cast_fp16 = slice_by_index(begin = var_53_begin_0, end = var_53_end_0, end_mask = var_53_end_mask_0, x = audio_to_fp16)[name = string("op_53_cast_fp16")]; + fp16 var_54_to_fp16 = const()[name = string("op_54_to_fp16"), val = fp16(0x1.f0cp-1)]; + tensor var_55_cast_fp16 = mul(x = var_53_cast_fp16, y = var_54_to_fp16)[name = string("op_55_cast_fp16")]; + tensor var_56_cast_fp16 = sub(x = var_51_cast_fp16, y = var_55_cast_fp16)[name = string("op_56_cast_fp16")]; + bool x_3_interleave_0 = const()[name = string("x_3_interleave_0"), val = bool(false)]; + tensor x_3_cast_fp16 = concat(axis = var_9, interleave = x_3_interleave_0, values = (var_49_cast_fp16, var_56_cast_fp16))[name = string("x_3_cast_fp16")]; + tensor var_59 = logical_not(x = timemask)[name = string("op_59")]; + fp16 var_16_to_fp16 = const()[name = string("op_16_to_fp16"), val = fp16(0x0p+0)]; + tensor input_1_cast_fp16 = select(a = var_16_to_fp16, b = x_3_cast_fp16, cond = var_59)[name = string("input_1_cast_fp16")]; + tensor concat_1x = const()[name = string("concat_1x"), val = tensor([1, 1, -1])]; + tensor input_3_cast_fp16 = reshape(shape = concat_1x, x = input_1_cast_fp16)[name = string("input_3_cast_fp16")]; + tensor input_5_pad_0 = const()[name = string("input_5_pad_0"), val = tensor([0, 0, 0, 0, 256, 256])]; + string input_5_mode_0 = const()[name = string("input_5_mode_0"), val = string("constant")]; + fp16 const_3_to_fp16 = const()[name = string("const_3_to_fp16"), val = fp16(0x0p+0)]; + tensor input_5_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = input_5_mode_0, pad = input_5_pad_0, x = input_3_cast_fp16)[name = string("input_5_cast_fp16")]; + tensor concat_2x = const()[name = string("concat_2x"), val = tensor([1, -1])]; + tensor input_cast_fp16 = reshape(shape = concat_2x, x = input_5_cast_fp16)[name = string("input_cast_fp16")]; + tensor expand_dims_3 = const()[name = string("expand_dims_3"), val = tensor([160])]; + tensor expand_dims_4_axes_0 = const()[name = string("expand_dims_4_axes_0"), val = tensor([1])]; + tensor expand_dims_4_cast_fp16 = expand_dims(axes = expand_dims_4_axes_0, x = input_cast_fp16)[name = string("expand_dims_4_cast_fp16")]; + string conv_0_pad_type_0 = const()[name = string("conv_0_pad_type_0"), val = string("valid")]; + tensor conv_0_pad_0 = const()[name = string("conv_0_pad_0"), val = tensor([0, 0])]; + tensor conv_0_dilations_0 = const()[name = string("conv_0_dilations_0"), val = tensor([1])]; + int32 conv_0_groups_0 = const()[name = string("conv_0_groups_0"), val = int32(1)]; + tensor expand_dims_1_to_fp16 = const()[name = string("expand_dims_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor conv_0_cast_fp16 = conv(dilations = conv_0_dilations_0, groups = conv_0_groups_0, pad = conv_0_pad_0, pad_type = conv_0_pad_type_0, strides = expand_dims_3, weight = expand_dims_1_to_fp16, x = expand_dims_4_cast_fp16)[name = string("conv_0_cast_fp16")]; + string conv_1_pad_type_0 = const()[name = string("conv_1_pad_type_0"), val = string("valid")]; + tensor conv_1_pad_0 = const()[name = string("conv_1_pad_0"), val = tensor([0, 0])]; + tensor conv_1_dilations_0 = const()[name = string("conv_1_dilations_0"), val = tensor([1])]; + int32 conv_1_groups_0 = const()[name = string("conv_1_groups_0"), val = int32(1)]; + tensor expand_dims_2_to_fp16 = const()[name = string("expand_dims_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263296)))]; + tensor conv_1_cast_fp16 = conv(dilations = conv_1_dilations_0, groups = conv_1_groups_0, pad = conv_1_pad_0, pad_type = conv_1_pad_type_0, strides = expand_dims_3, weight = expand_dims_2_to_fp16, x = expand_dims_4_cast_fp16)[name = string("conv_1_cast_fp16")]; + int32 stack_0_axis_0 = const()[name = string("stack_0_axis_0"), val = int32(-1)]; + tensor stack_0_cast_fp16 = stack(axis = stack_0_axis_0, values = (conv_0_cast_fp16, conv_1_cast_fp16))[name = string("stack_0_cast_fp16")]; + fp16 var_19_promoted_to_fp16 = const()[name = string("op_19_promoted_to_fp16"), val = fp16(0x1p+1)]; + tensor var_74_cast_fp16 = pow(x = stack_0_cast_fp16, y = var_19_promoted_to_fp16)[name = string("op_74_cast_fp16")]; + tensor var_76_axes_0 = const()[name = string("op_76_axes_0"), val = tensor([-1])]; + bool var_76_keep_dims_0 = const()[name = string("op_76_keep_dims_0"), val = bool(false)]; + tensor var_76_cast_fp16 = reduce_sum(axes = var_76_axes_0, keep_dims = var_76_keep_dims_0, x = var_74_cast_fp16)[name = string("op_76_cast_fp16")]; + tensor x_11_cast_fp16 = identity(x = var_76_cast_fp16)[name = string("x_11_cast_fp16")]; + bool x_13_transpose_x_0 = const()[name = string("x_13_transpose_x_0"), val = bool(false)]; + bool x_13_transpose_y_0 = const()[name = string("x_13_transpose_y_0"), val = bool(false)]; + tensor const_4_to_fp16 = const()[name = string("const_4_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(526528)))]; + tensor x_13_cast_fp16 = matmul(transpose_x = x_13_transpose_x_0, transpose_y = x_13_transpose_y_0, x = const_4_to_fp16, y = x_11_cast_fp16)[name = string("x_13_cast_fp16")]; + fp16 var_83_to_fp16 = const()[name = string("op_83_to_fp16"), val = fp16(0x1p-24)]; + tensor var_84_cast_fp16 = add(x = x_13_cast_fp16, y = var_83_to_fp16)[name = string("op_84_cast_fp16")]; + fp32 x_epsilon_0 = const()[name = string("x_epsilon_0"), val = fp32(0x1p-149)]; + tensor x_cast_fp16 = log(epsilon = x_epsilon_0, x = var_84_cast_fp16)[name = string("x_cast_fp16")]; + tensor var_86_shape_cast_fp16 = shape(x = x_cast_fp16)[name = string("op_86_shape_cast_fp16")]; + int32 gather_5_batch_dims_0 = const()[name = string("gather_5_batch_dims_0"), val = int32(0)]; + bool gather_5_validate_indices_0 = const()[name = string("gather_5_validate_indices_0"), val = bool(false)]; + string var_86_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_86_shape_cast_fp16_to_uint16_dtype_0"), val = string("uint16")]; + int32 gather_5_cast_uint16_axis_0 = const()[name = string("gather_5_cast_uint16_axis_0"), val = int32(0)]; + uint16 select_0_to_uint16 = const()[name = string("select_0_to_uint16"), val = uint16(2)]; + tensor var_86_shape_cast_fp16_to_uint16 = cast(dtype = var_86_shape_cast_fp16_to_uint16_dtype_0, x = var_86_shape_cast_fp16)[name = string("cast_7")]; + uint16 gather_5_cast_uint16_cast_uint16 = gather(axis = gather_5_cast_uint16_axis_0, batch_dims = gather_5_batch_dims_0, indices = select_0_to_uint16, validate_indices = gather_5_validate_indices_0, x = var_86_shape_cast_fp16_to_uint16)[name = string("gather_5_cast_uint16_cast_uint16")]; + string gather_5_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_5_cast_uint16_to_int32_dtype_0"), val = string("int32")]; + int32 const_5 = const()[name = string("const_5"), val = int32(0)]; + int32 const_6 = const()[name = string("const_6"), val = int32(1)]; + int32 gather_5_cast_uint16_to_int32 = cast(dtype = gather_5_cast_uint16_to_int32_dtype_0, x = gather_5_cast_uint16_cast_uint16)[name = string("cast_6")]; + tensor mask_1 = range_1d(end = gather_5_cast_uint16_to_int32, start = const_5, step = const_6)[name = string("mask_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([0])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = mask_1)[name = string("expand_dims_0")]; + tensor var_91_axes_0 = const()[name = string("op_91_axes_0"), val = tensor([1])]; + tensor var_91 = expand_dims(axes = var_91_axes_0, x = mel_length)[name = string("op_91")]; + tensor mask = greater_equal(x = expand_dims_0, y = var_91)[name = string("mask")]; + tensor var_93_axes_0 = const()[name = string("op_93_axes_0"), val = tensor([1])]; + tensor var_93 = expand_dims(axes = var_93_axes_0, x = mask)[name = string("op_93")]; + tensor processed_signal_cast_fp16 = select(a = var_16_to_fp16, b = x_cast_fp16, cond = var_93)[name = string("processed_signal_cast_fp16")]; + string processed_signal_cast_fp16_to_fp32_dtype_0 = const()[name = string("processed_signal_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor mel = cast(dtype = processed_signal_cast_fp16_to_fp32_dtype_0, x = processed_signal_cast_fp16)[name = string("cast_5")]; + } -> (mel, mel_length); +} \ No newline at end of file diff --git a/multilingual/2240ms/preprocessor.mlmodelc/weights/weight.bin b/multilingual/2240ms/preprocessor.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..86dd375f6649d262d58c9cf8b89006ceab216411 --- /dev/null +++ b/multilingual/2240ms/preprocessor.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:297514e2b211d14b0e53cb97193d679bb89ead98d28e578f3f1d049ddbcc36b3 +size 592384 diff --git a/multilingual/2240ms/preprocessor.mlpackage/Data/com.apple.CoreML/model.mlmodel b/multilingual/2240ms/preprocessor.mlpackage/Data/com.apple.CoreML/model.mlmodel new file mode 100644 index 0000000000000000000000000000000000000000..050fa97ca7a2aa4b7c4fa318f4fa2a51914287c4 --- /dev/null +++ b/multilingual/2240ms/preprocessor.mlpackage/Data/com.apple.CoreML/model.mlmodel @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0feb90f2cf9a5bc749fa763b6eb78a23755b6184b41a262fe08faebe4e709b3e +size 16035 diff --git a/multilingual/2240ms/preprocessor.mlpackage/Data/com.apple.CoreML/weights/weight.bin b/multilingual/2240ms/preprocessor.mlpackage/Data/com.apple.CoreML/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..86dd375f6649d262d58c9cf8b89006ceab216411 --- /dev/null +++ b/multilingual/2240ms/preprocessor.mlpackage/Data/com.apple.CoreML/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:297514e2b211d14b0e53cb97193d679bb89ead98d28e578f3f1d049ddbcc36b3 +size 592384 diff --git a/multilingual/2240ms/preprocessor.mlpackage/Manifest.json b/multilingual/2240ms/preprocessor.mlpackage/Manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..667b90978564849cb1c6d3c8b10f535ad524d4be --- /dev/null +++ b/multilingual/2240ms/preprocessor.mlpackage/Manifest.json @@ -0,0 +1,18 @@ +{ + "fileFormatVersion": "1.0.0", + "itemInfoEntries": { + "02ED6D10-6658-4E52-83B8-E0D3DEA2B8AC": { + "author": "com.apple.CoreML", + "description": "CoreML Model Weights", + "name": "weights", + "path": "com.apple.CoreML/weights" + }, + "8D115BDF-817A-4C02-9F24-4AF6137B0210": { + "author": "com.apple.CoreML", + "description": "CoreML Model Specification", + "name": "model.mlmodel", + "path": "com.apple.CoreML/model.mlmodel" + } + }, + "rootModelIdentifier": "8D115BDF-817A-4C02-9F24-4AF6137B0210" +} diff --git a/multilingual/2240ms/tokenizer.json b/multilingual/2240ms/tokenizer.json new file mode 100644 index 0000000000000000000000000000000000000000..5c9c31a266fd62950553b9d5fef65598813f55e0 --- /dev/null +++ b/multilingual/2240ms/tokenizer.json @@ -0,0 +1,13089 @@ +{ + "0": "", + "1": "", + "2": "\u2581", + "3": "\u0438", + "4": ".", + "5": "\u0435", + "6": ",", + "7": "\u0430", + "8": "\u0441", + "9": "\u043e", + "10": "\u043d", + "11": "\u0442", + "12": "\u0442\u0430", + "13": "\u044f", + "14": "\u043a", + "15": "\u2581\u043d\u0430", + "16": "\u043b", + "17": "\u0443", + "18": "\u0437", + "19": "\u0440", + "20": "\u0442\u043e", + "21": "\u043d\u0430", + "22": "\u2581\u0434\u0430", + "23": "\u0432\u0430", + "24": "\u0440\u0430", + "25": "\u0434", + "26": "e", + "27": "\u043a\u0430", + "28": "\u2581\u0437\u0430", + "29": "\u043d\u043e", + "30": "\u043c", + "31": "\u043d\u0438", + "32": "\u044a", + "33": "\u043c\u0435", + "34": "t", + "35": "\u0441\u0442", + "36": "\u043f", + "37": "\u2581\u043f\u043e", + "38": "a", + "39": "\u043d\u0435", + "40": "s", + "41": "\u2581\u0441\u0435", + "42": "\u0440\u0435", + "43": "\u0432", + "44": "\u043a\u043e", + "45": "\u2581\u0432", + "46": "o", + "47": "i", + "48": "\u2581\u0441", + "49": "\u0433", + "50": "\u0442\u0435", + "51": "\u043b\u0438", + "52": "\u0447\u0435", + "53": "\u0431", + "54": "\u2581\u0438", + "55": "\u0442\u0438", + "56": "\u0436", + "57": "\u2581\u043e\u0442", + "58": "\u0447", + "59": "r", + "60": "\u0438\u0442\u0435", + "61": "\u043c\u0430", + "62": "\u0445", + "63": "\u0432\u0435", + "64": "\u0432\u0438", + "65": "\u0440\u0438", + "66": "l", + "67": "\u0448", + "68": "u", + "69": "\u0432\u043e", + "70": "\u2581\u0435", + "71": "d", + "72": "\u0434\u0438", + "73": "c", + "74": "\u0433\u0430", + "75": "\u043c\u0438", + "76": "\u043b\u0430", + "77": "\u043f\u043e", + "78": "\u2581\u0441\u044a", + "79": "\u0439", + "80": "\u0449\u0435", + "81": "\u043b\u0435", + "82": "\u0440\u043e", + "83": "\u0434\u0435", + "84": "\u0433\u043e", + "85": "h", + "86": "m", + "87": "\u0446\u0438", + "88": "\u0449", + "89": "\u2581\u043f\u0440\u043e", + "90": "\u2581\u0438\u0437", + "91": "\u0434\u0430", + "92": "\u043c\u043e", + "93": "\u0441\u0438", + "94": "\u0418", + "95": "\u2581\u0442\u043e\u0432\u0430", + "96": "\u043d\u0438\u044f", + "97": "p", + "98": "\u043d\u0438\u0442\u0435", + "99": "\u0435\u043d\u0438\u0435", + "100": "\u043b\u043e", + "101": "\u041d", + "102": "\u0444", + "103": "\u2581\u0434\u043e", + "104": "\u041f", + "105": "\u2581\u043f\u0440\u0435\u0434", + "106": "\u2581\u043f\u0440\u0438", + "107": "\u043f\u0440\u0430\u0432", + "108": "\u0421", + "109": "\u2581\u0441\u0430", + "110": "\u2581\u0440\u0430\u0437", + "111": "\u0449\u043e", + "112": "\u2581\u043e\u0431", + "113": "n", + "114": "\u2581\u0438\u043c\u0430", + "115": "\u043f\u0430", + "116": "\u0441\u0442\u0430", + "117": "\u0412", + "118": "\u2581\u0442\u0440\u044f\u0431\u0432\u0430", + "119": "g", + "120": "\u043d\u043e\u0441\u0442", + "121": "f", + "122": "\u0431\u0438", + "123": "\u0447\u0430", + "124": "en", + "125": "in", + "126": "\u0446\u0438\u044f", + "127": "\u0432\u044a\u0440", + "128": "on", + "129": "y", + "130": "er", + "131": "an", + "132": "\u0422", + "133": "w", + "134": "\u0432\u0430\u043d\u0435", + "135": "\u041a", + "136": "\u2581\u043a\u043e\u0438\u0442\u043e", + "137": "\u2581\u043c\u043d\u043e\u0433\u043e", + "138": "\u2581\u043f\u0440\u0435", + "139": "0", + "140": "b", + "141": "v", + "142": "\u0434\u044a\u0440\u0436\u0430", + "143": "\u0446\u0435", + "144": "\u0410", + "145": "\u0436\u0434\u0430", + "146": "\u2581\u0411\u043b\u0430\u0433\u043e\u0434\u0430\u0440\u044f", + "147": "2", + "148": "\u041c", + "149": "1", + "150": "\u2581the", + "151": "\u041e", + "152": "\u2581\u043c\u043e\u0436\u0435", + "153": "\u0414", + "154": "\u0441\u043b\u0435\u0434", + "155": "\u0433\u043e\u0432\u043e\u0440", + "156": "\u0440\u0430\u0431\u043e\u0442", + "157": "\u2581\u0432\u044a\u043f\u0440\u043e\u0441", + "158": "\u0434\u043e\u0431", + "159": "\u0417", + "160": "\u0446", + "161": "k", + "162": "\u2581\u0422\u043e\u0432\u0430", + "163": "?", + "164": "\u2581\u0431\u044a\u0434\u0435", + "165": "\u2581\u043a\u043e\u043c\u0438\u0441\u0438\u044f", + "166": "\u0415", + "167": "\u2581\u0442\u043e\u0437\u0438", + "168": "\u2581\u0442\u0435\u0437\u0438", + "169": "\u2581\u0433\u043e\u0441\u043f\u043e\u0434\u0438\u043d", + "170": "\u2581\u0432\u044a\u0437", + "171": "\u2581\u0415\u0432\u0440\u043e\u043f\u0435\u0439\u0441\u043a\u0438\u044f", + "172": "\u0411", + "173": "\u0413", + "174": "\u0420", + "175": "\u044e", + "176": "3", + "177": "5", + "178": "q", + "179": "I", + "180": "\u00e9", + "181": "4", + "182": "z", + "183": "A", + "184": "6", + "185": "j", + "186": "E", + "187": "7", + "188": "\u0429", + "189": "T", + "190": "8", + "191": "9", + "192": "\u041b", + "193": "S", + "194": "\u0424", + "195": "x", + "196": "C", + "197": "\u0425", + "198": "\u044c", + "199": "M", + "200": "P", + "201": "\u0423", + "202": "D", + "203": "B", + "204": "\u0427", + "205": "U", + "206": "W", + "207": "\u0428", + "208": "\u0426", + "209": "N", + "210": "O", + "211": "G", + "212": "\u00e0", + "213": "F", + "214": "L", + "215": "\u00e8", + "216": "V", + "217": "R", + "218": "\u0627", + "219": "\u042e", + "220": "\u00f3", + "221": "\u042f", + "222": "H", + "223": "\u03b1", + "224": "\u00fc", + "225": "\u00e4", + "226": "\u0644", + "227": "\u0416", + "228": "J", + "229": "\u00ed", + "230": "\u03c4", + "231": "\u03b9", + "232": "\u00e1", + "233": "\u03bf", + "234": "K", + "235": "\u03b5", + "236": "\u064a", + "237": "\u00ea", + "238": "Y", + "239": "\u03bd", + "240": "\u0646", + "241": "\u00f6", + "242": "\u0645", + "243": "\u00e7", + "244": "\u03c1", + "245": "\u0419", + "246": "\u0648", + "247": "\u03c3", + "248": "\u03c0", + "249": "\u0119", + "250": "\u03c5", + "251": "\u062a", + "252": "\u03b7", + "253": "\u0631", + "254": "\u03bc", + "255": "\u03ba", + "256": "", + "257": "st", + "258": "ch", + "259": "n\u00ed", + "260": "\u2581s", + "261": "le", + "262": "li", + "263": "\u2581po", + "264": "\u2581v", + "265": "\u017e", + "266": "\u010d", + "267": "\u2581to", + "268": "no", + "269": "to", + "270": "\u2581z", + "271": "me", + "272": "\u2581se", + "273": "\u2581a", + "274": "te", + "275": "\u2581je", + "276": "ho", + "277": "\u2581pro", + "278": "\u016f", + "279": "n\u011b", + "280": "ro", + "281": "\u2581na", + "282": "ce", + "283": "\u2581o", + "284": "la", + "285": "\u0161", + "286": "\u2581ne", + "287": "ni", + "288": "ra", + "289": "ti", + "290": "lo", + "291": "ko", + "292": "\u2581\u017ee", + "293": "n\u00e1", + "294": "po", + "295": "je", + "296": "\u011b", + "297": "de", + "298": "na", + "299": "mi", + "300": "\u2581do", + "301": "ci", + "302": "\u2581k", + "303": "ku", + "304": "\u0159e", + "305": "\u2581by", + "306": "ve", + "307": "\u2581za", + "308": "m\u011b", + "309": "\u2581A", + "310": "\u00fd", + "311": "re", + "312": "v\u00e1", + "313": "ou", + "314": "vo", + "315": "n\u00e9", + "316": "va", + "317": "\u017ee", + "318": "mo", + "319": "v\u011b", + "320": "j\u00ed", + "321": "t\u011b", + "322": "v\u00fd", + "323": "\u2581tak", + "324": "ze", + "325": "\u0159\u00ed", + "326": "ne", + "327": "\u0161e", + "328": "\u2581vy", + "329": "ka", + "330": "ji", + "331": "ky", + "332": "r\u00e1", + "333": "ovat", + "334": "\u2581ob", + "335": "c\u00ed", + "336": "\u2581jak", + "337": "\u2581p\u0159e", + "338": "ny", + "339": "v\u00ed", + "340": "n\u00fd", + "341": "vi", + "342": "\u2581in", + "343": "pr\u00e1v", + "344": "\u00fa", + "345": "\u2581co", + "346": "\u2581tak\u00e9", + "347": "ent", + "348": "\u2581pan", + "349": "\u2581D\u011bkuji", + "350": "\u2581kter\u00e9", + "351": "\u0159i", + "352": "\u2581aby", + "353": "\u2581p\u0159\u00ed", + "354": "\u2581p\u0159i", + "355": "prav", + "356": "\u0159", + "357": "vrop", + "358": "\u2581bude", + "359": "\u2581roz", + "360": "\u2581jsou", + "361": "ov\u00e9", + "362": "\u2581jsme", + "363": "sk\u00e9", + "364": "ov\u00e1n\u00ed", + "365": "\u2581tady", + "366": "sk\u00fd", + "367": "d\u011bl", + "368": "\u2581mus\u00ed", + "369": "Z", + "370": "klad", + "371": "\u2581tedy", + "372": "dob", + "373": "\u2581To", + "374": "\u00e1ln\u00ed", + "375": "\u2581Je", + "376": "\u2581st\u00e1t", + "377": "\u0148", + "378": "oval", + "379": "\u2581proto\u017ee", + "380": "\u2581jsem", + "381": "\u2581kter\u00fd", + "382": "p\u0159edsed", + "383": "\u2581b\u00fdt", + "384": "\u010f", + "385": "\u010c", + "386": "\u0165", + "387": "\u0160", + "388": "\u0158", + "389": "\u017d", + "390": "\u0103", + "391": "\u0142", + "392": "\u017c", + "393": "\u0105", + "394": "X", + "395": "\u00da", + "396": "\u015b", + "397": "", + "398": "\u2581for", + "399": "\u2581det", + "400": "\u2581at", + "401": "\u00e6", + "402": "et", + "403": "\u2581og", + "404": "\u2581vi", + "405": "al", + "406": "\u2581de", + "407": "\u2581der", + "408": "\u2581til", + "409": "or", + "410": "\u2581er", + "411": "om", + "412": "\u00e5", + "413": "\u00f8", + "414": "and", + "415": "\u2581har", + "416": "at", + "417": "\u2581f", + "418": "\u2581i", + "419": "\u2581s\u00e5", + "420": "\u2581af", + "421": "ge", + "422": "ar", + "423": "is", + "424": "ing", + "425": "\u2581med", + "426": "\u2581p\u00e5", + "427": "\u2581be", + "428": "un", + "429": "lig", + "430": "\u2581ikke", + "431": "\u2581man", + "432": "ig", + "433": "\u2581som", + "434": "\u00f8r", + "435": "\u2581Og", + "436": "el", + "437": "ag", + "438": "\u2581skal", + "439": "erne", + "440": "\u2581Det", + "441": "\u2581den", + "442": "ste", + "443": "ning", + "444": "\u2581jeg", + "445": "id", + "446": "\u2581kan", + "447": "\u2581ogs\u00e5", + "448": "\u2581vil", + "449": "ske", + "450": "iv", + "451": "\u2581ud", + "452": "\u2581her", + "453": "ion", + "454": "am", + "455": "ur", + "456": "for", + "457": "\u2581pr", + "458": "else", + "459": "\u2581sig", + "460": "\u2581men", + "461": "\u2581ind", + "462": "\u2581jo", + "463": "ende", + "464": "\u2581v\u00e6re", + "465": "\u2581Vi", + "466": "ation", + "467": "\u2581m\u00e5", + "468": "mme", + "469": "ighed", + "470": "tage", + "471": "\u2581op", + "472": "\u2581Jeg", + "473": "\u2581hvor", + "474": "\u2581ved", + "475": "\u2581f\u00e5", + "476": "\u2581fra", + "477": "\u2581over", + "478": "\u2581have", + "479": "kke", + "480": "\u2581meget", + "481": "\u2581S\u00e5", + "482": "\u2581Tak", + "483": "\u2581noget", + "484": "\u2581alle", + "485": "brug", + "486": "\u2581komme", + "487": "\u2581Men", + "488": "\u2581var", + "489": "hold", + "490": "arbejde", + "491": "\u2581eller", + "492": "\u2581vores", + "493": "\u2581frem", + "494": "\u2581alts\u00e5", + "495": "\u2581vigtig", + "496": "v\u00e6r", + "497": "\u2581EU", + "498": "\u2581g\u00f8re", + "499": "\u2581nogle", + "500": "skab", + "501": "\u2581sp\u00f8rgsm\u00e5l", + "502": "\u2581kunne", + "503": "\u2581kommissionen", + "504": "\u2581hvis", + "505": "\u00d8", + "506": "\u00c6", + "507": "\u03c2", + "508": "\u03bb", + "509": "\u03af", + "510": "\u03cc", + "511": "\u0131", + "512": "\u03ad", + "513": "\u03ac", + "514": "\u03c9", + "515": "\u03b3", + "516": "\u03b4", + "517": "\u03ae", + "518": "", + "519": "\u2581die", + "520": "\u2581und", + "521": "\u2581das", + "522": "sch", + "523": "\u2581ist", + "524": "\u2581ich", + "525": "\u2581ein", + "526": "\u2581ge", + "527": "ung", + "528": "it", + "529": "\u2581wir", + "530": "\u2581zu", + "531": "\u2581so", + "532": "\u2581da", + "533": "\u2581S", + "534": "\u2581auch", + "535": "gen", + "536": "\u2581nicht", + "537": "\u2581W", + "538": "\u2581B", + "539": "\u2581E", + "540": "\u2581F", + "541": "ll", + "542": "\u2581es", + "543": "\u2581K", + "544": "ie", + "545": "au", + "546": "\u2581P", + "547": "ich", + "548": "\u2581eine", + "549": "lich", + "550": "ck", + "551": "ten", + "552": "mal", + "553": "ein", + "554": "\u2581T", + "555": "\u2581dann", + "556": "\u2581Und", + "557": "\u2581mit", + "558": "\u2581auf", + "559": "hr", + "560": "ter", + "561": "tz", + "562": "\u2581dass", + "563": "\u2581G", + "564": "ben", + "565": "um", + "566": "us", + "567": "cht", + "568": "il", + "569": "\u2581Das", + "570": "\u2581diese", + "571": "\u2581noch", + "572": "\u2581jetzt", + "573": "ut", + "574": "\u2581ver", + "575": "kt", + "576": "\u2581Ich", + "577": "\u2581hier", + "578": "\u2581hat", + "579": "\u2581haben", + "580": "\u2581von", + "581": "ri", + "582": "ach", + "583": "ol", + "584": "\u2581Da", + "585": "\u2581als", + "586": "sp", + "587": "\u2581f\u00fcr", + "588": "ell", + "589": "\u2581sich", + "590": "\u2581was", + "591": "\u2581ja", + "592": "uch", + "593": "\u2581kann", + "594": "\u2581sind", + "595": "wi", + "596": "\u2581aus", + "597": "rei", + "598": "\u2581wie", + "599": "\u2581Ge", + "600": "und", + "601": "\u2581St", + "602": "isch", + "603": "\u2581sie", + "604": "\u2581Ja", + "605": "\u2581du", + "606": "\u2581war", + "607": "\u2581im", + "608": "\u2581dem", + "609": "\u2581aber", + "610": "\u2581oder", + "611": "\u00df", + "612": "\u2581Sch", + "613": "\u2581uns", + "614": "\u2581habe", + "615": "\u2581wenn", + "616": "\u2581wo", + "617": "\u2581bei", + "618": "\u2581ihr", + "619": "\u2581Ma", + "620": "zu", + "621": "\u2581schon", + "622": "\u2581De", + "623": "\u2581Sie", + "624": "\u2581\u00fcber", + "625": "\u2581vor", + "626": "\u2581Die", + "627": "\u2581ganz", + "628": "iert", + "629": "\u2581Le", + "630": "\u2581viel", + "631": "\u2581In", + "632": "\u2581Also", + "633": "\u2581Ver", + "634": "\u2581sehr", + "635": "\u2581Re", + "636": "halt", + "637": "\u2581einfach", + "638": "\u2581werden", + "639": "\u2581sein", + "640": "\u2581Wir", + "641": "\u2581nur", + "642": "\u2581immer", + "643": "ieren", + "644": "\u2581muss", + "645": "\u2581wieder", + "646": "\u2581mir", + "647": "\u2581gut", + "648": "\u2581mehr", + "649": "\u2581Mi", + "650": "\u2581nach", + "651": "\u2581Ha", + "652": "\u2581weil", + "653": "\u2581Aber", + "654": "kommen", + "655": "\u2581gibt", + "656": "\u2581meine", + "657": "\u2581andere", + "658": "\u2581k\u00f6nnen", + "659": "\u2581machen", + "660": "\u2581nat\u00fcrlich", + "661": "\u2581bisschen", + "662": "\u2581durch", + "663": "sehen", + "664": "\u2581weiter", + "665": "\u2581keine", + "666": "\u2581sagen", + "667": "\u2581wirklich", + "668": "\u2581eigentlich", + "669": "\u2581jede", + "670": "schaft", + "671": "\u2581glaube", + "672": "\u00dc", + "673": "", + "674": "\u03c7", + "675": "\u03c4\u03b1", + "676": "\u2581\u03bd\u03b1", + "677": "\u03b5\u03b9", + "678": "\u2581\u03ba\u03b1\u03b9", + "679": "\u03bc\u03b1", + "680": "\u03b2", + "681": "\u03c3\u03b7", + "682": "\u03c4\u03b5", + "683": "\u03ce", + "684": "\u03b8", + "685": "\u03c6", + "686": "\u03c0\u03bf", + "687": "\u03cd", + "688": "\u2581\u03c4\u03bf", + "689": "\u03af\u03b1", + "690": "\u03c4\u03b9", + "691": "\u03b1\u03bd", + "692": "\u03bf\u03c5", + "693": "\u03c1\u03b1", + "694": "\u2581\u03b3\u03b9\u03b1", + "695": "\u03b5\u03af", + "696": "\u03c4\u03b7", + "697": "\u03be", + "698": "\u03ba\u03b1", + "699": "\u2581\u03c4\u03b7\u03bd", + "700": "\u2581\u03c4\u03b7", + "701": "\u03bc\u03b5", + "702": "\u03c4\u03bf", + "703": "\u03bf\u03cd", + "704": "\u2581\u03c4\u03bf\u03c5", + "705": "\u2581\u03c0\u03c1\u03bf", + "706": "\u2581\u03bc\u03b5", + "707": "\u03b6", + "708": "\u2581\u03b8\u03b1", + "709": "\u2581\u03b5\u03af\u03bd\u03b1\u03b9", + "710": "\u03c1\u03bf", + "711": "\u03c9\u03bd", + "712": "\u03bc\u03ad", + "713": "\u2581\u03c0\u03bf\u03c5", + "714": "\u03b9\u03b1", + "715": "\u03bd\u03bf", + "716": "\u03b9\u03ba\u03ae", + "717": "\u03ce\u03bd", + "718": "\u03c1\u03b9", + "719": "\u03b8\u03b5", + "720": "\u0395", + "721": "\u03c1\u03af", + "722": "\u2581\u03cc\u03c4\u03b9", + "723": "\u03bf\u03c5\u03bc\u03b5", + "724": "\u2581\u03b1\u03c0\u03cc", + "725": "\u03bb\u03bf", + "726": "\u03c1\u03ac", + "727": "\u03b9\u03bf", + "728": "\u2581\u03c4\u03c9\u03bd", + "729": "\u03b5\u03c5", + "730": "\u03bb\u03b7", + "731": "\u03bf\u03c5\u03bd", + "732": "\u0391", + "733": "\u2581\u03c3\u03b5", + "734": "\u03a0", + "735": "\u2581\u03c3\u03c5\u03bd", + "736": "\u03c6\u03bf\u03c1", + "737": "\u2581\u03b4\u03b5\u03bd", + "738": "\u03a3", + "739": "\u2581\u03c3\u03c4\u03bf", + "740": "\u2581\u03b4\u03b9", + "741": "\u03c4\u03ac", + "742": "\u2581\u03b1\u03c5\u03c4\u03cc", + "743": "\u2581\u03b4\u03b9\u03b1", + "744": "\u03b9\u03c3\u03c4", + "745": "\u2581\u03c0\u03bf\u03bb\u03cd", + "746": "\u2581\u03c0\u03c1\u03ad\u03c0\u03b5\u03b9", + "747": "\u2581\u03c3\u03c4\u03b7\u03bd", + "748": "\u03c3\u03bf\u03c5\u03bc\u03b5", + "749": "\u03b9\u03ba\u03ac", + "750": "\u03a4", + "751": "\u2581\u03b5\u03c0", + "752": "\u039a", + "753": "\u03c8", + "754": "\u2581\u03b1\u03c0\u03bf", + "755": "\u2581\u03bf\u03b9", + "756": "\u03b5\u03c4\u03b1\u03b9", + "757": "\u2581\u03b5\u03c0\u03b9", + "758": "\u2581\u03a5\u03c0\u03cc\u03c4\u03b9\u03c4\u03bb\u03bf\u03b9", + "759": "\u2581AUTHORWAVE", + "760": "\u03bf\u03cd\u03bc\u03b5", + "761": "\u03b9\u03ba\u03cc", + "762": "\u2581\u039a\u03b1\u03b9", + "763": "\u03c0\u03c1\u03cc", + "764": "\u2581\u0395\u03c5\u03c7\u03b1\u03c1\u03b9\u03c3\u03c4\u03ce", + "765": "\u2581\u03bc\u03b9\u03b1", + "766": "\u2581\u03ad\u03bd\u03b1", + "767": "\u2581\u03c3\u03c5\u03bc", + "768": "\u039c", + "769": "\u2581\u03c0\u03b5\u03c1\u03b9", + "770": "\u2581\u03b1\u03c5\u03c4\u03ae", + "771": "\u03ae\u03c3\u03b5\u03b9", + "772": "\u039f", + "773": "\u03b9\u03ba\u03ad", + "774": "\u2581\u03ba\u03b1\u03c4\u03ac", + "775": "\u0393", + "776": "\u0398", + "777": "\u2581\u0395\u03c5\u03c1\u03c9\u03c0\u03b1\u03ca\u03ba\u03ae", + "778": "\u2581\u03ad\u03c7\u03bf\u03c5\u03bc\u03b5", + "779": "\u2581\u03b1\u03bb\u03bb\u03ac", + "780": "\u03b5\u03c1\u03b3", + "781": "\u0397", + "782": "\u2581\u03b8\u03ad\u03bc\u03b1", + "783": "\u03bf\u03bb\u03bf\u03b3", + "784": "\u03cc\u03c4\u03b7\u03c4\u03b1", + "785": "\u2581\u03ad\u03c7\u03b5\u03b9", + "786": "\u03c0\u03bf\u03bb\u03b9\u03c4", + "787": "\u0394", + "788": "\u2581\u03bb\u03bf\u03b9\u03c0\u03cc\u03bd", + "789": "\u03bf\u03bd\u03c4\u03b1\u03b9", + "790": "\u039d", + "791": "\u03c6\u03ad\u03c1", + "792": "\u2581\u0395\u03c0\u03b9\u03c4\u03c1\u03bf\u03c0\u03ae", + "793": "\u2581\u03b1\u03c5\u03c4\u03ac", + "794": "\u2581\u0388\u03bd\u03c9\u03c3\u03b7", + "795": "\u03a5", + "796": "\u03ca", + "797": "\u2581\u0394\u03b5\u03bd", + "798": "\u2581\u03ad\u03c7\u03bf\u03c5\u03bd", + "799": "\u2581\u03c5\u03c0\u03ac\u03c1\u03c7\u03b5\u03b9", + "800": "\u0392", + "801": "\u0399", + "802": "\u039b", + "803": "\u03a6", + "804": "\u03a1", + "805": "\u03a7", + "806": "\u039e", + "807": "\u03a9", + "808": "\u0396", + "809": "\u03a8", + "810": "\u0389", + "811": "\u0386", + "812": "\u038c", + "813": "\u0388", + "814": "", + "815": "ma", + "816": "ta", + "817": "se", + "818": "da", + "819": "si", + "820": "\u2581on", + "821": "\u00f5", + "822": "ks", + "823": "ga", + "824": "\u2581et", + "825": "\u2581ka", + "826": "he", + "827": "mu", + "828": "tu", + "829": "ha", + "830": "ja", + "831": "gi", + "832": "\u2581selle", + "833": "\u2581ole", + "834": "nd", + "835": "oo", + "836": "gu", + "837": "ju", + "838": "est", + "839": "\u2581ei", + "840": "\u2581pa", + "841": "nud", + "842": "\u2581v\u00e4ga", + "843": "\u2581see", + "844": "tud", + "845": "\u2581pea", + "846": "nda", + "847": "\u00e4r", + "848": "\u2581Euroopa", + "849": "\u2581kui", + "850": "vad", + "851": "ke", + "852": "sta", + "853": "sed", + "854": "\u2581v\u00f5i", + "855": "di", + "856": "\u2581saa", + "857": "mise", + "858": "\u2581siis", + "859": "\u2581su", + "860": "ide", + "861": "pool", + "862": "val", + "863": "tus", + "864": "\u2581seda", + "865": "\u2581Me", + "866": "\u2581vastu", + "867": "\u2581j\u00e4", + "868": "\u2581tule", + "869": "selt", + "870": "ment", + "871": "\u2581kes", + "872": "ndus", + "873": "\u2581t\u00f6\u00f6", + "874": "\u2581k\u00f5ik", + "875": "dus", + "876": "\u2581m\u00f5", + "877": "eeri", + "878": "\u2581meie", + "879": "\u2581meil", + "880": "\u2581ning", + "881": "v\u00f5t", + "882": "\u2581mida", + "883": "\u2581arv", + "884": "\u2581See", + "885": "takse", + "886": "\u2581vaja", + "887": "\u2581osa", + "888": "\u00f5igus", + "889": "\u2581nende", + "890": "\u2581n\u00fc\u00fcd", + "891": "\u2581aasta", + "892": "tsiooni", + "893": "\u2581inim", + "894": "\u2581need", + "895": "tsus", + "896": "riigi", + "897": "\u2581t\u00e4h", + "898": "\u2581Liidu", + "899": "\u2581v\u00e4lja", + "900": "\u00c4", + "901": "\u00d5", + "902": "\u00e3", + "903": "Q", + "904": "\u0107", + "905": "\u0639", + "906": "\u00f1", + "907": "", + "908": "t\u00e4", + "909": "ssa", + "910": "lla", + "911": "\u2581ett\u00e4", + "912": "ksi", + "913": "ty", + "914": "ki", + "915": "v\u00e4", + "916": "pa", + "917": "lle", + "918": "lu", + "919": "tta", + "920": "st\u00e4", + "921": "isi", + "922": "ise", + "923": "ll\u00e4", + "924": "kin", + "925": "n\u00e4", + "926": "\u00e4\u00e4n", + "927": "kse", + "928": "tte", + "929": "j\u00e4", + "930": "tt\u00e4", + "931": "ss\u00e4", + "932": "ista", + "933": "inen", + "934": "k\u00e4", + "935": "llis", + "936": "t\u00f6", + "937": "\u2581my\u00f6s", + "938": "vu", + "939": "taan", + "940": "\u2581t\u00e4m\u00e4", + "941": "\u2581voi", + "942": "utta", + "943": "iden", + "944": "nyt", + "945": "\u2581niin", + "946": "\u2581Kiitos", + "947": "\u2581ovat", + "948": "h\u00e4n", + "949": "suu", + "950": "\u2581toimi", + "951": "aika", + "952": "\u2581T\u00e4m\u00e4", + "953": "\u2581p\u00e4\u00e4", + "954": "\u2581mutta", + "955": "\u2581k\u00e4y", + "956": "\u2581t\u00e4ss\u00e4", + "957": "\u2581asia", + "958": "\u2581T\u00e4", + "959": "\u2581jotka", + "960": "\u2581ty\u00f6", + "961": "neet", + "962": "\u2581t\u00e4ytyy", + "963": "\u2581sitten", + "964": "\u2581Euroopan", + "965": "\u2581puolesta", + "966": "\u2581halua", + "967": "\u2581siit\u00e4", + "968": "\u2581komissio", + "969": "\u2581hyv\u00e4", + "970": "\u2581hyvin", + "971": "\u2581puhu", + "972": "\u2581meid\u00e4n", + "973": "\u2581vastaan", + "974": "\u2581t\u00e4rke\u00e4", + "975": "\u2581kaikki", + "976": "\u2581Kiitoksia", + "977": "\u2581viel\u00e4", + "978": "\u2581muut", + "979": "\u2581paljon", + "980": "mahdollis", + "981": "parlament", + "982": "\u2581pit\u00e4isi", + "983": "\u2581hyv\u00e4ksy", + "984": "\u2581puheenjohtaja", + "985": "\u2581liitty", + "986": "\u0101", + "987": "\u10d0", + "988": "\u10d8", + "989": "\u012b", + "990": "\u0113", + "991": "\u00eb", + "992": "\u10d4", + "993": "", + "994": "\u2581est", + "995": "\u2581c", + "996": "\u2581d", + "997": "\u2581la", + "998": "\u2581p", + "999": "\u2581que", + "1000": "\u2581en", + "1001": "\u2581le", + "1002": "\u2581\u00e0", + "1003": "es", + "1004": "\u2581l", + "1005": "\u2581un", + "1006": "\u2581pas", + "1007": "\u2581les", + "1008": "\u2581qui", + "1009": "\u2581il", + "1010": "\u2581vous", + "1011": "\u2581des", + "1012": "\u2581ce", + "1013": "\u2581qu", + "1014": "\u2581pour", + "1015": "\u2581n", + "1016": "\u2581par", + "1017": "\u2581\u00e7a", + "1018": "\u2581une", + "1019": "\u2581b", + "1020": "ant", + "1021": "\u2581j", + "1022": "ais", + "1023": "ez", + "1024": "\u2581dans", + "1025": "\u2581va", + "1026": "\u2581C", + "1027": "tre", + "1028": "ir", + "1029": "elle", + "1030": "eur", + "1031": "\u2581sur", + "1032": "\u2581re", + "1033": "\u2581con", + "1034": "\u2581ma", + "1035": "\u2581Et", + "1036": "\u2581au", + "1037": "ement", + "1038": "tion", + "1039": "t\u00e9", + "1040": "\u2581tout", + "1041": "mp", + "1042": "ique", + "1043": "\u2581plus", + "1044": "eux", + "1045": "\u2581d\u00e9", + "1046": "\u2581fait", + "1047": "qu", + "1048": "\u2581ai", + "1049": "\u2581comme", + "1050": "ens", + "1051": "ac", + "1052": "\u2581l\u00e0", + "1053": "\u2581si", + "1054": "ait", + "1055": "che", + "1056": "\u2581mais", + "1057": "que", + "1058": "ul", + "1059": "\u2581avec", + "1060": "\u2581bien", + "1061": "\u2581tu", + "1062": "age", + "1063": "\u2581mon", + "1064": "\u2581Donc", + "1065": "end", + "1066": "\u2581faire", + "1067": "\u2581\u00eatre", + "1068": "ver", + "1069": "\u2581peu", + "1070": "\u2581m\u00eame", + "1071": "tra", + "1072": "cha", + "1073": "\u2581nous", + "1074": "\u2581donc", + "1075": "\u2581sont", + "1076": "\u2581moi", + "1077": "ille", + "1078": "ff", + "1079": "\u2581ex", + "1080": "ien", + "1081": "\u2581Il", + "1082": "\u2581tr\u00e8s", + "1083": "\u2581cette", + "1084": "im", + "1085": "it\u00e9", + "1086": "\u2581dire", + "1087": "\u2581peut", + "1088": "ance", + "1089": "aire", + "1090": "m\u00e9", + "1091": "\u2581app", + "1092": "\u2581aussi", + "1093": "\u2581petit", + "1094": "aux", + "1095": "\u2581parce", + "1096": "onne", + "1097": "mb", + "1098": "man", + "1099": "\u2581On", + "1100": "\u2581quand", + "1101": "\u2581autre", + "1102": "\u00f4", + "1103": "\u2581chose", + "1104": "\u2581puis", + "1105": "\u2581\u00e9tait", + "1106": "ndre", + "1107": "port", + "1108": "\u2581vraiment", + "1109": "ence", + "1110": "\u2581Mais", + "1111": "\u00ee", + "1112": "\u2581avoir", + "1113": "form", + "1114": "\u2581faut", + "1115": "\u2581Alors", + "1116": "ign", + "1117": "\u2581o\u00f9", + "1118": "pr\u00e8s", + "1119": "\u2581beaucoup", + "1120": "ture", + "1121": "\u00fb", + "1122": "\u00c7", + "1123": "\u00e2", + "1124": "\u00f9", + "1125": "", + "1126": "sz", + "1127": "\u2581az", + "1128": "\u2581hogy", + "1129": "\u0151", + "1130": "\u00e1s", + "1131": "ok", + "1132": "gy", + "1133": "ek", + "1134": "\u00e1l", + "1135": "\u00e9s", + "1136": "em", + "1137": "\u00e1r", + "1138": "\u2581meg", + "1139": "\u2581\u00e9s", + "1140": "\u2581is", + "1141": "\u2581ez", + "1142": "\u2581egy", + "1143": "os", + "1144": "ak", + "1145": "ban", + "1146": "nak", + "1147": "\u00edt", + "1148": "ik", + "1149": "unk", + "1150": "\u2581nem", + "1151": "oz", + "1152": "\u00fcl", + "1153": "\u00e1n", + "1154": "\u00e1t", + "1155": "cs", + "1156": "\u00e9l", + "1157": "\u00e9r", + "1158": "nek", + "1159": "\u2581mi", + "1160": "szer", + "1161": "bb", + "1162": "\u2581K\u00f6sz\u00f6n\u00f6m", + "1163": "s\u00e9g", + "1164": "\u2581kell", + "1165": "\u00e9n", + "1166": "hat", + "1167": "\u2581ha", + "1168": "s\u00e1g", + "1169": "\u2581sz\u00e9pen", + "1170": "\u00e9rt", + "1171": "\u00e9k", + "1172": "ott", + "1173": "\u00f6n", + "1174": "\u00e9p", + "1175": "el\u0151", + "1176": "\u00fcnk", + "1177": "\u2581van", + "1178": "\u2581ki", + "1179": "\u2581fel", + "1180": "\u00e9ny", + "1181": "v\u00e9", + "1182": "leg", + "1183": "eket", + "1184": "\u2581Az", + "1185": "juk", + "1186": "\u2581k\u00f6z", + "1187": "\u0171", + "1188": "\u2581nagyon", + "1189": "\u2581tud", + "1190": "\u2581jelen", + "1191": "\u2581amely", + "1192": "\u2581lehet", + "1193": "\u2581ami", + "1194": "\u2581k\u00e9rd\u00e9s", + "1195": "\u2581ellen", + "1196": "tart", + "1197": "r\u0151l", + "1198": "\u00c9", + "1199": "orsz\u00e1g", + "1200": "rend", + "1201": "r\u00f3l", + "1202": "\u2581vagy", + "1203": "\u2581fontos", + "1204": "\u2581Eur\u00f3pai", + "1205": "\u2581akkor", + "1206": "\u2581jog", + "1207": "\u2581fog", + "1208": "fogad", + "1209": "kapcsol", + "1210": "\u2581r\u00e9sz", + "1211": "\u00e1ci\u00f3", + "1212": "\u2581volt", + "1213": "\u2581eln\u00f6k", + "1214": "\u2581bizotts\u00e1g", + "1215": "\u2581gondol", + "1216": "\u2581olyan", + "1217": "\u2581illetve", + "1218": "\u2581tag\u00e1llam", + "1219": "\u2581pedig", + "1220": "\u2581Teh\u00e1t", + "1221": "\u2581eur\u00f3pai", + "1222": "\u2581sz\u00fcks\u00e9g", + "1223": "szavaz", + "1224": "\u2581teh\u00e1t", + "1225": "k\u00f6vetkez", + "1226": "\u2581\u00f6ssze", + "1227": "\u2581biztos", + "1228": "\u00d6", + "1229": "\u00c1", + "1230": "\u00cd", + "1231": "\u0150", + "1232": "", + "1233": "\u2581u", + "1234": "\u2581bi", + "1235": "\u2581sa", + "1236": "\u0107e", + "1237": "\u2581od", + "1238": "ru", + "1239": "\u2581iz", + "1240": "go", + "1241": "nje", + "1242": "sti", + "1243": "\u0111", + "1244": "\u2581pri", + "1245": "ima", + "1246": "nu", + "1247": "\u2581pre", + "1248": "\u2581Hvala", + "1249": "lje", + "1250": "\u2581\u0161to", + "1251": "\u010di", + "1252": "nja", + "1253": "zi", + "1254": "vr", + "1255": "\u0107i", + "1256": "\u010de", + "1257": "ca", + "1258": "\u2581koji", + "1259": "ba", + "1260": "\u2581raz", + "1261": "\u05d9", + "1262": "\u05d5", + "1263": "\u05d4", + "1264": "\u062f", + "1265": "\u05dc", + "1266": "\u0629", + "1267": "\u0628", + "1268": "\u0647", + "1269": "\u0623", + "1270": "\u05d0", + "1271": "\u0633", + "1272": "\u0643", + "1273": "\u05ea", + "1274": "\u05e8", + "1275": "\u021b", + "1276": "\u05de", + "1277": "\u0642", + "1278": "\u05e9", + "1279": "", + "1280": "\u2581di", + "1281": "\u2581e", + "1282": "\u2581che", + "1283": "\u2581\u00e8", + "1284": "co", + "1285": "\u2581per", + "1286": "\u2581al", + "1287": "\u2581non", + "1288": "do", + "1289": "gli", + "1290": "so", + "1291": "amo", + "1292": "sa", + "1293": "ndo", + "1294": "\u2581una", + "1295": "fi", + "1296": "pi", + "1297": "nti", + "1298": "tto", + "1299": "tro", + "1300": "\u2581fa", + "1301": "chi", + "1302": "\u2581qua", + "1303": "zione", + "1304": "bi", + "1305": "\u2581del", + "1306": "mente", + "1307": "pe", + "1308": "ssi", + "1309": "\u2581ri", + "1310": "\u2581sono", + "1311": "\u2581me", + "1312": "\u2581questo", + "1313": "nte", + "1314": "tti", + "1315": "t\u00e0", + "1316": "\u2581nel", + "1317": "\u2581anche", + "1318": "sso", + "1319": "\u2581perch\u00e9", + "1320": "\u2581pi\u00f9", + "1321": "nta", + "1322": "\u2581come", + "1323": "cu", + "1324": "\u2581quindi", + "1325": "ggi", + "1326": "nza", + "1327": "sto", + "1328": "\u2581ho", + "1329": "\u00f2", + "1330": "\u2581della", + "1331": "gra", + "1332": "\u2581fare", + "1333": "spe", + "1334": "cco", + "1335": "nde", + "1336": "mento", + "1337": "fe", + "1338": "gio", + "1339": "pu", + "1340": "\u2581questa", + "1341": "\u2581tra", + "1342": "zza", + "1343": "sci", + "1344": "\u2581ba", + "1345": "\u2581dei", + "1346": "\u2581poi", + "1347": "sco", + "1348": "stra", + "1349": "\u2581quel", + "1350": "qui", + "1351": "\u2581delle", + "1352": "\u2581cosa", + "1353": "\u2581molto", + "1354": "sse", + "1355": "zioni", + "1356": "\u2581vol", + "1357": "\u2581inter", + "1358": "sce", + "1359": "\u2581fatto", + "1360": "\u2581com", + "1361": "\u2581quello", + "1362": "\u2581essere", + "1363": "\u2581due", + "1364": "\u2581abbiamo", + "1365": "\u2581comp", + "1366": "\u2581tutti", + "1367": "\u00ec", + "1368": "\u2581prima", + "1369": "\u2581parte", + "1370": "\u2581cos\u00ec", + "1371": "\u2581sempre", + "1372": "\u2581tutto", + "1373": "\u2581video", + "1374": "\u2581maglia", + "1375": "\u2581imp", + "1376": "\u2581cui", + "1377": "\u2581dove", + "1378": "\u2581col", + "1379": "\u2581Quindi", + "1380": "sione", + "1381": "rebbe", + "1382": "scri", + "1383": "", + "1384": "\u0117", + "1385": "ai", + "1386": "\u0173", + "1387": "\u2581ir", + "1388": "as", + "1389": "\u012f", + "1390": "\u2581kad", + "1391": "\u0117s", + "1392": "\u2581tai", + "1393": "\u016b", + "1394": "t\u0173", + "1395": "\u2581yra", + "1396": "i\u0173", + "1397": "uo", + "1398": "\u2581ko", + "1399": "\u2581i\u0161", + "1400": "tin", + "1401": "\u2581vis", + "1402": "\u010dia", + "1403": "\u2581kuri", + "1404": "d\u0117", + "1405": "ly", + "1406": "gal", + "1407": "\u2581\u0161i", + "1408": "iau", + "1409": "jo", + "1410": "tar", + "1411": "yb", + "1412": "\u2581Ir", + "1413": "\u2581tik", + "1414": "ijos", + "1415": "sak", + "1416": "\u2581turi", + "1417": "oje", + "1418": "\u2581Tai", + "1419": "j\u0173", + "1420": "\u2581apie", + "1421": "\u2581nu", + "1422": "\u2581mes", + "1423": "\u2581u\u017e", + "1424": "i\u0161k", + "1425": "\u2581gali", + "1426": "\u2581d\u0117l", + "1427": "\u2581labai", + "1428": "imas", + "1429": "klaus", + "1430": "laik", + "1431": "\u2581Europos", + "1432": "\u2581a\u0161", + "1433": "veik", + "1434": "\u2581b\u016bt\u0173", + "1435": "darb", + "1436": "\u2581kaip", + "1437": "\u2581teis", + "1438": "\u2581daug", + "1439": "\u2581tikrai", + "1440": "\u2581pra", + "1441": "reik", + "1442": "\u2581buvo", + "1443": "tur\u0117", + "1444": "\u2581valstyb", + "1445": "\u2581reikia", + "1446": "\u2581b\u016bti", + "1447": "\u2581A\u0161", + "1448": "\u2581m\u016bs\u0173", + "1449": "\u2581j\u016bs", + "1450": "vyk", + "1451": "\u2581A\u010di\u016b", + "1452": "cija", + "1453": "\u012e", + "1454": "\u0146", + "1455": "", + "1456": "\u2581no", + "1457": "j\u0101", + "1458": "iem", + "1459": "t\u0101", + "1460": "\u0101k", + "1461": "\u2581ar", + "1462": "\u0101m", + "1463": "\u2581pie", + "1464": "ies", + "1465": "ot", + "1466": "k\u0101", + "1467": "\u013c", + "1468": "tr", + "1469": "\u2581t\u0101", + "1470": "\u012bt", + "1471": "n\u0101", + "1472": "\u2581uz", + "1473": "\u2581tas", + "1474": "\u0113t", + "1475": "dz", + "1476": "\u2581ar\u012b", + "1477": "\u2581vien", + "1478": "\u2581jau", + "1479": "\u2581k\u0101", + "1480": "\u2581ie", + "1481": "gad", + "1482": "\u2581kur", + "1483": "\u2581kas", + "1484": "\u2581Un", + "1485": "\u2581m\u0113s", + "1486": "iet", + "1487": "d\u0101", + "1488": "\u012bg", + "1489": "\u2581Ta", + "1490": "\u2581k\u0101d", + "1491": "kaut", + "1492": "\u0113m", + "1493": "\u2581lie", + "1494": "umu", + "1495": "ties", + "1496": "dar", + "1497": "l\u0113", + "1498": "\u2581vai", + "1499": "\u2581bija", + "1500": "\u2581mums", + "1501": "\u2581tad", + "1502": "\u2581bet", + "1503": "\u012bba", + "1504": "\u2581ga", + "1505": "\u2581Latvijas", + "1506": "ija", + "1507": "kr", + "1508": "v\u0113", + "1509": "sim", + "1510": "\u2581\u0161o", + "1511": "dien", + "1512": "gan", + "1513": "\u012bgi", + "1514": "\u2581ap", + "1515": "\u0123", + "1516": "\u2581b\u016bt", + "1517": "dom\u0101", + "1518": "\u2581tev", + "1519": "m\u0113r", + "1520": "\u2581daudz", + "1521": "\u2581aiz", + "1522": "\u2581T\u0101", + "1523": "\u2581t\u0101d", + "1524": "\u2581tur", + "1525": "\u2581mon\u0113t", + "1526": "\u2581v\u0113l", + "1527": "\u2581laik", + "1528": "\u2581cilv\u0113", + "1529": "\u2581nav", + "1530": "\u2581lab", + "1531": "\u2581\u013coti", + "1532": "aug", + "1533": "\u2581l\u012bdz", + "1534": "\u2581lai", + "1535": "\u0161ana", + "1536": "\u2581Nu", + "1537": "\u2581vi\u0146a", + "1538": "\u2581savu", + "1539": "\u2581cit", + "1540": "teik", + "1541": "\u2581darb", + "1542": "\u2581Ne", + "1543": "zin", + "1544": "\u2581pirm", + "1545": "\u2581Latvi", + "1546": "\u2581tie\u0161", + "1547": "\u2581vi\u0146i", + "1548": "\u0113ja", + "1549": "dz\u012bvo", + "1550": "\u2581vi\u0146\u0161", + "1551": "\u2581pils\u0113", + "1552": "in\u0101t", + "1553": "\u2581vi\u0146u", + "1554": "\u2581tagad", + "1555": "k\u0101rt", + "1556": "\u2581pats", + "1557": "\u2581vair\u0101k", + "1558": "reiz", + "1559": "\u2581tikai", + "1560": "sakta", + "1561": "\u2581bij", + "1562": "\u2581Vi\u0146", + "1563": "\u2581sev", + "1564": "\u2581m\u0101j", + "1565": "v\u0113rt", + "1566": "\u258120", + "1567": "\u2581ce\u013c", + "1568": "tiek", + "1569": "iski", + "1570": "\u2581dz\u012bv", + "1571": "\u2581k\u0101p\u0113c", + "1572": "\u2581Bet", + "1573": "\u2581p\u0113c", + "1574": "\u2581noz\u012bm\u0113", + "1575": "niek", + "1576": "\u012bb\u0101", + "1577": "\u2581pal\u012bdz", + "1578": "\u2581protams", + "1579": "\u2581stils", + "1580": "\u2581vajadz", + "1581": "\u2581att\u012bst\u012b", + "1582": "\u2581svar\u012bg", + "1583": "\u2581sievie", + "1584": "\u2581grib", + "1585": "\u2581da\u017e\u0101d", + "1586": "\u2581valst", + "1587": "\u2581banka", + "1588": "\u2581iesp\u0113ja", + "1589": "\u2581bez", + "1590": "pr\u0101t", + "1591": "v\u0113rt\u012bb", + "1592": "\u2581person", + "1593": "pasaules", + "1594": "\u2581varb\u016bt", + "1595": "\u2581vienk\u0101r\u0161i", + "1596": "\u2581nauda", + "1597": "mekl\u0113", + "1598": "brauc", + "1599": "\u2581nevar", + "1600": "\u0101cijas", + "1601": "sp\u0113j", + "1602": "\u0137", + "1603": "", + "1604": "\u2581een", + "1605": "\u2581het", + "1606": "\u2581dat", + "1607": "\u2581we", + "1608": "\u2581ik", + "1609": "ij", + "1610": "\u2581En", + "1611": "\u2581te", + "1612": "\u2581ook", + "1613": "\u2581niet", + "1614": "\u2581dan", + "1615": "\u2581zo", + "1616": "\u2581voor", + "1617": "\u2581met", + "1618": "\u2581aan", + "1619": "\u2581zijn", + "1620": "\u2581Ik", + "1621": "\u2581wel", + "1622": "\u2581wat", + "1623": "aar", + "1624": "\u2581ze", + "1625": "ken", + "1626": "\u2581heb", + "1627": "der", + "1628": "ui", + "1629": "den", + "1630": "\u2581daar", + "1631": "\u2581maar", + "1632": "op", + "1633": "\u2581heel", + "1634": "\u2581nog", + "1635": "\u2581Dus", + "1636": "oor", + "1637": "\u2581hebben", + "1638": "\u2581uit", + "1639": "\u2581of", + "1640": "ven", + "1641": "\u2581Maar", + "1642": "\u2581Dat", + "1643": "\u2581gaan", + "1644": "elijk", + "1645": "\u2581naar", + "1646": "\u2581moet", + "1647": "acht", + "1648": "\u2581waar", + "1649": "\u2581dus", + "1650": "\u2581ben", + "1651": "\u2581goed", + "1652": "\u2581Het", + "1653": "\u2581even", + "1654": "ond", + "1655": "eld", + "1656": "\u2581dit", + "1657": "\u2581wil", + "1658": "rij", + "1659": "\u2581echt", + "1660": "\u2581doen", + "1661": "\u2581gewoon", + "1662": "lijk", + "1663": "tijd", + "1664": "\u2581meer", + "1665": "\u2581mijn", + "1666": "\u2581We", + "1667": "\u2581gaat", + "1668": "werk", + "1669": "\u2581hoe", + "1670": "uw", + "1671": "\u2581eigenlijk", + "1672": "\u2581deze", + "1673": "zelf", + "1674": "vol", + "1675": "\u2581veel", + "1676": "atie", + "1677": "\u2581kunnen", + "1678": "\u2581door", + "1679": "llen", + "1680": "\u2581mee", + "1681": "\u2581onder", + "1682": "\u2581toe", + "1683": "\u2581zit", + "1684": "\u2581mensen", + "1685": "\u2581hij", + "1686": "\u2581denk", + "1687": "\u2581zie", + "1688": "\u2581heeft", + "1689": "\u2581kl", + "1690": "nnen", + "1691": "\u2581zien", + "1692": "komen", + "1693": "\u2581natuurlijk", + "1694": "heid", + "1695": "\u2581Dan", + "1696": "\u2581vind", + "1697": "\u2581wordt", + "1698": "\u2581iets", + "1699": "\u2581maken", + "1700": "\u2581doe", + "1701": "\u2581Wat", + "1702": "\u2581wij", + "1703": "\u2581beetje", + "1704": "\u2581worden", + "1705": "\u2581Want", + "1706": "\u2581twee", + "1707": "\u2581hem", + "1708": "\u2581had", + "1709": "\u2581jullie", + "1710": "\u2581Als", + "1711": "\u2581kijken", + "1712": "\u2581toch", + "1713": "\u2581tot", + "1714": "nieuw", + "1715": "lang", + "1716": "\u2581Nou", + "1717": "\u2581krijg", + "1718": "houd", + "1719": "\u2581hele", + "1720": "\u2581allemaal", + "1721": "\u2581want", + "1722": "\u2581zeggen", + "1723": "\u2581leuk", + "1724": "", + "1725": "nie", + "1726": "\u2581w", + "1727": "cz", + "1728": "wa", + "1729": "\u2581si\u0119", + "1730": "\u2581jest", + "1731": "my", + "1732": "\u0142a", + "1733": "cie", + "1734": "czy", + "1735": "\u2581nie", + "1736": "wie", + "1737": "\u2581wy", + "1738": "nia", + "1739": "wo", + "1740": "rze", + "1741": "\u0142o", + "1742": "\u2581\u017ce", + "1743": "dzi", + "1744": "ej", + "1745": "\u00f3w", + "1746": "dzie", + "1747": "\u2581prze", + "1748": "\u015bci", + "1749": "by", + "1750": "za", + "1751": "dy", + "1752": "ry", + "1753": "\u0144", + "1754": "j\u0105", + "1755": "we", + "1756": "cze", + "1757": "owa", + "1758": "ego", + "1759": "\u017ce", + "1760": "cy", + "1761": "rzy", + "1762": "mie", + "1763": "\u2581przy", + "1764": "\u0142y", + "1765": "rz", + "1766": "szy", + "1767": "sze", + "1768": "\u015b\u0107", + "1769": "wia", + "1770": "zy", + "1771": "\u017cy", + "1772": "\u2581tutaj", + "1773": "j\u0119", + "1774": "pie", + "1775": "nych", + "1776": "\u2581tym", + "1777": "\u2581mo\u017ce", + "1778": "cji", + "1779": "\u2581pod", + "1780": "\u2581ale", + "1781": "\u2581tego", + "1782": "owy", + "1783": "uje", + "1784": "\u2581bo", + "1785": "\u2581by\u0142", + "1786": "n\u0105", + "1787": "bie", + "1788": "sy", + "1789": "\u2581te\u017c", + "1790": "\u2581bardzo", + "1791": "\u2581s\u0105", + "1792": "\u2581b\u0119dzie", + "1793": "\u2581Po", + "1794": "ski", + "1795": "\u2581kt\u00f3re", + "1796": "\u017a", + "1797": "\u2581ju\u017c", + "1798": "\u2581dla", + "1799": "\u0142em", + "1800": "nego", + "1801": "\u2581Nie", + "1802": "\u2581No", + "1803": "\u2581praw", + "1804": "cja", + "1805": "\u2581ten", + "1806": "\u2581takie", + "1807": "owa\u0107", + "1808": "\u2581kt\u00f3ry", + "1809": "\u2581w\u0142a\u015bnie", + "1810": "\u2581jeszcze", + "1811": "\u2581tam", + "1812": "\u2581\u017ceby", + "1813": "\u2581by\u0107", + "1814": "\u2581wi\u0119c", + "1815": "\u2581czyli", + "1816": "\u2581sobie", + "1817": "\u2581sam", + "1818": "\u2581tylko", + "1819": "\u2581tej", + "1820": "\u2581spraw", + "1821": "\u2581Na", + "1822": "\u2581m\u00f3wi", + "1823": "\u2581osob", + "1824": "\u2581czas", + "1825": "\u2581prac", + "1826": "\u2581Czy", + "1827": "\u2581prostu", + "1828": "\u2581teraz", + "1829": "st\u0119p", + "1830": "\u2581Was", + "1831": "\u2581my\u015bl", + "1832": "\u2581powiedz", + "1833": "\u2581zrobi", + "1834": "li\u015bmy", + "1835": "\u2581jakie\u015b", + "1836": "aj\u0105c", + "1837": "\u2581widz", + "1838": "\u2581kart", + "1839": "\u2581musi", + "1840": "\u2581pyta", + "1841": "", + "1842": "pt", + "1843": "PT", + "1844": "<", + "1845": ">", + "1846": "-", + "1847": "\u2581\u00e9", + "1848": "\u2581n\u00e3o", + "1849": "\u2581eu", + "1850": "\u2581um", + "1851": "\u2581voc\u00ea", + "1852": "\u2581para", + "1853": "\u00e3o", + "1854": "\u2581aqui", + "1855": "\u2581uma", + "1856": "\u00e7\u00e3o", + "1857": "\u2581ca", + "1858": "\u2581pe", + "1859": "\u2581tem", + "1860": "\u2581em", + "1861": "\u2581gente", + "1862": "\u2581O", + "1863": "\u2581ele", + "1864": "pre", + "1865": "ria", + "1866": "\u2581fo", + "1867": "mos", + "1868": "nho", + "1869": "\u2581Ent\u00e3o", + "1870": "bo", + "1871": "io", + "1872": "nha", + "1873": "\u2581isso", + "1874": "\u2581por", + "1875": "\u2581muito", + "1876": "nto", + "1877": "\u2581Eu", + "1878": "\u2581est\u00e1", + "1879": "idade", + "1880": "\u2581a\u00ed", + "1881": "be", + "1882": "\u2581esse", + "1883": "\u2581pode", + "1884": "\u2581como", + "1885": "ente", + "1886": "\u2581tamb\u00e9m", + "1887": "\u2581essa", + "1888": "lha", + "1889": "\u2581j\u00e1", + "1890": "\u2581mas", + "1891": "\u2581pessoa", + "1892": "qua", + "1893": "\u2581n\u00e9", + "1894": "\u2581fazer", + "1895": "\u2581t\u00e1", + "1896": "lho", + "1897": "\u2581l\u00e1", + "1898": "fica", + "1899": "\u2581vou", + "1900": "\u2581porque", + "1901": "\u2581Se", + "1902": "\u2581fala", + "1903": "\u2581coisa", + "1904": "\u2581N\u00e3o", + "1905": "...", + "1906": "\u2581s\u00f3", + "1907": "\u2581n\u00f3s", + "1908": "\u00e7o", + "1909": "\u2581Por", + "1910": "\u2581assim", + "1911": "\u2581Co", + "1912": "iza", + "1913": "\u2581bem", + "1914": "\u2581todo", + "1915": "eira", + "1916": "\u2581sua", + "1917": "\u00eancia", + "1918": "\u00e7\u00f5es", + "1919": "\u2581Voc\u00ea", + "1920": "\u2581tudo", + "1921": "\u2581agora", + "1922": "eiro", + "1923": "\u00e1rio", + "1924": "\u2581at\u00e9", + "1925": "\u2581mesmo", + "1926": "\u2581vamos", + "1927": "\u2581quando", + "1928": "ciona", + "1929": "", + "1930": "\u2581\u00een", + "1931": "\u021bi", + "1932": "\u2581s\u0103", + "1933": "\u2581\u0219i", + "1934": "\u2581cu", + "1935": "\u2581c\u0103", + "1936": "\u2581care", + "1937": "\u2581mai", + "1938": "r\u0103", + "1939": "sc", + "1940": "c\u0103", + "1941": "\u2581am", + "1942": "are", + "1943": "\u2581din", + "1944": "\u2581fi", + "1945": "\u2581este", + "1946": "t\u0103", + "1947": "\u2581pentru", + "1948": "rea", + "1949": "\u0219ti", + "1950": "\u0219", + "1951": "ele", + "1952": "du", + "1953": "\u2581M", + "1954": "\u2581fac", + "1955": "\u00e2n", + "1956": "\u2581sunt", + "1957": "\u2581I", + "1958": "\u2581acest", + "1959": "ului", + "1960": "lor", + "1961": "\u2581mult", + "1962": "\u0219i", + "1963": "\u2581mo", + "1964": "\u2581fost", + "1965": "per", + "1966": "\u2581foarte", + "1967": "\u2581\u0218i", + "1968": "\u2581m\u0103", + "1969": "s\u0103", + "1970": "cur", + "1971": "tor", + "1972": "\u2581cum", + "1973": "inte", + "1974": "at\u0103", + "1975": "\u0219te", + "1976": "\u2581dac\u0103", + "1977": "\u00e2nd", + "1978": "\u2581subliniere", + "1979": "\u2581dar", + "1980": "\u2581sau", + "1981": "tat", + "1982": "ori", + "1983": "\u2581v\u0103", + "1984": "\u2581asta", + "1985": "n\u0103", + "1986": "\u2581prim", + "1987": "\u2581a\u0219a", + "1988": "eaz\u0103", + "1989": "\u2581\u00eentr", + "1990": "\u2581spun", + "1991": "\u2581lui", + "1992": "\u2581sub", + "1993": "itate", + "1994": "\u2581aici", + "1995": "\u2581bine", + "1996": "\u2581c\u00e2nd", + "1997": "\u2581prin", + "1998": "\u2581alt", + "1999": "\u2581nici", + "2000": "stru", + "2001": "\u2581c\u00e2t", + "2002": "\u2581vede", + "2003": "fer", + "2004": "\u2581dup\u0103", + "2005": "\u2581ju", + "2006": "\u2581despre", + "2007": "\u2581timp", + "2008": "\u2581acum", + "2009": "\u2581poate", + "2010": "\u2581spus", + "2011": "\u2581lucru", + "2012": "\u2581f\u0103cut", + "2013": "p\u0103r", + "2014": "\u2581urm\u0103", + "2015": "\u2581atunci", + "2016": "\u2581fr", + "2017": "\u2581chiar", + "2018": "\u2581\u00eencep", + "2019": "\u0218", + "2020": "\u00ce", + "2021": "", + "2022": "\u2581\u043d\u0435", + "2023": "\u044b", + "2024": "\u0442\u044c", + "2025": "\u2581\u044d\u0442\u043e", + "2026": "\u0436\u0435", + "2027": "\u2581\u0447\u0442\u043e", + "2028": "\u2581\u0442\u043e", + "2029": "\u043b\u044c", + "2030": "\u2581\u043e", + "2031": "\u2581\u0443", + "2032": "\u0430\u0442\u044c", + "2033": "\u2581\u0442\u0430\u043a", + "2034": "\u2581\u043a\u0430\u043a", + "2035": "\u043a\u0438", + "2036": "\u0441\u044f", + "2037": "\u0435\u043c", + "2038": "\u2581\u0432\u044b", + "2039": "\u2581\u0431\u044b", + "2040": "\u2581\u0432\u0441\u0435", + "2041": "\u0440\u0443", + "2042": "\u0431\u043e", + "2043": "\u2581\u0418", + "2044": "\u2581\u0432\u043e\u0442", + "2045": "\u043a\u0443", + "2046": "\u2581\u0412", + "2047": "\u0447\u0438", + "2048": "\u043e\u0439", + "2049": "\u043c\u0443", + "2050": "\u2581\u0441\u043e", + "2051": "\u0442\u044b", + "2052": "\u043d\u0443", + "2053": "\u0441\u044c", + "2054": "\u2581\u0435\u0441\u0442\u044c", + "2055": "\u0442\u0443", + "2056": "\u043d\u044b", + "2057": "\u0448\u0435", + "2058": "\u2581\u043c\u044b", + "2059": "\u0434\u0443", + "2060": "\u0438\u0442\u044c", + "2061": "\u044d", + "2062": "\u0434\u0435\u043b", + "2063": "\u043b\u044f", + "2064": "\u043c\u0435\u043d", + "2065": "\u0436\u0438", + "2066": "\u0441\u0442\u043e", + "2067": "\u0445\u043e", + "2068": "\u0441\u0442\u0432", + "2069": "\u0432\u044b", + "2070": "\u0432\u0435\u0440", + "2071": "\u0437\u043d\u0430", + "2072": "\u0441\u0442\u0438", + "2073": "\u0448\u0438", + "2074": "\u0435\u0442\u0441\u044f", + "2075": "\u0443\u044e", + "2076": "\u0440\u044b", + "2077": "\u0445\u043e\u0434", + "2078": "\u0430\u0435\u0442", + "2079": "\u043d\u044b\u0439", + "2080": "\u043f\u0435\u0440", + "2081": "\u2581\u041f\u043e", + "2082": "\u043b\u0443\u0447", + "2083": "\u043d\u044b\u0435", + "2084": "\u0442\u043e\u0440", + "2085": "\u2581\u0442\u0430\u043c", + "2086": "\u2581\u0431\u0443\u0434\u0435\u0442", + "2087": "\u2581\u0441\u0430\u043c", + "2088": "\u2581\u0434\u043b\u044f", + "2089": "\u2581\u043e\u0447\u0435\u043d\u044c", + "2090": "\u0435\u043d\u0438\u044f", + "2091": "\u0430\u044e\u0442", + "2092": "\u2581\u041d\u0443", + "2093": "\u2581\u042d\u0442\u043e", + "2094": "\u2581\u0414\u0430", + "2095": "\u2581\u043c\u0435\u043d\u044f", + "2096": "\u2581\u0435\u0441\u043b\u0438", + "2097": "\u2581\u0422\u043e", + "2098": "\u0435\u043d\u044c", + "2099": "\u043d\u044b\u0445", + "2100": "\u2581\u0435\u0449\u0435", + "2101": "\u2581\u0432\u0430\u043c", + "2102": "\u2581\u043f\u0435\u0440\u0435", + "2103": "\u2581\u0437\u0434\u0435\u0441\u044c", + "2104": "\u2581\u043f\u0440\u043e\u0441\u0442\u043e", + "2105": "\u2581\u0412\u043e\u0442", + "2106": "\u2581\u041d\u043e", + "2107": "\u2581\u0447\u0442\u043e\u0431\u044b", + "2108": "\u0441\u043c\u043e\u0442\u0440", + "2109": "\u2581\u0441\u0435\u0439\u0447\u0430\u0441", + "2110": "\u2581\u043c\u043e\u0436\u0435\u0442", + "2111": "\u2581\u044d\u0442\u0438", + "2112": "\u0430\u043b\u044c\u043d\u043e", + "2113": "\u0434\u043e\u043b", + "2114": "\u2581\u041d\u0430", + "2115": "\u2581\u0422\u0430\u043a", + "2116": "\u2581\u043a\u043e\u0433\u0434\u0430", + "2117": "\u0451", + "2118": "\u0430\u0439\u0442\u0435", + "2119": "\u043f\u0438\u0441", + "2120": "\u0442\u0435\u043b\u044c\u043d\u043e", + "2121": "\u0435\u0448\u044c", + "2122": "\u2581\u0434\u0440\u0443\u0433", + "2123": "\u042d", + "2124": "", + "2125": "ov", + "2126": "\u013e", + "2127": "sk", + "2128": "\u2581aj", + "2129": "ob", + "2130": "t\u00e1", + "2131": "a\u0165", + "2132": "\u2581bol", + "2133": "\u2581s\u00fa", + "2134": "\u2581ako", + "2135": "\u017ei", + "2136": "\u2581sme", + "2137": "\u2581V", + "2138": "ali", + "2139": "\u2581alebo", + "2140": "\u2581\u010do", + "2141": "i\u0165", + "2142": "\u2581m\u00e1", + "2143": "\u00fdch", + "2144": "\u2581z\u00e1", + "2145": "\u2581tie", + "2146": "\u2581nejak", + "2147": "\u2581v\u00fd", + "2148": "\u010das", + "2149": "nov", + "2150": "rov", + "2151": "\u2581ktor\u00e9", + "2152": "aj\u00fa", + "2153": "ova\u0165", + "2154": "\u2581ke\u010f", + "2155": "\u2581str", + "2156": "\u2581\u0161kol", + "2157": "n\u00fa", + "2158": "\u2581ktor", + "2159": "\u2581vlastne", + "2160": "\u2581pr\u00ed", + "2161": "nej", + "2162": "\u2581ve\u013emi", + "2163": "\u0161ie", + "2164": "rob", + "2165": "\u2581tr", + "2166": "n\u00fdch", + "2167": "enie", + "2168": "\u2581spo", + "2169": "\u2581rok", + "2170": "osti", + "2171": "\u2581t\u00fdm", + "2172": "\u2581m\u00f4\u017ee", + "2173": "\u2581ktor\u00fd", + "2174": "os\u0165", + "2175": "\u2581projekt", + "2176": "\u2581kon", + "2177": "\u2581vzdel\u00e1va", + "2178": "\u2581Tak\u017ee", + "2179": "\u2581e\u0161te", + "2180": "\u2581t\u00fdch", + "2181": "\u2581mal", + "2182": "\u2581cel", + "2183": "\u2581potom", + "2184": "\u2581svoj", + "2185": "enia", + "2186": "\u00e1lne", + "2187": "ie\u0165", + "2188": "\u2581teda", + "2189": "jedn", + "2190": "sled", + "2191": "\u2581mo\u017eno", + "2192": "\u2581v\u00e1m", + "2193": "chod", + "2194": "uj\u00fa", + "2195": "tvor", + "2196": "\u2581druh", + "2197": "\u2581Slovensk", + "2198": "h\u013ead", + "2199": "stup", + "2200": "\u2581\u013eud\u00ed", + "2201": "\u2581napr\u00edklad", + "2202": "\u2581ve\u013ek", + "2203": "\u2581nie\u010do", + "2204": "\u010e", + "2205": "", + "2206": "sl", + "2207": "lj", + "2208": "kot", + "2209": "ih", + "2210": "\u2581svet", + "2211": "\u2581ta", + "2212": "\u2581tako", + "2213": "\u2581kar", + "2214": "\u2581nek", + "2215": "jih", + "2216": "udi", + "2217": "\u2581vse", + "2218": "\u2581drug", + "2219": "\u2581ima", + "2220": "kaj", + "2221": "\u2581smo", + "2222": "del", + "2223": "\u2581sem", + "2224": "\u2581lahko", + "2225": "\u2581samo", + "2226": "\u2581ve\u010d", + "2227": "nih", + "2228": "\u2581dr\u017eav", + "2229": "\u2581zelo", + "2230": "\u2581zdaj", + "2231": "\u2581razum", + "2232": "\u2581\u0161e", + "2233": "\u2581tega", + "2234": "\u2581ljudi", + "2235": "\u2581pred", + "2236": "\u2581sta", + "2237": "nost", + "2238": "\u2581ampak", + "2239": "\u2581novinar", + "2240": "\u2581naprej", + "2241": "\u2581mora", + "2242": "\u2581Vs", + "2243": "krat", + "2244": "\u2581Ampak", + "2245": "\u2581vedno", + "2246": "\u2581velik", + "2247": "\u2581kako", + "2248": "\u2581najbolj", + "2249": "ziroma", + "2250": "\u2581vsi", + "2251": "\u2581nekaj", + "2252": "\u2581kater", + "2253": "\u2581res", + "2254": "\u2581tukaj", + "2255": "\u2581dogaja", + "2256": "\u2581svoje", + "2257": "\u2581let", + "2258": "daj", + "2259": "\u2581pripri\u010da", + "2260": "\u2581\u010dlovek", + "2261": "\u2581ho\u010de", + "2262": "\u2581vojn", + "2263": "\u2581Pre", + "2264": "\u2581dobr", + "2265": "ljan", + "2266": "\u2581moj", + "2267": "\u2581dejansko", + "2268": "\u2581ljudje", + "2269": "\u2581mediji", + "2270": "\u2581prot", + "2271": "\u2581narav", + "2272": "bilo", + "2273": "\u2581Afrik", + "2274": "\u2581vzhod", + "2275": "\u2581\u010dlove\u0161tva", + "2276": "\u2581kriz", + "2277": "\u2581pogled", + "2278": "\u2581medije", + "2279": "poved", + "2280": "\u2581za\u010del", + "2281": "\u2581ve\u010din", + "2282": "imajo", + "2283": "\u2581Ljudje", + "2284": "\u2581dru\u017eb", + "2285": "\u2581govorim", + "2286": "\u2581informacij", + "2287": "\u2581kultur", + "2288": "\u2581bli\u017enj", + "2289": "\u2581podobno", + "2290": "\u2581njihov", + "2291": "\u2581konc", + "2292": "\u2581pisa", + "2293": "\u2581zaveda", + "2294": "\u2581vsak", + "2295": "\u017eivel", + "2296": "\u2581funkcionira", + "2297": "\u2581internet", + "2298": "\u2581islamsk", + "2299": "\u2581film", + "2300": "\u2581otroci", + "2301": "\u2581prihaja", + "2302": "\u2581politi\u010dn", + "2303": "\u2581popoln", + "2304": "\u2581Velik", + "2305": "\u2581druga\u010den", + "2306": "\u2581recimo", + "2307": "\u2581resnic", + "2308": "solutno", + "2309": "\u2581Bli\u017en", + "2310": "\u2581Evropsk", + "2311": "\u2581muslimani", + "2312": "\u2581nadzoruje", + "2313": "\u2581socialne", + "2314": "\u2581zgodovin", + "2315": "\u2581\u010dlove\u0161k", + "2316": "\u2581\u017eivljenj", + "2317": "\u2581prijatelj", + "2318": "\u2581vendar", + "2319": "\u2581ljudem", + "2320": "\u2581\u0161tevil", + "2321": "\u2581Sirij", + "2322": "", + "2323": "\u2581att", + "2324": "\u2581och", + "2325": "\u2581\u00e4r", + "2326": "\u2581f\u00f6r", + "2327": "\u2581h\u00e4r", + "2328": "\u2581jag", + "2329": "\u00e4n", + "2330": "\u2581till", + "2331": "\u2581h", + "2332": "\u2581inte", + "2333": "\u2581Och", + "2334": "\u2581av", + "2335": "\u2581om", + "2336": "\u2581ska", + "2337": "\u2581ut", + "2338": "\u2581ett", + "2339": "all", + "2340": "\u2581ocks\u00e5", + "2341": "\u2581Jag", + "2342": "era", + "2343": "pp", + "2344": "\u2581upp", + "2345": "\u2581d\u00e5", + "2346": "\u2581d\u00e4r", + "2347": "\u2581lite", + "2348": "\u00e5r", + "2349": "sam", + "2350": "isk", + "2351": "het", + "2352": "f\u00f6r", + "2353": "\u2581kommer", + "2354": "\u2581vill", + "2355": "\u00f6r", + "2356": "erna", + "2357": "ande", + "2358": "s\u00e4tt", + "2359": "\u2581finns", + "2360": "\u2581n\u00e4r", + "2361": "\u2581vara", + "2362": "ade", + "2363": "s\u00f6k", + "2364": "\u2581hur", + "2365": "\u2581vad", + "2366": "bil", + "2367": "\u2581g\u00f6ra", + "2368": "\u2581f\u00e5r", + "2369": "verk", + "2370": "\u2581mycket", + "2371": "\u2581v\u00e4l", + "2372": "kom", + "2373": "\u2581g\u00f6r", + "2374": "\u2581ni", + "2375": "\u2581bara", + "2376": "\u2581fr\u00e5n", + "2377": "st\u00e4ll", + "2378": "\u2581v\u00e4ldigt", + "2379": "\u2581min", + "2380": "\u2581olika", + "2381": "\u2581alla", + "2382": "lev", + "2383": "\u2581fram", + "2384": "\u2581kanske", + "2385": "\u2581v\u00e5r", + "2386": "\u2581tid", + "2387": "skap", + "2388": "h\u00e5ll", + "2389": "\u2581F\u00f6r", + "2390": "\u2581g\u00e5r", + "2391": "\u2581blir", + "2392": "\u2581under", + "2393": "\u2581l\u00e4r", + "2394": "\u2581ny", + "2395": "\u2581D\u00e5", + "2396": "\u2581b\u00f6rja", + "2397": "r\u00e4tt", + "2398": "\u2581\u00f6ver", + "2399": "\u2581oss", + "2400": "\u2581exempel", + "2401": "\u2581skulle", + "2402": "g\u00e5ng", + "2403": "\u2581kunna", + "2404": "\u2581andra", + "2405": "\u2581n\u00e5gon", + "2406": "\u2581jobba", + "2407": "land", + "2408": "\u2581n\u00e5got", + "2409": "\u2581beh\u00f6ver", + "2410": "\u2581s\u00e4ga", + "2411": "klar", + "2412": "\u2581m\u00e5nga", + "2413": "\u2581skriv", + "2414": "\u2581anv\u00e4nda", + "2415": "\u2581sj\u00e4lv", + "2416": "\u2581samma", + "2417": "l\u00e4gg", + "2418": "\u2581m\u00e5ste", + "2419": "\u2581efter", + "2420": "text", + "2421": "\u2581prata", + "2422": "\u2581klicka", + "2423": "\u2581hitta", + "2424": "\u2581tror", + "2425": "\u2581n\u00e5gonting", + "2426": "fr\u00e5ga", + "2427": "\u2581titta", + "2428": "\u2581tycker", + "2429": "\u2581ganska", + "2430": "\u2581j\u00e4tte", + "2431": "\u2581Vad", + "2432": "\u2581genom", + "2433": "\u2581\u00e4ven", + "2434": "\u2581t\u00e4nker", + "2435": "arbete", + "2436": "\u2581faktiskt", + "2437": "person", + "2438": "\u2581komma", + "2439": "bygg", + "2440": "", + "2441": "\u0456", + "2442": "\u043d\u0456", + "2443": "\u0454", + "2444": "\u0457", + "2445": "\u2581\u0437", + "2446": "\u2581\u0449\u043e", + "2447": "\u0432\u0456", + "2448": "\u0440\u0456", + "2449": "\u0446\u0456", + "2450": "\u2581\u0456", + "2451": "\u043b\u0456", + "2452": "\u043c\u0456", + "2453": "\u0431\u0443", + "2454": "\u0434\u0456", + "2455": "\u043e\u0433\u043e", + "2456": "\u2581\u0432\u0438", + "2457": "\u2581\u0446\u0435", + "2458": "\u0435\u0440", + "2459": "\u0441\u0456", + "2460": "\u2581\u044f\u043a", + "2461": "\u043e\u043c\u0443", + "2462": "\u2581\u0432\u0456\u0434", + "2463": "\u0434\u043e", + "2464": "\u0442\u0456", + "2465": "\u0456\u043d", + "2466": "\u0431\u0430", + "2467": "\u2581\u0406", + "2468": "\u043f\u0456", + "2469": "\u043f\u0435", + "2470": "\u0435\u043d\u043d\u044f", + "2471": "\u043d\u044c", + "2472": "\u043a\u0456", + "2473": "\u0437\u0430", + "2474": "\u043d\u0438\u0445", + "2475": "\u2581\u043f\u0456\u0434", + "2476": "\u043d\u0438\u0439", + "2477": "\u0440\u0430\u0437", + "2478": "\u043d\u044f", + "2479": "\u043b\u044e", + "2480": "\u043f\u0438", + "2481": "\u0441\u043e", + "2482": "\u0431\u0456", + "2483": "\u0442\u044c\u0441\u044f", + "2484": "\u2581\u0440\u043e\u0437", + "2485": "\u0441\u0442\u0456", + "2486": "\u2581\u044f\u043a\u0456", + "2487": "\u0443\u0432\u0430\u0442\u0438", + "2488": "\u2581\u043d\u0430\u0441", + "2489": "\u0430\u043d\u043d\u044f", + "2490": "\u043d\u043e\u0433\u043e", + "2491": "\u2581\u0432\u043e\u043d\u0438", + "2492": "\u0430\u044e\u0442\u044c", + "2493": "\u2581\u0434\u0443\u0436\u0435", + "2494": "\u2581\u0417\u0430", + "2495": "\u043b\u0443", + "2496": "\u043a\u0456\u0432", + "2497": "\u2581\u041c\u0438", + "2498": "\u2581\u0442\u043e\u043c\u0443", + "2499": "\u2581\u0431\u0443\u0434\u0435", + "2500": "\u2581\u0432\u0436\u0435", + "2501": "\u2581\u0426\u0435", + "2502": "\u0446\u044c", + "2503": "\u2581\u0447\u0430\u0441", + "2504": "\u0456\u0441\u0442\u044c", + "2505": "\u0446\u044f", + "2506": "\u2581\u0431\u0443\u043b\u043e", + "2507": "\u2581\u0430\u043b\u0435", + "2508": "\u0431\u0456\u043b\u044c\u0448", + "2509": "\u043f\u0440\u0430\u0446", + "2510": "\u0442\u0440\u0438\u043c", + "2511": "\u0430\u0454\u043c\u043e", + "2512": "\u0430\u0454\u0442\u044c\u0441\u044f", + "2513": "\u2581\u0442\u0443\u0442", + "2514": "\u0443\u044e\u0442\u044c", + "2515": "\u0430\u0446\u0456\u0457", + "2516": "\u2581\u044f\u043a\u0438\u0439", + "2517": "\u043c\u0435\u043d\u0442", + "2518": "\u2581\u043b\u044e\u0434\u0438", + "2519": "\u0443\u0432\u0430\u043d\u043d\u044f", + "2520": "\u2581\u044f\u043a\u0449\u043e", + "2521": "\u0444\u043e\u0440", + "2522": "\u2581\u0431\u0435\u0437", + "2523": "\u0443\u043a\u0440\u0430\u0457\u043d", + "2524": "\u0443\u0432\u0430\u043b\u0438", + "2525": "\u0440\u043e\u0437\u0443\u043c\u0456", + "2526": "\u0404", + "2527": "\u0407", + "2528": "\u0406", + "2529": "", + "2530": "\u0641", + "2531": "\u062d", + "2532": "\u0650", + "2533": "\u064f", + "2534": "\u062c", + "2535": "\u2581\u0627\u0644", + "2536": "\u0635", + "2537": "\u2581\u0648", + "2538": "\u0652", + "2539": "\u0637", + "2540": "\u0634", + "2541": "\u064e", + "2542": "\u062e", + "2543": "\u0632", + "2544": "\u0627\u0646", + "2545": "\u2581\u0623", + "2546": "\u0636", + "2547": "\u0627\u0644", + "2548": "\u2581\u0628", + "2549": "\u2581\u0627\u0644\u0645", + "2550": "\u2581\u0641\u064a", + "2551": "\u2581\u0645\u0646", + "2552": "\u0649", + "2553": "\u0627\u062a", + "2554": "\u064e\u0651", + "2555": "\u064a\u0646", + "2556": "\u0647\u0627", + "2557": "\u064a\u0629", + "2558": "\u062b", + "2559": "\u063a", + "2560": "\u2581\u0645", + "2561": "\u0627\u0631", + "2562": "\u2581\u0648\u064e", + "2563": "\u0644\u0627", + "2564": "\u0630", + "2565": "\u0648\u0644", + "2566": "\u0626", + "2567": "\u064e\u0627", + "2568": "\u0627\u0645", + "2569": "\u0648\u0646", + "2570": "\u0648\u0627", + "2571": "\u2581\u0639\u0644\u0649", + "2572": "\u2581\u0648\u0627\u0644", + "2573": "\u2581\u0627\u0644\u0652", + "2574": "\u0646\u0627", + "2575": "\u2581\u0627\u0644\u0623", + "2576": "\u0645\u0627", + "2577": "\u064a\u0631", + "2578": "\u0644\u0650", + "2579": "\u0638", + "2580": "\u0627\u0621", + "2581": "\u0644\u064e", + "2582": "\u0648\u0631", + "2583": "\u2581\u0627\u0644\u062a", + "2584": "\u2581\u0623\u0646", + "2585": "\u0627\u0628", + "2586": "\u0645\u064e", + "2587": "\u0643\u064e", + "2588": "\u062a\u064e", + "2589": "\u0651", + "2590": "\u0647\u0645", + "2591": "\u0639\u064e", + "2592": "\u0627\u062f", + "2593": "\u2581\u0625", + "2594": "\u0646\u0652", + "2595": "\u2581\u0623\u064e", + "2596": "\u0627\u0633", + "2597": "\u2581\u0627\u0644\u0633", + "2598": "\u064f\u0648", + "2599": "\u0628\u0650", + "2600": "\u2581\u0627\u0644\u0639", + "2601": "\u0622", + "2602": "\u064a\u0647", + "2603": "\u0650\u0651", + "2604": "\u2581\u0644\u0644", + "2605": "\u064d", + "2606": "\u2581\u0627\u0644\u062d", + "2607": "\u0646\u064e", + "2608": "\u064a\u0627", + "2609": "\u2581\u0641\u0649", + "2610": "\u2581\u0628\u0627\u0644", + "2611": "\u0648\u0645", + "2612": "\u2581\u0639\u0646", + "2613": "\u2581\u0627\u0644\u0646", + "2614": "\u0645\u064e\u0627", + "2615": "\u064a\u062f", + "2616": "\u2581\u0645\u0627", + "2617": "\u0627\u0639", + "2618": "\u064e\u064a\u0652", + "2619": "\u0627\u064b", + "2620": "\u064b\u0627", + "2621": "\u2581\u0645\u0639", + "2622": "\u0633\u062a", + "2623": "\u0645\u064f", + "2624": "\u2581\u0627\u0644\u0634", + "2625": "\u0634\u0631", + "2626": "\u2581\u0643\u0627\u0646", + "2627": "\u0625", + "2628": "\u0625\u0650", + "2629": "\u0627\u0641", + "2630": "\u0627\u062d", + "2631": "\u064c", + "2632": "\u062d\u064e", + "2633": "\u0630\u0627", + "2634": "\u2581\u0641\u0650\u064a", + "2635": "\u0631\u0628", + "2636": "\u2581\u064a\u0639\u0646\u064a", + "2637": "\u2581\u064a\u064e", + "2638": "\u064e\u0629\u0650", + "2639": "\u2581\u0627\u0644\u0642", + "2640": "\u0642\u064e", + "2641": "\u0646\u064e\u0627", + "2642": "\u064f\u0651", + "2643": "\u2581\u0627\u0644\u062c", + "2644": "\u0633\u064e", + "2645": "\u2581\u0627\u0644\u0628", + "2646": "\u2581\u0627\u0644\u062f", + "2647": "\u0645\u0652", + "2648": "\u0645\u0631", + "2649": "\u064f\u0648\u0646\u064e", + "2650": "\u0624", + "2651": "\u2581\u0627\u0644\u0627", + "2652": "\u0645\u0650", + "2653": "\u064e\u0648\u0652", + "2654": "\u0628\u0631", + "2655": "\u2581\u0628\u064a", + "2656": "\u2581\u0627\u0644\u0631", + "2657": "\u0628\u064e", + "2658": "\u0647\u064e\u0627", + "2659": "\u0647\u064f", + "2660": "\u0648\u0642", + "2661": "\u0627\u062c", + "2662": "\u2581\u0641\u064e", + "2663": "\u2581\u0622\u0647", + "2664": "\u2581\u0627\u0644\u0641", + "2665": "\u0652\u062a\u064e", + "2666": "\u2581\u0643\u0644", + "2667": "\u2581\u0627\u0644\u0635", + "2668": "\u2581\u0625\u0644\u0649", + "2669": "\u2581\u0647\u0648", + "2670": "\u2581\u0645\u0650\u0646\u0652", + "2671": "\u0648\u062f", + "2672": "\u0648\u0628", + "2673": "\u2581\u0648\u0623", + "2674": "\u062e\u0644", + "2675": "\u0631\u064e", + "2676": "\u062d\u062f", + "2677": "\u064a\u0645", + "2678": "\u2581\u0627\u0644\u0625", + "2679": "\u062f\u064e", + "2680": "\u0641\u064e", + "2681": "\u0647\u064f\u0645\u0652", + "2682": "\u0646\u0650", + "2683": "\u062c\u064e", + "2684": "\u064f\u0648\u0627", + "2685": "\u0641\u0631", + "2686": "\u064e\u0639\u0652", + "2687": "\u2581\u0623\u0648", + "2688": "\u2581\u0625\u0646", + "2689": "\u0648\u0633", + "2690": "\u0644\u064e\u0627", + "2691": "\u062c\u0645", + "2692": "\u0650\u064a\u0646\u064e", + "2693": "\u064e\u0651\u0627", + "2694": "\u0648\u0641", + "2695": "\u0648\u062c", + "2696": "\u2581\u0627\u0644\u062e", + "2697": "\u0639\u0645\u0644", + "2698": "\u2581\u0644\u0645", + "2699": "\u064e\u0627\u062a\u0650", + "2700": "\u2581\u0647\u0630\u0627", + "2701": "\u2581\u0623\u064e\u0646\u0652", + "2702": "\u2581\u0645\u0634", + "2703": "\u2581\u0628\u0639\u062f", + "2704": "\u2581\u0627\u0644\u0652\u0645\u064f", + "2705": "\u2581\u0627\u0644\u0637", + "2706": "\u0650\u0647\u0650", + "2707": "\u2581\u0627\u0644\u0644\u0649", + "2708": "\u0621", + "2709": "\u2581\u0627\u0644\u0644\u064a", + "2710": "\u2581\u0639\u064e\u0644\u064e\u0649", + "2711": "\u0652\u062a\u0650", + "2712": "\u2581\u0627\u0644\u0652\u0623\u064e", + "2713": "\u0630\u064e\u0627", + "2714": "\u064e\u0631\u064e", + "2715": "\u2581\u0623\u0646\u0627", + "2716": "\u0643\u064f\u0645\u0652", + "2717": "\u2581\u0627\u0644\u0652\u0645\u064e", + "2718": "\u2581\u0625\u0650\u0646\u064e\u0651", + "2719": "\u0652\u0631\u064e", + "2720": "\u2581\u0647\u0630\u0647", + "2721": "\u064e\u0644\u064e", + "2722": "\u064e\u0631\u0652", + "2723": "\u2581\u0627\u0633\u062a", + "2724": "\u2581\u0645\u0635\u0631", + "2725": "\u0650\u064a\u064e", + "2726": "\u0652\u0631\u0650", + "2727": "\u064e\u062d\u0652", + "2728": "\u0631\u0650\u064a", + "2729": "\u064e\u062f\u0652", + "2730": "\u2581\u0645\u0650\u0646\u064e", + "2731": "\u2581\u0648\u064e\u0644\u064e", + "2732": "\u2581\u0648\u064e\u0627\u0644\u0652", + "2733": "\u2581\u0643\u0645\u0627", + "2734": "\u0628\u0642\u0649", + "2735": "\u062f\u0650\u064a", + "2736": "\u2581\u0627\u0644\u0644\u0647", + "2737": "\u2581\u0627\u0644\u064e\u0651\u0630\u0650\u064a", + "2738": "\u2581\u0627\u0644\u0630\u064a", + "2739": "\u0639\u0631\u0641", + "2740": "\u2581\u0627\u0644\u0652\u0639\u064e", + "2741": "\u064e\u0647\u064f", + "2742": "\u0634\u0639\u0631", + "2743": "\u2581\u0644\u0643\u0646", + "2744": "\u0639\u0644\u0645", + "2745": "\u064e\u0629\u064f", + "2746": "\u064b", + "2747": "\u2581\u0646\u0641\u0633", + "2748": "\u0650\u064a\u064e\u0651\u0629\u0650", + "2749": "\u064e\u062a\u0652", + "2750": "\u2581\u0648\u064e\u0623\u064e", + "2751": "\u064e\u0629\u064d", + "2752": "\u0645\u062b\u0644", + "2753": "\u2581\u063a\u064a\u0631", + "2754": "\u0627\u0626\u064a", + "2755": "\u2581\u0625\u0650\u0644\u064e\u0649", + "2756": "\u2581\u0648\u0627\u062d\u062f", + "2757": "\u2581\u0623\u064e\u0646\u064e\u0651", + "2758": "\u2581\u0647\u064e\u0630\u064e\u0627", + "2759": "\u2581\u0630\u0644\u0643", + "2760": "\u064e\u0629\u064e", + "2761": "\u2581\u062d\u062a\u0649", + "2762": "\u2581\u0647\u064e\u0644\u0652", + "2763": "\u061f", + "2764": "\u060c", + "2765": "vy", + "2766": "\u2581byl", + "2767": "\u0147", + "2768": "\u0164", + "2769": "\u00d3", + "2770": "\u00e6r", + "2771": "\u2581blev", + "2772": "ft", + "2773": "lige", + "2774": "ved", + "2775": "'", + "2776": "\u00c5", + "2777": "\u2581H", + "2778": "\u2581D", + "2779": "aus", + "2780": "\u2581N", + "2781": "\u2581Be", + "2782": "mm", + "2783": "ab", + "2784": "\u2581Er", + "2785": "ssen", + "2786": "hl", + "2787": "hn", + "2788": "ischen", + "2789": "\u2581wurde", + "2790": "rie", + "2791": "lei", + "2792": "\u2581An", + "2793": "\u2581Ein", + "2794": "etz", + "2795": "rau", + "2796": "ische", + "2797": "\u00e4h", + "2798": "\u2581mein", + "2799": "\u2581So", + "2800": "\u2581hatte", + "2801": "\u2581unter", + "2802": "\u2581Zu", + "2803": "\u2581ihn", + "2804": "\u2581Jahr", + "2805": "\u2581zwei", + "2806": "keit", + "2807": "\u2581ihm", + "2808": "\u2581Aus", + "2809": "", + "2810": "\u2581you", + "2811": "\u2581that", + "2812": "\u2581and", + "2813": "\u2581can", + "2814": "\u2581it", + "2815": "\u2581your", + "2816": "ed", + "2817": "\u2581Okay", + "2818": "\u2581just", + "2819": "ay", + "2820": "\u2581Yeah", + "2821": "\u2581with", + "2822": "th", + "2823": "\u2581Thank", + "2824": "\u2581thank", + "2825": "\u2581help", + "2826": "\u2581please", + "2827": "\u2581one", + "2828": "\u2581there", + "2829": "ic", + "2830": "\u2581much", + "2831": "\u2581what", + "2832": "\u2581my", + "2833": "hi", + "2834": "\u2581will", + "2835": "\u2581would", + "2836": "\u2581if", + "2837": "\u2581two", + "2838": "\u2581this", + "2839": "\u2581he", + "2840": "\u2581go", + "2841": "\u2581all", + "2842": "\u2581Oh", + "2843": "\u2581like", + "2844": "\u2581very", + "2845": "\u2581The", + "2846": "\u2581today", + "2847": "\u2581not", + "2848": "\u2581yeah", + "2849": "\u2581take", + "2850": "ight", + "2851": "ex", + "2852": "\u2581Ok", + "2853": "\u2581seven", + "2854": "\u2581number", + "2855": "\u2581know", + "2856": "\u2581about", + "2857": "\u2581four", + "2858": "\u2581okay", + "2859": "\u2581name", + "2860": "\u2581And", + "2861": "\u2581five", + "2862": "\u2581How", + "2863": "\u2581account", + "2864": "\u2581any", + "2865": "\u2581three", + "2866": "\u2581could", + "2867": "\u2581up", + "2868": "\u2581get", + "2869": "\u2581phone", + "2870": "\u2581great", + "2871": "\u2581six", + "2872": "\u2581eight", + "2873": "\u2581now", + "2874": "\u2581nine", + "2875": "\u2581That", + "2876": "\u2581address", + "2877": "\u2581look", + "2878": "\u2581call", + "2879": "ill", + "2880": "\u2581You", + "2881": "\u2581but", + "2882": "\u2581got", + "2883": "\u2581don", + "2884": "\u2581email", + "2885": "\u2581calling", + "2886": "\u2581problem", + "2887": "\u2581right", + "2888": "\u2581good", + "2889": "\u2581well", + "2890": "\u2581out", + "2891": "\u2581What", + "2892": "\u2581how", + "2893": "\u2581really", + "2894": "\u2581anything", + "2895": "\u2581actually", + "2896": "\u2581from", + "2897": "\u2581think", + "2898": "\u2581time", + "2899": "\u2581some", + "2900": "\u2581ask", + "2901": "\u2581else", + "2902": "other", + "2903": "\u2581fine", + "2904": "able", + "2905": "\u2581Good", + "2906": "\u2581when", + "2907": "\u2581full", + "2908": "\u2581confirm", + "2909": "\u2581give", + "2910": "\u2581more", + "2911": "ever", + "2912": "\u2581month", + "2913": "\u2581information", + "2914": "\u2581sure", + "2915": "\u2581survey", + "2916": "\u2581sorry", + "2917": "\u2581send", + "2918": "\u2581through", + "2919": "\u2581check", + "2920": "\u2581long", + "2921": "\u2581birth", + "2922": "\u2581should", + "2923": "\u2581twenty", + "2924": "\u2581make", + "2925": "\u2581zero", + "2926": "ful", + "2927": "\u2581store", + "2928": "\u2581policy", + "2929": "\u2581back", + "2930": "\u2581again", + "2931": "\u2581first", + "2932": "\u2581Could", + "2933": "\u2581work", + "2934": "\u2581afternoon", + "2935": "\u2581after", + "2936": "\u2581insurance", + "2937": "\u2581customer", + "2938": "\u2581payment", + "2939": "\u2581question", + "2940": "\u2581receive", + "2941": "\u2581possible", + "2942": "\u2581moment", + "2943": "\u2581system", + "2944": "\u2581change", + "2945": "\u2581hundred", + "2946": "\u2581nineteen", + "2947": "", + "2948": "\u2581.", + "2949": "\u2581,", + "2950": "\u2581st", + "2951": "\u2581are", + "2952": "ow", + "2953": "ive", + "2954": "ate", + "2955": "ad", + "2956": "ect", + "2957": "\u2581they", + "2958": "\u2581as", + "2959": "ng", + "2960": "ity", + "2961": "ther", + "2962": "act", + "2963": "ist", + "2964": "\u2581our", + "2965": "\u2581sp", + "2966": "ally", + "2967": "\u2581his", + "2968": "\u2581But", + "2969": "\u2581has", + "2970": "\u2581also", + "2971": "\u2581which", + "2972": "\u2581He", + "2973": "\u2581uh", + "2974": "day", + "2975": "\u2581people", + "2976": "\u2581who", + "2977": "\u2581thing", + "2978": "\u2581because", + "2979": "\u2581other", + "2980": "ough", + "2981": "\u2581part", + "2982": "\u2581say", + "2983": "\u2581year", + "2984": "side", + "2985": "\"", + "2986": "", + "2987": "\u2581y", + "2988": "\u2581el", + "2989": "ci\u00f3n", + "2990": "\u2581Es", + "2991": "res", + "2992": "\u2581los", + "2993": "\u2581La", + "2994": "dos", + "2995": "\u00eda", + "2996": "\u2581El", + "2997": "\u2581las", + "2998": "\u2581m\u00e1s", + "2999": "men", + "3000": "\u00f1o", + "3001": "\u2581esta", + "3002": "idad", + "3003": "par", + "3004": "\u00bf", + "3005": "r\u00eda", + "3006": "\u2581fue", + "3007": "rio", + "3008": "enta", + "3009": "\u00f3n", + "3010": "cho", + "3011": "ciones", + "3012": "ble", + "3013": "\u2581Ca", + "3014": "\u2581muy", + "3015": "\u2581tambi\u00e9n", + "3016": "\u2581tiene", + "3017": "\u00f1a", + "3018": "\u2581Su", + "3019": "\u2581pero", + "3020": "\u2581son", + "3021": "encia", + "3022": "si\u00f3n", + "3023": "\u2581hay", + "3024": "\u2581puede", + "3025": "ncia", + "3026": "\u2581mucho", + "3027": "\u2581Si", + "3028": "\u2581pues", + "3029": "miento", + "3030": "\u2581Con", + "3031": "ones", + "3032": "ecto", + "3033": "iendo", + "3034": "\u2581d\u00eda", + "3035": "\u2581sobre", + "3036": "\u2581primer", + "3037": "\u2581qu\u00e9", + "3038": "\u2581gusta", + "3039": "\u2581San", + "3040": "\u2581hacer", + "3041": "cional", + "3042": "\u2581verdad", + "3043": "\u2581persona", + "3044": "\u2581pasa", + "3045": "\u2581mejor", + "3046": "qu\u00ed", + "3047": "\u2581Fue", + "3048": "\u2581Com", + "3049": "\u2581ciudad", + "3050": "\u00d1", + "3051": "", + "3052": "cia", + "3053": "\u2581lo", + "3054": "\u2581Y", + "3055": "ron", + "3056": "les", + "3057": "\u2581mu", + "3058": "cio", + "3059": "\u2581yo", + "3060": "bu", + "3061": "\u2581s\u00ed", + "3062": "\u2581Pero", + "3063": "\u2581as\u00ed", + "3064": "", + "3065": "r\u00e9", + "3066": "\u00e9e", + "3067": "\u2581Les", + "3068": "nt", + "3069": "our", + "3070": "\u2581Ce", + "3071": "com", + "3072": "\u2581Elle", + "3073": "\u2581Cet", + "3074": "ux", + "3075": "ale", + "3076": "ier", + "3077": "ction", + "3078": "\u2581cha", + "3079": "\u2581pr\u00e9", + "3080": "\u2581deux", + "3081": "if", + "3082": "l\u00e9", + "3083": "\u00e8re", + "3084": "i\u00e8re", + "3085": "iste", + "3086": "\u2581parti", + "3087": "\u2581\u00e9t\u00e9", + "3088": "cette", + "3089": "avec", + "3090": "\u2581tou", + "3091": "jour", + "3092": "app", + "3093": "cul", + "3094": "\u2581\u00e9gale", + "3095": "aine", + "3096": "gue", + "3097": "\u2581tr\u00e8", + "3098": "\u2581nombre", + "3099": "\u2581\u00e9tai", + "3100": "tout", + "3101": "\u2581grand", + "3102": "\u2581commun", + "3103": "Une", + "3104": "\u0153", + "3105": "\u00ef", + "3106": "\u00c0", + "3107": "\u0152", + "3108": "\u00c8", + "3109": "\u014d", + "3110": "\u00ff", + "3111": "\u014c", + "3112": "\u00d4", + "3113": "\u00ca", + "3114": "\u00c2", + "3115": "\u2581m", + "3116": "av", + "3117": "ouv", + "3118": "\u00eat", + "3119": "ois", + "3120": "pri", + "3121": "voir", + "3122": "sion", + "3123": "ix", + "3124": "ang", + "3125": "\u00e9tait", + "3126": "ard", + "3127": "aient", + "3128": "\u0106", + "3129": "\u0130", + "3130": "\u00d9", + "3131": "\u00db", + "3132": "\u00cb", + "3133": "\u00cf", + "3134": "", + "3135": "\u05d1", + "3136": "\u05e2", + "3137": "\u05e7", + "3138": "\u05d7", + "3139": "\u05db", + "3140": "\u05d3", + "3141": "\u05e0", + "3142": "\u2581\u05d1", + "3143": "\u05d9\u05dd", + "3144": "\u05d2", + "3145": "\u2581\u05de", + "3146": "\u05e1", + "3147": "\u05dd", + "3148": "\u05d5\u05ea", + "3149": "\u05e6", + "3150": "\u05e4", + "3151": "\u05d8", + "3152": "\u05d5\u05e8", + "3153": "\u05d6", + "3154": "\u2581\u05dc", + "3155": "\u05e0\u05d9", + "3156": "\u2581\u05e9\u05dc", + "3157": "\u2581\u05d4\u05de", + "3158": "\u05df", + "3159": "\u05da", + "3160": "\u05de\u05d5", + "3161": "\u05d1\u05d9", + "3162": "\u05e0\u05d5", + "3163": "\u05d5\u05dc", + "3164": "\u05dc\u05d9", + "3165": "\u05d1\u05e8", + "3166": "\u05d3\u05d9", + "3167": "\u2581\u05d0\u05ea", + "3168": "\u2581\u05e2\u05dc", + "3169": "\u05e9\u05d9", + "3170": "\u05de\u05e9", + "3171": "\u05d5\u05df", + "3172": "\u05d9\u05e8", + "3173": "\u05e0\u05d4", + "3174": "\u05d9\u05ea", + "3175": "\u05e4\u05e8", + "3176": "\u05e3", + "3177": "\u05db\u05dc", + "3178": "\u2581\u05d4\u05d5\u05d0", + "3179": "\u05de\u05d9", + "3180": "\u05d5\u05d1", + "3181": "\u05e8\u05d5", + "3182": "\u2581\u05d1\u05de", + "3183": "\u05e4\u05d9", + "3184": "\u05d0\u05d9", + "3185": "\u2581\u05d4\u05e9", + "3186": "\u05d7\u05d9", + "3187": "\u05d7\u05d5", + "3188": "\u05dc\u05d5", + "3189": "\u05d1\u05e2", + "3190": "\u2581\u05d4\u05d0", + "3191": "\u05e7\u05e8", + "3192": "\u2581\u05dc\u05d0", + "3193": "\u05e0\u05d9\u05dd", + "3194": "\u05e1\u05d9", + "3195": "\u05e8\u05d9", + "3196": "\u2581\u05dc\u05d4", + "3197": "\u05e9\u05e8", + "3198": "\u05d5\u05d3", + "3199": "\u05d9\u05df", + "3200": "\u05d5\u05e4", + "3201": "\u05d0\u05dc", + "3202": "\u2581\u05d4\u05d7", + "3203": "\u05d3\u05e8", + "3204": "\u05e0\u05d5\u05ea", + "3205": "\u2581\u05d4\u05e2", + "3206": "\u05e8\u05d9\u05dd", + "3207": "\u05e4\u05d5", + "3208": "\u05e6\u05d9", + "3209": "\u2581\u05dc\u05de", + "3210": "\u05d0\u05e8", + "3211": "\u05d0\u05d5\u05ea", + "3212": "\u05d8\u05d9", + "3213": "\u2581\u05d4\u05e1", + "3214": "\u05d9\u05d5\u05ea", + "3215": "\u05db\u05d9", + "3216": "\u05e5", + "3217": "\u2581\u05d0\u05d5", + "3218": "\u2581\u05d5\u05d4", + "3219": "\u2581\u05d6\u05d4", + "3220": "\u2581\u05d4\u05d9\u05d0", + "3221": "\u2581\u05d4\u05e6", + "3222": "\u05de\u05e8", + "3223": "\u05e4\u05e2", + "3224": "\u2581\u05d4\u05e4", + "3225": "\u05db\u05df", + "3226": "\u2581\u05d4\u05d9\u05d4", + "3227": "\u05d8\u05e8", + "3228": "\u05d6\u05e8", + "3229": "\u2581\u05e9\u05e0", + "3230": "\u05d0\u05d7\u05e8", + "3231": "\u2581\u05e8\u05d1", + "3232": "\u2581\u05d6\u05d5", + "3233": "\u2581\u05d4\u05e8", + "3234": "\u05de\u05d9\u05dd", + "3235": "\u2581\u05d5\u05de", + "3236": "\u05e8\u05d0\u05e9", + "3237": "\u2581\u05dc\u05d0\u05d7\u05e8", + "3238": "\u05d7\u05dc\u05e7", + "3239": "\u05de\u05df", + "3240": "\u2581\u05d4\u05d9\u05d5", + "3241": "\u05de\u05e1\u05e4\u05e8", + "3242": "\u2581\u05d9\u05d5\u05ea\u05e8", + "3243": "\u05d0\u05d7\u05d3", + "3244": "\u2581\u05d4\u05d9\u05d9\u05ea", + "3245": "\u05e2\u05e6\u05de", + "3246": "\u05de\u05e7\u05d5\u05dd", + "3247": "", + "3248": "\u093e", + "3249": "\u0930", + "3250": "\u0928", + "3251": "\u0915", + "3252": "\u0938", + "3253": "\u0964", + "3254": "\u093f", + "3255": "\u092e", + "3256": "\u0940", + "3257": "\u0932", + "3258": "\u0947", + "3259": "\u092a", + "3260": "\u2581\u0939\u0948", + "3261": "\u094d", + "3262": "\u0939", + "3263": "\u091c", + "3264": "\u0935", + "3265": "\u0924", + "3266": "\u0902", + "3267": "\u091f", + "3268": "\u094b", + "3269": "\u0941", + "3270": "\u0917", + "3271": "\u2581\u0915\u0947", + "3272": "\u2581\u092c", + "3273": "\u2581\u092e\u0947\u0902", + "3274": "\u0936", + "3275": "\u0928\u0947", + "3276": "\u0942", + "3277": "\u092f", + "3278": "\u0928\u093e", + "3279": "\u0924\u093e", + "3280": "\u0926", + "3281": "\u091a", + "3282": "\u2581\u0906", + "3283": "\u092c", + "3284": "\u2581\u0915\u0930", + "3285": "\u2581\u0915\u0940", + "3286": "\u2581\u0905", + "3287": "\u0930\u094d", + "3288": "\u2581\u0939\u094b", + "3289": "\u2581\u0914\u0930", + "3290": "\u090f", + "3291": "\u0916", + "3292": "\u2581\u0924\u094b", + "3293": "\u2581\u0939\u0948\u0902", + "3294": "\u2581\u0938\u0947", + "3295": "\u2581\u0915\u093e", + "3296": "\u094b\u0902", + "3297": "\u2581\u0915\u094b", + "3298": "\u2581\u0915\u093f", + "3299": "\u0924\u0947", + "3300": "\u092b", + "3301": "\u0927", + "3302": "\u0930\u093e", + "3303": "\u0935\u093e", + "3304": "\u2581\u091c\u093e", + "3305": "\u0921", + "3306": "\u0948", + "3307": "\u2581\u0928\u0939\u0940\u0902", + "3308": "\u0909", + "3309": "\u094d\u092f", + "3310": "\u0908", + "3311": "\u2581\u092d\u0940", + "3312": "\u0915\u093e", + "3313": "\u2581\u0926", + "3314": "\u0921\u093c", + "3315": "\u0915\u0947", + "3316": "\u0930\u0940", + "3317": "\u0924\u0940", + "3318": "\u0907", + "3319": "\u2581\u090f\u0915", + "3320": "\u094d\u0930", + "3321": "\u2581\u0907\u0938", + "3322": "\u2581\u092a\u094d\u0930", + "3323": "\u2581\u0909\u0938", + "3324": "\u092f\u093e", + "3325": "\u2581\u092a\u0930", + "3326": "\u092e\u093e", + "3327": "\u092d", + "3328": "\u0947\u0902", + "3329": "\u0932\u0947", + "3330": "\u2581\u0935\u094b", + "3331": "\u0932\u093e", + "3332": "\u094c", + "3333": "\u0938\u0947", + "3334": "\u2581\u0939\u092e", + "3335": "\u2581\u091c\u094b", + "3336": "\u0915\u094d", + "3337": "\u0917\u093e", + "3338": "\u0923", + "3339": "\u2581\u0935\u093f", + "3340": "\u0939\u093e", + "3341": "\u0928\u0940", + "3342": "\u2581\u0906\u092a", + "3343": "\u093f\u092f\u093e", + "3344": "\u2581\u092e\u0948\u0902", + "3345": "\u0902\u0917", + "3346": "\u0938\u094d", + "3347": "\u2581\u0939\u0940", + "3348": "\u0925", + "3349": "\u0930\u0947", + "3350": "\u2581\u092a\u093e", + "3351": "\u093f\u0924", + "3352": "\u0949", + "3353": "\u092d\u093e", + "3354": "\u0938\u0940", + "3355": "\u0901", + "3356": "\u2581\u092f\u0947", + "3357": "\u0915\u094d\u0937", + "3358": "\u091b", + "3359": "\u2581\u0925\u093e", + "3360": "\u0924\u093f", + "3361": "\u2581\u0932\u093f\u090f", + "3362": "\u2581\u0926\u0947", + "3363": "\u0932\u0940", + "3364": "\u2581\u0915\u094d\u092f\u093e", + "3365": "\u2581\u0938\u0902", + "3366": "\u0937", + "3367": "\u2581\u092f\u0939", + "3368": "\u2581\u0939\u093e\u0901", + "3369": "\u0920", + "3370": "\u0924\u094d\u0930", + "3371": "\u0902\u0926", + "3372": "\u0918", + "3373": "\u2581\u092c\u0939\u0941\u0924", + "3374": "\u2581\u0938\u092e", + "3375": "\u094d\u092f\u093e", + "3376": "\u2581\u0932\u0917", + "3377": "\u2581\u0926\u094b", + "3378": "\u093c", + "3379": "\u2581\u0926\u0947\u0916", + "3380": "\u0913", + "3381": "\u0926\u093e", + "3382": "\u2581\u0928\u093f", + "3383": "\u0902\u0921", + "3384": "\u0926\u0940", + "3385": "\u2581\u0930\u0939\u0947", + "3386": "\u2581\u0932\u094b\u0917", + "3387": "\u2581\u092c\u093e\u0924", + "3388": "\u2581\u0915\u0941\u091b", + "3389": "\u093e\u0907", + "3390": "\u2581\u0905\u091a\u094d\u091b\u093e", + "3391": "\u2581\u0938\u0941", + "3392": "\u2581\u0938\u093e\u0925", + "3393": "\u2581\u0915\u0939\u093e", + "3394": "\u2581\u0915\u093f\u092f\u093e", + "3395": "\u0938\u094d\u091f", + "3396": "\u2581\u0938\u092c", + "3397": "\u0922\u093c", + "3398": "\u2581\u0930\u0939\u093e", + "3399": "\u2581\u0917\u092f\u093e", + "3400": "\u2581\u092b\u093f\u0930", + "3401": "\u2581\u092a\u0947", + "3402": "\u2581\u0905\u092c", + "3403": "\u0938\u094d\u0925", + "3404": "\u2581\u091c\u0940", + "3405": "\u2581\u091a\u0932", + "3406": "\u2581\u092c\u093e\u0930", + "3407": "\u2581\u0925\u0947", + "3408": "\u0938\u094d\u0924", + "3409": "\u2581\u0925\u0940", + "3410": "\u2581\u092e\u093f\u0932", + "3411": "\u2581\u0915\u094b\u0908", + "3412": "\u0943", + "3413": "\u2581\u092e\u0924\u0932\u092c", + "3414": "\u093f\u092f\u094b\u0902", + "3415": "\u2581\u0939\u0942\u0901", + "3416": "\u2581\u0905\u092d\u0940", + "3417": "\u0947\u0902\u0917\u0947", + "3418": "\u2581\u092c\u094b\u0932", + "3419": "\u091d", + "3420": "\u2581\u0930\u0939\u0940", + "3421": "\u091a\u093e\u0930", + "3422": "\u2581\u0905\u092a\u0928\u0947", + "3423": "\u2581\u092c\u093e\u0926", + "3424": "\u2581\u0932\u0947\u0915\u093f\u0928", + "3425": "\u0924\u094d\u0924", + "3426": "\u0910", + "3427": "\u2581\u092e\u0941\u091d\u0947", + "3428": "\u2581\u092e\u0947\u0930\u0947", + "3429": "\u0911", + "3430": "!", + "3431": "\u0906", + "3432": "\u090a", + "3433": "\u0922", + "3434": "\u091e", + "3435": "\u0905", + "3436": "\u0903", + "3437": "\u0914", + "3438": "\u090b", + "3439": "\u0945", + "3440": "\u0919", + "3441": "\u090d", + "3442": "\u0950", + "3443": "\u0960", + "3444": "\u0931", + "3445": "\u00cc", + "3446": "", + "3447": "\u2581\u3044", + "3448": "\u2581\u3002", + "3449": "\u2581\u3001", + "3450": "\u2581\u306e", + "3451": "\u2581\u3046", + "3452": "\u2581\u3093", + "3453": "\u2581\u306a", + "3454": "\u2581\u304b", + "3455": "\u2581\u3067", + "3456": "\u2581\u3063", + "3457": "\u2581\u3066", + "3458": "\u2581\u3042", + "3459": "\u2581\u305f", + "3460": "\u2581\u3068", + "3461": "\u2581\u3059", + "3462": "\u2581\u308b", + "3463": "\u2581\u306f", + "3464": "\u2581\u306b", + "3465": "\u2581\u3057", + "3466": "\u2581\u305d", + "3467": "\u2581\u3082", + "3468": "\u2581\u30fc", + "3469": "\u2581\u307e", + "3470": "\u2581\u304c", + "3471": "\u2581\u306d", + "3472": "\u2581\u3089", + "3473": "\u2581\u308c", + "3474": "\u2581\u3060", + "3475": "\u2581\u30f3", + "3476": "\u2581\u3053", + "3477": "\u2581\u3088", + "3478": "\u2581\u308a", + "3479": "\u2581\u3092", + "3480": "\u4ee5", + "3481": "\u4ed5", + "3482": "\u2581\u53cb", + "3483": "\u2581\u6771", + "3484": "\u2581\u9055", + "3485": "\u2581\u6587", + "3486": "\u2581\u30a1", + "3487": "\u2581\u30ce", + "3488": "\u2581\u6210", + "3489": "\u2581\u660e", + "3490": "\u2581\u4e16", + "3491": "\u2581\u5f37", + "3492": "\u2581\u66f2", + "3493": "\u2581\u8868", + "3494": "\u2581\u6708", + "3495": "\u2581\u60c5", + "3496": "\u2581\u6d3b", + "3497": "\u2581\u753a", + "3498": "\u2581\u4ed8", + "3499": "\u2581\u3075", + "3500": "\u2581\u3072", + "3501": "\u2581\u8cb7", + "3502": "\u2581\u9023", + "3503": "\u2581\u3080", + "3504": "\u2581\u533a", + "3505": "\u2581\u30da", + "3506": "\u2581\u78ba", + "3507": "\u2581\u6d41", + "3508": "\u2581\u671f", + "3509": "\u2581\u6d77", + "3510": "\u2581\u8a2d", + "3511": "\u2581\u8a9e", + "3512": "\u2581\u66f8", + "3513": "\u2581\u6599", + "3514": "\u2581\u8981", + "3515": "\u2581\u79d1", + "3516": "\u2581\u80b2", + "3517": "\u2581\u30b4", + "3518": "\u2581\u5b89", + "3519": "\u2581\u516d", + "3520": "\u2581\u6709", + "3521": "\u2581\u30b2", + "3522": "\u2581\u539f", + "3523": "\u2581\u80fd", + "3524": "\u2581\u58f2", + "3525": "\u2581\u611b", + "3526": "\u2581\u4eac", + "3527": "\u2581\u5236", + "3528": "\u2581\u30b6", + "3529": "\u2581\u826f", + "3530": "\u2581\u30ae", + "3531": "\u2581\u30e4", + "3532": "\u2581\u4e03", + "3533": "\u2581\u7121", + "3534": "\u2581\u8003", + "3535": "\u2581\u7279", + "3536": "\u2581\u767e", + "3537": "\u2581\u5c11", + "3538": "\u2581\u53c2", + "3539": "\u2581\u7537", + "3540": "\u2581\u4fdd", + "3541": "\u2581\u5712", + "3542": "\u666e", + "3543": "\u2581\u4ed6", + "3544": "\u2581\u30a9", + "3545": "\u2581\u6b21", + "3546": "\u2581\u512a", + "3547": "\u2581\u304e", + "3548": "\u2581\u8abf", + "3549": "\u2581\u6f14", + "3550": "\u2581\u53e3", + "3551": "\u2581\u98a8", + "3552": "\u2581\u9001", + "3553": "\u2581\u904b", + "3554": "\u2581\u99c5", + "3555": "\u2581\u5c40", + "3556": "\u53d7", + "3557": "\u2581\u7f6e", + "3558": "\u2581\u90fd", + "3559": "\u2581\u4fe1", + "3560": "\u2581\u7f8e", + "3561": "\u2581\u89aa", + "3562": "\u2581\u3005", + "3563": "\u2581\u96f6", + "3564": "\u2581\u5143", + "3565": "\u2581\u59cb", + "3566": "\u2581\u9078", + "3567": "\u2581\u5de5", + "3568": "\u2581\u754c", + "3569": "\u2581\u8eab", + "3570": "\u2581\u5e83", + "3571": "\u2581\u5411", + "3572": "\u2581\u7d44", + "3573": "\u2581\u5728", + "3574": "\u2581\u5354", + "3575": "\u2581\u6c34", + "3576": "\u2581\u5dde", + "3577": "\u2581\u4f11", + "3578": "\u2581\u548c", + "3579": "\u2581\u653e", + "3580": "\u2581\u69d8", + "3581": "\u2581\u7d42", + "3582": "\u2581\u969b", + "3583": "\u2581\u52a0", + "3584": "\u2581\u5357", + "3585": "\u2581\u5207", + "3586": "\u2581\u4e0d", + "3587": "\u2581\u50d5", + "3588": "\u2581\u4f8b", + "3589": "\u2581\u65e9", + "3590": "\u2581\u65cf", + "3591": "\u2581\u3047", + "3592": "\u2581\u7d4c", + "3593": "\u2581\u4f9b", + "3594": "\u2581\u5f62", + "3595": "\u2581\u767d", + "3596": "\u2581\u6728", + "3597": "\u2581\u7b49", + "3598": "\u2581\u5929", + "3599": "\u2581\u5229", + "3600": "\u2581\u73fe", + "3601": "\u2581\u5fdc", + "3602": "\u2581\u9928", + "3603": "\u2581\u5404", + "3604": "\u2581\u70b9", + "3605": "\u2581\u52d9", + "3606": "\u2581\u30f4", + "3607": "\u2581\u771f", + "3608": "\u2581\u6307", + "3609": "\u2581\u671d", + "3610": "\u2581\u97f3", + "3611": "\u2581\u4e88", + "3612": "\u2581\u5e73", + "3613": "\u2581\u984c", + "3614": "\u2581\u4f4f", + "3615": "\u2581\u5186", + "3616": "\u2581\u4f1d", + "3617": "\u2581\u56e3", + "3618": "\u2581\u6751", + "3619": "\u2581\u76f8", + "3620": "\u2581\u8853", + "3621": "\u2581\u5e30", + "3622": "\u2581\u53e4", + "3623": "\u2581\u8ab0", + "3624": "\u2581\u53ef", + "3625": "\u2581\u592b", + "3626": "\u2581\u5f7c", + "3627": "\u2581\u533b", + "3628": "\u2581\u7a7a", + "3629": "\u2581\u8cde", + "3630": "\u2581\u653f", + "3631": "\u2581\u4ea4", + "3632": "\u2581\u6c11", + "3633": "\u2581\u6c7a", + "3634": "\u5c02", + "3635": "\u2581\u7523", + "3636": "\u2581\u9650", + "3637": "\u2581\u795e", + "3638": "\u2581\u57fa", + "3639": "\u2581\u60aa", + "3640": "\u2581\u9662", + "3641": "\u2581\u7cfb", + "3642": "\u2581\u5f15", + "3643": "\u2581\u65c5", + "3644": "\u2581\u6280", + "3645": "\u2581\u53f0", + "3646": "\u2581\u52dd", + "3647": "\u2581\u3086", + "3648": "\u2581\u57df", + "3649": "\u2581\u518d", + "3650": "\u2581\u554f", + "3651": "\u2581\u8272", + "3652": "\u2581\u30d2", + "3653": "\u4f01", + "3654": "\u767b", + "3655": "\u7dcf", + "3656": "\u6539", + "3657": "\u63a2", + "3658": "\u7570", + "3659": "\u5b8c", + "3660": "\u4f0a", + "3661": "\u9811", + "3662": "\u6df1", + "3663": "\u7af6", + "3664": "\u63a8", + "3665": "\u8fb2", + "3666": "\u6628", + "3667": "\u639b", + "3668": "\u5bc4", + "3669": "\u4ed9", + "3670": "\u5371", + "3671": "\u8f9e", + "3672": "\u6f2b", + "3673": "\u57fc", + "3674": "\u8a73", + "3675": "\u5e7c", + "3676": "\u6271", + "3677": "\u4f59", + "3678": "\u63cf", + "3679": "\u63a1", + "3680": "\u88ab", + "3681": "\u30f6", + "3682": "\u4ff3", + "3683": "\u6803", + "3684": "\u56fa", + "3685": "\u526f", + "3686": "\u6df7", + "3687": "\u6551", + "3688": "\u7518", + "3689": "\u4e92", + "3690": "\u9589", + "3691": "\u75b2", + "3692": "\u4e9c", + "3693": "\u501f", + "3694": "\u690d", + "3695": "\u8cac", + "3696": "\u4eee", + "3697": "\u8da3", + "3698": "\u8f9b", + "3699": "\u8131", + "3700": "\u6050", + "3701": "\u6ce3", + "3702": "\u5951", + "3703": "\u60b2", + "3704": "\u96a0", + "3705": "\u662d", + "3706": "\u9ebb", + "3707": "\u54f2", + "3708": "\u5ba3", + "3709": "\u6c96", + "3710": "\u60a9", + "3711": "\u6d6e", + "3712": "\u8af8", + "3713": "\u5de8", + "3714": "\u5348", + "3715": "\u5360", + "3716": "\u8ddd", + "3717": "\u7e4b", + "3718": "\u6e0b", + "3719": "\u5fd9", + "3720": "\u6c5a", + "3721": "\u5ef6", + "3722": "\u5192", + "3723": "\u8a2a", + "3724": "\u6cbf", + "3725": "\u552f", + "3726": "\u6279", + "3727": "\u90f5", + "3728": "\u4f9d", + "3729": "\u63da", + "3730": "\u52c7", + "3731": "\u8a95", + "3732": "\u67d4", + "3733": "\u50be", + "3734": "\u5bc2", + "3735": "\u8a89", + "3736": "\u61f8", + "3737": "\u9ec4", + "3738": "\u90a6", + "3739": "\u81e8", + "3740": "\u5b09", + "3741": "\u7dba", + "3742": "\u5d29", + "3743": "\u8cfc", + "3744": "\u6d45", + "3745": "\u7e70", + "3746": "\u7dad", + "3747": "\u55ab", + "3748": "\u7a3c", + "3749": "\u71c3", + "3750": "\u65e2", + "3751": "\u8e0f", + "3752": "\u55a7", + "3753": "\u61a7", + "3754": "\u795d", + "3755": "\u6f01", + "3756": "\u8352", + "3757": "\u7dca", + "3758": "\u7372", + "3759": "\u98fe", + "3760": "\u70ad", + "3761": "\u642d", + "3762": "\u52aa", + "3763": "\u72d9", + "3764": "\u8a34", + "3765": "\u5bc5", + "3766": "\u9867", + "3767": "\u6311", + "3768": "\u61d0", + "3769": "\u72ed", + "3770": "\u96f0", + "3771": "\u62db", + "3772": "\u5857", + "3773": "\u6392", + "3774": "\u963f", + "3775": "\u596a", + "3776": "\u96c7", + "3777": "\u57cb", + "3778": "\u5c65", + "3779": "\u4fb5", + "3780": "\u61b2", + "3781": "\u8a72", + "3782": "\u786c", + "3783": "\u8caf", + "3784": "\u80f8", + "3785": "\u983b", + "3786": "\u52e7", + "3787": "\u9b45", + "3788": "\u5fe0", + "3789": "\u8328", + "3790": "\u6291", + "3791": "\u9a5a", + "3792": "\u75e9", + "3793": "\u5996", + "3794": "\u63c3", + "3795": "\u885d", + "3796": "\u54c0", + "3797": "\u829d", + "3798": "\u504f", + "3799": "\u5f27", + "3800": "\u4ef0", + "3801": "\u6f70", + "3802": "\u6dbc", + "3803": "\u8ae6", + "3804": "\u98fd", + "3805": "\u598a", + "3806": "\u633f", + "3807": "\u8010", + "3808": "\u8ce2", + "3809": "\u902e", + "3810": "\u62ab", + "3811": "\u6d69", + "3812": "\u900f", + "3813": "\u6328", + "3814": "\u4fc3", + "3815": "\u667a", + "3816": "\u507d", + "3817": "\u62d3", + "3818": "\u63a7", + "3819": "\u64a4", + "3820": "\u6f5c", + "3821": "\u6817", + "3822": "\u5553", + "3823": "\u7fa8", + "3824": "\u8d08", + "3825": "\u52b1", + "3826": "\u4f3a", + "3827": "\u5410", + "3828": "\u5faa", + "3829": "\u9700", + "3830": "\u6442", + "3831": "\u6dfb", + "3832": "\u7ffb", + "3833": "\u7761", + "3834": "\u5b64", + "3835": "\u7b20", + "3836": "\u6606", + "3837": "\u583a", + "3838": "\u6c88", + "3839": "\u4fd7", + "3840": "\u51fd", + "3841": "\u5302", + "3842": "\u906d", + "3843": "\u6eb6", + "3844": "\u52f2", + "3845": "\u7f70", + "3846": "\u8a87", + "3847": "\u659c", + "3848": "\u935b", + "3849": "\u8cb0", + "3850": "\u7de9", + "3851": "\u62bd", + "3852": "\u7652", + "3853": "\u53e9", + "3854": "\u4f46", + "3855": "\u683d", + "3856": "\u8cbf", + "3857": "\u8107", + "3858": "\u5036", + "3859": "\u9022", + "3860": "\u5949", + "3861": "\u662f", + "3862": "\u8912", + "3863": "\u9271", + "3864": "\u8cbc", + "3865": "\u4f73", + "3866": "\u75be", + "3867": "\u5e61", + "3868": "\u67b6", + "3869": "\u546a", + "3870": "\u4e32", + "3871": "\u5e7e", + "3872": "\u6c99", + "3873": "\u62d2", + "3874": "\u8105", + "3875": "\u8b21", + "3876": "\u631f", + "3877": "\u62cd", + "3878": "\u938c", + "3879": "\u80c3", + "3880": "\u99b4", + "3881": "\u9077", + "3882": "\u5197", + "3883": "\u7b51", + "3884": "\u6f2c", + "3885": "\u6068", + "3886": "\u6bb4", + "3887": "\u66c7", + "3888": "\u7fcc", + "3889": "\u8ecc", + "3890": "\u5378", + "3891": "\u6b53", + "3892": "\u6de1", + "3893": "\u6f0f", + "3894": "\u8986", + "3895": "\u72e9", + "3896": "\u755c", + "3897": "\u84b8", + "3898": "\u854e", + "3899": "\u6cf0", + "3900": "\u7d1b", + "3901": "\u7d5e", + "3902": "\u8a50", + "3903": "\u6905", + "3904": "\u6052", + "3905": "\u5132", + "3906": "\u64ec", + "3907": "\u53d4", + "3908": "\u53ec", + "3909": "\u5e7d", + "3910": "\u80ba", + "3911": "\u7b87", + "3912": "\u80a5", + "3913": "\u758e", + "3914": "\u9676", + "3915": "\u65e8", + "3916": "\u90b8", + "3917": "\u5449", + "3918": "\u51c6", + "3919": "\u8df3", + "3920": "\u757f", + "3921": "\u5ef7", + "3922": "\u920d", + "3923": "\u6e9c", + "3924": "\u6170", + "3925": "\u72a0", + "3926": "\u7e4a", + "3927": "\u82b3", + "3928": "\u7272", + "3929": "\u773a", + "3930": "\u90ca", + "3931": "\u618e", + "3932": "\u514b", + "3933": "\u731b", + "3934": "\u63aa", + "3935": "\u9a19", + "3936": "\u6d78", + "3937": "\u6148", + "3938": "\u52a3", + "3939": "\u93ae", + "3940": "\u8650", + "3941": "\u8e74", + "3942": "\u82d7", + "3943": "\u9665", + "3944": "\u5f90", + "3945": "\u62ed", + "3946": "\u58cc", + "3947": "\u614c", + "3948": "\u6349", + "3949": "\u819c", + "3950": "\u508d", + "3951": "\u565b", + "3952": "\u819a", + "3953": "\u6f20", + "3954": "\u606d", + "3955": "\u81a8", + "3956": "\u6a3d", + "3957": "\u820c", + "3958": "\u611a", + "3959": "\u7881", + "3960": "\u82a6", + "3961": "\u5eca", + "3962": "\u5674", + "3963": "\u7f8a", + "3964": "\u85ab", + "3965": "\u7be0", + "3966": "\u59a5", + "3967": "\u78ef", + "3968": "\u6851", + "3969": "\u7092", + "3970": "\u62d8", + "3971": "\u690e", + "3972": "\u7c98", + "3973": "\u5208", + "3974": "\u8061", + "3975": "\u537f", + "3976": "\u80e1", + "3977": "\u5d07", + "3978": "\u84b2", + "3979": "\u5270", + "3980": "\u745e", + "3981": "\u6e13", + "3982": "\u8ced", + "3983": "\u6e67", + "3984": "\u70f9", + "3985": "\u51dd", + "3986": "\u7d3a", + "3987": "\u9038", + "3988": "\u7261", + "3989": "\u58a8", + "3990": "\u840c", + "3991": "\u622f", + "3992": "\u8429", + "3993": "\u79e9", + "3994": "\u6367", + "3995": "\u69fb", + "3996": "\u8154", + "3997": "\u8776", + "3998": "\u8d05", + "3999": "\u7a4f", + "4000": "\u6562", + "4001": "\u64c1", + "4002": "\u8d74", + "4003": "\u78d0", + "4004": "\u58ee", + "4005": "\u8a93", + "4006": "\u62b9", + "4007": "\u6ea2", + "4008": "\u53f1", + "4009": "\u53f6", + "4010": "\u59a8", + "4011": "\u6cb8", + "4012": "\u7d33", + "4013": "\u963b", + "4014": "\u5984", + "4015": "\u6590", + "4016": "\u5983", + "4017": "\u5de7", + "4018": "\u540a", + "4019": "\u60da", + "4020": "\u8236", + "4021": "\u52ff", + "4022": "\u61c7", + "4023": "\u7525", + "4024": "\u60dc", + "4025": "\u7b39", + "4026": "\u6b86", + "4027": "\u6fe1", + "4028": "\u60e3", + "4029": "\u6020", + "4030": "\u6dc0", + "4031": "\u5265", + "4032": "\u66d6", + "4033": "\u6b64", + "4034": "\u85dd", + "4035": "\u8fb0", + "4036": "\u632b", + "4037": "\u66ab", + "4038": "\u6155", + "4039": "\u78a7", + "4040": "\u5634", + "4041": "\u3062", + "4042": "\u6d2a", + "4043": "\u865c", + "4044": "\u9065", + "4045": "\u92ed", + "4046": "\u5a2f", + "4047": "\u814e", + "4048": "\u871c", + "4049": "\u8a02", + "4050": "\u74e6", + "4051": "\u5944", + "4052": "\u64ad", + "4053": "\u75d5", + "4054": "\u7db4", + "4055": "\u7a40", + "4056": "\u9699", + "4057": "\u5384", + "4058": "\u5448", + "4059": "\u66f0", + "4060": "\u5d16", + "4061": "\u64e6", + "4062": "\u70cf", + "4063": "\u62c9", + "4064": "\u8861", + "4065": "\u6731", + "4066": "\u5606", + "4067": "\u8339", + "4068": "\u5cef", + "4069": "\u6ff1", + "4070": "\u84bc", + "4071": "\u30f1", + "4072": "\u6d12", + "4073": "\u85a9", + "4074": "\u8acf", + "4075": "\u55c5", + "4076": "\u689d", + "4077": "\u8096", + "4078": "\u785d", + "4079": "\u8a63", + "4080": "\u8cd1", + "4081": "\u67a2", + "4082": "\u6e9d", + "4083": "\u7a00", + "4084": "\u6a58", + "4085": "\u7766", + "4086": "\u9673", + "4087": "\u91e7", + "4088": "\u91b8", + "4089": "\u55aa", + "4090": "\u67af", + "4091": "\u6881", + "4092": "\u86cd", + "4093": "\u7ce7", + "4094": "\u90ed", + "4095": "\u7058", + "4096": "\u723d", + "4097": "\u7c97", + "4098": "\u8702", + "4099": "\u636e", + "4100": "\u5112", + "4101": "\u80a1", + "4102": "\u978d", + "4103": "\u61f2", + "4104": "\u5b54", + "4105": "\u6f06", + "4106": "\u8499", + "4107": "\u693f", + "4108": "\u7345", + "4109": "\u73c8", + "4110": "\u7554", + "4111": "\u9a28", + "4112": "\u675c", + "4113": "\u7984", + "4114": "\u52c3", + "4115": "\u9ac4", + "4116": "\u5f0a", + "4117": "\u77ef", + "4118": "\u9df2", + "4119": "\u58ec", + "4120": "\u6666", + "4121": "\u6e15", + "4122": "\u85cd", + "4123": "\u533f", + "4124": "\u582a", + "4125": "\u7aaa", + "4126": "\u5289", + "4127": "\u6182", + "4128": "\u5091", + "4129": "\u63b4", + "4130": "\u540e", + "4131": "\u916a", + "4132": "\u5176", + "4133": "\u82eb", + "4134": "\u30c5", + "4135": "\u63c9", + "4136": "\u73a9", + "4137": "\u80f4", + "4138": "\u8910", + "4139": "\u8afe", + "4140": "\u5598", + "4141": "\u559a", + "4142": "\u8594", + "4143": "\u8cc4", + "4144": "\u7fe0", + "4145": "\u5023", + "4146": "\u576a", + "4147": "\u6109", + "4148": "\u6276", + "4149": "\u670b", + "4150": "\u5351", + "4151": "\u66fe", + "4152": "\u786b", + "4153": "\u51a8", + "4154": "\u5b78", + "4155": "\u6c7d", + "4156": "\u837b", + "4157": "\u8461", + "4158": "\u6eba", + "4159": "\u8fbf", + "4160": "\u4e91", + "4161": "\u5fcc", + "4162": "\u7815", + "4163": "\u6734", + "4164": "\u6a8e", + "4165": "\u9320", + "4166": "\u5e63", + "4167": "\u80af", + "4168": "\u81b5", + "4169": "\u52c5", + "4170": "\u65bc", + "4171": "\u7947", + "4172": "\u8304", + "4173": "\u6591", + "4174": "\u50c5", + "4175": "\u8a60", + "4176": "\u96bc", + "4177": "\u98e2", + "4178": "\u7a3d", + "4179": "\u5dba", + "4180": "\u6df5", + "4181": "\u8b83", + "4182": "\u7aae", + "4183": "\u7be4", + "4184": "\u97fb", + "4185": "\u6897", + "4186": "\u72f8", + "4187": "\u69cd", + "4188": "\u8b17", + "4189": "\u8ab9", + "4190": "\u9010", + "4191": "\u53d9", + "4192": "\u5420", + "4193": "\u725f", + "4194": "\u9838", + "4195": "\u52fe", + "4196": "\u717d", + "4197": "\u7460", + "4198": "\u4fb6", + "4199": "\u68b6", + "4200": "\u8997", + "4201": "\u95a4", + "4202": "\u51a5", + "4203": "\u5dfe", + "4204": "\u5f04", + "4205": "\u83e9", + "4206": "\u8526", + "4207": "\u99a8", + "4208": "\u6fc1", + "4209": "\u714e", + "4210": "\u8218", + "4211": "\u6876", + "4212": "\u79e6", + "4213": "\u9061", + "4214": "\u5806", + "4215": "\u6afb", + "4216": "\u6e07", + "4217": "\u77ad", + "4218": "\u81c6", + "4219": "\u4fe3", + "4220": "\u7169", + "4221": "\u54b3", + "4222": "\u5506", + "4223": "\u60f9", + "4224": "\u6775", + "4225": "\u7c9f", + "4226": "\u9091", + "4227": "\u553e", + "4228": "\u6756", + "4229": "\u6960", + "4230": "\u6b6a", + "4231": "\u711a", + "4232": "\u8fb1", + "4233": "\u559d", + "4234": "\u6e58", + "4235": "\u76f2", + "4236": "\u8b39", + "4237": "\u8e2a", + "4238": "\u965b", + "4239": "\u589f", + "4240": "\u64b0", + "4241": "\u6ccc", + "4242": "\u6f15", + "4243": "\u8a6b", + "4244": "\u771e", + "4245": "\u90c1", + "4246": "\u6e1a", + "4247": "\u8210", + "4248": "\u8235", + "4249": "\u8e8a", + "4250": "\u58f9", + "4251": "\u5c6f", + "4252": "\u7435", + "4253": "\u7436", + "4254": "\u7a92", + "4255": "\u82af", + "4256": "\u8e87", + "4257": "\u4e1e", + "4258": "\u7262", + "4259": "\u8305", + "4260": "\u5f57", + "4261": "\u699b", + "4262": "\u7b95", + "4263": "\u82ad", + "4264": "\u918d", + "4265": "\u9190", + "4266": "\u9945", + "4267": "\u5815", + "4268": "\u5deb", + "4269": "\u6a9c", + "4270": "\u914c", + "4271": "\u96eb", + "4272": "\u6b3e", + "4273": "\u9d3b", + "4274": "\u4f10", + "4275": "\u7901", + "4276": "\u7a83", + "4277": "\u8389", + "4278": "\u929a", + "4279": "\u6191", + "4280": "\u639f", + "4281": "\u6492", + "4282": "\u6a0b", + "4283": "\u7336", + "4284": "\u868a", + "4285": "\u88fe", + "4286": "\u96cc", + "4287": "\u6216", + "4288": "\u643e", + "4289": "\u6cc4", + "4290": "\u7109", + "4291": "\u7940", + "4292": "\u7b8b", + "4293": "\u919c", + "4294": "\u9d5c", + "4295": "\u51f1", + "4296": "\u5c16", + "4297": "\u6c23", + "4298": "\u75d2", + "4299": "\u830e", + "4300": "\u745b", + "4301": "\u602f", + "4302": "\u698e", + "4303": "\u6feb", + "4304": "\u7099", + "4305": "\u97ad", + "4306": "\u9b4f", + "4307": "\u4f5b", + "4308": "\u51b6", + "4309": "\u55dc", + "4310": "\u5750", + "4311": "\u6144", + "4312": "\u61c9", + "4313": "\u6c50", + "4314": "\u73c2", + "4315": "\u8fc5", + "4316": "\u62f7", + "4317": "\u9019", + "4318": "\u5ae1", + "4319": "\u60bc", + "4320": "\u637b", + "4321": "\u6a3a", + "4322": "\u85c1", + "4323": "\u932c", + "4324": "\u50ad", + "4325": "\u5243", + "4326": "\u5d4c", + "4327": "\u727d", + "4328": "\u937c", + "4329": "\u4fae", + "4330": "\u5f59", + "4331": "\u6bec", + "4332": "\u4ea8", + "4333": "\u4f86", + "4334": "\u5b8d", + "4335": "\u8a1b", + "4336": "\u9ab8", + "4337": "\u4ec7", + "4338": "\u5df4", + "4339": "\u6c3e", + "4340": "\u71e6", + "4341": "\u783a", + "4342": "\u79df", + "4343": "\u8549", + "4344": "\u5614", + "4345": "\u6703", + "4346": "\u67da", + "4347": "\u69cc", + "4348": "\u83ab", + "4349": "\u88d4", + "4350": "\u91d8", + "4351": "\u51a4", + "4352": "\u51b4", + "4353": "\u64ab", + "4354": "\u8d0b", + "4355": "\u30f5", + "4356": "\u4e9b", + "4357": "\u4f43", + "4358": "\u72d0", + "4359": "\u56a2", + "4360": "\u92f3", + "4361": "\u5dbd", + "4362": "\u9f4b", + "4363": "\u51f9", + "4364": "\u54fa", + "4365": "\u57f4", + "4366": "\u65fa", + "4367": "\u86cb", + "4368": "\u8cdc", + "4369": "\u4f0d", + "4370": "\u545f", + "4371": "\u5937", + "4372": "\u5dbc", + "4373": "\u6c4e", + "4374": "\u9739", + "4375": "\u5875", + "4376": "\u6101", + "4377": "\u8106", + "4378": "\u97ee", + "4379": "\u540f", + "4380": "\u5957", + "4381": "\u5993", + "4382": "\u68b1", + "4383": "\u6d1b", + "4384": "\u6f31", + "4385": "\u725d", + "4386": "\u798d", + "4387": "\u7d21", + "4388": "\u810a", + "4389": "\u8cd3", + "4390": "\u586b", + "4391": "\u673d", + "4392": "\u6a2b", + "4393": "\u8299", + "4394": "\u84c9", + "4395": "\u9310", + "4396": "\u5835", + "4397": "\u5f14", + "4398": "\u633d", + "4399": "\u6955", + "4400": "\u6c72", + "4401": "\u5294", + "4402": "\u5eb8", + "4403": "\u694a", + "4404": "\u7826", + "4405": "\u9c57", + "4406": "\u61a4", + "4407": "\u634f", + "4408": "\u6d29", + "4409": "\u723e", + "4410": "\u750d", + "4411": "\u817f", + "4412": "\u9c52", + "4413": "\u685f", + "4414": "\u6a7f", + "4415": "\u82db", + "4416": "\u982c", + "4417": "\u55da", + "4418": "\u5751", + "4419": "\u5b75", + "4420": "\u5e87", + "4421": "\u68a2", + "4422": "\u6b05", + "4423": "\u7560", + "4424": "\u7a7f", + "4425": "\u8513", + "4426": "\u8d99", + "4427": "\u927e", + "4428": "\u4f51", + "4429": "\u5dcc", + "4430": "\u5f77", + "4431": "\u65a7", + "4432": "\u68d8", + "4433": "\u6dd8", + "4434": "\u7b94", + "4435": "\u7d2f", + "4436": "\u8729", + "4437": "\u908a", + "4438": "\u9ebf", + "4439": "\u5713", + "4440": "\u66a2", + "4441": "\u69ae", + "4442": "\u6b89", + "4443": "\u6d8c", + "4444": "\u7aea", + "4445": "\u8aee", + "4446": "\u96db", + "4447": "\u9bf5", + "4448": "\u4e14", + "4449": "\u5347", + "4450": "\u5954", + "4451": "\u5ce8", + "4452": "\u7149", + "4453": "\u7791", + "4454": "\u8276", + "4455": "\u840e", + "4456": "\u8568", + "4457": "\u85aa", + "4458": "\u8f0c", + "4459": "\u5dfd", + "4460": "\u66f3", + "4461": "\u6cab", + "4462": "\u82a5", + "4463": "\u8511", + "4464": "\u93a7", + "4465": "\u9f20", + "4466": "\u51ea", + "4467": "\u5c51", + "4468": "\u5d14", + "4469": "\u5d6f", + "4470": "\u6a59", + "4471": "\u6e38", + "4472": "\u7a1c", + "4473": "\u8072", + "4474": "\u511a", + "4475": "\u695a", + "4476": "\u8006", + "4477": "\u82b9", + "4478": "\u83d6", + "4479": "\u88f3", + "4480": "\u9017", + "4481": "\u905c", + "4482": "\u9640", + "4483": "\u4ff8", + "4484": "\u5a29", + "4485": "\u5cd9", + "4486": "\u6190", + "4487": "\u6241", + "4488": "\u626e", + "4489": "\u6faa", + "4490": "\u7729", + "4491": "\u7f75", + "4492": "\u8036", + "4493": "\u8058", + "4494": "\u9c3b", + "4495": "\u309d", + "4496": "\u56c3", + "4497": "\u5f7f", + "4498": "\u6167", + "4499": "\u66dd", + "4500": "\u6fe0", + "4501": "\u8309", + "4502": "\u976d", + "4503": "\u9daf", + "4504": "\u9e92", + "4505": "\u30f0", + "4506": "\u4e5e", + "4507": "\u50b2", + "4508": "\u54e8", + "4509": "\u5f6c", + "4510": "\u73c0", + "4511": "\u79e4", + "4512": "\u84ec", + "4513": "\u8ebe", + "4514": "\u9075", + "4515": "\u51f8", + "4516": "\u53a9", + "4517": "\u6168", + "4518": "\u698a", + "4519": "\u6c8c", + "4520": "\u75b1", + "4521": "\u8fe6", + "4522": "\u53e1", + "4523": "\u543b", + "4524": "\u5b2c", + "4525": "\u5d69", + "4526": "\u660f", + "4527": "\u8171", + "4528": "\u8888", + "4529": "\u9c39", + "4530": "\u57e0", + "4531": "\u5be1", + "4532": "\u5cfb", + "4533": "\u5df7", + "4534": "\u62d7", + "4535": "\u62d9", + "4536": "\u63c4", + "4537": "\u63f6", + "4538": "\u65a1", + "4539": "\u6962", + "4540": "\u6dcb", + "4541": "\u722c", + "4542": "\u7425", + "4543": "\u805a", + "4544": "\u80da", + "4545": "\u81a0", + "4546": "\u8292", + "4547": "\u8703", + "4548": "\u87ba", + "4549": "\u9910", + "4550": "\u9dfa", + "4551": "\u51e0", + "4552": "\u52ab", + "4553": "\u5321", + "4554": "\u63d6", + "4555": "\u6b3d", + "4556": "\u7422", + "4557": "\u7825", + "4558": "\u877f", + "4559": "\u8adc", + "4560": "\u8ae7", + "4561": "\u8dbe", + "4562": "\u50d1", + "4563": "\u5a9a", + "4564": "\u5b5f", + "4565": "\u5b95", + "4566": "\u5bd3", + "4567": "\u5f8a", + "4568": "\u5f98", + "4569": "\u6357", + "4570": "\u66d9", + "4571": "\u7e82", + "4572": "\u7fc1", + "4573": "\u81bf", + "4574": "\u85ea", + "4575": "\u8a0a", + "4576": "\u8fc2", + "4577": "\u932b", + "4578": "\u4fef", + "4579": "\u5a3c", + "4580": "\u689f", + "4581": "\u6e3e", + "4582": "\u6ffe", + "4583": "\u79bf", + "4584": "\u7ce0", + "4585": "\u8180", + "4586": "\u82c5", + "4587": "\u8877", + "4588": "\u8c79", + "4589": "\u9798", + "4590": "\u9eb9", + "4591": "\u9ece", + "4592": "\u6abb", + "4593": "\u6e25", + "4594": "\u9149", + "4595": "\u97a0", + "4596": "\u567a", + "4597": "\u60f0", + "4598": "\u646f", + "4599": "\u65db", + "4600": "\u6bc0", + "4601": "\u6d38", + "4602": "\u6dd1", + "4603": "\u71fb", + "4604": "\u77b0", + "4605": "\u7ac8", + "4606": "\u7cfe", + "4607": "\u86d9", + "4608": "\u8e44", + "4609": "\u502d", + "4610": "\u536f", + "4611": "\u56c1", + "4612": "\u5830", + "4613": "\u6652", + "4614": "\u6a13", + "4615": "\u72db", + "4616": "\u84fc", + "4617": "\u86db", + "4618": "\u8718", + "4619": "\u8b33", + "4620": "\u52be", + "4621": "\u5403", + "4622": "\u5484", + "4623": "\u5631", + "4624": "\u6070", + "4625": "\u60b6", + "4626": "\u69c7", + "4627": "\u7325", + "4628": "\u7396", + "4629": "\u792b", + "4630": "\u7977", + "4631": "\u7ad9", + "4632": "\u7ae3", + "4633": "\u7d68", + "4634": "\u7e1e", + "4635": "\u966a", + "4636": "\u4e58", + "4637": "\u53e2", + "4638": "\u5c39", + "4639": "\u61be", + "4640": "\u62ee", + "4641": "\u633a", + "4642": "\u6582", + "4643": "\u6714", + "4644": "\u701e", + "4645": "\u7587", + "4646": "\u77a5", + "4647": "\u7a63", + "4648": "\u7f79", + "4649": "\u8aeb", + "4650": "\u9013", + "4651": "\u96f9", + "4652": "\u981a", + "4653": "\u4f3d", + "4654": "\u5eff", + "4655": "\u60df", + "4656": "\u63bb", + "4657": "\u6523", + "4658": "\u6bb2", + "4659": "\u6c5d", + "4660": "\u6d59", + "4661": "\u806f", + "4662": "\u8a54", + "4663": "\u96bb", + "4664": "\u9801", + "4665": "\u9913", + "4666": "\u50b3", + "4667": "\u51b2", + "4668": "\u65a5", + "4669": "\u7e3d", + "4670": "\u8151", + "4671": "\u92f8", + "4672": "\u9695", + "4673": "\u9812", + "4674": "\u9837", + "4675": "\u4ec0", + "4676": "\u54ed", + "4677": "\u5718", + "4678": "\u5851", + "4679": "\u59e6", + "4680": "\u5bf5", + "4681": "\u615f", + "4682": "\u6b12", + "4683": "\u7953", + "4684": "\u79bd", + "4685": "\u7c50", + "4686": "\u8695", + "4687": "\u8ce6", + "4688": "\u8f62", + "4689": "\u912d", + "4690": "\u92d2", + "4691": "\u985b", + "4692": "\u9c48", + "4693": "\u4e11", + "4694": "\u5b30", + "4695": "\u5ba6", + "4696": "\u5be6", + "4697": "\u5c4d", + "4698": "\u67e9", + "4699": "\u6d9b", + "4700": "\u7473", + "4701": "\u75bc", + "4702": "\u7aa9", + "4703": "\u7dfb", + "4704": "\u811b", + "4705": "\u936c", + "4706": "\u4eab", + "4707": "\u53ad", + "4708": "\u54bd", + "4709": "\u5632", + "4710": "\u6a05", + "4711": "\u71ed", + "4712": "\u75d9", + "4713": "\u7624", + "4714": "\u7e23", + "4715": "\u808b", + "4716": "\u809b", + "4717": "\u8654", + "4718": "\u895f", + "4719": "\u9583", + "4720": "\u9b6f", + "4721": "\u55a9", + "4722": "\u55fd", + "4723": "\u56a5", + "4724": "\u58d5", + "4725": "\u601c", + "4726": "\u634c", + "4727": "\u7b4f", + "4728": "\u7baa", + "4729": "\u7e6d", + "4730": "\u85cf", + "4731": "\u86fe", + "4732": "\u8a03", + "4733": "\u8caa", + "4734": "\u98af", + "4735": "\u531d", + "4736": "\u5480", + "4737": "\u548e", + "4738": "\u56bc", + "4739": "\u5c53", + "4740": "\u5e9a", + "4741": "\u6115", + "4742": "\u6ef8", + "4743": "\u707c", + "4744": "\u7b25", + "4745": "\u8700", + "4746": "\u8a36", + "4747": "\u8a85", + "4748": "\u8d14", + "4749": "\u91ac", + "4750": "\u9c10", + "4751": "\u4fc4", + "4752": "\u5026", + "4753": "\u5039", + "4754": "\u5239", + "4755": "\u5699", + "4756": "\u5859", + "4757": "\u685d", + "4758": "\u6adb", + "4759": "\u7119", + "4760": "\u76e7", + "4761": "\u7ac4", + "4762": "\u7d18", + "4763": "\u7d62", + "4764": "\u83f0", + "4765": "\u8466", + "4766": "\u849c", + "4767": "\u8541", + "4768": "\u8599", + "4769": "\u8606", + "4770": "\u8b01", + "4771": "\u8fa3", + "4772": "\u9761", + "4773": "\u99d5", + "4774": "\u9d0e", + "4775": "\u4ec4", + "4776": "\u4f98", + "4777": "\u5016", + "4778": "\u5080", + "4779": "\u50fb", + "4780": "\u5121", + "4781": "\u524b", + "4782": "\u5f45", + "4783": "\u6802", + "4784": "\u6854", + "4785": "\u68b5", + "4786": "\u6ef2", + "4787": "\u6fb3", + "4788": "\u6fe4", + "4789": "\u7368", + "4790": "\u7577", + "4791": "\u75d4", + "4792": "\u7626", + "4793": "\u7960", + "4794": "\u79b0", + "4795": "\u81a3", + "4796": "\u834f", + "4797": "\u8944", + "4798": "\u8a25", + "4799": "\u8de8", + "4800": "\u8e93", + "4801": "\u90b1", + "4802": "\u9264", + "4803": "\u93d1", + "4804": "\u95ca", + "4805": "\u96c9", + "4806": "\u9d6c", + "4807": "\u53db", + "4808": "\u543c", + "4809": "\u59d0", + "4810": "\u5f4c", + "4811": "\u66fc", + "4812": "\u6c83", + "4813": "\u6f23", + "4814": "\u6f38", + "4815": "\u700b", + "4816": "\u721b", + "4817": "\u7690", + "4818": "\u7c3e", + "4819": "\u7fe1", + "4820": "\u82d3", + "4821": "\u839e", + "4822": "\u84d1", + "4823": "\u857e", + "4824": "\u874b", + "4825": "\u8766", + "4826": "\u892a", + "4827": "\u9119", + "4828": "\u914b", + "4829": "\u92e4", + "4830": "\u937e", + "4831": "\u9435", + "4832": "\u5191", + "4833": "\u557c", + "4834": "\u5617", + "4835": "\u5c4f", + "4836": "\u65af", + "4837": "\u6900", + "4838": "\u6e20", + "4839": "\u71be", + "4840": "\u7280", + "4841": "\u76ba", + "4842": "\u7768", + "4843": "\u78cb", + "4844": "\u7b67", + "4845": "\u7cca", + "4846": "\u837c", + "4847": "\u83b1", + "4848": "\u8fa8", + "4849": "\u901e", + "4850": "\u9081", + "4851": "\u936e", + "4852": "\u968b", + "4853": "\u9786", + "4854": "\u978b", + "4855": "\u4e56", + "4856": "\u55df", + "4857": "\u5700", + "4858": "\u5fd6", + "4859": "\u60e0", + "4860": "\u61ba", + "4861": "\u6518", + "4862": "\u6727", + "4863": "\u675e", + "4864": "\u69d9", + "4865": "\u6b98", + "4866": "\u6deb", + "4867": "\u7015", + "4868": "\u70b8", + "4869": "\u71d0", + "4870": "\u7b50", + "4871": "\u7ff3", + "4872": "\u813e", + "4873": "\u81c0", + "4874": "\u8b49", + "4875": "\u9318", + "4876": "\u9d2c", + "4877": "\u308e", + "4878": "\u4e8e", + "4879": "\u5055", + "4880": "\u54ac", + "4881": "\u5516", + "4882": "\u555c", + "4883": "\u5703", + "4884": "\u58fd", + "4885": "\u59da", + "4886": "\u59e5", + "4887": "\u5a49", + "4888": "\u5b0c", + "4889": "\u5b55", + "4890": "\u5c60", + "4891": "\u5cb1", + "4892": "\u5ed3", + "4893": "\u61ab", + "4894": "\u621f", + "4895": "\u6309", + "4896": "\u637a", + "4897": "\u6853", + "4898": "\u6939", + "4899": "\u6977", + "4900": "\u6ac2", + "4901": "\u704c", + "4902": "\u71d7", + "4903": "\u7526", + "4904": "\u788d", + "4905": "\u795f", + "4906": "\u79ae", + "4907": "\u7a79", + "4908": "\u7b4d", + "4909": "\u7c17", + "4910": "\u814b", + "4911": "\u832b", + "4912": "\u8494", + "4913": "\u8afa", + "4914": "\u8cb6", + "4915": "\u9059", + "4916": "\u9211", + "4917": "\u9328", + "4918": "\u9771", + "4919": "\u98c4", + "4920": "\u9af7", + "4921": "\u9d60", + "4922": "\u9f0e", + "4923": "\u4ea6", + "4924": "\u4f47", + "4925": "\u5072", + "4926": "\u526a", + "4927": "\u5271", + "4928": "\u57d2", + "4929": "\u59f6", + "4930": "\u5c0d", + "4931": "\u5e47", + "4932": "\u5fbd", + "4933": "\u606b", + "4934": "\u652b", + "4935": "\u6b78", + "4936": "\u72e1", + "4937": "\u77bc", + "4938": "\u786f", + "4939": "\u7afa", + "4940": "\u7b0f", + "4941": "\u7bdd", + "4942": "\u7c00", + "4943": "\u7c7e", + "4944": "\u7f6b", + "4945": "\u807e", + "4946": "\u8139", + "4947": "\u8521", + "4948": "\u8557", + "4949": "\u876e", + "4950": "\u8cfd", + "4951": "\u8d16", + "4952": "\u8fad", + "4953": "\u92ea", + "4954": "\u9b93", + "4955": "\u9c2f", + "4956": "\u9c3a", + "4957": "\u4e24", + "4958": "\u4e4e", + "4959": "\u5118", + "4960": "\u530d", + "4961": "\u5310", + "4962": "\u5686", + "4963": "\u5f1b", + "4964": "\u5fa8", + "4965": "\u60e1", + "4966": "\u619a", + "4967": "\u6698", + "4968": "\u68c9", + "4969": "\u6a02", + "4970": "\u6bb7", + "4971": "\u6beb", + "4972": "\u6c40", + "4973": "\u70d9", + "4974": "\u72c4", + "4975": "\u73ea", + "4976": "\u7433", + "4977": "\u74e3", + "4978": "\u7b8f", + "4979": "\u7e5a", + "4980": "\u8207", + "4981": "\u822b", + "4982": "\u8237", + "4983": "\u8317", + "4984": "\u849f", + "4985": "\u84bb", + "4986": "\u86ed", + "4987": "\u88a2", + "4988": "\u8956", + "4989": "\u8966", + "4990": "\u8cf4", + "4991": "\u8d04", + "4992": "\u8e59", + "4993": "\u8f4d", + "4994": "\u8f9f", + "4995": "\u8faf", + "4996": "\u9182", + "4997": "\u9187", + "4998": "\u947d", + "4999": "\u9846", + "5000": "\u9870", + "5001": "\u9c2d", + "5002": "\u51f0", + "5003": "\u5475", + "5004": "\u566a", + "5005": "\u5bf6", + "5006": "\u61fa", + "5007": "\u6372", + "5008": "\u63a0", + "5009": "\u69b4", + "5010": "\u71df", + "5011": "\u7370", + "5012": "\u754f", + "5013": "\u755d", + "5014": "\u7566", + "5015": "\u76c8", + "5016": "\u7827", + "5017": "\u7a62", + "5018": "\u7d06", + "5019": "\u7fc6", + "5020": "\u803d", + "5021": "\u8205", + "5022": "\u8569", + "5023": "\u86f8", + "5024": "\u8882", + "5025": "\u893b", + "5026": "\u8eaf", + "5027": "\u8fed", + "5028": "\u9005", + "5029": "\u9082", + "5030": "\u9089", + "5031": "\u920e", + "5032": "\u929b", + "5033": "\u95dc", + "5034": "\u9e1e", + "5035": "\u9f67", + "5036": "\u4ea5", + "5037": "\u52f8", + "5038": "\u543d", + "5039": "\u54a5", + "5040": "\u5967", + "5041": "\u598d", + "5042": "\u5a62", + "5043": "\u5c24", + "5044": "\u5c41", + "5045": "\u6134", + "5046": "\u65b7", + "5047": "\u65f1", + "5048": "\u6688", + "5049": "\u67b7", + "5050": "\u67d8", + "5051": "\u6ac3", + "5052": "\u6adf", + "5053": "\u6bd8", + "5054": "\u6c6a", + "5055": "\u6f74", + "5056": "\u6fb1", + "5057": "\u7164", + "5058": "\u7194", + "5059": "\u7576", + "5060": "\u777e", + "5061": "\u7893", + "5062": "\u7a84", + "5063": "\u7bc1", + "5064": "\u7c2a", + "5065": "\u7e79", + "5066": "\u7ff9", + "5067": "\u8000", + "5068": "\u8387", + "5069": "\u83f4", + "5070": "\u8602", + "5071": "\u8737", + "5072": "\u8904", + "5073": "\u890c", + "5074": "\u8b2c", + "5075": "\u8ce3", + "5076": "\u8eb0", + "5077": "\u8ecb", + "5078": "\u903c", + "5079": "\u93ac", + "5080": "\u975c", + "5081": "\u9b43", + "5082": "\u9b9f", + "5083": "\u9cf6", + "5084": "\u9f5f", + "5085": "\u9f6c", + "5086": "\u301c", + "5087": "\u30ee", + "5088": "\u4e9f", + "5089": "\u4ec6", + "5090": "\u51cb", + "5091": "\u54a4", + "5092": "\u5544", + "5093": "\u57dc", + "5094": "\u5a11", + "5095": "\u5a36", + "5096": "\u6089", + "5097": "\u620a", + "5098": "\u620e", + "5099": "\u64bc", + "5100": "\u64f2", + "5101": "\u6578", + "5102": "\u6726", + "5103": "\u687f", + "5104": "\u6a1f", + "5105": "\u6aae", + "5106": "\u6c81", + "5107": "\u6d63", + "5108": "\u6d9c", + "5109": "\u6ed3", + "5110": "\u703e", + "5111": "\u71e7", + "5112": "\u7232", + "5113": "\u733e", + "5114": "\u7464", + "5115": "\u7469", + "5116": "\u766c", + "5117": "\u776b", + "5118": "\u77ee", + "5119": "\u788c", + "5120": "\u7a1f", + "5121": "\u7a4e", + "5122": "\u7be5", + "5123": "\u7bf3", + "5124": "\u7cb9", + "5125": "\u7dec", + "5126": "\u7f77", + "5127": "\u7f9e", + "5128": "\u8216", + "5129": "\u847a", + "5130": "\u8acd", + "5131": "\u8af7", + "5132": "\u8b04", + "5133": "\u8da8", + "5134": "\u8e4a", + "5135": "\u8e81", + "5136": "\u8f3b", + "5137": "\u900d", + "5138": "\u970d", + "5139": "\u9b06", + "5140": "\u9baa", + "5141": "\u9ef4", + "5142": "\u4f7b", + "5143": "\u5167", + "5144": "\u51c9", + "5145": "\u525d", + "5146": "\u52d2", + "5147": "\u5396", + "5148": "\u53b6", + "5149": "\u5538", + "5150": "\u5556", + "5151": "\u5885", + "5152": "\u592d", + "5153": "\u5ba5", + "5154": "\u5be2", + "5155": "\u5df2", + "5156": "\u608d", + "5157": "\u62c7", + "5158": "\u6350", + "5159": "\u6426", + "5160": "\u649a", + "5161": "\u64a5", + "5162": "\u64d4", + "5163": "\u652a", + "5164": "\u665d", + "5165": "\u6753", + "5166": "\u6763", + "5167": "\u6787", + "5168": "\u6867", + "5169": "\u6930", + "5170": "\u6a47", + "5171": "\u6b23", + "5172": "\u6cd7", + "5173": "\u6db8", + "5174": "\u6df9", + "5175": "\u6e2d", + "5176": "\u6eff", + "5177": "\u6f58", + "5178": "\u6fd4", + "5179": "\u6fd8", + "5180": "\u6fdf", + "5181": "\u70ac", + "5182": "\u7147", + "5183": "\u71a8", + "5184": "\u71f5", + "5185": "\u72fd", + "5186": "\u73bb", + "5187": "\u763b", + "5188": "\u7647", + "5189": "\u779e", + "5190": "\u7895", + "5191": "\u79a7", + "5192": "\u79be", + "5193": "\u79c9", + "5194": "\u7d72", + "5195": "\u7d89", + "5196": "\u7e0b", + "5197": "\u7e37", + "5198": "\u7e6b", + "5199": "\u81fa", + "5200": "\u8271", + "5201": "\u856a", + "5202": "\u867b", + "5203": "\u8778", + "5204": "\u89ba", + "5205": "\u8a1d", + "5206": "\u8abc", + "5207": "\u8b6f", + "5208": "\u8f15", + "5209": "\u9438", + "5210": "\u958f", + "5211": "\u9a5b", + "5212": "\u9ad9", + "5213": "\u9b18", + "5214": "\u9b4d", + "5215": "\u9b4e", + "5216": "\u9bf0", + "5217": "\u9bf1", + "5218": "\u9d61", + "5219": "\u9e1a", + "5220": "\u9edb", + "5221": "\u9f3e", + "5222": "\u4e9e", + "5223": "\u4f83", + "5224": "\u4fad", + "5225": "\u4fce", + "5226": "\u5011", + "5227": "\u52de", + "5228": "\u5319", + "5229": "\u541e", + "5230": "\u54b8", + "5231": "\u54c8", + "5232": "\u564e", + "5233": "\u5664", + "5234": "\u56d3", + "5235": "\u58de", + "5236": "\u5abd", + "5237": "\u5ff8", + "5238": "\u5ffd", + "5239": "\u6029", + "5240": "\u604d", + "5241": "\u6063", + "5242": "\u60c7", + "5243": "\u61ae", + "5244": "\u622a", + "5245": "\u6258", + "5246": "\u64bb", + "5247": "\u6572", + "5248": "\u658c", + "5249": "\u660a", + "5250": "\u6919", + "5251": "\u69ce", + "5252": "\u6d8e", + "5253": "\u6dee", + "5254": "\u6dfa", + "5255": "\u6e5b", + "5256": "\u6eaf", + "5257": "\u6f09", + "5258": "\u6f6f", + "5259": "\u6fb9", + "5260": "\u7114", + "5261": "\u711c", + "5262": "\u7156", + "5263": "\u71d4", + "5264": "\u7337", + "5265": "\u736a", + "5266": "\u73ca", + "5267": "\u743f", + "5268": "\u745a", + "5269": "\u751c", + "5270": "\u752b", + "5271": "\u7564", + "5272": "\u7586", + "5273": "\u766a", + "5274": "\u76ea", + "5275": "\u77a0", + "5276": "\u783f", + "5277": "\u7957", + "5278": "\u798a", + "5279": "\u7aba", + "5280": "\u7b08", + "5281": "\u7b19", + "5282": "\u7bad", + "5283": "\u7c38", + "5284": "\u80e4", + "5285": "\u81cd", + "5286": "\u821b", + "5287": "\u827e", + "5288": "\u8318", + "5289": "\u83aa", + "5290": "\u8403", + "5291": "\u8431", + "5292": "\u848b", + "5293": "\u8597", + "5294": "\u85f9", + "5295": "\u86ce", + "5296": "\u86ef", + "5297": "\u8815", + "5298": "\u88b1", + "5299": "\u8977", + "5300": "\u89af", + "5301": "\u89c0", + "5302": "\u8a48", + "5303": "\u8aa6", + "5304": "\u8acc", + "5305": "\u8ae4", + "5306": "\u8b7d", + "5307": "\u8c50", + "5308": "\u8cce", + "5309": "\u8ce4", + "5310": "\u8d6d", + "5311": "\u8dcb", + "5312": "\u8e42", + "5313": "\u8e99", + "5314": "\u8f46", + "5315": "\u8f64", + "5316": "\u9041", + "5317": "\u9248", + "5318": "\u9249", + "5319": "\u932e", + "5320": "\u96d9", + "5321": "\u98ee", + "5322": "\u991e", + "5323": "\u9952", + "5324": "\u9957", + "5325": "\u99c8", + "5326": "\u99dd", + "5327": "\u9a57", + "5328": "\u9d44", + "5329": "\u9dd7", + "5330": "\u9eb4", + "5331": "\u9ed1", + "5332": "\ud857\udc4b", + "5333": "\u4e15", + "5334": "\u4e2a", + "5335": "\u4e99", + "5336": "\u4eb0", + "5337": "\u4efd", + "5338": "\u5047", + "5339": "\u50d6", + "5340": "\u50ed", + "5341": "\u524c", + "5342": "\u528d", + "5343": "\u52bf", + "5344": "\u5377", + "5345": "\u53c3", + "5346": "\u548b", + "5347": "\u54ab", + "5348": "\u54ea", + "5349": "\u5583", + "5350": "\u55ae", + "5351": "\u56b4", + "5352": "\u56c2", + "5353": "\u56d1", + "5354": "\u57b3", + "5355": "\u5852", + "5356": "\u58d8", + "5357": "\u5919", + "5358": "\u5934", + "5359": "\u5987", + "5360": "\u59b2", + "5361": "\u59c6", + "5362": "\u5ae3", + "5363": "\u5be5", + "5364": "\u5bf9", + "5365": "\u5c07", + "5366": "\u5c08", + "5367": "\u5d5c", + "5368": "\u5e08", + "5369": "\u5e1a", + "5370": "\u5e36", + "5371": "\u5e96", + "5372": "\u5eec", + "5373": "\u5f61", + "5374": "\u5f9e", + "5375": "\u5fb7", + "5376": "\u60fb", + "5377": "\u613f", + "5378": "\u6147", + "5379": "\u618a", + "5380": "\u61c3", + "5381": "\u61ff", + "5382": "\u6208", + "5383": "\u6230", + "5384": "\u6237", + "5385": "\u6289", + "5386": "\u62c2", + "5387": "\u62cc", + "5388": "\u62d4", + "5389": "\u6369", + "5390": "\u63ac", + "5391": "\u6451", + "5392": "\u6493", + "5393": "\u64b9", + "5394": "\u652c", + "5395": "\u6656", + "5396": "\u678c", + "5397": "\u6837", + "5398": "\u68b3", + "5399": "\u69ff", + "5400": "\u6a31", + "5401": "\u6a84", + "5402": "\u6aa2", + "5403": "\u6aaa", + "5404": "\u6aac", + "5405": "\u6ab8", + "5406": "\u6ae8", + "5407": "\u6b1d", + "5408": "\u6c9b", + "5409": "\u6cbd", + "5410": "\u6d35", + "5411": "\u6da6", + "5412": "\u6e8c", + "5413": "\u6ec9", + "5414": "\u6eef", + "5415": "\u6efe", + "5416": "\u6f11", + "5417": "\u6f32", + "5418": "\u6f6d", + "5419": "\u7165", + "5420": "\u71fc", + "5421": "\u7252", + "5422": "\u72f7", + "5423": "\u7463", + "5424": "\u7511", + "5425": "\u758b", + "5426": "\u75cd", + "5427": "\u75f0", + "5428": "\u7672", + "5429": "\u767c", + "5430": "\u76c2", + "5431": "\u775b", + "5432": "\u77dc", + "5433": "\u77e9", + "5434": "\u787c", + "5435": "\u78a9", + "5436": "\u7941", + "5437": "\u798e", + "5438": "\u79b9", + "5439": "\u7b1e", + "5440": "\u7b45", + "5441": "\u7b86", + "5442": "\u7c11", + "5443": "\u7cae", + "5444": "\u7d45", + "5445": "\u7d7d", + "5446": "\u7d93", + "5447": "\u7da0", + "5448": "\u7dac", + "5449": "\u7db8", + "5450": "\u7dd8", + "5451": "\u7e12", + "5452": "\u7e61", + "5453": "\u7e69", + "5454": "\u7e6a", + "5455": "\u7e8c", + "5456": "\u7eb8", + "5457": "\u7ec8", + "5458": "\u804a", + "5459": "\u8070", + "5460": "\u8085", + "5461": "\u80c4", + "5462": "\u820d", + "5463": "\u8229", + "5464": "\u8258", + "5465": "\u8278", + "5466": "\u83eb", + "5467": "\u8514", + "5468": "\u851a", + "5469": "\u860a", + "5470": "\u863f", + "5471": "\u86de", + "5472": "\u870a", + "5473": "\u8753", + "5474": "\u8755", + "5475": "\u87c4", + "5476": "\u87e0", + "5477": "\u884d", + "5478": "\u88dd", + "5479": "\u89bd", + "5480": "\u89bf", + "5481": "\u8a3b", + "5482": "\u8ac4", + "5483": "\u8b74", + "5484": "\u8b80", + "5485": "\u8b93", + "5486": "\u8bf7", + "5487": "\u8c6c", + "5488": "\u8c98", + "5489": "\u8d39", + "5490": "\u8d6b", + "5491": "\u8de3", + "5492": "\u8e89", + "5493": "\u8efe", + "5494": "\u8f49", + "5495": "\u8ff8", + "5496": "\u8ff9", + "5497": "\u914a", + "5498": "\u9169", + "5499": "\u91aa", + "5500": "\u923f", + "5501": "\u929c", + "5502": "\u934d", + "5503": "\u943a", + "5504": "\u945a", + "5505": "\u94bf", + "5506": "\u95bb", + "5507": "\u95ee", + "5508": "\u965e", + "5509": "\u96dc", + "5510": "\u9706", + "5511": "\u9730", + "5512": "\u97cb", + "5513": "\u985a", + "5514": "\u9986", + "5515": "\u99c1", + "5516": "\u99f1", + "5517": "\u9a55", + "5518": "\u9b51", + "5519": "\u93b9", + "5520": "\u6248", + "5521": "\u9e7c", + "5522": "\u9c24", + "5523": "\u8757", + "5524": "\u6777", + "5525": "\u66c9", + "5526": "\u9c67", + "5527": "\u9c47", + "5528": "\u9214", + "5529": "\u6eaa", + "5530": "\u65a4", + "5531": "\u734f", + "5532": "\u6670", + "5533": "\u76d2", + "5534": "\u5e5f", + "5535": "\u8f5f", + "5536": "\u8ad2", + "5537": "\u7b92", + "5538": "\u75e3", + "5539": "\u9ea9", + "5540": "\u699c", + "5541": "\u9b92", + "5542": "\u5398", + "5543": "\u8cc2", + "5544": "\u84a1", + "5545": "\u85af", + "5546": "\u6a80", + "5547": "\u8e35", + "5548": "\u5366", + "5549": "\u7962", + "5550": "\u60b8", + "5551": "\u7b48", + "5552": "\u76c3", + "5553": "\u67a1", + "5554": "\u87a2", + "5555": "\u9b41", + "5556": "\u7fb9", + "5557": "\u6bef", + "5558": "\u7bed", + "5559": "\u7621", + "5560": "\u5653", + "5561": "\u535c", + "5562": "\u7d2c", + "5563": "\u58f7", + "5564": "\u55e3", + "5565": "\u80f1", + "5566": "\u96c1", + "5567": "\u6634", + "5568": "\u6602", + "5569": "\u647a", + "5570": "\u8b02", + "5571": "\u818f", + "5572": "\u7d9c", + "5573": "\u87fb", + "5574": "\u81e5", + "5575": "\u9bab", + "5576": "\u6ad3", + "5577": "\u88df", + "5578": "\u59be", + "5579": "\u74dc", + "5580": "\u9eb5", + "5581": "\u87f2", + "5582": "\u9e78", + "5583": "\u515c", + "5584": "\u7e8f", + "5585": "\u9306", + "5586": "\u88b4", + "5587": "\u74e2", + "5588": "\u4e19", + "5589": "\u7aff", + "5590": "\u5962", + "5591": "\u852d", + "5592": "\u67ca", + "5593": "\u55ac", + "5594": "\u9921", + "5595": "\u8fc4", + "5596": "\u676d", + "5597": "\u7c95", + "5598": "\u64e2", + "5599": "\u9784", + "5600": "\u8e5f", + "5601": "\u7e55", + "5602": "\u8087", + "5603": "\u9742", + "5604": "\u907d", + "5605": "\u57c3", + "5606": "\u6813", + "5607": "\u751a", + "5608": "\u714c", + "5609": "\u67f5", + "5610": "\u51cc", + "5611": "\u853d", + "5612": "\u71c8", + "5613": "\u9949", + "5614": "\u91c7", + "5615": "\u8463", + "5616": "\u696f", + "5617": "\u57a2", + "5618": "\u6e26", + "5619": "\u6bc5", + "5620": "\u6028", + "5621": "\u5687", + "5622": "\u9e9f", + "5623": "\u67d1", + "5624": "\u6689", + "5625": "\u7dcb", + "5626": "\u75e2", + "5627": "\u6893", + "5628": "\u6e4a", + "5629": "\u901d", + "5630": "\u7aaf", + "5631": "\u5740", + "5632": "\u7e4d", + "5633": "\u63c6", + "5634": "\u60e7", + "5635": "\u5df3", + "5636": "\u58fa", + "5637": "\u7483", + "5638": "\u80b4", + "5639": "\u8098", + "5640": "\u9b8e", + "5641": "\u8a6e", + "5642": "\u514e", + "5643": "\u9aed", + "5644": "\u8471", + "5645": "\u5840", + "5646": "\u53ea", + "5647": "\u7ca5", + "5648": "\u8a23", + "5649": "\u6284", + "5650": "\u5f10", + "5651": "\u5446", + "5652": "\u8338", + "5653": "\u5ec9", + "5654": "\u7078", + "5655": "\u681e", + "5656": "\u5e25", + "5657": "\u82fa", + "5658": "\u6953", + "5659": "\u724c", + "5660": "\u7d79", + "5661": "\u68af", + "5662": "\u6234", + "5663": "\u4e98", + "5664": "\u5bb5", + "5665": "\u8b5a", + "5666": "\u5efb", + "5667": "\u9bdb", + "5668": "\u99b3", + "5669": "\u51e7", + "5670": "\u7a14", + "5671": "\u7f60", + "5672": "\u9192", + "5673": "\u75b9", + "5674": "\u7dbb", + "5675": "\u589c", + "5676": "\u9262", + "5677": "\u72d7", + "5678": "\u6912", + "5679": "\u4ed4", + "5680": "\u7cde", + "5681": "\u8d66", + "5682": "\u8404", + "5683": "\u82d4", + "5684": "\u7027", + "5685": "\u8823", + "5686": "\u59d1", + "5687": "\u8017", + "5688": "\u51db", + "5689": "\u98f4", + "5690": "\u68fa", + "5691": "\u60a6", + "5692": "\u9bad", + "5693": "\u87f9", + "5694": "\u7709", + "5695": "\u6816", + "5696": "\u9bc9", + "5697": "\u8587", + "5698": "\u541f", + "5699": "\u9591", + "5700": "\u86ee", + "5701": "\u85fb", + "5702": "\u7a9f", + "5703": "\u8c8c", + "5704": "\u5a7f", + "5705": "\u817a", + "5706": "\u75fa", + "5707": "\u9688", + "5708": "\u81fc", + "5709": "\u7d10", + "5710": "\u7dbf", + "5711": "\u69fd", + "5712": "\u9be8", + "5713": "\u7409", + "5714": "\u53c9", + "5715": "\u4ff5", + "5716": "\u7259", + "5717": "\u831c", + "5718": "\u7432", + "5719": "\u5e16", + "5720": "\u906e", + "5721": "\u6ef4", + "5722": "\u932f", + "5723": "\u907c", + "5724": "\u9bd6", + "5725": "\u59dc", + "5726": "\u8749", + "5727": "\u9813", + "5728": "\u7897", + "5729": "\u732a", + "5730": "\u9a30", + "5731": "\u5b9b", + "5732": "\u914e", + "5733": "\u71d5", + "5734": "\u9cf3", + "5735": "\u5ac9", + "5736": "\u5766", + "5737": "\u6c70", + "5738": "\u9d28", + "5739": "\u8f3f", + "5740": "\u984e", + "5741": "\u8aed", + "5742": "\u760d", + "5743": "\u6841", + "5744": "\u842c", + "5745": "\u904d", + "5746": "\u67d0", + "5747": "\u9756", + "5748": "\u58f1", + "5749": "\u971e", + "5750": "\u865a", + "5751": "\u5e06", + "5752": "\u7a6b", + "5753": "\u81b3", + "5754": "\u9ba8", + "5755": "\u6681", + "5756": "\u62d0", + "5757": "\u5b8b", + "5758": "\u51e1", + "5759": "\u6ce1", + "5760": "\u5451", + "5761": "\u9ce9", + "5762": "\u55b0", + "5763": "\u56da", + "5764": "\u59ea", + "5765": "\u584a", + "5766": "\u59ac", + "5767": "\u7d17", + "5768": "\u74f6", + "5769": "\u5c3a", + "5770": "\u77db", + "5771": "\u5ee3", + "5772": "\u9e93", + "5773": "\u84cb", + "5774": "\u6f02", + "5775": "\u6643", + "5776": "\u5f84", + "5777": "\u5146", + "5778": "\u67ff", + "5779": "\u4fa0", + "5780": "\u9b31", + "5781": "\u5bf8", + "5782": "\u638c", + "5783": "\u5b9c", + "5784": "\u8ce0", + "5785": "\u6f84", + "5786": "\u674f", + "5787": "\u59fb", + "5788": "\u53a8", + "5789": "\u95a5", + "5790": "\u68f2", + "5791": "\u4faf", + "5792": "\u731f", + "5793": "\u674e", + "5794": "\u7985", + "5795": "\u8b19", + "5796": "\u86c7", + "5797": "\u80c6", + "5798": "\u30c2", + "5799": "\u6627", + "5800": "\u971c", + "5801": "\u845b", + "5802": "\u65ac", + "5803": "\u7c60", + "5804": "\u66f9", + "5805": "\u60e8", + "5806": "\u7e2b", + "5807": "\u7070", + "5808": "\u6842", + "5809": "\u8fbb", + "5810": "\u864e", + "5811": "\u7c92", + "5812": "\u7b1b", + "5813": "\u5507", + "5814": "\u9175", + "5815": "\u80ce", + "5816": "\u722a", + "5817": "\u73e0", + "5818": "\u76fe", + "5819": "\u6bbb", + "5820": "\u9418", + "5821": "\u925b", + "5822": "\u9685", + "5823": "\u821f", + "5824": "\u9285", + "5825": "\u570b", + "5826": "\u9326", + "5827": "\u70c8", + "5828": "\u9df9", + "5829": "\u92fc", + "5830": "\u6795", + "5831": "\u5824", + "5832": "\u8a1f", + "5833": "\u51f6", + "5834": "\u673a", + "5835": "\u5eb6", + "5836": "\u5c3c", + "5837": "\u5589", + "5838": "\u6850", + "5839": "\u819d", + "5840": "\u58c7", + "5841": "\u84c4", + "5842": "\u82bd", + "5843": "\u8607", + "5844": "\u7bb8", + "5845": "\u5ce0", + "5846": "\u8c9e", + "5847": "\u7089", + "5848": "\u5ce1", + "5849": "\u7d46", + "5850": "\u6ecb", + "5851": "\u8896", + "5852": "\u74a7", + "5853": "\u5609", + "5854": "\u7f36", + "5855": "\u8679", + "5856": "\u88f8", + "5857": "\u8015", + "5858": "\u60a0", + "5859": "\u8475", + "5860": "\u642c", + "5861": "\u664b", + "5862": "\u5f26", + "5863": "\u990c", + "5864": "\u8247", + "5865": "\u4eae", + "5866": "\u816b", + "5867": "\u72fc", + "5868": "\u697c", + "5869": "\u9905", + "5870": "\u723a", + "5871": "\u53c8", + "5872": "\u4f8d", + "5873": "\u68df", + "5874": "\u596e", + "5875": "\u50e7", + "5876": "\u84ee", + "5877": "\u828b", + "5878": "\u7573", + "5879": "\u5bb4", + "5880": "\u99ff", + "5881": "\u916c", + "5882": "\u68da", + "5883": "\u5256", + "5884": "\u8cca", + "5885": "\u8870", + "5886": "\u5841", + "5887": "\u8b5c", + "5888": "\u65cb", + "5889": "\u8b90", + "5890": "\u80aa", + "5891": "\u8178", + "5892": "\u83f1", + "5893": "\u95b2", + "5894": "\u7b52", + "5895": "\u54c9", + "5896": "\u9675", + "5897": "\u5a46", + "5898": "\u6b04", + "5899": "\u9855", + "5900": "\u9042", + "5901": "\u7e1b", + "5902": "\u8ef8", + "5903": "\u585e", + "5904": "\u5b8f", + "5905": "\u7def", + "5906": "\u7434", + "5907": "\u5bb0", + "5908": "\u91dc", + "5909": "\u862d", + "5910": "\u9298", + "5911": "\u6f64", + "5912": "\u66a6", + "5913": "\u4f0e", + "5914": "\u75f4", + "5915": "\u73b2", + "5916": "\u75ab", + "5917": "\u660c", + "5918": "\u73ed", + "5919": "\u5f80", + "5920": "\u5203", + "5921": "\u6f54", + "5922": "\u6d32", + "5923": "\u5982", + "5924": "\u5cac", + "5925": "\u7950", + "5926": "\u67cf", + "5927": "\u518a", + "5928": "\u96c0", + "5929": "\u88c2", + "5930": "\u53eb", + "5931": "\u7f85", + "5932": "\u7c8b", + "5933": "\u67f1", + "5934": "\u7948", + "5935": "\u6566", + "5936": "\u30f2", + "5937": "\u5c09", + "5938": "\u3045", + "5939": "\u7db1", + "5940": "\u4e4f", + "5941": "\u6b3a", + "5942": "\u66fd", + "5943": "\u6df3", + "5944": "\u7fd4", + "5945": "\u628a", + "5946": "\u6b96", + "5947": "\u6daf", + "5948": "\u6212", + "5949": "\u5a92", + "5950": "\u7b26", + "5951": "\u9162", + "5952": "\u9177", + "5953": "\u8c9d", + "5954": "\u5cf0", + "5955": "\u5bdb", + "5956": "\u96b7", + "5957": "\u733f", + "5958": "\u764c", + "5959": "\u7dbe", + "5960": "\u6ce5", + "5961": "\u7c9b", + "5962": "\u6249", + "5963": "\u5a20", + "5964": "\u8f14", + "5965": "\u76bf", + "5966": "\u9f13", + "5967": "\u719f", + "5968": "\u6717", + "5969": "\u99d2", + "5970": "\u92ad", + "5971": "\u82d1", + "5972": "\u9396", + "5973": "\u809d", + "5974": "\u5782", + "5975": "\u5104", + "5976": "\u78c1", + "5977": "\u6d1e", + "5978": "\u95c7", + "5979": "\u8987", + "5980": "\u51a0", + "5981": "\u58b3", + "5982": "\u4e3c", + "5983": "\u5be7", + "5984": "\u77b3", + "5985": "\u7656", + "5986": "\u525b", + "5987": "\u83ca", + "5988": "\u5b22", + "5989": "\u9047", + "5990": "\u80a2", + "5991": "\u654f", + "5992": "\u5c0b", + "5993": "\u72c2", + "5994": "\u67f4", + "5995": "\u5f6b", + "5996": "\u5805", + "5997": "\u679d", + "5998": "\u7d2b", + "5999": "\u62fe", + "6000": "\u5d8b", + "6001": "\u9084", + "6002": "\u7e26", + "6003": "\u80de", + "6004": "\u6069", + "6005": "\u3043", + "6006": "\u91c8", + "6007": "\u5c3b", + "6008": "\u5eb5", + "6009": "\u5a01", + "6010": "\u5c1a", + "6011": "\u62f3", + "6012": "\u64b2", + "6013": "\u5320", + "6014": "\u6676", + "6015": "\u61a9", + "6016": "\u7965", + "6017": "\u7832", + "6018": "\u7126", + "6019": "\u5c3f", + "6020": "\u9b42", + "6021": "\u7a42", + "6022": "\u8f44", + "6023": "\u62dd", + "6024": "\u91a4", + "6025": "\u658e", + "6026": "\u621a", + "6027": "\u5de3", + "6028": "\u572d", + "6029": "\u9727", + "6030": "\u6f6e", + "6031": "\u57f9", + "6032": "\u5f81", + "6033": "\u5f25", + "6034": "\u5b5d", + "6035": "\u8150", + "6036": "\u8ca2", + "6037": "\u6ca1", + "6038": "\u68cb", + "6039": "\u5f70", + "6040": "\u5e3d", + "6041": "\u83cc", + "6042": "\u7891", + "6043": "\u6597", + "6044": "\u63fa", + "6045": "\u7cf8", + "6046": "\u9d8f", + "6047": "\u9f3b", + "6048": "\u7235", + "6049": "\u85a6", + "6050": "\u808c", + "6051": "\u5c48", + "6052": "\u7d0b", + "6053": "\u67a0", + "6054": "\u57a3", + "6055": "\u65ec", + "6056": "\u614e", + "6057": "\u968f", + "6058": "\u8ed2", + "6059": "\u4e59", + "6060": "\u7384", + "6061": "\u5200", + "6062": "\u8df5", + "6063": "\u4f0f", + "6064": "\u5642", + "6065": "\u5e84", + "6066": "\u78e8", + "6067": "\u9694", + "6068": "\u9686", + "6069": "\u7a74", + "6070": "\u76c6", + "6071": "\u8ca7", + "6072": "\u9375", + "6073": "\u5cb3", + "6074": "\u616e", + "6075": "\u5374", + "6076": "\u62f6", + "6077": "\u5f13", + "6078": "\u5373", + "6079": "\u59d3", + "6080": "\u6398", + "6081": "\u6d6a", + "6082": "\u8b72", + "6083": "\u6589", + "6084": "\u9a0e", + "6085": "\u5968", + "6086": "\u8b00", + "6087": "\u5854", + "6088": "\u6ed1", + "6089": "\u5098", + "6090": "\u96f7", + "6091": "\u4fca", + "6092": "\u8edf", + "6093": "\u8f1d", + "6094": "\u6458", + "6095": "\u6176", + "6096": "\u6c57", + "6097": "\u6fa4", + "6098": "\u7bc7", + "6099": "\u8b0e", + "6100": "\u5e7b", + "6101": "\u9903", + "6102": "\u5339", + "6103": "\u543e", + "6104": "\u93e1", + "6105": "\u68d2", + "6106": "\u6d99", + "6107": "\u8cc3", + "6108": "\u8302", + "6109": "\u609f", + "6110": "\u5504", + "6111": "\u846c", + "6112": "\u6469", + "6113": "\u7c3f", + "6114": "\u6e09", + "6115": "\u511f", + "6116": "\u50da", + "6117": "\u65ed", + "6118": "\u8102", + "6119": "\u5e8f", + "6120": "\u8cab", + "6121": "\u3049", + "6122": "\u8aa4", + "6123": "\u7ffc", + "6124": "\u5bee", + "6125": "\u6edd", + "6126": "\u6c37", + "6127": "\u91dd", + "6128": "\u96c5", + "6129": "\u502b", + "6130": "\u85e9", + "6131": "\u5237", + "6132": "\u9663", + "6133": "\u7551", + "6134": "\u5a9b", + "6135": "\u5c3d", + "6136": "\u8cdb", + "6137": "\u50b5", + "6138": "\u8aa0", + "6139": "\u716e", + "6140": "\u6843", + "6141": "\u52d8", + "6142": "\u7a32", + "6143": "\u68c4", + "6144": "\u8170", + "6145": "\u83c5", + "6146": "\u7344", + "6147": "\u67f3", + "6148": "\u7ca7", + "6149": "\u5f18", + "6150": "\u9db4", + "6151": "\u80a9", + "6152": "\u9283", + "6153": "\u6c41", + "6154": "\u706f", + "6155": "\u773c", + "6156": "\u7db2", + "6157": "\u5c01", + "6158": "\u564c", + "6159": "\u7a3f", + "6160": "\u9644", + "6161": "\u676f", + "6162": "\u6094", + "6163": "\u9ea6", + "6164": "\u6e7f", + "6165": "\u9774", + "6166": "\u307a", + "6167": "\u6b8a", + "6168": "\u62b5", + "6169": "\u790e", + "6170": "\u8c5a", + "6171": "\u9ed9", + "6172": "\u7de0", + "6173": "\u9154", + "6174": "\u4e43", + "6175": "\u71e5", + "6176": "\u934b", + "6177": "\u8c6a", + "6178": "\u8a0e", + "6179": "\u6fc3", + "6180": "\u7d05", + "6181": "\u7968", + "6182": "\u708e", + "6183": "\u76ae", + "6184": "\u7e2e", + "6185": "\u5fb9", + "6186": "\u6749", + "6187": "\u8f03", + "6188": "\u7a1a", + "6189": "\u5800", + "6190": "\u5e33", + "6191": "\u5fcd", + "6192": "\u4f2f", + "6193": "\u8a17", + "6194": "\u77e2", + "6195": "\u8a69", + "6196": "\u8feb", + "6197": "\u5076", + "6198": "\u6838", + "6199": "\u5100", + "6200": "\u53cc", + "6201": "\u5230", + "6202": "\u524a", + "6203": "\u6db2", + "6204": "\u99c6", + "6205": "\u4e80", + "6206": "\u8972", + "6207": "\u8846", + "6208": "\u5510", + "6209": "\u7cd6", + "6210": "\u5de1", + "6211": "\u7e41", + "6212": "\u81ed", + "6213": "\u708a", + "6214": "\u9670", + "6215": "\u8155", + "6216": "\u6d44", + "6217": "\u5629", + "6218": "\u54b2", + "6219": "\u76d7", + "6220": "\u8108", + "6221": "\u6ede", + "6222": "\u7267", + "6223": "\u574a", + "6224": "\u5305", + "6225": "\u81f3", + "6226": "\u679a", + "6227": "\u5049", + "6228": "\u81f4", + "6229": "\u8a13", + "6230": "\u8ca8", + "6231": "\u8033", + "6232": "\u6f22", + "6233": "\u65d7", + "6234": "\u5df1", + "6235": "\u6247", + "6236": "\u6885", + "6237": "\u63e1", + "6238": "\u6b27", + "6239": "\u8584", + "6240": "\u6065", + "6241": "\u732e", + "6242": "\u9810", + "6243": "\u4ec1", + "6244": "\u9f8d", + "6245": "\u8a70", + "6246": "\u73cd", + "6247": "\u5f69", + "6248": "\u5feb", + "6249": "\u6cbc", + "6250": "\u6bd2", + "6251": "\u4e39", + "6252": "\u53e5", + "6253": "\u9234", + "6254": "\u91e3", + "6255": "\u7e01", + "6256": "\u5fae", + "6257": "\u5999", + "6258": "\u62ec", + "6259": "\u6669", + "6260": "\u7c89", + "6261": "\u9eba", + "6262": "\u5353", + "6263": "\u570f", + "6264": "\u517c", + "6265": "\u6b20", + "6266": "\u8e0a", + "6267": "\u8133", + "6268": "\u7adc", + "6269": "\u9cf4", + "6270": "\u5f66", + "6271": "\u5ac1", + "6272": "\u9a12", + "6273": "\u9aea", + "6274": "\u8acb", + "6275": "\u5375", + "6276": "\u640d", + "6277": "\u8377", + "6278": "\u68a8", + "6279": "\u5531", + "6280": "\u5d50", + "6281": "\u5e4c", + "6282": "\u4f34", + "6283": "\u624d", + "6284": "\u961c", + "6285": "\u81d3", + "6286": "\u7363", + "6287": "\u7bb1", + "6288": "\u7956", + "6289": "\u7532", + "6290": "\u6d74", + "6291": "\u5c0a", + "6292": "\u907f", + "6293": "\u6607", + "6294": "\u718a", + "6295": "\u58c1", + "6296": "\u4e18", + "6297": "\u6790", + "6298": "\u5b6b", + "6299": "\u5e72", + "6300": "\u7687", + "6301": "\u539a", + "6302": "\u4ead", + "6303": "\u970a", + "6304": "\u7b46", + "6305": "\u627f", + "6306": "\u5747", + "6307": "\u878d", + "6308": "\u5f8b", + "6309": "\u7dd1", + "6310": "\u5426", + "6311": "\u9b3c", + "6312": "\u587e", + "6313": "\u811a", + "6314": "\u9808", + "6315": "\u90aa", + "6316": "\u888b", + "6317": "\u6e56", + "6318": "\u4e73", + "6319": "\u88d5", + "6320": "\u63ee", + "6321": "\u51cd", + "6322": "\u6ec5", + "6323": "\u4e7e", + "6324": "\u3074", + "6325": "\u7fbd", + "6326": "\u6162", + "6327": "\u5019", + "6328": "\u62e1", + "6329": "\u6fef", + "6330": "\u8cb8", + "6331": "\u7802", + "6332": "\u656c", + "6333": "\u6982", + "6334": "\u5e81", + "6335": "\u7159", + "6336": "\u57f7", + "6337": "\u5e95", + "6338": "\u88c1", + "6339": "\u558b", + "6340": "\u9e97", + "6341": "\u9732", + "6342": "\u96f2", + "6343": "\u9aa8", + "6344": "\u500d", + "6345": "\u6bbf", + "6346": "\u5947", + "6347": "\u6613", + "6348": "\u5c64", + "6349": "\u6577", + "6350": "\u5e55", + "6351": "\u6bdb", + "6352": "\u7206", + "6353": "\u6687", + "6354": "\u68b0", + "6355": "\u8cb4", + "6356": "\u96a3", + "6357": "\u8f38", + "6358": "\u3077", + "6359": "\u67c4", + "6360": "\u59eb", + "6361": "\u7bc4", + "6362": "\u63b2", + "6363": "\u5618", + "6364": "\u585a", + "6365": "\u5264", + "6366": "\u5145", + "6367": "\u4f75", + "6368": "\u5974", + "6369": "\u675f", + "6370": "\u5893", + "6371": "\u702c", + "6372": "\u520a", + "6373": "\u8863", + "6374": "\u629e", + "6375": "\u7e04", + "6376": "\u77ac", + "6377": "\u5c04", + "6378": "\u83d3", + "6379": "\u52df", + "6380": "\u4e71", + "6381": "\u8fce", + "6382": "\u62b1", + "6383": "\u6c38", + "6384": "\u7af9", + "6385": "\u9178", + "6386": "\u523a", + "6387": "\u95a3", + "6388": "\u90f7", + "6389": "\u4e5f", + "6390": "\u61b6", + "6391": "\u5263", + "6392": "\u529f", + "6393": "\u9e7f", + "6394": "\u725b", + "6395": "\u79d8", + "6396": "\u4ecf", + "6397": "\u96c4", + "6398": "\u866b", + "6399": "\u5584", + "6400": "\u5c4a", + "6401": "\u8266", + "6402": "\u7247", + "6403": "\u8907", + "6404": "\u70ba", + "6405": "\u6cf3", + "6406": "\u5b9d", + "6407": "\u6fc0", + "6408": "\u5e79", + "6409": "\u81e3", + "6410": "\u4e4b", + "6411": "\u6691", + "6412": "\u6d66", + "6413": "\u770b", + "6414": "\u7591", + "6415": "\u8a98", + "6416": "\u66b4", + "6417": "\u8056", + "6418": "\u6368", + "6419": "\u677f", + "6420": "\u685c", + "6421": "\u7834", + "6422": "\u9769", + "6423": "\u5e0c", + "6424": "\u5e45", + "6425": "\u5442", + "6426": "\u6298", + "6427": "\u8a3a", + "6428": "\u4f38", + "6429": "\u60d1", + "6430": "\u6e2c", + "6431": "\u99d0", + "6432": "\u7a93", + "6433": "\u7d00", + "6434": "\u820e", + "6435": "\u7f72", + "6436": "\u60a3", + "6437": "\u5cb8", + "6438": "\u7e3e", + "6439": "\u6e7e", + "6440": "\u5c90", + "6441": "\u6a39", + "6442": "\u7d0d", + "6443": "\u79c0", + "6444": "\u514d", + "6445": "\u8b1d", + "6446": "\u6c60", + "6447": "\u7981", + "6448": "\u80cc", + "6449": "\u8e8d", + "6450": "\u8074", + "6451": "\u6297", + "6452": "\u8c46", + "6453": "\u7a0e", + "6454": "\u594f", + "6455": "\u8349", + "6456": "\u5f3e", + "6457": "\u6075", + "6458": "\u8001", + "6459": "\u793c", + "6460": "\u89d2", + "6461": "\u7ae5", + "6462": "\u5be9", + "6463": "\u88cf", + "6464": "\u5439", + "6465": "\u7720", + "6466": "\u6b6f", + "6467": "\u62e0", + "6468": "\u5bd2", + "6469": "\u6163", + "6470": "\u89e6", + "6471": "\u98fc", + "6472": "\u8358", + "6473": "\u7fa4", + "6474": "\u8ff7", + "6475": "\u6cca", + "6476": "\u5b97", + "6477": "\u65e6", + "6478": "\u50b7", + "6479": "\u984d", + "6480": "\u5869", + "6481": "\u5238", + "6482": "\u5e8a", + "6483": "\u9759", + "6484": "\u7559", + "6485": "\u8457", + "6486": "\u6cb9", + "6487": "\u8a8c", + "6488": "\u7f6a", + "6489": "\u7d14", + "6490": "\u8179", + "6491": "\u5075", + "6492": "\u5247", + "6493": "\u58ca", + "6494": "\u672d", + "6495": "\u8f2a", + "6496": "\u6383", + "6497": "\u707d", + "6498": "\u95d8", + "6499": "\u5f31", + "6500": "\u523b", + "6501": "\u822a", + "6502": "\u7b54", + "6503": "\u6804", + "6504": "\u59ff", + "6505": "\u4ea1", + "6506": "\u7e54", + "6507": "\u6557", + "6508": "\u7ae0", + "6509": "\u5438", + "6510": "\u4ee4", + "6511": "\u9bae", + "6512": "\u88dc", + "6513": "\u5915", + "6514": "\u635c", + "6515": "\u6012", + "6516": "\u6a21", + "6517": "\u76ca", + "6518": "\u559c", + "6519": "\u83ef", + "6520": "\u7d75", + "6521": "\u7533", + "6522": "\u76e4", + "6523": "\u8efd", + "6524": "\u7a4d", + "6525": "\u6a19", + "6526": "\u968e", + "6527": "\u7701", + "6528": "\u5bc6", + "6529": "\u9805", + "6530": "\u732b", + "6531": "\u5f93", + "6532": "\u975e", + "6533": "\u5e1d", + "6534": "\u5b63", + "6535": "\u6355", + "6536": "\u515a", + "6537": "\u6211", + "6538": "\u5727", + "6539": "\u9999", + "6540": "\u7b4b", + "6541": "\u8f29", + "6542": "\u7c4d", + "6543": "\u4e01", + "6544": "\u62bc", + "6545": "\u5c3e", + "6546": "\u97d3", + "6547": "\u64cd", + "6548": "\u6697", + "6549": "\u75c7", + "6550": "\u6563", + "6551": "\u7a81", + "6552": "\u9069", + "6553": "\u96d1", + "6554": "\u8de1", + "6555": "\u53b3", + "6556": "\u4e86", + "6557": "\u9ce5", + "6558": "\u9003", + "6559": "\u8b1b", + "6560": "\u6674", + "6561": "\u5fb4", + "6562": "\u5211", + "6563": "\u99c4", + "6564": "\u5009", + "6565": "\u56f0", + "6566": "\u77ed", + "6567": "\u5a66", + "6568": "\u9063", + "6569": "\u7565", + "6570": "\u9f62", + "6571": "\u9707", + "6572": "\u6575", + "6573": "\u8535", + "6574": "\u535a", + "6575": "\u8840", + "6576": "\u6e80", + "6577": "\u5fd7", + "6578": "\u8217", + "6579": "\u5b99", + "6580": "\u90e1", + "6581": "\u90a3", + "6582": "\u5bff", + "6583": "\u907a", + "6584": "\u79cb", + "6585": "\u6975", + "6586": "\u91cc", + "6587": "\u5ec3", + "6588": "\u56e0", + "6589": "\u5178", + "6590": "\u67d3", + "6591": "\u5f92", + "6592": "\u5dfb", + "6593": "\u9802", + "6594": "\u5742", + "6595": "\u8d85", + "6596": "\u6cb3", + "6597": "\u76db", + "6598": "\u72ac", + "6599": "\u8c4a", + "6600": "\u7aef", + "6601": "\u7d39", + "6602": "\u9996", + "6603": "\u6e6f", + "6604": "\u967d", + "6605": "\u7cbe", + "6606": "\u7949", + "6607": "\u6b73", + "6608": "\u7df4", + "6609": "\u6c5f", + "6610": "\u602a", + "6611": "\u5370", + "6612": "\u7b97", + "6613": "\u7d19", + "6614": "\u6255", + "6615": "\u6c42", + "6616": "\u969c", + "6617": "\u7c21", + "6618": "\u5fa1", + "6619": "\u9014", + "6620": "\u5275", + "6621": "\u8cc0", + "6622": "\u8239", + "6623": "\u5802", + "6624": "\u83dc", + "6625": "\u30a5", + "6626": "\u52e4", + "6627": "\u75db", + "6628": "\u4e26", + "6629": "\u666f", + "6630": "\u96ea", + "6631": "\u7bc0", + "6632": "\u9451", + "6633": "\u6d5c", + "6634": "\u663c", + "6635": "\u6e05", + "6636": "\u629c", + "6637": "\u52e2", + "6638": "\u66ae", + "6639": "\u9280", + "6640": "\u76df", + "6641": "\u9b5a", + "6642": "\u7387", + "6643": "\u6d0b", + "6644": "\u5bfa", + "6645": "\u5f01", + "6646": "\u7686", + "6647": "\u5fb3", + "6648": "\u8336", + "6649": "\u7b11", + "6650": "\u6e21", + "6651": "\u5948", + "6652": "\u9806", + "6653": "\u6cc1", + "6654": "\u8ac7", + "6655": "\u821e", + "6656": "\u6848", + "6657": "\u5ca9", + "6658": "\u8ca0", + "6659": "\u65e7", + "6660": "\u8ca1", + "6661": "\u8a31", + "6662": "\u6545", + "6663": "\u51ac", + "6664": "\u6a2a", + "6665": "\u5965", + "6666": "\u8a33", + "6667": "\u6bd4", + "6668": "\u56f2", + "6669": "\u505c", + "6670": "\u7bc9", + "6671": "\u6ce2", + "6672": "\u59b9", + "6673": "\u6797", + "6674": "\u6696", + "6675": "\u7d22", + "6676": "\u8d64", + "6677": "\u7d66", + "6678": "\u672b", + "6679": "\u50ac", + "6680": "\u6b66", + "6681": "\u6d17", + "6682": "\u9045", + "6683": "\u8ff0", + "6684": "\u9ed2", + "6685": "\u72af", + "6686": "\u5de6", + "6687": "\u6e90", + "6688": "\u9b54", + "6689": "\u7d30", + "6690": "\u4e45", + "6691": "\u4e0e", + "6692": "\u6e1b", + "6693": "\u7d1a", + "6694": "\u8cbb", + "6695": "\u8d8a", + "6696": "\u5dee", + "6697": "\u59bb", + "6698": "\u9818", + "6699": "\u885b", + "6700": "\u4e38", + "6701": "\u7d61", + "6702": "\u968a", + "6703": "\u85ac", + "6704": "\u6c0f", + "6705": "\u671b", + "6706": "\u4f3c", + "6707": "\u5c31", + "6708": "\u53f3", + "6709": "\u6761", + "6710": "\u5e03", + "6711": "\u51e6", + "6712": "\u8c37", + "6713": "\u7b56", + "6714": "\u52b9", + "6715": "\u5fd8", + "6716": "\u71b1", + "6717": "\u5fa9", + "6718": "\u59c9", + "6719": "\u30cc", + "6720": "\u632f", + "6721": "\u8ab2", + "6722": "\u898f", + "6723": "\u5012", + "6724": "\u6e2f", + "6725": "\u6ce8", + "6726": "\u68ee", + "6727": "\u9632", + "6728": "\u7d99", + "6729": "\u9000", + "6730": "\u6839", + "6731": "\u706b", + "6732": "\u66ff", + "6733": "\u9678", + "6734": "\u53bb", + "6735": "\u8996", + "6736": "\u6574", + "6737": "\u6e96", + "6738": "\u5ead", + "6739": "\u30be", + "6740": "\u72ec", + "6741": "\u6483", + "6742": "\u5150", + "6743": "\u6a4b", + "6744": "\u307d", + "6745": "\u63db", + "6746": "\u5ff5", + "6747": "\u8b58", + "6748": "\u306c", + "6749": "\u6253", + "6750": "\u6d25", + "6751": "\u96e8", + "6752": "\u5e78", + "6753": "\u542b", + "6754": "\u796d", + "6755": "\u97ff", + "6756": "\u52b4", + "6757": "\u51c4", + "6758": "\u5c06", + "6759": "\u5b98", + "6760": "\u82e6", + "6761": "\u8ffd", + "6762": "\u9060", + "6763": "\u672a", + "6764": "\u8ca9", + "6765": "\u5a18", + "6766": "\u8857", + "6767": "\u66dc", + "6768": "\u7a0b", + "6769": "\u63d0", + "6770": "\u7389", + "6771": "\u5224", + "6772": "\u79fb", + "6773": "\u653b", + "6774": "\u4f4e", + "6775": "\u88c5", + "6776": "\u65ad", + "6777": "\u53ca", + "6778": "\u8a3c", + "6779": "\u8c61", + "6780": "\u5b88", + "6781": "\u9752", + "6782": "\u5bcc", + "6783": "\u623b", + "6784": "\u8a5e", + "6785": "\u5409", + "6786": "\u6295", + "6787": "\u6b74", + "6788": "\u6ca2", + "6789": "\u8f09", + "6790": "\u5177", + "6791": "\u5eab", + "6792": "\u9664", + "6793": "\u74b0", + "6794": "\u5c55", + "6795": "\u5352", + "6796": "\u4e89", + "6797": "\u5931", + "6798": "\u623f", + "6799": "\u6625", + "6800": "\u6319", + "6801": "\u6f5f", + "6802": "\u8fd4", + "6803": "\u99ac", + "6804": "\u6b32", + "6805": "\u6750", + "6806": "\u6238", + "6807": "\u56f3", + "6808": "\u5bdd", + "6809": "\u990a", + "6810": "\u713c", + "6811": "\u5c0e", + "6812": "\u5922", + "6813": "\u7c73", + "6814": "\u51b7", + "6815": "\u606f", + "6816": "\u5175", + "6817": "\u5e2d", + "6818": "\u6e08", + "6819": "\u5287", + "6820": "\u63f4", + "6821": "\u98ef", + "6822": "\u592e", + "6823": "\u967a", + "6824": "\u670d", + "6825": "\u614b", + "6826": "\u8d70", + "6827": "\u8a55", + "6828": "\u5c45", + "6829": "\u6a29", + "6830": "\u8ad6", + "6831": "\u5f1f", + "6832": "\u3085", + "6833": "\u5883", + "6834": "\u5bdf", + "6835": "\u6388", + "6836": "\u983c", + "6837": "\u6d3e", + "6838": "\u64ae", + "6839": "\u7d20", + "6840": "\u4fee", + "6841": "\u7b2c", + "6842": "\u8cea", + "6843": "\u544a", + "6844": "\u8208", + "6845": "\u79d2", + "6846": "\u5b87", + "6847": "\u8089", + "6848": "\u5144", + "6849": "\u50cf", + "6850": "\u79f0", + "6851": "\u5024", + "6852": "\u982d", + "6853": "\u9031", + "6854": "\u7763", + "6855": "\u6d88", + "6856": "\u5b85", + "6857": "\u82b8", + "6858": "\u9854", + "6859": "\u8aad", + "6860": "\u4ef2", + "6861": "\u904a", + "6862": "\u8a66", + "6863": "\u901f", + "6864": "\u9152", + "6865": "\u5bbf", + "6866": "\u96e2", + "6867": "\u677e", + "6868": "\u5897", + "6869": "\u6bba", + "6870": "\u9244", + "6871": "\u53f8", + "6872": "\u5bb3", + "6873": "\u5272", + "6874": "\u77f3", + "6875": "\u590f", + "6876": "\u7248", + "6877": "\u4f50", + "6878": "\u52a9", + "6879": "\u82f1", + "6880": "\u53f7", + "6881": "\u60f3", + "6882": "\u7ba1", + "6883": "\u6025", + "6884": "\u9803", + "6885": "\u3065", + "6886": "\u82e5", + "6887": "\u604b", + "6888": "\u9020", + "6889": "\u53f2", + "6890": "\u6cc9", + "6891": "\u91cf", + "6892": "\u88fd", + "6893": "\u5e9c", + "6894": "\u8db3", + "6895": "\u6016", + "6896": "\u738b", + "6897": "\u59d4", + "6898": "\u4e21", + "6899": "\u8fba", + "6900": "\u6b8b", + "6901": "\u9006", + "6902": "\u5099", + "6903": "\u8ecd", + "6904": "\u8b66", + "6905": "\u67fb", + "6906": "\u5217", + "6907": "\u7de8", + "6908": "\u6bb5", + "6909": "\u53cd", + "6910": "\u30bc", + "6911": "\u643a", + "6912": "\u6b69", + "6913": "\u682a", + "6914": "\u5668", + "6915": "\u5ea7", + "6916": "\u98db", + "6917": "\u4e08", + "6918": "\u82b1", + "6919": "\u4fa1", + "6920": "\u76e3", + "6921": "\u5d0e", + "6922": "\u85e4", + "6923": "\u30d8", + "6924": "\u5468", + "6925": "\u6bce", + "6926": "\u7d71", + "6927": "\u53ce", + "6928": "\u843d", + "6929": "\u661f", + "6930": "\u964d", + "6931": "\u62c5", + "6932": "\u5074", + "6933": "\u7642", + "6934": "\u5e2b", + "6935": "\u5199", + "6936": "\u985e", + "6937": "\u547d", + "6938": "\u4ecb", + "6939": "\u9858", + "6940": "\u8b77", + "6941": "\u57ce", + "6942": "\u6b7b", + "6943": "\u679c", + "6944": "\u962a", + "6945": "\u4efb", + "6946": "\u66f4", + "6947": "\u5e38", + "6948": "\u4fbf", + "6949": "\u305c", + "6950": "\u691c", + "6951": "\u904e", + "6952": "\u8cc7", + "6953": "\u50cd", + "6954": "\u8a8d", + "6955": "\u822c", + "6956": "\u793a", + "6957": "\u5ba2", + "6958": "\u7fd2", + "6959": "\u7a76", + "6960": "\u534a", + "6961": "\u9332", + "6962": "\u5b57", + "6963": "\u6614", + "6964": "\u5eb7", + "6965": "\u90ce", + "6966": "\u5f71", + "6967": "\u899a", + "6968": "\u578b", + "6969": "\u58f0", + "6970": "\u4ef6", + "6971": "\u7fa9", + "6972": "\u65bd", + "6973": "\u798f", + "6974": "\u5bb9", + "6975": "\u8def", + "6976": "\u547c", + "6977": "\u5f79", + "6978": "\u5358", + "6979": "\u4e95", + "6980": "\u72b6", + "6981": "\u5efa", + "6982": "\u7531", + "6983": "\u5c5e", + "6984": "\u52c9", + "6985": "\u571f", + "6986": "\u8449", + "6987": "\u8d77", + "6988": "\u89a7", + "6989": "\u914d", + "6990": "\u5f35", + "6991": "\u63a5", + "6992": "\u8fbc", + "6993": "\u5f85", + "6994": "\u5ba4", + "6995": "\u75c5", + "6996": "\u5e2f", + "6997": "\u5acc", + "6998": "\u5a5a", + "6999": "\u5149", + "7000": "\u500b", + "7001": "\u8077", + "7002": "\u55b6", + "7003": "\u307c", + "7004": "\u7814", + "7005": "\u8a08", + "7006": "\u76f4", + "7007": "\u96e3", + "7008": "\u305e", + "7009": "\u7d76", + "7010": "\u30e8", + "7011": "\u7167", + "7012": "\u897f", + "7013": "\u7d04", + "7014": "\u5b58", + "7015": "\u9a13", + "7016": "\u6cbb", + "7017": "\u7236", + "7018": "\u89e3", + "7019": "\u5ca1", + "7020": "\u8ee2", + "7021": "\u5546", + "7022": "\u9032", + "7023": "\u4fc2", + "7024": "\u8aac", + "7025": "\u89b3", + "7026": "\u7403", + "7027": "\u4e57", + "7028": "\u5bae", + "7029": "\u652f", + "7030": "\u5f97", + "7031": "\u541b", + "7032": "\u8b70", + "7033": "\u5065", + "7034": "\u9580", + "7035": "\u6b62", + "7036": "\u91cd", + "7037": "\u6e29", + "7038": "\u7dd2", + "7039": "\u7740", + "7040": "\u98f2", + "7041": "\u6bcd", + "7042": "\u58eb", + "7043": "\u3056", + "7044": "\u96c6", + "7045": "\u4e07", + "7046": "\u592a", + "7047": "\u7d9a", + "7048": "\u7dda", + "7049": "\u7a2e", + "7050": "\u683c", + "7051": "\u4f4d", + "7052": "\u30e6", + "7053": "\u6b4c", + "7054": "\u591c", + "7055": "\u5171", + "7056": "\u6b63", + "7057": "\u5fc5", + "7058": "\u30d2", + "7059": "\u8272", + "7060": "\u554f", + "7061": "\u518d", + "7062": "\u57df", + "7063": "\u3086", + "7064": "\u52dd", + "7065": "\u53f0", + "7066": "\u6280", + "7067": "\u65c5", + "7068": "\u5f15", + "7069": "\u7cfb", + "7070": "\u9662", + "7071": "\u60aa", + "7072": "\u57fa", + "7073": "\u795e", + "7074": "\u9650", + "7075": "\u7523", + "7076": "\u6c7a", + "7077": "\u6c11", + "7078": "\u4ea4", + "7079": "\u653f", + "7080": "\u8cde", + "7081": "\u7a7a", + "7082": "\u533b", + "7083": "\u5f7c", + "7084": "\u592b", + "7085": "\u53ef", + "7086": "\u8ab0", + "7087": "\u53e4", + "7088": "\u5e30", + "7089": "\u8853", + "7090": "\u76f8", + "7091": "\u6751", + "7092": "\u56e3", + "7093": "\u4f1d", + "7094": "\u5186", + "7095": "\u4f4f", + "7096": "\u984c", + "7097": "\u5e73", + "7098": "\u4e88", + "7099": "\u97f3", + "7100": "\u671d", + "7101": "\u6307", + "7102": "\u771f", + "7103": "\u30f4", + "7104": "\u52d9", + "7105": "\u70b9", + "7106": "\u5404", + "7107": "\u9928", + "7108": "\u5fdc", + "7109": "\u73fe", + "7110": "\u5229", + "7111": "\u5929", + "7112": "\u7b49", + "7113": "\u6728", + "7114": "\u767d", + "7115": "\u5f62", + "7116": "\u4f9b", + "7117": "\u7d4c", + "7118": "\u3047", + "7119": "\u65cf", + "7120": "\u65e9", + "7121": "\u4f8b", + "7122": "\u50d5", + "7123": "\u4e0d", + "7124": "\u5207", + "7125": "\u5357", + "7126": "\u52a0", + "7127": "\u969b", + "7128": "\u7d42", + "7129": "\u69d8", + "7130": "\u653e", + "7131": "\u548c", + "7132": "\u4f11", + "7133": "\u5dde", + "7134": "\u6c34", + "7135": "\u5354", + "7136": "\u5728", + "7137": "\u7d44", + "7138": "\u5411", + "7139": "\u5e83", + "7140": "\u8eab", + "7141": "\u754c", + "7142": "\u5de5", + "7143": "\u9078", + "7144": "\u59cb", + "7145": "\u5143", + "7146": "\u96f6", + "7147": "\u3005", + "7148": "\u89aa", + "7149": "\u7f8e", + "7150": "\u4fe1", + "7151": "\u90fd", + "7152": "\u7f6e", + "7153": "\u5c40", + "7154": "\u99c5", + "7155": "\u904b", + "7156": "\u9001", + "7157": "\u98a8", + "7158": "\u53e3", + "7159": "\u6f14", + "7160": "\u8abf", + "7161": "\u304e", + "7162": "\u512a", + "7163": "\u6b21", + "7164": "\u30a9", + "7165": "\u4ed6", + "7166": "\u5712", + "7167": "\u4fdd", + "7168": "\u7537", + "7169": "\u53c2", + "7170": "\u5c11", + "7171": "\u767e", + "7172": "\u7279", + "7173": "\u8003", + "7174": "\u7121", + "7175": "\u4e03", + "7176": "\u30e4", + "7177": "\u30ae", + "7178": "\u826f", + "7179": "\u30b6", + "7180": "\u5236", + "7181": "\u4eac", + "7182": "\u611b", + "7183": "\u58f2", + "7184": "\u80fd", + "7185": "\u539f", + "7186": "\u30b2", + "7187": "\u6709", + "7188": "\u516d", + "7189": "\u5b89", + "7190": "\u30b4", + "7191": "\u80b2", + "7192": "\u79d1", + "7193": "\u8981", + "7194": "\u6599", + "7195": "\u66f8", + "7196": "\u8a9e", + "7197": "\u8a2d", + "7198": "\u6d77", + "7199": "\u671f", + "7200": "\u6d41", + "7201": "\u78ba", + "7202": "\u30da", + "7203": "\u533a", + "7204": "\u3080", + "7205": "\u9023", + "7206": "\u8cb7", + "7207": "\u3072", + "7208": "\u3075", + "7209": "\u4ed8", + "7210": "\u753a", + "7211": "\u6d3b", + "7212": "\u60c5", + "7213": "\u6708", + "7214": "\u8868", + "7215": "\u66f2", + "7216": "\u5f37", + "7217": "\u4e16", + "7218": "\u660e", + "7219": "\u6210", + "7220": "\u30ce", + "7221": "\u30a1", + "7222": "\u6587", + "7223": "\u9055", + "7224": "\u6771", + "7225": "\u53cb", + "7226": "\u610f", + "7227": "\u529b", + "7228": "\u5f0f", + "7229": "\u6cd5", + "7230": "\u5831", + "7231": "\u54e1", + "7232": "\u5fc3", + "7233": "\u5c4b", + "7234": "\u54c1", + "7235": "\u5317", + "7236": "\u5148", + "7237": "\u5cf6", + "7238": "\u5473", + "7239": "\u5ddd", + "7240": "\u958b", + "7241": "\u5343", + "7242": "\u95a2", + "7243": "\u516b", + "7244": "\u96fb", + "7245": "\u7136", + "7246": "\u5ea6", + "7247": "\u4ffa", + "7248": "\u9054", + "7249": "\u9762", + "7250": "\u4e5d", + "7251": "\u6570", + "7252": "\u53d6", + "7253": "\u697d", + "7254": "\u91d1", + "7255": "\u6027", + "7256": "\u91ce", + "7257": "\u5225", + "7258": "\u6226", + "7259": "\u516c", + "7260": "\u6a5f", + "7261": "\u9053", + "7262": "\u76ee", + "7263": "\u8a18", + "7264": "\u3073", + "7265": "\u767a", + "7266": "\u5bfe", + "7267": "\u7acb", + "7268": "\u521d", + "7269": "\u5316", + "7270": "\u30bd", + "7271": "\u56db", + "7272": "\u30ef", + "7273": "\u7530", + "7274": "\u6301", + "7275": "\u30ac", + "7276": "\u8eca", + "7277": "\u756a", + "7278": "\u30d4", + "7279": "\u805e", + "7280": "\u56de", + "7281": "\u3041", + "7282": "\u3076", + "7283": "\u30d9", + "7284": "\u4e94", + "7285": "\u3052", + "7286": "\u5b9f", + "7287": "\u30dc", + "7288": "\u5e97", + "7289": "\u5c0f", + "7290": "\u5b9a", + "7291": "\u30e2", + "7292": "\u9577", + "7293": "\u65b0", + "7294": "\u30cf", + "7295": "\u30b1", + "7296": "\u5916", + "7297": "\u30dd", + "7298": "\u8fd1", + "7299": "\u6240", + "7300": "\u3078", + "7301": "\u770c", + "7302": "\u540c", + "7303": "\u30cd", + "7304": "\u5185", + "7305": "\u5973", + "7306": "\u30db", + "7307": "\u4f53", + "7308": "\u597d", + "7309": "\u30c4", + "7310": "\u30bb", + "7311": "\u77e5", + "7312": "\u5c71", + "7313": "\u6765", + "7314": "\u30a7", + "7315": "\u4f7f", + "7316": "\u30e7", + "7317": "\u30ba", + "7318": "\u4e3b", + "7319": "\u52d5", + "7320": "\u7406", + "7321": "\u7269", + "7322": "\u6620", + "7323": "\u8005", + "7324": "\u3050", + "7325": "\u7684", + "7326": "\u4ee3", + "7327": "\u5909", + "7328": "\u6559", + "7329": "\u793e", + "7330": "\u7528", + "7331": "\u8a71", + "7332": "\u540d", + "7333": "\u69cb", + "7334": "\u9ad8", + "7335": "\u6700", + "7336": "\u305a", + "7337": "\u30df", + "7338": "\u6821", + "7339": "\u30c0", + "7340": "\u98df", + "7341": "\u5f8c", + "7342": "\u624b", + "7343": "\u4e09", + "7344": "\u901a", + "7345": "\u611f", + "7346": "\u5408", + "7347": "\u591a", + "7348": "\u696d", + "7349": "\u5165", + "7350": "\u30a8", + "7351": "\u5834", + "7352": "\u3079", + "7353": "\u4e0a", + "7354": "\u5bb6", + "7355": "\u79c1", + "7356": "\u5e74", + "7357": "\u9593", + "7358": "\u753b", + "7359": "\u524d", + "7360": "\u4e0b", + "7361": "\u30e3", + "7362": "\u5730", + "7363": "\u4e8c", + "7364": "\u30a6", + "7365": "\u30ca", + "7366": "\u30d3", + "7367": "\u81ea", + "7368": "\u5168", + "7369": "\u30d1", + "7370": "\u7d50", + "7371": "\u30d6", + "7372": "\u30e5", + "7373": "\u5e02", + "7374": "\u30b5", + "7375": "\u6c17", + "7376": "\u65b9", + "7377": "\u30c7", + "7378": "\u5341", + "7379": "\u30ad", + "7380": "\u5f53", + "7381": "\u56fd", + "7382": "\u4f5c", + "7383": "\u30a3", + "7384": "\u90e8", + "7385": "\u30aa", + "7386": "\u30cb", + "7387": "\u30c1", + "7388": "\u30e0", + "7389": "\u30b0", + "7390": "\u30e1", + "7391": "\u3054", + "7392": "\u5b50", + "7393": "\u3070", + "7394": "\u751f", + "7395": "\u307b", + "7396": "\u3071", + "7397": "\u305b", + "7398": "\u4f55", + "7399": "\u51fa", + "7400": "\u8a00", + "7401": "\u4eca", + "7402": "\u30d0", + "7403": "\u4e8b", + "7404": "\u4e2d", + "7405": "\u30d7", + "7406": "\u6642", + "7407": "\u30b3", + "7408": "\u898b", + "7409": "\u30c6", + "7410": "\u4f1a", + "7411": "\u30de", + "7412": "\u30ab", + "7413": "\u601d", + "7414": "\u30ed", + "7415": "\u30b8", + "7416": "\u30d5", + "7417": "\u30b7", + "7418": "\u3081", + "7419": "\u30ec", + "7420": "\u30c9", + "7421": "\u5206", + "7422": "\u3087", + "7423": "\u308d", + "7424": "\u5b66", + "7425": "\u884c", + "7426": "\u30bf", + "7427": "\u5927", + "7428": "\u3064", + "7429": "\u672c", + "7430": "\u65e5", + "7431": "\u308f", + "7432": "\u4e00", + "7433": "\u30af", + "7434": "\u307f", + "7435": "\u30ea", + "7436": "\u30a2", + "7437": "\u30c3", + "7438": "\u4eba", + "7439": "\u30e9", + "7440": "\uff1f", + "7441": "\u304a", + "7442": "\u3058", + "7443": "\u30a4", + "7444": "\u30eb", + "7445": "\u30c8", + "7446": "\u3083", + "7447": "\u304d", + "7448": "\u3055", + "7449": "\u3061", + "7450": "\u3084", + "7451": "\u30b9", + "7452": "\u3069", + "7453": "\u3051", + "7454": "\u304f", + "7455": "\u3048", + "7456": "\u3092", + "7457": "\u308a", + "7458": "\u3088", + "7459": "\u3053", + "7460": "\u30f3", + "7461": "\u3060", + "7462": "\u308c", + "7463": "\u3089", + "7464": "\u306d", + "7465": "\u304c", + "7466": "\u307e", + "7467": "\u30fc", + "7468": "\u3082", + "7469": "\u305d", + "7470": "\u3057", + "7471": "\u306b", + "7472": "\u306f", + "7473": "\u308b", + "7474": "\u3059", + "7475": "\u3068", + "7476": "\u305f", + "7477": "\u3042", + "7478": "\u3066", + "7479": "\u3063", + "7480": "\u3067", + "7481": "\u304b", + "7482": "\u306a", + "7483": "\u3093", + "7484": "\u3046", + "7485": "\u306e", + "7486": "\u3001", + "7487": "\u3002", + "7488": "\u3044", + "7489": "", + "7490": "\uc774", + "7491": "\uac00", + "7492": "\uc744", + "7493": "\ub294", + "7494": "\uc5d0", + "7495": "\ub3c4", + "7496": "\uace0", + "7497": "\uc758", + "7498": "\uc9c0", + "7499": "\ub97c", + "7500": "\u2581\uadf8", + "7501": "\ub2e4", + "7502": "\uc740", + "7503": "\uae30", + "7504": "\ud55c", + "7505": "\uc5b4", + "7506": "\uc2dc", + "7507": "\uc790", + "7508": "\uc11c", + "7509": "\ub85c", + "7510": "\ud574", + "7511": "\ub9ac", + "7512": "\uc694", + "7513": "\uc0ac", + "7514": "\u2581\ubb50", + "7515": "\uc778", + "7516": "\uac8c", + "7517": "\uc5d0\uc11c", + "7518": "\u2581\uc774\uc81c", + "7519": "\uc815", + "7520": "\ud558", + "7521": "\u2581\uc5b4", + "7522": "\u2581\uac70", + "7523": "\ud558\ub294", + "7524": "\ub098", + "7525": "\ub300", + "7526": "\u2581\uc880", + "7527": "\ud558\uace0", + "7528": "\ub9cc", + "7529": "\u2581\uc218", + "7530": "\u2581\uc544", + "7531": "\uc7a5", + "7532": "\uba74", + "7533": "\uc73c\ub85c", + "7534": "\uc6d0", + "7535": "\uc57c", + "7536": "\uc8fc", + "7537": "\uacfc", + "7538": "\uc0c1", + "7539": "\uad6c", + "7540": "\uc2a4", + "7541": "\uc77c", + "7542": "\u2581\uadf8\ub7f0", + "7543": "\ub77c", + "7544": "\uc218", + "7545": "\ud560", + "7546": "\uc544", + "7547": "\ub4e4", + "7548": "\u2581\uc774\ub7f0", + "7549": "\u2581\uc608", + "7550": "\uac70", + "7551": "\u2581\uc9c0\uae08", + "7552": "\uc131", + "7553": "\u2581\ubcf4", + "7554": "\u2581\uc548", + "7555": "\ubcf4", + "7556": "\u2581\ub610", + "7557": "\ub3d9", + "7558": "\uc18c", + "7559": "\uc2e0", + "7560": "\u2581\uc788\ub294", + "7561": "\uc2ed", + "7562": "\u2581\uac83", + "7563": "\uac04", + "7564": "\uc81c", + "7565": "\ub294\ub370", + "7566": "\uac74", + "7567": "\u2581\ub300", + "7568": "\ubd80", + "7569": "\ud654", + "7570": "\uc804", + "7571": "\u2581\uc804", + "7572": "\u2581\uc774\ub807\uac8c", + "7573": "\u2581\uc77c", + "7574": "\u2581\uadfc\ub370", + "7575": "\ub4e4\uc774", + "7576": "\u2581\uadf8\ub798\uc11c", + "7577": "\ub370", + "7578": "\ud588", + "7579": "\uce58", + "7580": "\uc120", + "7581": "\ub4dc", + "7582": "\u2581\ub9ce\uc774", + "7583": "\uc138", + "7584": "\uc9c4", + "7585": "\uc5f0", + "7586": "\uc5ec", + "7587": "\uad00", + "7588": "\ubd84", + "7589": "\u2581\ub124", + "7590": "\ub9c8", + "7591": "\uc624", + "7592": "\ubbf8", + "7593": "\uc704", + "7594": "\uc8e0", + "7595": "\uc2b5\ub2c8\ub2e4", + "7596": "\uacc4", + "7597": "\uc2dd", + "7598": "\ubb34", + "7599": "\uc788", + "7600": "\ubb38", + "7601": "\ub2f9", + "7602": "\uc7ac", + "7603": "\ub144", + "7604": "\uccb4", + "7605": "\u2581\ub098", + "7606": "\uc640", + "7607": "\uc6b0", + "7608": "\ub77c\uace0", + "7609": "\uc2e4", + "7610": "\u2581\ub54c", + "7611": "\ub2e8", + "7612": "\ud1b5", + "7613": "\uc601", + "7614": "\u2581\uc8fc", + "7615": "\uc801", + "7616": "\uba85", + "7617": "\u2581\uc54a", + "7618": "\u2581\ub9d0", + "7619": "\u2581\uc624", + "7620": "\u2581\uc788\ub2e4", + "7621": "\ud574\uc11c", + "7622": "\ub824", + "7623": "\u2581\uc5b4\ub5a4", + "7624": "\ubc29", + "7625": "\uc0b0", + "7626": "\u2581\uc6b0\ub9ac", + "7627": "\ucc28", + "7628": "\u2581\uc800", + "7629": "\ubb3c", + "7630": "\ub2c8", + "7631": "\u2581\uc544\ub2c8", + "7632": "\u2581\ub354", + "7633": "\u2581\uc0ac", + "7634": "\ubc18", + "7635": "\ub2c8\ub2e4", + "7636": "\uc810", + "7637": "\u2581\ube44", + "7638": "\ud2b8", + "7639": "\u2581\uc74c", + "7640": "\uc6a9", + "7641": "\uc5c5", + "7642": "\uacbd", + "7643": "\uc0dd", + "7644": "\uc801\uc73c\ub85c", + "7645": "\uacf5", + "7646": "\u2581\ub0b4", + "7647": "\u2581\uadf8\ub9ac\uace0", + "7648": "\uad6d", + "7649": "\ub7ec", + "7650": "\uc548", + "7651": "\ube44", + "7652": "\uae4c\uc9c0", + "7653": "\ub2c8\uae4c", + "7654": "\uae08", + "7655": "\uc6b4", + "7656": "\u2581\uc774\uac8c", + "7657": "\u2581\uacf5", + "7658": "\ub0b4", + "7659": "\ud68c", + "7660": "\u2581\uc798", + "7661": "\ud558\uac8c", + "7662": "\ud589", + "7663": "\uc870", + "7664": "\ubaa8", + "7665": "\uac10", + "7666": "\uac00\uc9c0\uace0", + "7667": "\u2581\ub9c9", + "7668": "\uc9d1", + "7669": "\ub41c", + "7670": "\uac83", + "7671": "\ubc1c", + "7672": "\ud559", + "7673": "\uc2ec", + "7674": "\ub358", + "7675": "\ubc31", + "7676": "\u2581\uc720", + "7677": "\ub77c\ub294", + "7678": "\ub0a8", + "7679": "\u2581\ub54c\ubb38\uc5d0", + "7680": "\u2581\uadf8\ub7ec\ub2c8\uae4c", + "7681": "\ub418\ub294", + "7682": "\uc785\ub2c8\ub2e4", + "7683": "\ud0c0", + "7684": "\uad50", + "7685": "\u2581\ub4e4\uc5b4", + "7686": "\u2581\uc5c6", + "7687": "\uc5b4\uc694", + "7688": "\ubc95", + "7689": "\uc801\uc778", + "7690": "\uc5ed", + "7691": "\u2581\uc0dd\uac01", + "7692": "\ub9e4", + "7693": "\ubbfc", + "7694": "\ud55c\ub2e4", + "7695": "\u2581\uac19\uc740", + "7696": "\u2581\uadf8\ub0e5", + "7697": "\ubc30", + "7698": "\ub974", + "7699": "\u2581\ub418", + "7700": "\ubd80\ubd84", + "7701": "\uc721", + "7702": "\u2581\uc598\uae30", + "7703": "\ud638", + "7704": "\ud504", + "7705": "\ub0a0", + "7706": "\u2581\ubabb", + "7707": "\u2581\uc0ac\uc2e4", + "7708": "\uac70\ub4e0\uc694", + "7709": "\ucc9c", + "7710": "\ub4f1", + "7711": "\u2581\uc5b4\ub5bb\uac8c", + "7712": "\u2581\uc81c", + "7713": "\uc9c0\ub9cc", + "7714": "\ud788", + "7715": "\u2581\uc81c\uac00", + "7716": "\u2581\uadf8\ub807\uac8c", + "7717": "\ub354", + "7718": "\uad8c", + "7719": "\ud558\uba74", + "7720": "\ucd9c", + "7721": "\ub2e4\uace0", + "7722": "\ub2ec", + "7723": "\uaca0", + "7724": "\uc791", + "7725": "\uc785", + "7726": "\u2581\uc800\ub294", + "7727": "\ud574\uc57c", + "7728": "\u2581\ubd80", + "7729": "\u2581\uc9c4\uc9dc", + "7730": "\ud45c", + "7731": "\uc9c1", + "7732": "\uc591", + "7733": "\u2581\ubc14", + "7734": "\ud569\ub2c8\ub2e4", + "7735": "\uc0b4", + "7736": "\ub825", + "7737": "\uc5c8", + "7738": "\ud588\ub2e4", + "7739": "\u2581\ub108\ubb34", + "7740": "\u2581\uac00\uc7a5", + "7741": "\u2581\uc870", + "7742": "\ud314", + "7743": "\uc911", + "7744": "\ub2d8", + "7745": "\u2581\ub0b4\uac00", + "7746": "\uc720", + "7747": "\ub798", + "7748": "\ubc84", + "7749": "\ubc88", + "7750": "\uac1c", + "7751": "\ud6c4", + "7752": "\uc796\uc544\uc694", + "7753": "\ud558\uc9c0", + "7754": "\ud53c", + "7755": "\uc885", + "7756": "\ub124", + "7757": "\ud604", + "7758": "\u2581\uc788\uc2b5\ub2c8\ub2e4", + "7759": "\u2581\uc88b", + "7760": "\ub9de", + "7761": "\ub09c", + "7762": "\uac19", + "7763": "\u2581\uad49\uc7a5\ud788", + "7764": "\u2581\uc911", + "7765": "\ucd94", + "7766": "\uc6d4", + "7767": "\uc5d0\ub294", + "7768": "\uccad", + "7769": "\uc18d", + "7770": "\u2581\uc0ac\ub78c", + "7771": "\ubc1b", + "7772": "\uc9c8", + "7773": "\ub178", + "7774": "\ud615", + "7775": "\u2581\uac78", + "7776": "\uad70", + "7777": "\uc600", + "7778": "\ud30c", + "7779": "\ub514", + "7780": "\ubcf8", + "7781": "\ub3fc", + "7782": "\ub108", + "7783": "\u2581\uadf8\ub7ec\uba74", + "7784": "\u2581\ubd88", + "7785": "\u2581\ub450", + "7786": "\u2581\uc624\ub298", + "7787": "\u2581\uac1c", + "7788": "\ucd5c", + "7789": "\u2581\uc0bc", + "7790": "\ud06c", + "7791": "\ub410", + "7792": "\ud3b8", + "7793": "\ucabd", + "7794": "\ud310", + "7795": "\ub54c", + "7796": "\u2581\ub418\uac8c", + "7797": "\u2581\ub098\ub294", + "7798": "\ubd80\ud130", + "7799": "\ub791", + "7800": "\u2581\uadf8\uac70", + "7801": "\u2581\ub300\ud574\uc11c", + "7802": "\u2581\uc815\ub3c4", + "7803": "\ub808", + "7804": "\u2581\uae40", + "7805": "\u2581\uc774\uac70", + "7806": "\u2581\uc788\uace0", + "7807": "\u2581\uac15", + "7808": "\u2581\ub300\ud55c", + "7809": "\uc73c\uba74", + "7810": "\u2581\uadf8\uac8c", + "7811": "\u2581\ubb38\uc81c", + "7812": "\ud3ec", + "7813": "\ubaa9", + "7814": "\uacb0", + "7815": "\uc900", + "7816": "\ud0dc", + "7817": "\u2581\ud558\ub098", + "7818": "\uc678", + "7819": "\uc528", + "7820": "\uc796\uc544", + "7821": "\uc784", + "7822": "\uce60", + "7823": "\uc5f4", + "7824": "\ubcc0", + "7825": "\ub41c\ub2e4", + "7826": "\uc608\uc694", + "7827": "\ud0a4", + "7828": "\ubc15", + "7829": "\u2581\uadf8\ub807", + "7830": "\uae4c", + "7831": "\u2581\ub9d0\uc500", + "7832": "\u2581\uc870\uae08", + "7833": "\ud130", + "7834": "\u2581\uc6b0\ub9ac\uac00", + "7835": "\uc57d", + "7836": "\uc778\ub370", + "7837": "\uae34", + "7838": "\ub9ce", + "7839": "\ud558\uae30", + "7840": "\ub4e0", + "7841": "\u2581\uc57d\uac04", + "7842": "\u2581\uc788\uc5c8", + "7843": "\u2581\ub420", + "7844": "\uaca9", + "7845": "\uc6cc", + "7846": "\ub4e4\uc740", + "7847": "\ud558\ub2e4", + "7848": "\u2581\ub2e4\ub978", + "7849": "\uba39", + "7850": "\u2581\uc815\ub9d0", + "7851": "\u2581\uc65c", + "7852": "\uba74\uc11c", + "7853": "\uc220", + "7854": "\ud569", + "7855": "\uc99d", + "7856": "\u2581\uacc4\uc18d", + "7857": "\uce74", + "7858": "\u2581\uacbd\uc6b0", + "7859": "\ud3c9", + "7860": "\ub0d0", + "7861": "\uc774\ub2e4", + "7862": "\ubd24", + "7863": "\ub4e4\uc744", + "7864": "\uc11d", + "7865": "\uac01", + "7866": "\ubcf4\ub2e4", + "7867": "\ubd84\ub4e4", + "7868": "\uadfc", + "7869": "\ub9b0", + "7870": "\ubcfc", + "7871": "\uae09", + "7872": "\uc54c", + "7873": "\uc124", + "7874": "\uc558", + "7875": "\ub418", + "7876": "\ucd08", + "7877": "\uc4f0", + "7878": "\uc74c", + "7879": "\u2581\ub098\uc624", + "7880": "\uc73c", + "7881": "\uc62c", + "7882": "\uc838", + "7883": "\ucc45", + "7884": "\ud655", + "7885": "\uac08", + "7886": "\ub3c8", + "7887": "\u2581\uc788\ub294\ub370", + "7888": "\ubcf5", + "7889": "\uc751", + "7890": "\ub418\uace0", + "7891": "\uc904", + "7892": "\u2581\ub9ce\uc740", + "7893": "\ub839", + "7894": "\ud5a5", + "7895": "\uac70\uc8e0", + "7896": "\u2581\ubcf4\uba74", + "7897": "\ub8e8", + "7898": "\uc5b8", + "7899": "\uc808", + "7900": "\uc5d0\uc11c\ub294", + "7901": "\ud2f0", + "7902": "\u2581\ud55c\uad6d", + "7903": "\ud1a0", + "7904": "\ud55c\ud14c", + "7905": "\u2581\ub9de\uc544", + "7906": "\uc5d0\uac8c", + "7907": "\u2581\uadf8\ub7f0\ub370", + "7908": "\ub2e4\ub294", + "7909": "\u2581\uc0c1\ud669", + "7910": "\u2581\uadf8\ub7ec", + "7911": "\uc774\ub77c\uace0", + "7912": "\ub8cc", + "7913": "\uc774\ub098", + "7914": "\u2581\uc5ec\uae30", + "7915": "\ubc14", + "7916": "\u2581\uc544\uc774", + "7917": "\uc560", + "7918": "\ub300\ub85c", + "7919": "\u2581\uac70\uae30", + "7920": "\u2581\uc88b\uc544", + "7921": "\ucc38", + "7922": "\uace0\uc694", + "7923": "\uadf8", + "7924": "\uba74\uc740", + "7925": "\uc0bc", + "7926": "\uad6c\uc694", + "7927": "\ub984", + "7928": "\ucc98\ub7fc", + "7929": "\ub2f4", + "7930": "\u2581\uc788\uc744", + "7931": "\u2581\uc88b\uc740", + "7932": "\ud488", + "7933": "\uc800", + "7934": "\uc2b9", + "7935": "\u2581\ubbf8\uad6d", + "7936": "\u2581\uac19\uc560", + "7937": "\ud558\uc2dc", + "7938": "\ubcd1", + "7939": "\ud658", + "7940": "\u2581\ud544\uc694", + "7941": "\u2581\uc0ac\ub78c\ub4e4", + "7942": "\uc9c0\ub294", + "7943": "\uc545", + "7944": "\u2581\ud55c\ubc88", + "7945": "\u2581\uc778\uc81c", + "7946": "\ub860", + "7947": "\uc21c", + "7948": "\uc628", + "7949": "\ucc98", + "7950": "\uc84c", + "7951": "\uc168", + "7952": "\u2581\uadf8\ub54c", + "7953": "\ub450", + "7954": "\uac14", + "7955": "\uc190", + "7956": "\uc6b8", + "7957": "\ubc8c", + "7958": "\ucf54", + "7959": "\u2581\uadf8\ub2c8\uae4c", + "7960": "\ucde8", + "7961": "\u2581\uc788\uc5b4", + "7962": "\uc804\uc5d0", + "7963": "\u2581\uac83\uc774", + "7964": "\ub204", + "7965": "\u2581\uc0ac\ub78c\ub4e4\uc774", + "7966": "\u2581\uc790\uae30", + "7967": "\ub838", + "7968": "\u2581\uc544\ub2c8\ub77c", + "7969": "\uc608", + "7970": "\ud22c", + "7971": "\uc2b5\ub2c8\uae4c", + "7972": "\u2581\uc77c\ub2e8", + "7973": "\u2581\uc5c6\ub294", + "7974": "\ud070", + "7975": "\u2581\uc0dd\uac01\uc744", + "7976": "\ub978", + "7977": "\uc0c8", + "7978": "\uae38", + "7979": "\ud0dd", + "7980": "\ud50c", + "7981": "\uac81", + "7982": "\u2581\uc694\uc998", + "7983": "\u2581\uadf8\ub7fc", + "7984": "\uba38", + "7985": "\u2581\ubb54\uac00", + "7986": "\uc2f6", + "7987": "\uac80", + "7988": "\uba54", + "7989": "\uac70\ub098", + "7990": "\ub780", + "7991": "\ub3c5", + "7992": "\uba87", + "7993": "\uc654", + "7994": "\ubd81", + "7995": "\ud588\uc2b5\ub2c8\ub2e4", + "7996": "\u2581\uadf8\ub798", + "7997": "\uacf3", + "7998": "\uc871", + "7999": "\uaca8", + "8000": "\ube60", + "8001": "\u2581\ubcf4\ub2c8\uae4c", + "8002": "\u2581\uc815\ubd80", + "8003": "\ub9dd", + "8004": "\uc788\ub294", + "8005": "\ub098\uc694", + "8006": "\uc6c0", + "8007": "\u2581\uc0ac\ub78c\uc774", + "8008": "\u2581\uc598\uae30\ub97c", + "8009": "\ub193", + "8010": "\ud2b9", + "8011": "\u2581\uac83\ub3c4", + "8012": "\u2581\uc774\uc57c\uae30", + "8013": "\uad11", + "8014": "\uba70", + "8015": "\uac15", + "8016": "\ud558\uba74\uc11c", + "8017": "\ub85d", + "8018": "\ub9d0", + "8019": "\ube0c", + "8020": "\u2581\uad00\ub828", + "8021": "\u2581\uc2dc\uc791", + "8022": "\uae00", + "8023": "\ud588\ub358", + "8024": "\u2581\uacbd\uc81c", + "8025": "\uc644", + "8026": "\uaca0\ub2e4", + "8027": "\uaca0\uc2b5\ub2c8\ub2e4", + "8028": "\u2581\uce5c\uad6c", + "8029": "\u2581\uad6d\ubbfc", + "8030": "\u2581\uadf8\uac83", + "8031": "\ubd10", + "8032": "\ud65c", + "8033": "\ub192", + "8034": "\u2581\uc788\uc5b4\uc694", + "8035": "\uc774\ub77c\ub294", + "8036": "\u2581\ub2e4\uc2dc", + "8037": "\u2581\uc5ec\ub7ec", + "8038": "\ucd1d", + "8039": "\uc7a1", + "8040": "\ub2a5", + "8041": "\ud56d", + "8042": "\ub958", + "8043": "\uaddc", + "8044": "\ub530", + "8045": "\ucc44", + "8046": "\uc874", + "8047": "\ub9bd", + "8048": "\uce5c", + "8049": "\uc7c1", + "8050": "\ub298", + "8051": "\ubc94", + "8052": "\ubcc4", + "8053": "\uce21", + "8054": "\ud14c", + "8055": "\ucca0", + "8056": "\ub531", + "8057": "\uc5d4", + "8058": "\uc5b5", + "8059": "\ub05d", + "8060": "\ub77d", + "8061": "\ub9b4", + "8062": "\ucc3d", + "8063": "\uadf9", + "8064": "\uc918", + "8065": "\ud611", + "8066": "\ud328", + "8067": "\ucee4", + "8068": "\uc55e", + "8069": "\ub3cc", + "8070": "\ucda9", + "8071": "\uc0c9", + "8072": "\ub208", + "8073": "\uc154", + "8074": "\uc775", + "8075": "\uc811", + "8076": "\uc1a1", + "8077": "\ud798", + "8078": "\ub0ac", + "8079": "\uafb8", + "8080": "\uaed8", + "8081": "\uc2f8", + "8082": "\ub420", + "8083": "\ub2f5", + "8084": "\ud5d8", + "8085": "\uce68", + "8086": "\uc728", + "8087": "\ub7fd", + "8088": "\ud398", + "8089": "\uac78", + "8090": "\ub4a4", + "8091": "\ud76c", + "8092": "\ucf1c", + "8093": "\ubab0", + "8094": "\ud600", + "8095": "\uc368", + "8096": "\ud300", + "8097": "\uc158", + "8098": "\ucd95", + "8099": "\ub7c9", + "8100": "\ud63c", + "8101": "\uccd0", + "8102": "\ub4dd", + "8103": "\ubc00", + "8104": "\ubca0", + "8105": "\ud669", + "8106": "\ud3ed", + "8107": "\ub140", + "8108": "\uc27d", + "8109": "\ud2c0", + "8110": "\ud6a8", + "8111": "\uace8", + "8112": "\ubd88", + "8113": "\ub78c", + "8114": "\ub840", + "8115": "\ub290", + "8116": "\uc988", + "8117": "\ube14", + "8118": "\ub180", + "8119": "\ud568", + "8120": "\ub5a8", + "8121": "\ud154", + "8122": "\ud5c8", + "8123": "\ub17c", + "8124": "\uad81", + "8125": "\ub9bc", + "8126": "\ud0c4", + "8127": "\ub7f4", + "8128": "\uacac", + "8129": "\uc529", + "8130": "\ub7f0", + "8131": "\ub5a0", + "8132": "\ub118", + "8133": "\ud480", + "8134": "\uc8fd", + "8135": "\uc8c4", + "8136": "\uc2b5", + "8137": "\ud575", + "8138": "\uadc0", + "8139": "\uc61b", + "8140": "\uc724", + "8141": "\ud64d", + "8142": "\ub07c", + "8143": "\ub18d", + "8144": "\ub828", + "8145": "\uac16", + "8146": "\uccab", + "8147": "\ub458", + "8148": "\ud639", + "8149": "\uc5e0", + "8150": "\uc9d5", + "8151": "\ud6c8", + "8152": "\ud0c8", + "8153": "\ucf00", + "8154": "\uaf2d", + "8155": "\ub960", + "8156": "\ud601", + "8157": "\uc12f", + "8158": "\ucc29", + "8159": "\ud734", + "8160": "\ubd09", + "8161": "\ud074", + "8162": "\uc5fc", + "8163": "\ubc1d", + "8164": "\ud48d", + "8165": "\uc2ac", + "8166": "\ubd99", + "8167": "\uace1", + "8168": "\uc5bc", + "8169": "\ucc0d", + "8170": "\ubfd0", + "8171": "\uc9dc", + "8172": "\ubab8", + "8173": "\uc7a0", + "8174": "\ub123", + "8175": "\ub79c", + "8176": "\uc26c", + "8177": "\ub4ef", + "8178": "\ub110", + "8179": "\ub790", + "8180": "\ubc0f", + "8181": "\uc655", + "8182": "\ud37c", + "8183": "\uc555", + "8184": "\ub9db", + "8185": "\ub0ae", + "8186": "\ud0c1", + "8187": "\uc561", + "8188": "\uc92c", + "8189": "\ucc2c", + "8190": "\uc9f8", + "8191": "\ud544", + "8192": "\uc288", + "8193": "\uc13c", + "8194": "\ub099", + "8195": "\ud3d0", + "8196": "\ub73b", + "8197": "\uac11", + "8198": "\uc695", + "8199": "\ud3f0", + "8200": "\uc77d", + "8201": "\uc5c6", + "8202": "\ud2bc", + "8203": "\uc6c3", + "8204": "\ub9e8", + "8205": "\uce35", + "8206": "\ucc3e", + "8207": "\uc219", + "8208": "\ud1f4", + "8209": "\uad74", + "8210": "\uade0", + "8211": "\uc6e0", + "8212": "\ud765", + "8213": "\ub150", + "8214": "\ub4e3", + "8215": "\uc637", + "8216": "\ub0c8", + "8217": "\ud754", + "8218": "\ud61c", + "8219": "\uc554", + "8220": "\uac1d", + "8221": "\uc5d8", + "8222": "\ub274", + "8223": "\uae68", + "8224": "\ubb58", + "8225": "\ub04c", + "8226": "\ub6f0", + "8227": "\uc2eb", + "8228": "\ub054", + "8229": "\uce90", + "8230": "\ub2d0", + "8231": "\uacbc", + "8232": "\uc559", + "8233": "\ud750", + "8234": "\ub7b5", + "8235": "\ubcbd", + "8236": "\uc598", + "8237": "\ub9c9", + "8238": "\uaebc", + "8239": "\uc9d3", + "8240": "\uc990", + "8241": "\ucc30", + "8242": "\uba3c", + "8243": "\uc4f8", + "8244": "\ub355", + "8245": "\uae50", + "8246": "\ubc25", + "8247": "\uc5c7", + "8248": "\ub728", + "8249": "\ucdb0", + "8250": "\ubd05", + "8251": "\ubbff", + "8252": "\ub807", + "8253": "\uce59", + "8254": "\ub0b8", + "8255": "\ubc24", + "8256": "\uc9dd", + "8257": "\uc5bb", + "8258": "\uc794", + "8259": "\uc058", + "8260": "\ud0ac", + "8261": "\ud138", + "8262": "\uc6b1", + "8263": "\uac12", + "8264": "\ub9c1", + "8265": "\uc88c", + "8266": "\ube4c", + "8267": "\ucee8", + "8268": "\ub86d", + "8269": "\ud5cc", + "8270": "\uac54", + "8271": "\ucfe0", + "8272": "\ud305", + "8273": "\ube7c", + "8274": "\uc228", + "8275": "\uce20", + "8276": "\uc5c4", + "8277": "\ud614", + "8278": "\ub545", + "8279": "\uafc8", + "8280": "\ub2e5", + "8281": "\ub0bc", + "8282": "\uacf1", + "8283": "\uc0b6", + "8284": "\ub9e5", + "8285": "\uc98c", + "8286": "\uc606", + "8287": "\uc54a", + "8288": "\uad34", + "8289": "\ube48", + "8290": "\ud134", + "8291": "\uc625", + "8292": "\uc6e8", + "8293": "\ud0a8", + "8294": "\ub9ad", + "8295": "\ud32c", + "8296": "\ud5e4", + "8297": "\ud718", + "8298": "\uc12d", + "8299": "\uba40", + "8300": "\uce6d", + "8301": "\uc05c", + "8302": "\ub0a9", + "8303": "\ub1cc", + "8304": "\ucc99", + "8305": "\uad73", + "8306": "\uc37c", + "8307": "\ub4ed", + "8308": "\uaef4", + "8309": "\ub9e1", + "8310": "\uc1fc", + "8311": "\uace4", + "8312": "\ube68", + "8313": "\ucce4", + "8314": "\uc568", + "8315": "\ucbe4", + "8316": "\ub313", + "8317": "\ub179", + "8318": "\uc989", + "8319": "\ucf58", + "8320": "\ub2a6", + "8321": "\ube5b", + "8322": "\ud608", + "8323": "\ubf51", + "8324": "\uae4a", + "8325": "\ub538", + "8326": "\uc4f4", + "8327": "\uaf43", + "8328": "\ud39c", + "8329": "\ub04a", + "8330": "\ud3b4", + "8331": "\ub78d", + "8332": "\ud648", + "8333": "\ub0c9", + "8334": "\ud508", + "8335": "\ub220", + "8336": "\ud0d5", + "8337": "\ubc11", + "8338": "\uae54", + "8339": "\ub8b0", + "8340": "\ucc0c", + "8341": "\ub800", + "8342": "\ud551", + "8343": "\ud758", + "8344": "\uce7c", + "8345": "\ucb49", + "8346": "\uc2f1", + "8347": "\uc2b7", + "8348": "\ub35c", + "8349": "\uafd4", + "8350": "\ubb54", + "8351": "\ub799", + "8352": "\uc0ad", + "8353": "\ud478", + "8354": "\ub86f", + "8355": "\ub529", + "8356": "\ucf69", + "8357": "\ud68d", + "8358": "\ubd07", + "8359": "\ud150", + "8360": "\ub8f9", + "8361": "\ub9f9", + "8362": "\ud31d", + "8363": "\uc2fc", + "8364": "\uc878", + "8365": "\ubca4", + "8366": "\ub044", + "8367": "\ub80c", + "8368": "\ub7ab", + "8369": "\ubba4", + "8370": "\ud610", + "8371": "\ud0b9", + "8372": "\uc313", + "8373": "\uc635", + "8374": "\ud78c", + "8375": "\ucf13", + "8376": "\ud380", + "8377": "\ud3f4", + "8378": "\uc820", + "8379": "\ubc97", + "8380": "\ucd09", + "8381": "\uace7", + "8382": "\uacaa", + "8383": "\ub113", + "8384": "\uc783", + "8385": "\ubca8", + "8386": "\ub1a8", + "8387": "\ubd04", + "8388": "\ubb3b", + "8389": "\ub784", + "8390": "\uba58", + "8391": "\uceec", + "8392": "\ud761", + "8393": "\ucd98", + "8394": "\uba4b", + "8395": "\ucd0c", + "8396": "\ub378", + "8397": "\uc12c", + "8398": "\ucc59", + "8399": "\ucf30", + "8400": "\uae5d", + "8401": "\ud578", + "8402": "\ud649", + "8403": "\ucd78", + "8404": "\ucf5c", + "8405": "\uaf64", + "8406": "\uc9d0", + "8407": "\uc22b", + "8408": "\uc998", + "8409": "\ub454", + "8410": "\ucef5", + "8411": "\uc194", + "8412": "\uae0d", + "8413": "\ub7ac", + "8414": "\ud3fc", + "8415": "\ub141", + "8416": "\ub5bb", + "8417": "\ud329", + "8418": "\ub5a1", + "8419": "\uaf2c", + "8420": "\ud799", + "8421": "\uc0c0", + "8422": "\uca54", + "8423": "\uaf08", + "8424": "\ud0d0", + "8425": "\ucea0", + "8426": "\uc2b4", + "8427": "\ubfcc", + "8428": "\uc9da", + "8429": "\uc1c4", + "8430": "\ubb18", + "8431": "\ub9bf", + "8432": "\uc564", + "8433": "\ud640", + "8434": "\uc14b", + "8435": "\ud1a1", + "8436": "\uc130", + "8437": "\uc78a", + "8438": "\ub465", + "8439": "\ub2eb", + "8440": "\ucda4", + "8441": "\ube59", + "8442": "\ubaac", + "8443": "\uaf3c", + "8444": "\ub7a8", + "8445": "\ube75", + "8446": "\uc2a8", + "8447": "\ub7fc", + "8448": "\ud3bc", + "8449": "\uc140", + "8450": "\ub864", + "8451": "\ub82c", + "8452": "\ud0d1", + "8453": "\uc384", + "8454": "\ub137", + "8455": "\ub4b7", + "8456": "\uc5d1", + "8457": "\ub7ed", + "8458": "\ucef4", + "8459": "\ub611", + "8460": "\ub2dd", + "8461": "\ud5ec", + "8462": "\ucca8", + "8463": "\ub904", + "8464": "\ub51c", + "8465": "\uae5c", + "8466": "\ud2f1", + "8467": "\ub744", + "8468": "\uc0e4", + "8469": "\ube45", + "8470": "\ub834", + "8471": "\uc81d", + "8472": "\uca4c", + "8473": "\uc300", + "8474": "\ubc0d", + "8475": "\ud751", + "8476": "\ub194", + "8477": "\uac77", + "8478": "\uba78", + "8479": "\ud1a4", + "8480": "\uc5fd", + "8481": "\ud050", + "8482": "\ucd2c", + "8483": "\ucb64", + "8484": "\ud29c", + "8485": "\ud790", + "8486": "\uc88b", + "8487": "\ub959", + "8488": "\ube5a", + "8489": "\ucf8c", + "8490": "\uafc0", + "8491": "\ud540", + "8492": "\ub871", + "8493": "\ub2cc", + "8494": "\ub5bc", + "8495": "\uba48", + "8496": "\ub550", + "8497": "\ud034", + "8498": "\uae40", + "8499": "\ub985", + "8500": "\ub048", + "8501": "\ub20c", + "8502": "\uc30d", + "8503": "\ubb35", + "8504": "\ub534", + "8505": "\uc789", + "8506": "\ubbc0", + "8507": "\ub369", + "8508": "\uc6c5", + "8509": "\uc5c9", + "8510": "\ub0ab", + "8511": "\ud280", + "8512": "\uc67c", + "8513": "\ub0c4", + "8514": "\uaf3d", + "8515": "\uacb8", + "8516": "\ubc16", + "8517": "\ub10c", + "8518": "\uc148", + "8519": "\ub0e5", + "8520": "\uafbc", + "8521": "\ub188", + "8522": "\uba4d", + "8523": "\ubabd", + "8524": "\ubd95", + "8525": "\uacb9", + "8526": "\ubc34", + "8527": "\uc950", + "8528": "\ub080", + "8529": "\ubb50", + "8530": "\ub8f0", + "8531": "\ub809", + "8532": "\ub561", + "8533": "\ub69c", + "8534": "\ub8f8", + "8535": "\ubcbc", + "8536": "\ub2c9", + "8537": "\ub9d8", + "8538": "\ud15c", + "8539": "\ub2f7", + "8540": "\ub518", + "8541": "\ub0ad", + "8542": "\uc11e", + "8543": "\uc6ec", + "8544": "\uce78", + "8545": "\uc787", + "8546": "\uc881", + "8547": "\ub989", + "8548": "\uc571", + "8549": "\ub9fa", + "8550": "\ud6fc", + "8551": "\ubed4", + "8552": "\uac24", + "8553": "\ub010", + "8554": "\ud6cc", + "8555": "\ub084", + "8556": "\ub36e", + "8557": "\uc3d8", + "8558": "\ub057", + "8559": "\ubf08", + "8560": "\ucabc", + "8561": "\uc798", + "8562": "\ubb36", + "8563": "\ub154", + "8564": "\ud53d", + "8565": "\ucca9", + "8566": "\ucef8", + "8567": "\ub2ed", + "8568": "\uc735", + "8569": "\uc1e0", + "8570": "\ud2f4", + "8571": "\ub374", + "8572": "\uc90d", + "8573": "\ud14d", + "8574": "\ubdd4", + "8575": "\uc6f9", + "8576": "\uc0f5", + "8577": "\ub2ff", + "8578": "\ubdf0", + "8579": "\ub367", + "8580": "\ubbf9", + "8581": "\ub364", + "8582": "\ub42c", + "8583": "\uc796", + "8584": "\uacfd", + "8585": "\uad04", + "8586": "\uad1c", + "8587": "\ub8ec", + "8588": "\ub987", + "8589": "\ubd59", + "8590": "\ub760", + "8591": "\ub8e1", + "8592": "\ub155", + "8593": "\ub4ec", + "8594": "\ubabb", + "8595": "\uaf34", + "8596": "\ub69d", + "8597": "\ub801", + "8598": "\ucc2e", + "8599": "\ub610", + "8600": "\ub96d", + "8601": "\uc15c", + "8602": "\ud31f", + "8603": "\ud33d", + "8604": "\ubed0", + "8605": "\uc2f9", + "8606": "\ud0d3", + "8607": "\ub451", + "8608": "\ud1b1", + "8609": "\ucf64", + "8610": "\ub6b1", + "8611": "\uae0b", + "8612": "\uba64", + "8613": "\ub9d9", + "8614": "\uc824", + "8615": "\uc708", + "8616": "\uac90", + "8617": "\ud587", + "8618": "\ube57", + "8619": "\uacf0", + "8620": "\uae65", + "8621": "\uba5c", + "8622": "\ub0af", + "8623": "\uc639", + "8624": "\ucfe8", + "8625": "\ubcf6", + "8626": "\uc232", + "8627": "\ub365", + "8628": "\ubc1f", + "8629": "\ud2f8", + "8630": "\ud2c8", + "8631": "\ub52a", + "8632": "\uae4e", + "8633": "\uac89", + "8634": "\uce69", + "8635": "\ub480", + "8636": "\uc717", + "8637": "\uc090", + "8638": "\uc575", + "8639": "\ub125", + "8640": "\uafe8", + "8641": "\ubb49", + "8642": "\uc22d", + "8643": "\ud321", + "8644": "\ubc45", + "8645": "\ud56b", + "8646": "\ud749", + "8647": "\uce94", + "8648": "\ub46c", + "8649": "\ub540", + "8650": "\uc53b", + "8651": "\ud6a1", + "8652": "\ucfc4", + "8653": "\ubc2d", + "8654": "\uc369", + "8655": "\ub014", + "8656": "\uac31", + "8657": "\ubc38", + "8658": "\ud514", + "8659": "\uc880", + "8660": "\ud1a8", + "8661": "\uc580", + "8662": "\ube61", + "8663": "\uaecf", + "8664": "\ub258", + "8665": "\ub2ee", + "8666": "\ub1e8", + "8667": "\ud131", + "8668": "\ub304", + "8669": "\ud760", + "8670": "\ub7ad", + "8671": "\uc78e", + "8672": "\ub835", + "8673": "\ube8f", + "8674": "\ucad9", + "8675": "\ub0c5", + "8676": "\uc557", + "8677": "\uca4d", + "8678": "\ud770", + "8679": "\uad49", + "8680": "\ud584", + "8681": "\uada4", + "8682": "\uae43", + "8683": "\uc90c", + "8684": "\uc0d8", + "8685": "\ub55c", + "8686": "\uc0cc", + "8687": "\ucdc4", + "8688": "\ud0f1", + "8689": "\ud0a5", + "8690": "\ubc85", + "8691": "\uc3e0", + "8692": "\uc74d", + "8693": "\ubccd", + "8694": "\ud230", + "8695": "\uca0c", + "8696": "\ube10", + "8697": "\ub3d5", + "8698": "\ud140", + "8699": "\uc9e4", + "8700": "\ucef7", + "8701": "\uc0bd", + "8702": "\uaf42", + "8703": "\ub837", + "8704": "\uc139", + "8705": "\ud54f", + "8706": "\ud5e8", + "8707": "\uad7d", + "8708": "\ub8e9", + "8709": "\ucda5", + "8710": "\ub2e6", + "8711": "\ub7a9", + "8712": "\ud47c", + "8713": "\uc660", + "8714": "\ub3cb", + "8715": "\ud30d", + "8716": "\ucc14", + "8717": "\ub72c", + "8718": "\ud5f7", + "8719": "\ubaab", + "8720": "\ud399", + "8721": "\ubd93", + "8722": "\ud0e0", + "8723": "\ub72f", + "8724": "\uc149", + "8725": "\uad90", + "8726": "\ub625", + "8727": "\uae41", + "8728": "\ud0d4", + "8729": "\uba55", + "8730": "\uc816", + "8731": "\ub4c0", + "8732": "\ud4e8", + "8733": "\ub9f5", + "8734": "\uc587", + "8735": "\uc068", + "8736": "\uaecd", + "8737": "\uac13", + "8738": "\ub109", + "8739": "\uc9f1", + "8740": "\uce6b", + "8741": "\ud759", + "8742": "\uaf49", + "8743": "\uc501", + "8744": "\ub428", + "8745": "\ud3a0", + "8746": "\ubf40", + "8747": "\uac07", + "8748": "\uc465", + "8749": "\ud5d0", + "8750": "\ub299", + "8751": "\uc500", + "8752": "\ubc40", + "8753": "\ub618", + "8754": "\uc370", + "8755": "\ud23c", + "8756": "\ub95c", + "8757": "\ub86c", + "8758": "\ubed7", + "8759": "\ud301", + "8760": "\ud48b", + "8761": "\uc274", + "8762": "\ucea1", + "8763": "\ub584", + "8764": "\uc0f7", + "8765": "\uc539", + "8766": "\uc7a3", + "8767": "\uc3f4", + "8768": "\ubb47", + "8769": "\uc270", + "8770": "\ub81b", + "8771": "\uc65c", + "8772": "\ud729", + "8773": "\uc36c", + "8774": "\uc5ce", + "8775": "\ud5db", + "8776": "\ubfd4", + "8777": "\uc27c", + "8778": "\uc813", + "8779": "\ub729", + "8780": "\uc719", + "8781": "\uc29b", + "8782": "\uc170", + "8783": "\uc19f", + "8784": "\uc9e0", + "8785": "\ud6d4", + "8786": "\uc6f0", + "8787": "\uc634", + "8788": "\ud384", + "8789": "\uaf41", + "8790": "\ub730", + "8791": "\ubf55", + "8792": "\ub2ac", + "8793": "\ucc1c", + "8794": "\ud391", + "8795": "\ubbac", + "8796": "\uccbc", + "8797": "\ud241", + "8798": "\ub5b4", + "8799": "\ub284", + "8800": "\ub291", + "8801": "\ucf08", + "8802": "\ud0b4", + "8803": "\uc3d9", + "8804": "\uce98", + "8805": "\uad7c", + "8806": "\ud22d", + "8807": "\ub968", + "8808": "\ub6f8", + "8809": "\uc5ff", + "8810": "\uc610", + "8811": "\ubd90", + "8812": "\uc7ad", + "8813": "\ub315", + "8814": "\uafc9", + "8815": "\ucf67", + "8816": "\ud479", + "8817": "\uc70c", + "8818": "\ucffc", + "8819": "\uac9f", + "8820": "\uc060", + "8821": "\uc5ee", + "8822": "\ud69f", + "8823": "\uad7f", + "8824": "\uae61", + "8825": "\ub2d9", + "8826": "\uc2e3", + "8827": "\ucf55", + "8828": "\ubc43", + "8829": "\uc5b9", + "8830": "\uc9ec", + "8831": "\ud234", + "8832": "\ubee5", + "8833": "\ud07c", + "8834": "\uc290", + "8835": "\uc7a6", + "8836": "\ud720", + "8837": "\uc19c", + "8838": "\ud38c", + "8839": "\uca61", + "8840": "\ud5dd", + "8841": "\ud1b0", + "8842": "\uc0d0", + "8843": "\uae01", + "8844": "\ud31c", + "8845": "\ube54", + "8846": "\ub3d7", + "8847": "\uac2f", + "8848": "\ucf04", + "8849": "\ub7ff", + "8850": "\ub301", + "8851": "\ub310", + "8852": "\uc570", + "8853": "\ud0ed", + "8854": "\ube90", + "8855": "\ub308", + "8856": "\ub2db", + "8857": "\ud6c5", + "8858": "\ube7d", + "8859": "\ub12c", + "8860": "\uc9d6", + "8861": "\ub460", + "8862": "\uce84", + "8863": "\ub461", + "8864": "\ucac4", + "8865": "\ub528", + "8866": "\ubca1", + "8867": "\uc7a4", + "8868": "\ud004", + "8869": "\ubfdc", + "8870": "\uac2d", + "8871": "\ub3d4", + "8872": "\ucf70", + "8873": "\uc653", + "8874": "\uc96c", + "8875": "\ub314", + "8876": "\ub5b3", + "8877": "\ub0b1", + "8878": "\ud168", + "8879": "\ubcd5", + "8880": "\ub38c", + "8881": "\ud30e", + "8882": "\uad88", + "8883": "\ud0b5", + "8884": "\uadc4", + "8885": "\ucc10", + "8886": "\ucc1d", + "8887": "\uae4d", + "8888": "\ub7b4", + "8889": "\ud145", + "8890": "\ube80", + "8891": "\ud325", + "8892": "\ubd48", + "8893": "\uba67", + "8894": "\uc52c", + "8895": "\ubc99", + "8896": "\ubcb3", + "8897": "\uc1a5", + "8898": "\uc82f", + "8899": "\ub6f4", + "8900": "\ucb48", + "8901": "\ub810", + "8902": "\uc250", + "8903": "\uaec4", + "8904": "\uc584", + "8905": "\uc5e3", + "8906": "\uc324", + "8907": "\uc3dc", + "8908": "\ucff5", + "8909": "\ud0b7", + "8910": "\uc648", + "8911": "\ub764", + "8912": "\ube74", + "8913": "\ube84", + "8914": "\uafc7", + "8915": "\ub525", + "8916": "\ub544", + "8917": "\ub818", + "8918": "\ud54d", + "8919": "\uc378", + "8920": "\ub5a4", + "8921": "\ub215", + "8922": "\ub9f7", + "8923": "\ucc3c", + "8924": "\uc308", + "8925": "\ub2a0", + "8926": "\uc999", + "8927": "\uaf30", + "8928": "\ub371", + "8929": "\uc20d", + "8930": "\uc5ca", + "8931": "\uc0bf", + "8932": "\uaf80", + "8933": "\ub9e3", + "8934": "\uc7bc", + "8935": "\uac40", + "8936": "\ud018", + "8937": "\uc464", + "8938": "\ubfb0", + "8939": "\uca50", + "8940": "\ubb44", + "8941": "\uade4", + "8942": "\ub797", + "8943": "\uca0b", + "8944": "\ucee5", + "8945": "\uaff0", + "8946": "\uc123", + "8947": "\uc379", + "8948": "\ud3c8", + "8949": "\ud0ec", + "8950": "\ub2aa", + "8951": "\ub738", + "8952": "\uce89", + "8953": "\ubab9", + "8954": "\uc298", + "8955": "\ub975", + "8956": "\uc4f1", + "8957": "\ud6d7", + "8958": "\ub9cf", + "8959": "\ud15d", + "8960": "\ub51b", + "8961": "\ucc39", + "8962": "\ubb8c", + "8963": "\uce04", + "8964": "\ud3ad", + "8965": "\ube64", + "8966": "\ub08c", + "8967": "\ubed8", + "8968": "\uc6c1", + "8969": "\uafb9", + "8970": "\ub205", + "8971": "\uc6cd", + "8972": "\ud4f0", + "8973": "\uca5c", + "8974": "\uc21f", + "8975": "\uac94", + "8976": "\ud038", + "8977": "\ucf65", + "8978": "\ub8fb", + "8979": "\ub515", + "8980": "\ube91", + "8981": "\uc167", + "8982": "\uceeb", + "8983": "\uc388", + "8984": "\ubf18", + "8985": "\ub128", + "8986": "\ucc4c", + "8987": "\ud3ab", + "8988": "\ud390", + "8989": "\ucbd4", + "8990": "\ud5c9", + "8991": "\ud2ac", + "8992": "\ub9f4", + "8993": "\uc58c", + "8994": "\ud143", + "8995": "\uc69c", + "8996": "\ubed1", + "8997": "\uce85", + "8998": "\ubc0b", + "8999": "\uc574", + "9000": "\ud295", + "9001": "\ub214", + "9002": "\uc0f9", + "9003": "\ubc09", + "9004": "\uac71", + "9005": "\uae45", + "9006": "\uc408", + "9007": "\ud3c4", + "9008": "\uc204", + "9009": "\ub4c8", + "9010": "\ubee3", + "9011": "\ubc27", + "9012": "\uac20", + "9013": "\ud401", + "9014": "\uaf5d", + "9015": "\uce30", + "9016": "\ucfe1", + "9017": "\ucfe4", + "9018": "\ucf10", + "9019": "\uaecc", + "9020": "\uce75", + "9021": "\ub6a4", + "9022": "\ucc60", + "9023": "\uadd3", + "9024": "\ub11b", + "9025": "\ud6d1", + "9026": "\ud37d", + "9027": "\uc329", + "9028": "\uae7c", + "9029": "\ucea3", + "9030": "\ubea8", + "9031": "\ud6e8", + "9032": "\ub78f", + "9033": "\uccb8", + "9034": "\uc0ec", + "9035": "\ucb10", + "9036": "\uae6c", + "9037": "\uca84", + "9038": "\uc5cc", + "9039": "\ub07d", + "9040": "\uc9f0", + "9041": "\uac38", + "9042": "\uadc8", + "9043": "\ub385", + "9044": "\ub748", + "9045": "\uac4d", + "9046": "\ub08d", + "9047": "\ucf85", + "9048": "\ud0e4", + "9049": "\ud17c", + "9050": "\ub311", + "9051": "\ucc3b", + "9052": "\uad18", + "9053": "\uc73d", + "9054": "\uc309", + "9055": "\ub527", + "9056": "\ub7a0", + "9057": "\ucc21", + "9058": "\uafcb", + "9059": "\ub00c", + "9060": "\ubb63", + "9061": "\ub524", + "9062": "\ud64b", + "9063": "\ub8fd", + "9064": "\ud338", + "9065": "\ud5e5", + "9066": "\uaebd", + "9067": "\uafcd", + "9068": "\ud058", + "9069": "\ucef9", + "9070": "\uac1b", + "9071": "\uae70", + "9072": "\uc314", + "9073": "\ub775", + "9074": "\ud6e4", + "9075": "\uc53d", + "9076": "\ud141", + "9077": "\ub93c", + "9078": "\uc20f", + "9079": "\uc9ed", + "9080": "\ubbc4", + "9081": "\uc258", + "9082": "\ud33b", + "9083": "\uaed0", + "9084": "\uacf6", + "9085": "\ub400", + "9086": "\uc14c", + "9087": "\uca98", + "9088": "\uc641", + "9089": "\uc90f", + "9090": "\ucdcc", + "9091": "\ud330", + "9092": "\uc9ca", + "9093": "\uc60c", + "9094": "\uc37d", + "9095": "\uc83c", + "9096": "\ud719", + "9097": "\uba71", + "9098": "\uc2a5", + "9099": "\ucf20", + "9100": "\ub217", + "9101": "\uc954", + "9102": "\uc2ef", + "9103": "\ub796", + "9104": "\ubcb5", + "9105": "\uc0db", + "9106": "\uc7c8", + "9107": "\ucb50", + "9108": "\ucda7", + "9109": "\ub5d0", + "9110": "\ucc57", + "9111": "\uc178", + "9112": "\uc6dc", + "9113": "\ubc08", + "9114": "\ud248", + "9115": "\ud57c", + "9116": "\ubf48", + "9117": "\uc5e5", + "9118": "\ubd4c", + "9119": "\uaf48", + "9120": "\uc330", + "9121": "\ubd5c", + "9122": "\uaf3f", + "9123": "\ube73", + "9124": "\uc7b0", + "9125": "\ud2a0", + "9126": "\uc605", + "9127": "\uaec0", + "9128": "\uc37b", + "9129": "\uc538", + "9130": "\ucac0", + "9131": "\ub5c0", + "9132": "\ubfe1", + "9133": "\uc886", + "9134": "\uc42c", + "9135": "\ub761", + "9136": "\uc0e8", + "9137": "\uad82", + "9138": "\uac30", + "9139": "\ube55", + "9140": "\uccc7", + "9141": "\ub554", + "9142": "\uba69", + "9143": "\uba4e", + "9144": "\ubb88", + "9145": "\ub0c7", + "9146": "\ud000", + "9147": "\ud035", + "9148": "\ub4d0", + "9149": "\ud2bf", + "9150": "\uc573", + "9151": "\ud2a4", + "9152": "\ub3db", + "9153": "\uc607", + "9154": "\ud6e0", + "9155": "\ub5b5", + "9156": "\ubcd0", + "9157": "\uae7d", + "9158": "\uad9c", + "9159": "\uc211", + "9160": "\ubbc8", + "9161": "\ubd40", + "9162": "\ucf24", + "9163": "\ub289", + "9164": "\ucc48", + "9165": "\uc22f", + "9166": "\ubc28", + "9167": "\ucad1", + "9168": "\ub380", + "9169": "\ud5f9", + "9170": "\ub819", + "9171": "\ub138", + "9172": "\ub15c", + "9173": "\uc29d", + "9174": "\uc3ed", + "9175": "\ud54c", + "9176": "\uc58f", + "9177": "\ub9f8", + "9178": "\uc7bd", + "9179": "\ubf41", + "9180": "\uc468", + "9181": "\uc698", + "9182": "\ub119", + "9183": "\ub9ec", + "9184": "\ud188", + "9185": "\uba84", + "9186": "\uad38", + "9187": "\ubafc", + "9188": "\ucad2", + "9189": "\ub158", + "9190": "\uc958", + "9191": "\uac84", + "9192": "\uae60", + "9193": "\ubeb4", + "9194": "\ub135", + "9195": "\ubfcd", + "9196": "\ub020", + "9197": "\ud3a9", + "9198": "\uc174", + "9199": "\ucc58", + "9200": "\ub189", + "9201": "\ucd10", + "9202": "\uc5e1", + "9203": "\ub381", + "9204": "\uc0a5", + "9205": "\ucf78", + "9206": "\uc8e4", + "9207": "\ucf71", + "9208": "\ub74c", + "9209": "\ubccf", + "9210": "\uac2c", + "9211": "\ub0b5", + "9212": "\uc6a4", + "9213": "\ucc55", + "9214": "\uae7b", + "9215": "\uca30", + "9216": "\ub1fd", + "9217": "\uc38c", + "9218": "\ube8c", + "9219": "\uac85", + "9220": "\uc705", + "9221": "\uce87", + "9222": "\uc7b4", + "9223": "\uceac", + "9224": "\uc714", + "9225": "\ub768", + "9226": "\ub11c", + "9227": "\ubb4d", + "9228": "\ubd87", + "9229": "\ucea5", + "9230": "\ub383", + "9231": "\uc7c0", + "9232": "\ub700", + "9233": "\ubdf4", + "9234": "\uc58d", + "9235": "\ub404", + "9236": "\ud03c", + "9237": "\ud144", + "9238": "\ubdf8", + "9239": "\ub844", + "9240": "\uc82d", + "9241": "\uc730", + "9242": "\ud6d9", + "9243": "\ubd2c", + "9244": "\ud667", + "9245": "\ucd28", + "9246": "\uae31", + "9247": "\ub5b0", + "9248": "\ubb45", + "9249": "\ud5c0", + "9250": "\uafce", + "9251": "\uba49", + "9252": "\ucd25", + "9253": "\ucea4", + "9254": "\ud590", + "9255": "\uc6e1", + "9256": "\uccb5", + "9257": "\ud38d", + "9258": "\uc530", + "9259": "\uc200", + "9260": "\uce24", + "9261": "\uc229", + "9262": "\uc19d", + "9263": "\uc398", + "9264": "\ud789", + "9265": "\ubeec", + "9266": "\ud73c", + "9267": "\ub0d4", + "9268": "\uc0dc", + "9269": "\ub134", + "9270": "\uc094", + "9271": "\ud5f8", + "9272": "\uac9c", + "9273": "\uc234", + "9274": "\uc82c", + "9275": "\uacc8", + "9276": "\ub11d", + "9277": "\ub588", + "9278": "\uc894", + "9279": "\ud5f4", + "9280": "\uc61c", + "9281": "\uc974", + "9282": "\uc9fc", + "9283": "\uac17", + "9284": "\ub599", + "9285": "\ub9df", + "9286": "\uba53", + "9287": "\uca5d", + "9288": "\uc74f", + "9289": "\ud339", + "9290": "\uc289", + "9291": "\uaf10", + "9292": "\uc0fe", + "9293": "\uc3bc", + "9294": "\ud3ff", + "9295": "\ud489", + "9296": "\uacd8", + "9297": "\uc479", + "9298": "\uac8b", + "9299": "\ub8d4", + "9300": "\ud690", + "9301": "\ubc0e", + "9302": "\ud71c", + "9303": "\ub0e0", + "9304": "\ud5d9", + "9305": "\uaea0", + "9306": "\ubf09", + "9307": "\uc883", + "9308": "\uc0e5", + "9309": "\ub4f8", + "9310": "\ub81d", + "9311": "\ubbd0", + "9312": "\uadff", + "9313": "\ub0d8", + "9314": "\ub40f", + "9315": "\uc6e9", + "9316": "\uca08", + "9317": "\ucb58", + "9318": "\ub463", + "9319": "\ub3e0", + "9320": "\ud06d", + "9321": "\uc0d9", + "9322": "\ud56c", + "9323": "\uc53c", + "9324": "\uc619", + "9325": "\uc394", + "9326": "\uca09", + "9327": "\ub5f4", + "9328": "\ub9dc", + "9329": "\uc14d", + "9330": "\ud3a8", + "9331": "\uc9f9", + "9332": "\ub614", + "9333": "\uacbb", + "9334": "\uc8c8", + "9335": "\ub664", + "9336": "\ucc1f", + "9337": "\uaedc", + "9338": "\ud23d", + "9339": "\uae5f", + "9340": "\uc84d", + "9341": "\ub878", + "9342": "\ud2c9", + "9343": "\ubc0c", + "9344": "\uad54", + "9345": "\uaf07", + "9346": "\ub543", + "9347": "\ub81c", + "9348": "\ub877", + "9349": "\ub879", + "9350": "\uc315", + "9351": "\ucb2c", + "9352": "\uc651", + "9353": "\ud79d", + "9354": "\ud460", + "9355": "\uae37", + "9356": "\uba04", + "9357": "\uae84", + "9358": "\uc2f0", + "9359": "\uc6df", + "9360": "\ud763", + "9361": "\ubba8", + "9362": "\ubfb1", + "9363": "\uc248", + "9364": "\uc814", + "9365": "\ud081", + "9366": "\uc345", + "9367": "\uc0a3", + "9368": "\uacef", + "9369": "\uc0b5", + "9370": "\ub139", + "9371": "\ucb14", + "9372": "\uae79", + "9373": "\uaf4c", + "9374": "\ud1b3", + "9375": "\ud3c5", + "9376": "\ucff1", + "9377": "\uc8d7", + "9378": "\uce61", + "9379": "\ucacd", + "9380": "\ubca7", + "9381": "\ub620", + "9382": "\ub594", + "9383": "\ud207", + "9384": "\uc79b", + "9385": "\uc098", + "9386": "\uc448", + "9387": "\ubb00", + "9388": "\uc18e", + "9389": "\ub5cd", + "9390": "\uaf79", + "9391": "\uc62f", + "9392": "\ub2a1", + "9393": "\ubd91", + "9394": "\uad1e", + "9395": "\uac02", + "9396": "\ube7b", + "9397": "\uc88d", + "9398": "\uc36a", + "9399": "\uc318", + "9400": "\ucc3f", + "9401": "\uacd7", + "9402": "\ud711", + "9403": "\ucc27", + "9404": "\ub754", + "9405": "\ub5ab", + "9406": "\uc80b", + "9407": "\uc62d", + "9408": "\ud613", + "9409": "\uc27f", + "9410": "\uc6f8", + "9411": "\ud25c", + "9412": "\ud0c9", + "9413": "\ubb90", + "9414": "\ub5cf", + "9415": "\ucad8", + "9416": "\uc54e", + "9417": "\ub2fb", + "9418": "\uca44", + "9419": "\ub701", + "9420": "\uc59c", + "9421": "\uc1f3", + "9422": "\ub055", + "9423": "\ud651", + "9424": "\ud0ef", + "9425": "\ucbe7", + "9426": "\ub269", + "9427": "\ud284", + "9428": "\ud744", + "9429": "\ub260", + "9430": "\uc737", + "9431": "\ubb3d", + "9432": "\ud0f0", + "9433": "\uc091", + "9434": "\uac58", + "9435": "\ud585", + "9436": "\ube8d", + "9437": "\uacea", + "9438": "\ud683", + "9439": "\ud2d4", + "9440": "\uc9e2", + "9441": "\ubc9b", + "9442": "\ubfc5", + "9443": "\uc5f7", + "9444": "\ub091", + "9445": "\uc100", + "9446": "\uac09", + "9447": "\uafe9", + "9448": "\uc733", + "9449": "\uc251", + "9450": "\ud2cb", + "9451": "\uc74a", + "9452": "\ub4b9", + "9453": "\uad2d", + "9454": "\ubf1b", + "9455": "\ub739", + "9456": "\uc887", + "9457": "\ub01c", + "9458": "\ube70", + "9459": "\ub3a0", + "9460": "\uc7bf", + "9461": "\ub10b", + "9462": "\ud288", + "9463": "\ub4e6", + "9464": "\ud565", + "9465": "\ub755", + "9466": "\uc2ad", + "9467": "\uc0c5", + "9468": "\uc410", + "9469": "\ub059", + "9470": "\ud515", + "9471": "\uc231", + "9472": "\uca0d", + "9473": "\ub2f3", + "9474": "\ub36b", + "9475": "\ubd50", + "9476": "\uc069", + "9477": "\ub01d", + "9478": "\uad0c", + "9479": "\uc597", + "9480": "\ucb59", + "9481": "\ubf50", + "9482": "\ubee4", + "9483": "\uc595", + "9484": "\uc30c", + "9485": "\ub5c4", + "9486": "\ubc9a", + "9487": "\uc0f4", + "9488": "\ubc49", + "9489": "\ucacc", + "9490": "\uc9d9", + "9491": "\uad76", + "9492": "\ud769", + "9493": "\ub25c", + "9494": "\ucd18", + "9495": "\ub0a1", + "9496": "\uc50c", + "9497": "\ub234", + "9498": "\ucc22", + "9499": "\ud320", + "9500": "\uaed1", + "9501": "\uc5bd", + "9502": "\uad75", + "9503": "\ud07d", + "9504": "\uc553", + "9505": "\ub918", + "9506": "\uacc1", + "9507": "\uc7e4", + "9508": "\ubd89", + "9509": "\ub053", + "9510": "\ub9d1", + "9511": "\ub98e", + "9512": "\ub09a", + "9513": "\ucd1b", + "9514": "\uaebe", + "9515": "\uac1a", + "9516": "\ucc54", + "9517": "\ubb61", + "9518": "\ub560", + "9519": "\ub6ab", + "9520": "\uc633", + "9521": "\ucad3", + "9522": "\uc3df", + "9523": "\uc62e", + "9524": "\ub35f", + "9525": "\ub0b3", + "9526": "\uc549", + "9527": "\uc80a", + "9528": "\uc9e7", + "9529": "\ub429", + "9530": "\ucf2f", + "9531": "\ud290", + "9532": "", + "9533": "ene", + "9534": "\u2581ble", + "9535": "ikk", + "9536": "opp", + "9537": "\u2581Han", + "9538": "\u2581Den", + "9539": "unn", + "9540": "\u2581han", + "9541": "asjon", + "9542": "\u2581word", + "9543": "\u2581werd", + "9544": "", + "9545": "eg", + "9546": "\u2581ikkje", + "9547": "\u2581bok", + "9548": "lik", + "9549": "\u2581eit", + "9550": "s\u00e5", + "9551": "kk", + "9552": "\u2581nok", + "9553": "\u2581god", + "9554": "\u2581lese", + "9555": "dde", + "9556": "inga", + "9557": "\u2581denn", + "9558": "inn", + "9559": "kkje", + "9560": "dig", + "9561": "tid", + "9562": "\u2581b\u00f8ke", + "9563": "ord", + "9564": "\u2581tru", + "9565": "skje", + "9566": "\u2581sei", + "9567": "ller", + "9568": "\u2581fle", + "9569": "skriv", + "9570": "\u2581heil", + "9571": "wy", + "9572": "\u015a", + "9573": "\u0141", + "9574": "\u0179", + "9575": "\u017b", + "9576": "car", + "9577": "t\u00e3o", + "9578": "ia", + "9579": "\u2581foi", + "9580": "ito", + "9581": "ram", + "9582": "fa", + "9583": "\u2581meu", + "9584": "\u00e7a", + "9585": "\u2581dois", + "9586": "a\u00e7\u00e3o", + "9587": "\u2581ter", + "9588": "n\u00e7a", + "9589": "\u2581compra", + "9590": "\u2581mil", + "9591": "\u2581minha", + "9592": "\u2581passa", + "9593": "\u2581casa", + "9594": "\u00c3", + "9595": "\u00b7", + "9596": "", + "9597": "das", + "9598": "\u2581s\u00e3o", + "9599": "\u2581Pa", + "9600": "tura", + "9601": "\u2581ser", + "9602": "\u2581Ele", + "9603": "forma", + "9604": "\u2581Esta", + "9605": "\u00f5es", + "9606": "\u2581pelo", + "9607": "tua", + "9608": "\u2581pela", + "9609": "mar", + "9610": "\u2581Foi", + "9611": "\u2581foram", + "9612": "este", + "9613": "\u2581Um", + "9614": "\u2581S\u00e3o", + "9615": "\u2581entre", + "9616": "fun", + "9617": "agem", + "9618": "gua", + "9619": "\u2581Brasil", + "9620": "\u2581grande", + "9621": "icos", + "9622": "\u2581cidade", + "9623": "inda", + "9624": "\u2581Este", + "9625": "\u2581maior", + "9626": "\u2581brasileiro", + "9627": "\u2581munic\u00edpio", + "9628": "\u2581nome", + "9629": "\u2581encontra", + "9630": "amb\u00e9m", + "9631": "\u2581Sua", + "9632": "\u2581tr\u00eas", + "9633": "\u2581\u0421", + "9634": "\u2581\u0410", + "9635": "\u2581\u041a", + "9636": "\u0431\u0435", + "9637": "\u2581\u041e", + "9638": "\u0441\u0435", + "9639": "\u2581\u041f", + "9640": "\u2581\u043c\u043d\u0435", + "9641": "\u2581\u043e\u043d", + "9642": "\u0446\u0430", + "9643": "\u043d\u0438\u0435", + "9644": "\u0436\u0430", + "9645": "\u0441\u0442\u044c", + "9646": "\u043f\u0443", + "9647": "\u043c\u044b", + "9648": "\u0441\u043a\u0430", + "9649": "\u0441\u0430", + "9650": "\u2581\u0442\u0435\u0431\u044f", + "9651": "\u0433\u0438", + "9652": "\u2581\u0444\u0438\u043b\u044c\u043c", + "9653": "\u0442\u0440\u0435", + "9654": "\u0433\u0440\u0430", + "9655": "\u043c\u0435\u0440", + "9656": "\u0448\u0430", + "9657": "\u2581\u0412\u043a\u043b\u044e\u0447\u0438", + "9658": "\u043b\u0441\u044f", + "9659": "\u0449\u0438", + "9660": "\u2581\u0441\u0435\u0437\u043e\u043d", + "9661": "\u2581\u041a\u0430\u043a", + "9662": "\u2581\u0441\u043c\u043e\u0442\u0440\u0435\u0448\u043a\u0435", + "9663": "\u2581\u0421\u0431\u0435\u0440", + "9664": "\u2581\u0422\u0432", + "9665": "\u2581\u041d\u0435", + "9666": "\u2581\u0414\u0436\u043e\u0439", + "9667": "\u2581\u043e\u0434\u0438\u043d", + "9668": "\u2581\u0410\u0444\u0438\u043d\u0430", + "9669": "\u2581\u041c\u0430", + "9670": "\u2581\u0441\u0435\u043c\u044c", + "9671": "\u2581\u0422\u0430", + "9672": "\u2581\u0421\u0430\u043b\u044e\u0442", + "9673": "\u0431\u043e\u043b\u044c\u0448", + "9674": "\u0441\u043a\u0438\u0439", + "9675": "\u2581\u043f\u044f\u0442\u044c", + "9676": "\u2581\u0441\u0435\u0440\u0438\u0430\u043b", + "9677": "\u2581\u0447\u0435\u0442\u044b\u0440\u0435", + "9678": "\u043a\u043b\u044e\u0447", + "9679": "\u2581\u0448\u0435\u0441\u0442\u044c", + "9680": "\u0438\u0442\u0441\u044f", + "9681": "\u2581\u0432\u043e\u0441\u0435\u043c\u044c", + "9682": "\u2581\u0432\u043e\u043e\u0431\u0449\u0435", + "9683": "\u2581\u041f\u043e\u043a\u0430\u0436\u0438", + "9684": "\u2581\u043f\u043e\u0442\u043e\u043c\u0443", + "9685": "\u2581\u0434\u0432\u0430\u0434\u0446\u0430\u0442\u044c", + "9686": "\u2581\u043a\u0430\u043d\u0430\u043b", + "9687": "\u2581\u0432\u043a\u043b\u044e\u0447\u0438", + "9688": "\u2581\u0440\u0430\u0431\u043e\u0442", + "9689": "\u2581\u043a\u0430\u0440\u0442", + "9690": "\u0438\u0448\u044c", + "9691": "\u2581\u0434\u0435\u043d\u044c", + "9692": "\u042b", + "9693": "ska", + "9694": "var", + "9695": "", + "9696": "\u2581\u0e32", + "9697": "\u2581\u0e19", + "9698": "\u2581\u0e23", + "9699": "\u2581\u0e01", + "9700": "\u2581\u0e2d", + "9701": "\u0e40", + "9702": "\u2581\u0e48", + "9703": "\u2581\u0e07", + "9704": "\u0e31", + "9705": "\u2581\u0e21", + "9706": "\u2581\u0e49", + "9707": "\u2581\u0e22", + "9708": "\u2581\u0e35", + "9709": "\u2581\u0e25", + "9710": "\u2581\u0e27", + "9711": "\u2581\u0e14", + "9712": "\u2581\u0e17", + "9713": "\u2581\u0e2a", + "9714": "\u2581\u0e15", + "9715": "\u2581\u0e34", + "9716": "\u2581\u0e1a", + "9717": "\u2581\u0e1b", + "9718": "\u2581\u0e30", + "9719": "\u2581\u0e2b", + "9720": "\u0e41", + "9721": "\u2581\u0e04", + "9722": "\u2581\u0e08", + "9723": "\u2581\u0e02", + "9724": "\u0e43", + "9725": "\u0e44", + "9726": "\u0e37", + "9727": "\u2581\u0e1e", + "9728": "\u2581\u0e0a", + "9729": "\u2581\u0e47", + "9730": "\u2581\u0e39", + "9731": "\u2581\u0e38", + "9732": "\u2581\u0e4c", + "9733": "\u0e42", + "9734": "\u0e4d", + "9735": "\u2581\u0e36", + "9736": "\u2581\u0e28", + "9737": "\u2581\u0e16", + "9738": "\u2581\u0e0b", + "9739": "\u0e1c", + "9740": "\u2581\u0e20", + "9741": "\u2581\u0e29", + "9742": "\u2581\u0e13", + "9743": "\u2581\u0e18", + "9744": "\u2581\u0e0d", + "9745": "\u0e32", + "9746": "\u0e19", + "9747": "\u2581\u0e1f", + "9748": "\u0e23", + "9749": "\u0e01", + "9750": "\u0e2d", + "9751": "\u0e48", + "9752": "\u0e07", + "9753": "\u0e21", + "9754": "\u0e49", + "9755": "\u0e09", + "9756": "\u0e22", + "9757": "\u2581\u0e10", + "9758": "\u0e35", + "9759": "\u0e25", + "9760": "\u0e27", + "9761": "\u0e14", + "9762": "\u0e17", + "9763": "\u2581\u0e1d", + "9764": "\u0e2a", + "9765": "\u0e15", + "9766": "\u0e34", + "9767": "\u0e1a", + "9768": "\u2581\u0e2e", + "9769": "\u0e1b", + "9770": "\u0e30", + "9771": "\u0e2b", + "9772": "\u0e24", + "9773": "\u0e04", + "9774": "\u0e08", + "9775": "\u2581\u0e0f", + "9776": "\u0e12", + "9777": "\u0e02", + "9778": "\u0e1e", + "9779": "\u0e0a", + "9780": "\u0e47", + "9781": "\u0e39", + "9782": "\u0e38", + "9783": "\u0e4c", + "9784": "\u0e4a", + "9785": "\u2581\u0e2c", + "9786": "\u2581\u0e0e", + "9787": "\u0e11", + "9788": "\u0e36", + "9789": "\u0e28", + "9790": "\u0e16", + "9791": "\u0e0b", + "9792": "\u0e20", + "9793": "\u2581\u0e4b", + "9794": "\u0e29", + "9795": "\u0e13", + "9796": "\u0e18", + "9797": "\u0e0d", + "9798": "\u2581\u0e06", + "9799": "\u0e1f", + "9800": "\u0e10", + "9801": "\u0e1d", + "9802": "\u0e2e", + "9803": "\u0e0c", + "9804": "\u0e0f", + "9805": "\u0e2c", + "9806": "\u0e0e", + "9807": "\u0e45", + "9808": "\u0e4b", + "9809": "\u0e06", + "9810": "\u2581\u0e46", + "9811": "\u0e03", + "9812": "\u0e3a", + "9813": "\u0e05", + "9814": "\u0e46", + "9815": "", + "9816": "\u015f", + "9817": "\u011f", + "9818": "ya", + "9819": "\u2581ve", + "9820": "lar", + "9821": "\u2581bir", + "9822": "l\u0131", + "9823": "d\u0131", + "9824": "ler", + "9825": "ye", + "9826": "s\u0131", + "9827": "lar\u0131", + "9828": "leri", + "9829": "\u0131nda", + "9830": "t\u0131", + "9831": "\u2581bu", + "9832": "lan", + "9833": "ara", + "9834": "\u2581Bu", + "9835": "inde", + "9836": "\u0131n\u0131", + "9837": "y\u0131", + "9838": "yo", + "9839": "d\u00fc", + "9840": "\u2581olarak", + "9841": "\u2581i\u00e7in", + "9842": "maktad\u0131r", + "9843": "ar\u0131", + "9844": "\u2581ba\u015f", + "9845": "\u015e", + "9846": "\u011e", + "9847": "", + "9848": "\u2581\u987b", + "9849": "\u2581\u8d28", + "9850": "\u2581\u6237", + "9851": "\u2581\u4e91", + "9852": "\u2581\u697c", + "9853": "\u2581\u77f3", + "9854": "\u2581\u5ba1", + "9855": "\u2581\u663e", + "9856": "\u2581\u7559", + "9857": "\u2581\u5c3d", + "9858": "\u2581\u96f7", + "9859": "\u2581\u6597", + "9860": "\u2581\u667a", + "9861": "\u2581\u6740", + "9862": "\u2581\u62ec", + "9863": "\u2581\u6267", + "9864": "\u2581\u6548", + "9865": "\u2581\u9752", + "9866": "\u2581\u5584", + "9867": "\u2581\u793c", + "9868": "\u2581\u9760", + "9869": "\u2581\u674e", + "9870": "\u2581\u9ec4", + "9871": "\u2581\u54cd", + "9872": "\u2581\u8425", + "9873": "\u2581\u8865", + "9874": "\u2581\u52bf", + "9875": "\u2581\u8db3", + "9876": "\u2581\u6781", + "9877": "\u2581\u6c5f", + "9878": "\u2581\u7701", + "9879": "\u2581\u9999", + "9880": "\u2581\u7a76", + "9881": "\u2581\u8ffd", + "9882": "\u2581\u7ef4", + "9883": "\u2581\u7fa4", + "9884": "\u2581\u5347", + "9885": "\u2581\u7c73", + "9886": "\u2581\u4ebf", + "9887": "\u2581\u5e1d", + "9888": "\u2581\u7968", + "9889": "\u2581\u5b9d", + "9890": "\u2581\u62cd", + "9891": "\u2581\u613f", + "9892": "\u2581\u7075", + "9893": "\u2581\u6b66", + "9894": "\u2581\u6562", + "9895": "\u2581\u5df4", + "9896": "\u2581\u53e5", + "9897": "\u2581\u5f8b", + "9898": "\u2581\u5c14", + "9899": "\u2581\u72d7", + "9900": "\u2581\u68c0", + "9901": "\u2581\u8f7b", + "9902": "\u2581\u4f01", + "9903": "\u2581\u7b56", + "9904": "\u2047", + "9905": "\u962e", + "9906": "\u6c22", + "9907": "\u53f5", + "9908": "\u8859", + "9909": "\u6cf8", + "9910": "\u90af", + "9911": "\u9c7f", + "9912": "\u95f0", + "9913": "\u6c82", + "9914": "\u5315", + "9915": "\u6860", + "9916": "\u90a1", + "9917": "\u99a5", + "9918": "\u6fee", + "9919": "\u988d", + "9920": "\u5c8c", + "9921": "\u5162", + "9922": "\u8340", + "9923": "\u7fdf", + "9924": "\u86af", + "9925": "\u6d3c", + "9926": "\u7f8c", + "9927": "\u627c", + "9928": "\u8543", + "9929": "\u86df", + "9930": "\u9b13", + "9931": "\u6538", + "9932": "\u5e27", + "9933": "\u9050", + "9934": "\u81fb", + "9935": "\u61ca", + "9936": "\u6d9f", + "9937": "\u6c2f", + "9938": "\u6ea7", + "9939": "\u9570", + "9940": "\u5b6a", + "9941": "\u9ebe", + "9942": "\u608c", + "9943": "\u606c", + "9944": "\u8bd9", + "9945": "\u5ebe", + "9946": "\u8dfb", + "9947": "\u6dc4", + "9948": "\u73b7", + "9949": "\u607b", + "9950": "\u85d0", + "9951": "\u501c", + "9952": "\u5f87", + "9953": "\u911e", + "9954": "\u60cb", + "9955": "\u5fd0", + "9956": "\u6f29", + "9957": "\u87fe", + "9958": "\u4fe8", + "9959": "\u5f3c", + "9960": "\u69d0", + "9961": "\u7f2d", + "9962": 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"\u7281", + "10012": "\u90f4", + "10013": "\u5a13", + "10014": "\u607f", + "10015": "\u70bd", + "10016": "\u6485", + "10017": "\u759f", + "10018": "\u853b", + "10019": "\u835e", + "10020": "\u94b3", + "10021": "\u595a", + "10022": "\u8c7a", + "10023": "\u4f09", + "10024": "\u5f11", + "10025": "\u6c5e", + "10026": "\u871a", + "10027": "\u634b", + "10028": "\u777d", + "10029": "\u81c3", + "10030": "\u9a8a", + "10031": "\u6d9d", + "10032": "\u82b7", + "10033": "\u86f0", + "10034": "\u527d", + "10035": "\u630e", + "10036": "\u8037", + "10037": "\u817c", + "10038": "\u82aa", + "10039": "\u7619", + "10040": "\u9e9d", + "10041": "\u5b34", + "10042": "\u606a", + "10043": "\u8fe2", + "10044": "\u63a3", + "10045": "\u7ff1", + "10046": "\u9cd5", + "10047": "\u90ac", + "10048": "\u9b03", + "10049": "\u83e1", + "10050": "\u9068", + "10051": "\u577b", + "10052": "\u62c4", + "10053": "\u91ba", + "10054": "\u9e35", + "10055": "\u5b62", + "10056": "\u8862", + "10057": "\u6dbf", + "10058": "\u8c19", + "10059": "\u5156", + "10060": "\u8343", + "10061": "\u9773", + "10062": "\u665f", + "10063": "\u6f2f", + "10064": "\u86d0", + "10065": "\u86f3", + "10066": "\u92ae", + "10067": "\u59a9", + "10068": "\u6b92", + "10069": "\u7f42", + "10070": "\u8012", + "10071": "\u8c06", + "10072": "\u8c00", + "10073": "\u8f72", + "10074": "\u9713", + "10075": "\u83b4", + "10076": "\u96bd", + "10077": "\u6f7a", + "10078": "\u8e0c", + "10079": "\u90eb", + "10080": "\u5555", + "10081": "\u77d7", + "10082": "\u7a88", + "10083": "\u89de", + "10084": "\u94ec", + "10085": "\u988c", + "10086": "\u5d82", + "10087": "\u6da3", + "10088": "\u6e49", + "10089": "\u81ca", + "10090": "\u5522", + "10091": "\u6026", + "10092": "\u9aa5", + "10093": "\u6ee6", + "10094": "\u76f1", + "10095": "\u9e8b", + "10096": "\u535e", + "10097": "\u8200", + "10098": "\u916e", + "10099": "\u93d6", + "10100": "\u951a", + "10101": "\u9aa1", + "10102": "\u9ed4", + "10103": "\u6cf1", + "10104": "\u73de", + "10105": "\u74ef", + "10106": "\u77bf", + "10107": "\u9cb6", + "10108": "\u6175", + "10109": "\u6886", + "10110": "\u6ee2", + "10111": "\u8d5d", + "10112": "\u5a40", + "10113": "\u6c26", + "10114": "\u6dec", + "10115": "\u724d", + "10116": "\u740a", + "10117": "\u8446", + "10118": "\u57ed", + "10119": "\u8707", + "10120": "\u9642", + "10121": "\u62c8", + "10122": "\u7751", + "10123": "\u7ee5", + "10124": "\u8ddb", + "10125": "\u9122", + "10126": "\u5639", + "10127": "\u5d02", + "10128": "\u642a", + "10129": "\u655d", + "10130": "\u8c49", + "10131": "\u8d45", + "10132": "\u98d2", + "10133": "\u5c91", + "10134": "\u7ba9", + "10135": "\u87a8", + "10136": "\u6e0c", + "10137": "\u961a", + "10138": "\u998a", + "10139": "\u704f", + "10140": "\u70b7", + "10141": "\u712f", + "10142": "\u752c", + "10143": "\u8748", + "10144": "\u55e4", + "10145": "\u5cb7", + "10146": "\u62bf", + "10147": "\u6d9e", + "10148": "\u75b8", + "10149": "\u779f", + "10150": "\u7eb0", + "10151": "\u701b", + "10152": "\u75c9", + "10153": "\u7601", + "10154": "\u8368", + "10155": "\u88c6", + "10156": "\u9e51", + "10157": "\u5b6c", + "10158": "\u7c0b", + "10159": "\u7ec9", + "10160": "\u8331", + "10161": "\u839c", + "10162": "\u86d4", + "10163": "\u6800", + "10164": "\u72f0", + "10165": "\u78a3", + "10166": "\u909d", + "10167": "\u94c6", + "10168": "\u6cad", + "10169": "\u80e5", + "10170": "\u858f", + "10171": "\u8941", + "10172": "\u8f76", + "10173": "\u9537", + "10174": "\u504c", + "10175": "\u57c2", + "10176": "\u6035", + "10177": "\u6cd4", + "10178": "\u80db", + "10179": "\u5482", + "10180": "\u5676", + "10181": "\u5d27", + "10182": "\u623e", + "10183": "\u781d", + "10184": "\u8d2e", + "10185": "\u6715", + "10186": "\u6773", + "10187": "\u705e", + "10188": "\u7a37", + "10189": "\u8e2e", + "10190": "\u9506", + "10191": "\u542e", + "10192": "\u6525", + "10193": "\u6bd3", + "10194": "\u6ca3", + "10195": "\u85ff", + "10196": "\u88f1", + "10197": "\u4fda", + "10198": "\u51bd", + "10199": "\u77ec", + "10200": "\u852b", + "10201": "\u998f", + "10202": "\u7812", + "10203": "\u8983", + "10204": "\u8e09", + "10205": "\u949c", + "10206": "\u57a4", + "10207": "\u6dde", + "10208": "\u891a", + "10209": "\u8e52", + "10210": "\u8e69", + "10211": "\u90dc", + "10212": "\u6c68", + "10213": "\u7548", + "10214": "\u75e8", + "10215": "\u7823", + "10216": "\u785a", + "10217": "\u8c1f", + "10218": "\u9528", + "10219": "\u5773", + "10220": "\u57ad", + "10221": "\u5b51", + "10222": "\u5d4b", + "10223": "\u5d99", + "10224": "\u664c", + "10225": "\u6654", + "10226": "\u684e", + "10227": "\u6c85", + "10228": "\u6dc5", + "10229": "\u6ed8", + "10230": "\u714a", + "10231": "\u7284", + "10232": "\u7ea8", + "10233": "\u8188", + "10234": "\u9563", + "10235": "\u510b", + "10236": "\u51c7", + "10237": "\u5d03", + "10238": "\u5fe4", + "10239": "\u6004", + "10240": "\u6a28", + "10241": "\u7430", + "10242": "\u75fc", + "10243": "\u8238", + "10244": "\u853a", + "10245": "\u87cb", + "10246": "\u94a8", + "10247": "\u94e8", + "10248": "\u9cb3", + "10249": "\u9edd", + "10250": "\u4f91", + "10251": "\u5d06", + "10252": "\u69ab", + "10253": "\u72b8", + "10254": "\u742c", + "10255": "\u7eeb", + "10256": "\u8d48", + "10257": "\u909b", + "10258": "\u9995", + "10259": "\u9a77", + "10260": "\u56cd", + "10261": "\u57a1", + "10262": "\u59dd", + "10263": "\u6414", + "10264": "\u6ddd", + "10265": "\u6f78", + "10266": "\u70c3", + "10267": "\u73b3", + "10268": "\u73ee", + "10269": "\u768b", + "10270": "\u8174", + "10271": "\u8dec", + "10272": "\u9ca0", + "10273": "\u9f2c", + "10274": "\u4f22", + "10275": "\u5043", + "10276": "\u5d4a", + "10277": "\u60b1", + "10278": "\u63e9", + "10279": "\u6636", + "10280": "\u6ceb", + "10281": "\u6da0", + "10282": "\u6e6b", + "10283": "\u784c", + "10284": "\u7aa8", + "10285": "\u7ed4", + "10286": "\u7fb8", + "10287": "\u8148", + "10288": "\u8671", + "10289": "\u8d30", + "10290": "\u8db5", + "10291": "\u948e", + "10292": "\u94f7", + "10293": "\u4f2b", + "10294": "\u57a9", + "10295": "\u57dd", + "10296": "\u59af", + "10297": "\u5a09", + "10298": "\u626a", + "10299": "\u63ae", + "10300": "\u6d2e", + "10301": "\u6d43", + "10302": "\u7173", + "10303": "\u737e", + "10304": "\u73f2", + "10305": "\u7583", + "10306": "\u7800", + "10307": "\u7b71", + "10308": "\u7da6", + "10309": "\u826e", + "10310": "\u8306", + "10311": "\u891b", + "10312": "\u8bd3", + "10313": "\u8c94", + "10314": "\u902f", + "10315": "\u90e7", + "10316": "\u539d", + "10317": "\u56d4", + "10318": "\u584d", + "10319": "\u5889", + "10320": "\u5a9e", + "10321": "\u5f9c", + "10322": "\u6387", + "10323": "\u63b8", + "10324": "\u665e", + "10325": "\u66b9", + "10326": "\u6cee", + "10327": "\u6e9f", + "10328": "\u6f5e", + "10329": "\u7287", + "10330": "\u749f", + "10331": "\u7747", + "10332": "\u82cb", + "10333": "\u83c0", + "10334": "\u8473", + "10335": "\u8dda", + "10336": "\u90c5", + "10337": "\u94b4", + "10338": "\u9f39", + "10339": "\u4edf", + "10340": "\u4f97", + "10341": "\u4ffe", + "10342": "\u53c1", + "10343": "\u573b", + "10344": "\u5785", + "10345": "\u59a4", + "10346": "\u65cc", + "10347": "\u67b3", + "10348": "\u6954", + "10349": "\u6978", + "10350": "\u6e86", + "10351": "\u6fc2", + "10352": "\u77f8", + "10353": "\u7efb", + "10354": "\u7f31", + "10355": "\u8153", + "10356": "\u84e5", + "10357": "\u8c11", + "10358": "\u8c15", + "10359": "\u8e31", + "10360": "\u9099", + "10361": "\u94af", + "10362": "\u9512", + "10363": "\u95f3", + "10364": "\u9621", + "10365": "\u98a2", + "10366": "\u9a90", + "10367": "\u9cad", + "10368": "\u9cb7", + "10369": "\u9e5e", + "10370": "\u52ad", + "10371": "\u5575", + "10372": "\u5d47", + "10373": "\u5eb9", + "10374": "\u62da", + "10375": "\u65fb", + "10376": "\u67de", + "10377": "\u6a2f", + "10378": "\u6e8f", + "10379": "\u6f8d", + "10380": "\u740f", + "10381": "\u7762", + "10382": "\u7837", + "10383": "\u795a", + "10384": "\u7afd", + "10385": "\u82e1", + "10386": "\u8347", + "10387": "\u8385", + "10388": "\u8572", + "10389": "\u8731", + "10390": "\u87ca", + "10391": "\u88e8", + "10392": "\u89d0", + "10393": "\u8bc3", + "10394": "\u8c27", + "10395": "\u9095", + "10396": "\u90d3", + "10397": "\u9170", + "10398": "\u94d6", + "10399": "\u94df", + "10400": "\u954c", + "10401": "\u9606", + "10402": "\u9615", + "10403": "\u96d2", + "10404": "\u9701", + "10405": "\u9acb", + "10406": "\u9c85", + "10407": "\u9c91", + "10408": "\u9ca2", + "10409": "\u9eb8", + "10410": "\u523d", + "10411": "\u5511", + "10412": "\u559f", + "10413": "\u55ea", + "10414": "\u5658", + "10415": "\u56f9", + "10416": "\u572a", + "10417": "\u579a", + "10418": "\u57f8", + "10419": "\u5807", + "10420": "\u5aeb", + "10421": "\u5b17", + "10422": "\u5b5b", + "10423": "\u5b73", + "10424": "\u5cc1", + "10425": "\u5d6c", + "10426": "\u5f0b", + "10427": "\u60bb", + "10428": "\u625e", + "10429": "\u6448", + "10430": "\u64ba", + "10431": "\u64d8", + "10432": "\u6710", + "10433": "\u680e", + "10434": "\u6c8f", + "10435": "\u6d60", + "10436": "\u6de6", + "10437": "\u6e11", + "10438": "\u6f4b", + "10439": "\u7094", + "10440": "\u7117", + "10441": "\u7118", + "10442": "\u7168", + "10443": "\u7424", + "10444": "\u742e", + "10445": "\u7477", + "10446": "\u759d", + "10447": "\u75bd", + "10448": "\u7aa0", + "10449": "\u7cbc", + "10450": "\u7ebe", + "10451": "\u7f19", + "10452": "\u7f54", + "10453": "\u816d", + "10454": "\u830c", + "10455": "\u832f", + "10456": "\u8360", + "10457": "\u8438", + "10458": "\u8788", + "10459": "\u8872", + "10460": "\u8c2f", + "10461": "\u8e3a", + "10462": "\u8f6b", + "10463": "\u90b3", + "10464": "\u90ef", + "10465": "\u94e3", + "10466": "\u94e9", + "10467": "\u94f0", + "10468": "\u9532", + "10469": "\u9616", + "10470": "\u9708", + "10471": "\u9aa0", + "10472": "\u9ecd", + "10473": "\u4dae", + "10474": "\u4ee1", + "10475": "\u5053", + "10476": "\u520d", + "10477": "\u525c", + "10478": "\u5416", + "10479": "\u549d", + "10480": "\u54bb", + "10481": "\u54c2", + "10482": "\u5537", + "10483": "\u5581", + "10484": "\u55c4", + "10485": "\u562d", + "10486": "\u5659", + "10487": "\u5739", + "10488": "\u5769", + "10489": "\u57c7", + "10490": "\u57d5", + "10491": "\u57da", + "10492": "\u59ab", + "10493": "\u5a0c", + "10494": "\u5ada", + "10495": "\u5b71", + "10496": "\u5b93", + "10497": "\u5c05", + "10498": "\u5d9d", + "10499": "\u5f2d", + "10500": "\u6006", + "10501": "\u603f", + "10502": "\u6041", + "10503": "\u6078", + "10504": "\u6266", + "10505": "\u678b", + "10506": "\u690b", + "10507": "\u6a3e", + "10508": "\u6bc2", + "10509": "\u6c4a", + "10510": "\u6c69", + "10511": "\u6ce0", + "10512": "\u6d39", + "10513": "\u6d48", + "10514": "\u7113", + "10515": "\u727e", + "10516": "\u73b9", + "10517": "\u73d9", + "10518": "\u75a3", + "10519": "\u75b4", + "10520": "\u7633", + "10521": "\u772c", + "10522": "\u77fd", + "10523": "\u79e3", + "10524": "\u7b33", + "10525": "\u7be6", + "10526": "\u7c7c", + "10527": "\u7cb2", + "10528": "\u7ec0", + "10529": "\u7ecb", + "10530": "\u82a9", + "10531": "\u84e6", + "10532": "\u8821", + "10533": "\u8934", + "10534": "\u8a3e", + "10535": "\u8ba3", + "10536": "\u8bd8", + "10537": "\u8dba", + "10538": "\u8e2f", + "10539": "\u8e5a", + "10540": "\u8e85", + "10541": "\u8f78", + "10542": "\u9021", + "10543": "\u9150", + "10544": "\u9487", + "10545": "\u94b2", + "10546": "\u94e7", + "10547": "\u9509", + "10548": "\u951f", + "10549": "\u95e9", + "10550": "\u9697", + "10551": "\u9880", + "10552": "\u98e7", + "10553": "\u9ac2", + "10554": "\u9b49", + "10555": "\u9cdf", + "10556": "\u9e22", + "10557": "\uff21", + "10558": "\u9980", + "10559": "\u966c", + "10560": "\u8914", + "10561": "\u7596", + "10562": "\u68c2", + "10563": "\u6677", + "10564": "\u643d", + "10565": "\u9011", + "10566": "\u82f7", + "10567": "\u783c", + "10568": "\u76c5", + "10569": "\u746d", + "10570": "\u61b7", + "10571": "\u5fff", + "10572": "\u5c50", + "10573": "\u5c15", + "10574": "\u586c", + "10575": "\u500c", + "10576": "\u8df9", + "10577": "\u845a", + "10578": "\u6b93", + "10579": "\u51bc", + "10580": "\u50ee", + "10581": "\u8f73", + "10582": "\u8df6", + "10583": "\u8dce", + "10584": "\u8c85", + "10585": "\u831b", + "10586": "\u73fa", + "10587": "\u67d2", + "10588": "\u4f76", + "10589": "\u94e1", + "10590": "\u7cb3", + "10591": "\u71ee", + "10592": "\u67b0", + "10593": "\u547b", + "10594": "\u9534", + "10595": "\u5cd2", + "10596": "\u551b", + "10597": "\u9c9f", + "10598": "\u9a9d", + "10599": "\u975b", + "10600": "\u8db8", + "10601": "\u8019", + "10602": "\u78b4", + "10603": "\u71ca", + "10604": "\u6dd6", + "10605": "\u948f", + "10606": "\u886e", + "10607": "\u7428", + "10608": "\u5f89", + "10609": "\u5501", + "10610": "\u80d7", + "10611": "\u7ecc", + "10612": "\u5a4a", + "10613": "\u54ad", + "10614": "\u9a85", + "10615": "\u794e", + "10616": "\u7663", + "10617": "\u72d2", + "10618": "\u90ba", + "10619": "\u87c0", + "10620": "\u7a1e", + "10621": "\u6e4e", + "10622": "\u659b", + "10623": "\u688f", + "10624": "\u679e", + "10625": "\u9549", + "10626": "\u7bb4", + "10627": "\u7166", + "10628": "\u55d4", + "10629": "\u82e3", + "10630": "\u7fca", + "10631": "\u765c", + "10632": "\u8e7c", + "10633": "\u86c6", + "10634": "\u7441", + "10635": "\u6600", + "10636": "\u9a9e", + "10637": "\u77fe", + "10638": "\u749e", + "10639": "\u6849", + "10640": "\u5d58", + "10641": "\u5662", + "10642": "\u8bb4", + "10643": "\u7691", + "10644": "\u73c9", + "10645": "\u835a", + "10646": "\u7fce", + "10647": "\u5a75", + "10648": "\u8d53", + "10649": "\u7f30", + "10650": "\u7f28", + "10651": "\u7620", + "10652": "\u61cb", + "10653": "\u789c", + "10654": "\u70e9", + "10655": "\u5b37", + "10656": "\u5472", + "10657": "\u9e4c", + "10658": "\u9604", + "10659": "\u9555", + "10660": "\u7b60", + "10661": "\u7080", + "10662": "\u6c1f", + "10663": "\u5729", + "10664": "\u71a0", + "10665": "\u6f2a", + "10666": "\u6b46", + "10667": "\u64c0", + "10668": "\u9a9c", + "10669": "\u956d", + "10670": "\u8d4a", + "10671": "\u83c1", + "10672": "\u7bea", + "10673": "\u7708", + "10674": "\u5ffb", + "10675": "\u5b40", + "10676": "\u85dc", + "10677": "\u70f7", + "10678": "\u5bb8", + "10679": "\u504e", + "10680": "\u9539", + "10681": "\u94c9", + "10682": "\u8913", + "10683": "\u768e", + "10684": "\u72b7", + "10685": "\u7292", + "10686": "\u55d6", + "10687": "\u9e5c", + "10688": "\u950c", + "10689": "\u73cf", + "10690": "\u85d3", + "10691": "\u8dc4", + "10692": "\u69ad", + "10693": "\u5ad4", + "10694": "\u5a23", + "10695": "\u8d3b", + "10696": "\u870d", + "10697": "\u7f04", + "10698": "\u7738", + "10699": "\u7719", + "10700": "\u9e6b", + "10701": "\u8734", + "10702": "\u81ba", + "10703": "\u762a", + "10704": "\u6c93", + "10705": "\u6593", + "10706": "\u64de", + "10707": "\u5d2e", + "10708": "\u9541", + "10709": "\u7eab", + "10710": "\u789a", + "10711": "\u6862", + "10712": "\u98da", + "10713": "\u840b", + "10714": "\u7131", + "10715": "\u6a35", + "10716": "\u576f", + "10717": "\u5636", + "10718": "\u954a", + "10719": "\u8869", + "10720": "\u86f9", + "10721": "\u83a0", + "10722": "\u783e", + "10723": "\u6e0d", + "10724": "\u6be1", + "10725": "\u65ef", + "10726": "\u579b", + "10727": "\u9530", + "10728": "\u915a", + "10729": "\u9ccd", + "10730": "\u9968", + "10731": "\u94c0", + "10732": "\u5ccb", + "10733": "\u9a9b", + "10734": "\u8169", + "10735": "\u754a", + "10736": "\u5530", + "10737": "\u4ede", + "10738": "\u9609", + "10739": "\u72de", + "10740": "\u6631", + "10741": "\u6421", + "10742": "\u8f67", + "10743": "\u81e7", + "10744": "\u7a95", + "10745": "\u781a", + "10746": "\u70ca", + "10747": "\u6963", + "10748": "\u5fe1", + "10749": "\u9e42", + "10750": "\u6868", + "10751": "\u645e", + "10752": "\u612b", + "10753": "\u949b", + "10754": "\u797a", + "10755": "\u8e76", + "10756": "\u6043", + "10757": "\u5477", + "10758": "\u7b06", + "10759": "\u62a1", + "10760": "\u5ff1", + "10761": "\u5b05", + "10762": "\u520e", + "10763": "\u94b5", + "10764": "\u8ba7", + "10765": "\u86c0", + "10766": "\u6748", + "10767": "\u992e", + "10768": "\u948a", + "10769": "\u7f0e", + "10770": "\u954d", + "10771": "\u89ce", + "10772": "\u5a67", + "10773": "\u98a7", + "10774": "\u989a", + "10775": "\u874c", + "10776": "\u810d", + "10777": "\u55f2", + "10778": "\u5323", + "10779": "\u9f8a", + "10780": "\u82de", + "10781": "\u9cab", + "10782": "\u8e8f", + "10783": "\u8885", + "10784": "\u7ee2", + "10785": "\u5a7a", + "10786": "\u94ff", + "10787": "\u86b1", + "10788": "\u7bd1", + "10789": "\u94e4", + "10790": "\u8113", + "10791": "\u5ab2", + "10792": "\u94c4", + "10793": "\u7bab", + "10794": "\u5a06", + "10795": "\u4f58", + "10796": "\u90b0", + "10797": "\u83ba", + "10798": "\u7f22", + "10799": "\u6410", + "10800": "\u916f", + "10801": "\u8426", + "10802": "\u6f3e", + "10803": "\u6c7e", + "10804": "\u6bfd", + "10805": "\u8902", + "10806": "\u6684", + "10807": "\u9e3e", + "10808": "\u9b1f", + "10809": "\u7f07", + "10810": "\u7bd3", + "10811": "\u6cfe", + "10812": "\u8d73", + "10813": "\u8146", + "10814": "\u9cdd", + "10815": "\u97ec", + "10816": "\u950f", + "10817": "\u8be9", + "10818": "\u79f8", + "10819": "\u622c", + "10820": "\u89d1", + "10821": "\u8559", + "10822": "\u9e6d", + "10823": "\u86a4", + "10824": "\u828a", + "10825": "\u780c", + "10826": "\u7352", + "10827": "\u6b87", + "10828": "\u5942", + "10829": "\u94a3", + "10830": "\u8191", + "10831": "\u7cd7", + "10832": "\u76f9", + "10833": "\u73a5", + "10834": "\u9083", + "10835": "\u8713", + "10836": "\u71ce", + "10837": "\u5567", + "10838": "\u7f44", + "10839": "\u873b", + "10840": "\u776c", + "10841": "\u732c", + "10842": "\u9984", + "10843": "\u7696", + "10844": "\u5140", + "10845": "\u970e", + "10846": "\u84d3", + "10847": "\u7634", + "10848": "\u75eb", + "10849": "\u9550", + "10850": "\u8936", + "10851": "\u8dfa", + "10852": "\u70ec", + "10853": "\u6cd3", + "10854": "\u9535", + "10855": "\u8bb7", + "10856": "\u86aa", + "10857": "\u79c6", + "10858": "\u6cde", + "10859": "\u9cd7", + "10860": "\u8725", + "10861": "\u7085", + "10862": "\u65ee", + "10863": "\u6382", + "10864": "\u58d1", + "10865": "\u54a3", + "10866": "\u9b47", + "10867": "\u7898", + "10868": "\u7699", + "10869": "\u5bd0", + "10870": "\u7600", + "10871": "\u6005", + "10872": "\u869d", + "10873": "\u8398", + "10874": "\u5bf0", + "10875": "\u832c", + "10876": "\u51a2", + "10877": "\u9cde", + "10878": "\u9573", + "10879": "\u8f8d", + "10880": "\u9a8b", + "10881": "\u85b0", + "10882": "\u7b75", + "10883": "\u76ce", + "10884": "\u6988", + "10885": "\u5498", + "10886": "\u4fac", + "10887": "\u8f98", + "10888": "\u812f", + "10889": "\u695e", + "10890": "\u997d", + "10891": "\u82ef", + "10892": "\u9ab0", + "10893": "\u970f", + "10894": "\u8722", + "10895": "\u6d54", + "10896": "\u631b", + "10897": "\u5a04", + "10898": "\u60b4", + "10899": "\u5a55", + "10900": "\u55b3", + "10901": "\u557e", + "10902": "\u8baa", + "10903": "\u5e1b", + "10904": "\u5b7a", + "10905": "\u8bcb", + "10906": "\u8bff", + "10907": "\u78fa", + "10908": "\u7693", + "10909": "\u62f4", + "10910": "\u709c", + "10911": "\u5e44", + "10912": "\u5d3d", + "10913": "\u50a5", + "10914": "\u9cc5", + "10915": "\u94ee", + "10916": "\u6da7", + "10917": "\u94be", + "10918": "\u819b", + "10919": "\u7d0a", + "10920": "\u75e7", + "10921": "\u728a", + "10922": "\u6d5a", + "10923": "\u9163", + "10924": "\u6479", + "10925": "\u5e62", + "10926": "\u5ced", + "10927": "\u59e3", + "10928": "\u5406", + "10929": "\u7ead", + "10930": "\u8301", + "10931": "\u6ec7", + "10932": "\u4f57", + "10933": "\u9035", + "10934": "\u6e4d", + "10935": "\u8c29", + "10936": "\u836b", + "10937": "\u7a96", + "10938": "\u715c", + "10939": "\u9955", + "10940": "\u9062", + "10941": "\u67ad", + "10942": "\u60e6", + "10943": "\u8f7c", + "10944": "\u7bf1", + "10945": "\u7abf", + "10946": "\u795b", + "10947": "\u54fd", + "10948": "\u9e43", + "10949": "\u7c41", + "10950": "\u69b7", + "10951": "\u6635", + "10952": "\u5657", + "10953": "\u8925", + "10954": "\u7638", + "10955": "\u6cef", + "10956": "\u5b5c", + "10957": "\u8bb9", + "10958": "\u8537", + "10959": "\u729f", + "10960": "\u5c96", + "10961": "\u9791", + "10962": "\u91c9", + "10963": "\u8e4b", + "10964": "\u7c91", + "10965": "\u6d93", + "10966": "\u6cf7", + "10967": "\u9c88", + "10968": "\u988a", + "10969": "\u6d19", + "10970": "\u952d", + "10971": "\u7116", + "10972": "\u60ec", + "10973": "\u9a6e", + "10974": "\u998b", + "10975": "\u6995", + "10976": "\u996f", + "10977": "\u9776", + "10978": "\u9542", + "10979": "\u6cb1", + "10980": "\u6452", + "10981": "\u54d0", + "10982": "\u9eef", + "10983": "\u8be7", + "10984": "\u64ac", + "10985": "\u94d0", + "10986": "\u83cf", + "10987": "\u5671", + "10988": "\u82ae", + "10989": "\u739f", + "10990": "\u6dae", + "10991": "\u94c2", + "10992": "\u80ed", + "10993": "\u7459", + "10994": "\u5c79", + "10995": "\u55dd", + "10996": "\u9cd6", + "10997": "\u9602", + "10998": "\u5693", + "10999": "\u86a3", + "11000": "\u7c7d", + "11001": "\u7095", + "11002": "\u568f", + "11003": "\u8fe9", + "11004": "\u9981", + "11005": "\u72c8", + "11006": "\u631d", + "11007": "\u95f5", + "11008": "\u8c0f", + "11009": "\u7bc6", + "11010": "\u75a1", + "11011": "\u6dfc", + "11012": "\u631e", + "11013": "\u61e6", + "11014": "\u6059", + "11015": "\u5f5d", + "11016": "\u5958", + "11017": "\u4f36", + "11018": "\u6dcc", + "11019": "\u9ccc", + "11020": "\u80ef", + "11021": "\u6c74", + "11022": "\u9497", + "11023": "\u8de4", + "11024": "\u68e3", + "11025": "\u6657", + "11026": "\u5fd1", + "11027": "\u56f1", + "11028": "\u7405", + "11029": "\u5f99", + "11030": "\u7f9a", + "11031": "\u6a90", + "11032": "\u853c", + "11033": "\u8334", + "11034": "\u9997", + "11035": "\u8c1b", + "11036": "\u7444", + "11037": "\u6866", + "11038": "\u64b5", + "11039": "\u9e25", + "11040": "\u87b3", + "11041": "\u7edb", + "11042": "\u7ea3", + "11043": "\u7a57", + "11044": "\u69bb", + "11045": "\u6942", + "11046": "\u607a", + "11047": "\u592f", + "11048": "\u54ee", + "11049": "\u9e2f", + "11050": "\u60fa", + "11051": "\u9131", + "11052": "\u8f84", + "11053": "\u567c", + "11054": "\u53ae", + "11055": "\u533e", + "11056": "\u5014", + "11057": "\u7736", + "11058": "\u6829", + "11059": "\u664f", + "11060": "\u55d2", + "11061": "\u4f7c", + "11062": "\u6376", + "11063": "\u9a81", + "11064": "\u9504", + "11065": "\u80eb", + "11066": "\u9977", + "11067": "\u7b8d", + "11068": "\u70e8", + "11069": "\u8892", + "11070": "\u7578", + "11071": "\u60ee", + "11072": "\u7357", + "11073": "\u6ed5", + "11074": "\u5e3c", + "11075": "\u74a8", + "11076": "\u667e", + "11077": "\u8df7", + "11078": "\u62a8", + "11079": "\u74ee", + "11080": "\u82c7", + "11081": "\u621b", + "11082": "\u8e6c", + "11083": "\u556c", + "11084": "\u4f5f", + "11085": "\u5c9a", + "11086": "\u5b1b", + "11087": "\u956f", + "11088": "\u7f81", + "11089": "\u98d3", + "11090": "\u905b", + "11091": "\u6e85", + "11092": "\u9522", + "11093": "\u8386", + "11094": "\u63b3", + "11095": "\u7172", + "11096": "\u9698", + "11097": "\u6f4d", + "11098": "\u8be3", + "11099": "\u5c49", + "11100": "\u5b5a", + "11101": "\u4f70", + "11102": "\u9a6f", + "11103": "\u66a8", + "11104": "\u4fd1", + "11105": "\u835f", + "11106": "\u5cea", + "11107": "\u9890", + "11108": "\u919b", + "11109": "\u62e3", + "11110": "\u87d2", + "11111": "\u6ca5", + "11112": "\u6096", + "11113": "\u9ae6", + "11114": "\u63b7", + "11115": "\u4ee8", + "11116": "\u998d", + "11117": "\u94e0", + "11118": "\u75ca", + "11119": "\u6fd1", + "11120": "\u5623", + "11121": "\u8693", + "11122": "\u7830", + "11123": "\u8dc6", + "11124": "\u6d52", + "11125": "\u5ce5", + "11126": "\u4ea2", + "11127": "\u7329", + "11128": "\u6c76", + "11129": "\u79ba", + "11130": "\u73d1", + "11131": "\u53fc", + "11132": "\u8638", + "11133": "\u9e20", + "11134": "\u7fe9", + "11135": "\u7f24", + "11136": "\u7c27", + "11137": "\u747e", + "11138": "\u552c", + "11139": "\u748b", + "11140": "\u68a7", + "11141": "\u75f1", + "11142": "\u9a6d", + "11143": "\u741b", + "11144": "\u6c2a", + "11145": "\u84bf", + "11146": "\u78f7", + "11147": "\u949d", + "11148": "\u8fab", + "11149": "\u84df", + "11150": "\u7cb1", + "11151": "\u67b8", + "11152": "\u8717", + "11153": "\u7a98", + "11154": "\u9975", + "11155": "\u5228", + "11156": "\u7629", + "11157": "\u54c6", + "11158": "\u88f4", + "11159": "\u804b", + "11160": "\u7316", + "11161": "\u80e7", + "11162": "\u609a", + "11163": "\u8884", + "11164": "\u8364", + "11165": "\u80fa", + "11166": "\u6805", + "11167": "\u5fd2", + "11168": "\u9611", + "11169": "\u8f97", + "11170": "\u8e1d", + "11171": "\u6fd2", + "11172": "\u6d31", + "11173": "\u6a71", + "11174": "\u9a7f", + "11175": "\u7b5d", + "11176": "\u85c9", + "11177": "\u7ede", + "11178": "\u6bcb", + "11179": "\u80f0", + "11180": "\u70fd", + "11181": "\u701a", + "11182": "\u8f99", + "11183": "\u5ae6", + "11184": "\u6f7c", + "11185": "\u6e0e", + "11186": "\u6e32", + "11187": "\u55f7", + "11188": "\u7a20", + "11189": "\u5ad6", + "11190": "\u622e", + "11191": "\u6b83", + "11192": "\u9a78", + "11193": "\u8d58", + "11194": "\u56b7", + "11195": "\u5a34", + "11196": "\u5586", + "11197": "\u8327", + "11198": "\u7f2a", + "11199": "\u9e49", + "11200": "\u9abc", + "11201": "\u7f15", + "11202": "\u5dcd", + "11203": "\u9e66", + "11204": "\u8d43", + "11205": "\u8715", + "11206": "\u6ea5", + "11207": "\u7b03", + "11208": "\u952f", + "11209": "\u94b0", + "11210": "\u9a79", + "11211": "\u8c82", + "11212": "\u766b", + "11213": "\u759a", + "11214": "\u8708", + "11215": "\u5412", + "11216": "\u9704", + "11217": "\u968d", + "11218": "\u9e33", + "11219": "\u7eca", + "11220": "\u6da1", + "11221": "\u5e37", + "11222": "\u94db", + "11223": "\u4fea", + "11224": "\u9716", + "11225": "\u8517", + "11226": "\u692d", + "11227": "\u6e89", + "11228": "\u5ce6", + "11229": "\u5a05", + "11230": "\u532e", + "11231": "\u6994", + "11232": "\u4fd0", + "11233": "\u541d", + "11234": "\u8bec", + "11235": "\u97ed", + "11236": "\u4fde", + "11237": "\u70ef", + "11238": "\u574d", + "11239": "\u7599", + "11240": "\u6cae", + "11241": "\u7750", + "11242": "\u6c55", + "11243": "\u50a3", + "11244": "\u9885", + "11245": "\u865e", + "11246": "\u9619", + "11247": "\u7487", + "11248": "\u8bdf", + "11249": "\u659f", + "11250": "\u816e", + "11251": "\u70af", + "11252": "\u6b7c", + "11253": "\u90f8", + "11254": "\u75f9", + "11255": "\u66e6", + "11256": "\u64c2", + "11257": "\u9525", + "11258": "\u8eac", + "11259": "\u772f", + "11260": "\u8c4c", + "11261": "\u8bfd", + "11262": "\u60eb", + "11263": "\u9e4a", + "11264": "\u854a", + "11265": "\u6151", + "11266": "\u7ec5", + "11267": "\u64d2", + "11268": "\u6342", + "11269": "\u7efd", + "11270": "\u5b70", + "11271": "\u6664", + "11272": "\u5d2d", + "11273": "\u6f62", + "11274": "\u5e42", + "11275": "\u62e7", + "11276": "\u80ae", + "11277": "\u9176", + "11278": "\u6c2e", + "11279": "\u566c", + "11280": "\u9893", + "11281": "\u821c", + "11282": "\u683e", + "11283": "\u9523", + "11284": "\u86e4", + "11285": "\u9ac5", + "11286": "\u95eb", + "11287": "\u6cf5", + "11288": "\u996a", + "11289": "\u6002", + "11290": "\u814c", + "11291": "\u9cb8", + "11292": "\u752d", + "11293": "\u57a6", + "11294": "\u5180", + "11295": "\u78c5", + "11296": "\u5f29", + "11297": "\u796f", + "11298": "\u68ad", + "11299": "\u6615", + "11300": "\u4fa5", + "11301": "\u6123", + "11302": "\u77aa", + "11303": "\u6da4", + "11304": "\u68f1", + "11305": "\u7eef", + "11306": "\u6f9c", + "11307": "\u59d7", + "11308": "\u85d5", + "11309": "\u973e", + "11310": "\u9502", + "11311": "\u9540", + "11312": "\u6c79", + "11313": "\u9ca4", + "11314": "\u6e43", + "11315": "\u7c07", + "11316": "\u6e3a", + "11317": "\u9074", + "11318": "\u4e4d", + "11319": "\u6273", + "11320": "\u8018", + "11321": "\u9102", + "11322": "\u75ae", + "11323": "\u9ab7", + "11324": "\u8680", + "11325": "\u8042", + "11326": "\u75a4", + "11327": "\u6de4", + "11328": "\u5777", + "11329": "\u79fd", + "11330": "\u77a9", + "11331": "\u97f6", + "11332": "\u94a7", + "11333": "\u87d1", + "11334": "\u8335", + "11335": "\u829c", + "11336": "\u620c", + "11337": "\u52b5", + "11338": "\u5520", + "11339": "\u7eee", + "11340": "\u6d4a", + "11341": "\u6f13", + "11342": "\u6ba1", + "11343": "\u7728", + "11344": "\u60ed", + "11345": "\u502a", + "11346": "\u715e", + "11347": "\u6ed4", + "11348": "\u5018", + "11349": "\u67ab", + "11350": "\u6f88", + "11351": "\u5b7d", + "11352": "\u96f3", + "11353": "\u6c28", + "11354": "\u7ef0", + "11355": "\u8f95", + "11356": "\u9551", + "11357": "\u7184", + "11358": "\u6064", + "11359": "\u631a", + "11360": "\u98a4", + "11361": "\u778c", + "11362": "\u56e7", + "11363": "\u8bb3", + "11364": "\u75ea", + "11365": "\u70c1", + "11366": "\u7f94", + "11367": "\u79c3", + "11368": "\u6177", + "11369": "\u5c94", + "11370": "\u6f33", + "11371": "\u75de", + "11372": "\u5f64", + "11373": "\u69a8", + "11374": "\u76cf", + "11375": "\u6c90", + "11376": "\u68e0", + "11377": "\u5d34", + "11378": "\u575e", + "11379": "\u5429", + "11380": "\u6808", + "11381": "\u67e0", + "11382": "\u6556", + "11383": "\u4f88", + "11384": "\u7faf", + "11385": "\u6e1d", + "11386": "\u7ef7", + "11387": "\u7eb6", + "11388": "\u7cef", + "11389": "\u8354", + "11390": "\u6dc6", + "11391": "\u9661", + "11392": "\u4fcf", + "11393": "\u58a9", + "11394": "\u7cbd", + "11395": "\u67ec", + "11396": "\u5600", + "11397": "\u53a5", + "11398": "\u5254", + "11399": "\u903e", + "11400": "\u7fb2", + "11401": "\u8beb", + "11402": "\u7f00", + "11403": "\u5768", + "11404": "\u8d42", + "11405": "\u603c", + "11406": "\u5669", + "11407": "\u9647", + "11408": "\u94a6", + "11409": "\u94a0", + "11410": "\u5527", + "11411": "\u51ff", + "11412": "\u55e1", + "11413": "\u5431", + "11414": "\u5349", + "11415": "\u5455", + "11416": "\u6c5b", + "11417": "\u5f08", + "11418": "\u79e7", + "11419": "\u7cd9", + "11420": "\u7115", + "11421": "\u6da9", + "11422": "\u7d6e", + "11423": "\u7490", + "11424": "\u6d95", + "11425": "\u75b5", + "11426": "\u8110", + "11427": "\u6c13", + "11428": "\u7fbf", + "11429": "\u8c24", + "11430": "\u8759", + "11431": "\u904f", + "11432": "\u8760", + "11433": "\u7076", + "11434": "\u6789", + "11435": "\u54a9", + "11436": "\u61f5", + "11437": "\u5a6a", + "11438": "\u60d5", + "11439": "\u8bc5", + "11440": "\u5580", + "11441": "\u6320", + "11442": "\u9753", + "11443": "\u90dd", + "11444": "\u6cfb", + "11445": "\u97e7", + "11446": "\u618b", + "11447": "\u94dd", + "11448": "\u777f", + "11449": "\u5189", + "11450": "\u7a8d", + "11451": "\u78be", + "11452": "\u60f6", + "11453": "\u6f47", + "11454": "\u5dc5", + "11455": "\u9668", + "11456": "\u73ba", + "11457": "\u8d63", + "11458": "\u9c8d", + "11459": "\u54d7", + "11460": "\u7ca4", + "11461": "\u5a25", + "11462": "\u56e4", + "11463": "\u7011", + "11464": "\u68d5", + "11465": "\u53fd", + "11466": "\u710a", + "11467": "\u9e3d", + "11468": "\u6292", + "11469": "\u527f", + "11470": "\u82df", + "11471": "\u915d", + "11472": "\u8046", + "11473": "\u7845", + "11474": "\u7779", + "11475": "\u8782", + "11476": "\u6252", + "11477": "\u4eb5", + "11478": "\u9508", + "11479": "\u4e10", + "11480": "\u731d", + "11481": "\u964b", + "11482": "\u8845", + "11483": "\u599e", + "11484": "\u5478", + "11485": "\u7f1a", + "11486": "\u9a87", + "11487": "\u9f9a", + "11488": "\u5241", + "11489": "\u73ae", + "11490": "\u7785", + "11491": "\u4fd8", + "11492": "\u6986", + "11493": "\u5a76", + "11494": "\u761f", + "11495": "\u655b", + "11496": "\u8747", + "11497": "\u4fed", + "11498": "\u9556", + "11499": "\u9a8f", + "11500": "\u51f3", + "11501": "\u501a", + "11502": "\u5578", + "11503": "\u7b77", + "11504": "\u7ef8", + "11505": "\u6caa", + "11506": "\u886b", + "11507": "\u7455", + "11508": "\u6d3d", + "11509": "\u89c5", + "11510": "\u818a", + "11511": "\u4f6c", + "11512": "\u7f2e", + "11513": "\u63ba", + "11514": "\u80f3", + "11515": "\u7682", + "11516": "\u90a2", + "11517": "\u7ed2", + "11518": "\u78b1", + "11519": "\u7aa5", + "11520": "\u66a7", + "11521": "\u61c8", + "11522": "\u69df", + "11523": "\u56a3", + "11524": "\u7caa", + "11525": "\u9499", + "11526": "\u846b", + "11527": "\u5201", + "11528": "\u54d2", + "11529": "\u90b9", + "11530": "\u6a61", + "11531": "\u8165", + "11532": "\u9985", + "11533": "\u77f6", + "11534": "\u9cc4", + "11535": "\u545b", + "11536": "\u61ac", + "11537": "\u76b1", + "11538": "\u55b1", + "11539": "\u960e", + "11540": "\u55e6", + "11541": "\u96ef", + "11542": "\u5570", + "11543": "\u7a9c", + "11544": "\u9992", + "11545": "\u655e", + "11546": "\u8d41", + "11547": "\u7980", + "11548": "\u6402", + "11549": "\u5288", + "11550": "\u8038", + "11551": "\u8574", + "11552": "\u7bf7", + "11553": "\u8c41", + "11554": "\u8214", + "11555": "\u6bd9", + "11556": "\u7aa6", + "11557": "\u565c", + "11558": "\u8a79", + "11559": "\u762b", + "11560": "\u5f6a", + "11561": "\u6380", + "11562": "\u94f2", + "11563": "\u987d", + "11564": "\u7be1", + "11565": "\u4e53", + "11566": "\u9600", + "11567": "\u5a1f", + "11568": "\u946b", + "11569": "\u5e1c", + "11570": "\u4e2b", + "11571": "\u9ad3", + "11572": "\u6ca6", + "11573": "\u53e8", + "11574": "\u9576", + "11575": "\u55d3", + "11576": "\u8bf2", + "11577": "\u548f", + "11578": "\u997a", + "11579": "\u9e26", + "11580": "\u6984", + "11581": "\u5e90", + "11582": "\u864f", + "11583": "\u9a86", + "11584": "\u874e", + "11585": "\u54d4", + "11586": "\u8f7f", + "11587": "\u63cd", + "11588": "\u61a8", + "11589": "\u4f84", + "11590": "\u9165", + "11591": "\u8e39", + "11592": "\u6a44", + "11593": "\u7eba", + "11594": "\u516e", + "11595": "\u70db", + "11596": "\u60af", + "11597": "\u8783", + "11598": "\u8424", + "11599": "\u53a2", + "11600": "\u6ca7", + "11601": "\u5543", + "11602": "\u8f9c", + "11603": "\u7f55", + "11604": "\u9972", + "11605": "\u8c1c", + "11606": "\u5364", + "11607": "\u6d47", + "11608": "\u57d4", + "11609": "\u7426", + "11610": "\u8469", + "11611": "\u6073", + "11612": "\u7b0b", + "11613": "\u5490", + "11614": "\u5c7f", + "11615": "\u949e", + "11616": "\u8bc0", + "11617": "\u96cf", + "11618": "\u63b0", + "11619": "\u9610", + "11620": "\u5c4e", + "11621": "\u5495", + "11622": "\u6467", + "11623": "\u9ecf", + "11624": "\u6441", + "11625": "\u6055", + "11626": "\u7f09", + "11627": "\u6e24", + "11628": "\u7eac", + "11629": "\u64b8", + "11630": "\u840d", + "11631": "\u6512", + "11632": "\u64ce", + "11633": "\u7741", + "11634": "\u70b3", + "11635": "\u4e52", + "11636": "\u7ad6", + "11637": "\u7f14", + "11638": "\u4ed1", + "11639": "\u95f8", + "11640": "\u8be1", + "11641": "\u5564", + "11642": "\u7410", + "11643": "\u8682", + "11644": "\u8774", + "11645": "\u5955", + "11646": "\u8c34", + "11647": "\u63fd", + "11648": "\u53ee", + "11649": "\u7ece", + "11650": "\u77eb", + "11651": "\u6363", + "11652": "\u6b47", + "11653": "\u888d", + "11654": "\u8c0d", + "11655": "\u67a3", + "11656": "\u55b5", + "11657": "\u9ca8", + "11658": "\u8bcf", + "11659": "\u5960", + "11660": "\u5029", + "11661": "\u8e6d", + "11662": "\u64a9", + "11663": "\u7fd8", + "11664": "\u4fa8", + "11665": "\u8f90", + "11666": "\u7792", + "11667": "\u7130", + "11668": "\u9965", + "11669": "\u54a6", + "11670": "\u889c", + "11671": "\u634d", + "11672": "\u6a0a", + "11673": "\u95fd", + "11674": "\u94f8", + "11675": "\u58f6", + "11676": "\u8611", + "11677": "\u7f38", + "11678": "\u90b5", + "11679": "\u76d4", + "11680": "\u7096", + "11681": "\u6f8e", + "11682": "\u8c2c", + "11683": "\u6dc7", + "11684": "\u94c5", + "11685": "\u5d1b", + "11686": "\u803f", + "11687": "\u63e3", + "11688": "\u7504", + "11689": "\u575d", + "11690": "\u4ea9", + "11691": "\u9631", + "11692": "\u96a7", + "11693": "\u7538", + "11694": "\u5c27", + "11695": "\u78d5", + "11696": "\u6233", + "11697": "\u6ee4", + "11698": "\u8bb6", + "11699": "\u7574", + "11700": "\u917f", + "11701": "\u8206", + "11702": "\u5c82", + "11703": "\u5ac2", + "11704": "\u707f", + "11705": "\u886c", + "11706": "\u75d8", + "11707": "\u8393", + "11708": "\u549a", + "11709": "\u5fcf", + "11710": "\u9882", + "11711": "\u9521", + "11712": "\u563b", + "11713": "\u5188", + "11714": "\u7ee3", + "11715": "\u8d31", + "11716": "\u7eb1", + "11717": "\u96cd", + "11718": "\u98d9", + "11719": "\u7737", + "11720": "\u7784", + "11721": "\u5195", + "11722": "\u5ed6", + "11723": "\u62e2", + "11724": "\u6390", + "11725": "\u6d51", + "11726": "\u69c3", + "11727": "\u9489", + "11728": "\u6487", + "11729": "\u9a74", + "11730": "\u6ee5", + "11731": "\u88f9", + "11732": "\u545c", + "11733": "\u5e10", + "11734": "\u7aed", + "11735": "\u8d3f", + "11736": "\u6d46", + "11737": "\u8116", + "11738": "\u5306", + "11739": "\u9a7c", + "11740": "\u859b", + "11741": "\u9b44", + "11742": "\u8bf5", + "11743": "\u5792", + "11744": "\u7f05", + "11745": "\u8e66", + "11746": "\u9709", + "11747": "\u63ea", + "11748": "\u5784", + "11749": "\u5300", + "11750": "\u7ea4", + "11751": "\u6405", + "11752": "\u574e", + "11753": "\u7a3b", + "11754": "\u6869", + "11755": "\u73ab", + "11756": "\u8367", + "11757": "\u7a91", + "11758": "\u54d1", + "11759": "\u6413", + "11760": "\u94ed", + "11761": "\u5151", + "11762": "\u8086", + "11763": "\u5494", + "11764": "\u575f", + "11765": "\u56ca", + "11766": "\u9a70", + "11767": "\u77a7", + "11768": "\u58e4", + "11769": "\u5bde", + "11770": "\u9887", + "11771": "\u62ce", + "11772": "\u65f7", + "11773": "\u8721", + "11774": "\u7fa1", + "11775": "\u5594", + "11776": "\u6d85", + "11777": "\u94a5", + "11778": "\u7199", + "11779": "\u6495", + "11780": "\u70eb", + "11781": "\u9a73", + "11782": "\u7f06", + "11783": "\u8e48", + "11784": "\u77bb", + "11785": "\u7470", + "11786": "\u8854", + "11787": "\u803b", + "11788": "\u8681", + "11789": "\u95fa", + "11790": "\u6346", + "11791": "\u9877", + "11792": "\u5858", + "11793": "\u7476", + "11794": "\u8c2d", + "11795": "\u83b9", + "11796": "\u743c", + "11797": "\u62e6", + "11798": "\u7a46", + "11799": "\u83e0", + "11800": "\u54aa", + "11801": "\u68f5", + "11802": "\u8bbd", + "11803": "\u5ae9", + "11804": "\u8bdb", + "11805": "\u57ae", + "11806": "\u5499", + "11807": "\u9e64", + "11808": "\u74f7", + "11809": "\u9e70", + "11810": "\u5021", + "11811": "\u5471", + "11812": "\u964c", + "11813": "\u6084", + "11814": "\u70d8", + "11815": "\u62f1", + "11816": "\u62ef", + "11817": "\u8231", + "11818": "\u71b9", + "11819": "\u5de9", + "11820": "\u6d4f", + "11821": "\u7529", + "11822": "\u9888", + "11823": "\u5c61", + "11824": "\u62fd", + "11825": "\u584c", + "11826": "\u8d2c", + "11827": "\u8822", + "11828": "\u82ac", + "11829": "\u7ef5", + "11830": "\u5308", + "11831": "\u640f", + "11832": "\u8d4e", + "11833": "\u658b", + "11834": "\u8c10", + "11835": "\u852c", + "11836": "\u800d", + "11837": "\u789f", + "11838": "\u83c7", + "11839": "\u4e1b", + "11840": "\u5de2", + "11841": "\u5e18", + "11842": "\u83bd", + "11843": "\u5bc7", + "11844": "\u88d9", + "11845": "\u8c6b", + "11846": "\u64c5", + "11847": "\u4f63", + "11848": "\u567b", + "11849": "\u9976", + "11850": "\u6e17", + "11851": "\u953b", + "11852": "\u8be0", + "11853": "\u8482", + "11854": "\u52fa", + "11855": "\u96b6", + "11856": "\u5a77", + "11857": "\u8d9f", + "11858": "\u6401", + "11859": "\u561f", + "11860": "\u5760", + "11861": "\u594e", + "11862": "\u814a", + "11863": "\u6cfc", + "11864": "\u532a", + "11865": "\u9510", + "11866": "\u54e9", + "11867": "\u8270", + "11868": "\u5428", + "11869": "\u8c23", + "11870": "\u59ec", + "11871": "\u4fa3", + "11872": "\u6fa1", + "11873": "\u69db", + "11874": "\u8346", + "11875": "\u72e0", + "11876": "\u6e23", + "11877": "\u9655", + "11878": "\u638f", + "11879": "\u5f17", + "11880": "\u8c0a", + "11881": "\u9881", + "11882": "\u6500", + "11883": "\u6124", + "11884": "\u5992", + "11885": "\u94a9", + "11886": "\u80c0", + "11887": "\u625b", + "11888": "\u6254", + "11889": "\u51d1", + "11890": "\u70ab", + "11891": "\u57ab", + "11892": "\u94ae", + "11893": "\u5783", + "11894": "\u9e45", + "11895": "\u6127", + "11896": "\u50f5", + "11897": "\u6e34", + "11898": "\u632a", + "11899": "\u8c05", + "11900": "\u94c3", + "11901": "\u7b3c", + "11902": "\u8dea", + "11903": "\u745c", + "11904": "\u6e83", + "11905": "\u60ac", + "11906": "\u8d3e", + "11907": "\u6b79", + "11908": "\u9f7f", + "11909": "\u8d81", + "11910": "\u63a9", + "11911": "\u8bbc", + "11912": "\u8d29", + "11913": "\u6ee9", + "11914": "\u9524", + "11915": "\u76ef", + "11916": "\u6251", + "11917": "\u727a", + "11918": "\u58f3", + "11919": "\u573e", + "11920": "\u52cb", + "11921": "\u54fc", + "11922": "\u763e", + "11923": "\u82cd", + "11924": "\u59ae", + "11925": "\u9896", + "11926": "\u9614", + "11927": "\u718f", + "11928": "\u778e", + "11929": "\u6e0a", + "11930": "\u5764", + "11931": "\u9e23", + "11932": "\u6108", + "11933": "\u900a", + "11934": "\u817b", + "11935": "\u9a84", + "11936": "\u8d1e", + "11937": "\u5524", + "11938": "\u97f5", + "11939": "\u5a74", + "11940": "\u6cbe", + "11941": "\u97e6", + "11942": "\u98a0", + "11943": "\u68cd", + "11944": "\u4e54", + "11945": "\u5c4c", + "11946": "\u8083", + "11947": "\u80c1", + "11948": "\u5f6d", + "11949": "\u78ca", + "11950": "\u556a", + "11951": "\u53a6", + "11952": "\u742a", + "11953": "\u7ef3", + "11954": "\u59ca", + "11955": "\u9a9a", + "11956": "\u7eb2", + "11957": "\u8f96", + "11958": "\u867e", + "11959": "\u8c0e", + "11960": "\u8881", + "11961": "\u7f20", + "11962": "\u7f50", + "11963": "\u5be8", + "11964": "\u5e9e", + "11965": "\u95ef", + "11966": "\u5a07", + "11967": "\u8e72", + "11968": "\u53ed", + "11969": "\u5e15", + "11970": "\u8427", + "11971": "\u5401", + "11972": "\u745f", + "11973": "\u6c1b", + "11974": "\u838e", + "11975": "\u6454", + "11976": "\u76fc", + "11977": "\u5ab3", + "11978": "\u95f7", + "11979": "\u635e", + "11980": "\u4ff1", + "11981": "\u9e3f", + "11982": "\u9e4f", + "11983": "\u9988", + "11984": "\u7545", + "11985": "\u8c26", + "11986": "\u5509", + "11987": "\u62a0", + "11988": "\u8fc8", + "11989": "\u7b5b", + "11990": "\u8d3a", + "11991": "\u841d", + "11992": "\u8c28", + "11993": "\u7ebd", + "11994": "\u7239", + "11995": "\u80be", + "11996": "\u9aa4", + "11997": "\u51af", + "11998": "\u626d", + "11999": "\u5587", + "12000": "\u7816", + "12001": "\u8bde", + "12002": "\u65a9", + "12003": "\u72ee", + "12004": "\u7f62", + "12005": "\u8bf1", + "12006": "\u5492", + "12007": "\u7855", + "12008": "\u7f1d", + "12009": "\u6345", + "12010": "\u9a71", + "12011": "\u55bb", + "12012": "\u76d0", + "12013": "\u8fbd", + "12014": "\u54c4", + "12015": "\u9171", + "12016": "\u62e8", + "12017": "\u53f9", + "12018": "\u60e9", + "12019": "\u6cdb", + "12020": "\u5986", + "12021": "\u9601", + "12022": "\u6ee8", + "12023": "\u4fa6", + "12024": "\u6021", + "12025": "\u5978", + "12026": "\u5733", + "12027": "\u7b28", + "12028": "\u8eba", + "12029": "\u5179", + "12030": "\u6b67", + "12031": "\u4ed7", + "12032": "\u7fc5", + "12033": "\u7a9d", + "12034": "\u7ff0", + "12035": "\u5c97", + "12036": "\u88e4", + "12037": "\u7ed8", + "12038": "\u8bc8", + "12039": "\u9971", + "12040": "\u8b6c", + "12041": "\u8f69", + "12042": "\u8d2b", + "12043": "\u77e3", + "12044": "\u6323", + "12045": "\u67ef", + "12046": "\u8dc3", + "12047": "\u9493", + "12048": "\u63ed", + "12049": "\u6361", + "12050": "\u59e8", + "12051": "\u81c2", + "12052": "\u8db4", + "12053": "\u98d8", + "12054": "\u4eff", + "12055": "\u8f74", + "12056": "\u5939", + "12057": "\u758f", + "12058": "\u7838", + "12059": "\u94bb", + "12060": "\u54a7", + "12061": "\u80bf", + "12062": "\u997f", + "12063": "\u626f", + "12064": "\u7eb9", + "12065": "\u644a", + "12066": "\u4f2a", + "12067": "\u8c31", + "12068": "\u8d2f", + "12069": "\u809a", + "12070": "\u7f34", + "12071": "\u8361", + "12072": "\u629b", + "12073": "\u80a0", + "12074": "\u5415", + "12075": "\u5cad", + "12076": "\u78b3", + "12077": "\u90bb", + "12078": "\u9a7b", + "12079": "\u9e2d", + "12080": "\u629a", + "12081": "\u5154", + "12082": "\u7ea0", + "12083": "\u9f9f", + "12084": "\u71ac", + "12085": "\u5435", + "12086": "\u6d53", + "12087": "\u503e", + "12088": "\u5395", + "12089": "\u6d82", + "12090": "\u4fe9", + "12091": "\u9093", + "12092": "\u96fe", + "12093": "\u7eb5", + "12094": "\u5367", + "12095": "\u80a4", + "12096": "\u4e27", + "12097": "\u80f6", + "12098": "\u80d6", + "12099": "\u6377", + "12100": "\u6db5", + "12101": "\u8d60", + "12102": "\u8d4c", + "12103": "\u90ae", + "12104": "\u6655", + "12105": "\u7bee", + "12106": "\u5362", + "12107": "\u7ed1", + "12108": "\u575b", + "12109": "\u7978", + "12110": "\u83b2", + "12111": "\u6760", + "12112": "\u730e", + "12113": "\u8f70", + "12114": "\u53e0", + "12115": "\u5c38", + "12116": "\u67dc", + "12117": "\u5821", + "12118": "\u5242", + "12119": "\u607c", + "12120": "\u5220", + "12121": "\u594b", + "12122": "\u6296", + "12123": "\u70e4", + "12124": "\u5f7b", + "12125": "\u9189", + "12126": "\u950b", + "12127": "\u7cdf", + "12128": "\u6746", + "12129": "\u4f1e", + "12130": "\u7eb7", + "12131": "\u538c", + "12132": "\u6846", + "12133": "\u680f", + "12134": "\u4f69", + "12135": "\u529d", + "12136": "\u901b", + "12137": "\u918b", + "12138": "\u8be6", + "12139": "\u8273", + "12140": "\u70bc", + "12141": "\u522e", + "12142": "\u6062", + "12143": "\u5938", + "12144": "\u9012", + "12145": "\u739b", + "12146": "\u5c18", + "12147": "\u8d50", + "12148": "\u8fdf", + "12149": "\u83f2", + "12150": "\u8d4b", + "12151": "\u75af", + "12152": "\u7efc", + "12153": "\u8350", + "12154": "\u6cea", + "12155": "\u5c34", + "12156": "\u507f", + "12157": "\u6324", + "12158": "\u50a8", + "12159": "\u8fc1", + "12160": "\u9677", + "12161": "\u9a76", + "12162": "\u8230", + "12163": "\u5457", + "12164": "\u72b9", + "12165": "\u52b2", + "12166": "\u624e", + "12167": "\u518c", + "12168": "\u7275", + "12169": "\u7b79", + "12170": "\u50bb", + "12171": "\u8f89", + "12172": "\u6668", + "12173": "\u4ed3", + "12174": "\u8e22", + "12175": "\u9970", + "12176": "\u7f69", + "12177": "\u51bb", + "12178": "\u7ed5", + "12179": "\u55b7", + "12180": "\u7eea", + "12181": "\u8d54", + "12182": "\u780d", + "12183": "\u8d21", + "12184": "\u8e29", + "12185": "\u6491", + "12186": "\u4fa7", + "12187": "\u95f2", + "12188": "\u8fa9", + "12189": "\u6b49", + "12190": "\u5baa", + "12191": "\u94dc", + "12192": "\u94fe", + "12193": "\u6c27", + "12194": "\u817e", + "12195": "\u9f84", + "12196": "\u5a31", + "12197": "\u8f86", + "12198": "\u8d2a", + "12199": "\u89c8", + "12200": "\u5899", + "12201": "\u9274", + "12202": "\u5561", + "12203": "\u8109", + "12204": "\u5413", + "12205": "\u72f1", + "12206": "\u517d", + "12207": "\u7a97", + "12208": "\u5f2f", + "12209": "\u70ae", + "12210": "\u54a8", + "12211": "\u5fe7", + "12212": "\u96d5", + "12213": "\u5ba0", + "12214": "\u5c2c", + "12215": "\u6e14", + "12216": "\u806a", + "12217": "\u77ff", + "12218": "\u94fa", + "12219": "\u684c", + "12220": "\u6bc1", + "12221": "\u6735", + "12222": "\u88ad", + "12223": "\u6270", + "12224": "\u5a1c", + "12225": "\u9526", + "12226": "\u6321", + "12227": "\u680b", + "12228": "\u903b", + "12229": "\u90d1", + "12230": "\u568e", + "12231": "\u51ef", + "12232": "\u8f68", + "12233": "\u5e99", + "12234": "\u51ed", + "12235": "\u62df", + "12236": "\u5c1d", + "12237": "\u5565", + "12238": "\u55e8", + "12239": "\u6cfd", + "12240": "\u731c", + "12241": "\u5085", + "12242": "\u5141", + "12243": "\u95f9", + "12244": "\u9ed8", + "12245": "\u7a77", + "12246": "\u5466", + "12247": "\u7f13", + "12248": "\u9e1f", + "12249": "\u7f29", + "12250": "\u8d38", + "12251": "\u8eb2", + "12252": "\u8d4f", + "12253": "\u626c", + "12254": "\u7cd5", + "12255": "\u649e", + "12256": "\u8d37", + "12257": "\u593a", + "12258": "\u8212", + "12259": "\u5fc6", + "12260": "\u6d01", + "12261": "\u61d2", + "12262": "\u6c47", + "12263": "\u8f85", + "12264": "\u62d6", + "12265": "\u8bd1", + "12266": "\u788e", + "12267": "\u4f19", + "12268": "\u4eea", + "12269": "\u5496", + "12270": "\u6e10", + "12271": "\u8d24", + "12272": "\u810f", + "12273": "\u996e", + "12274": "\u6478", + "12275": "\u9080", + "12276": "\u8f88", + "12277": "\u563f", + "12278": "\u6653", + "12279": "\u62e5", + "12280": "\u9897", + "12281": "\u5a03", + "12282": "\u5e05", + "12283": "\u8d56", + "12284": "\u62c6", + "12285": "\u5e9f", + "12286": "\u70c2", + "12287": "\u9605", + "12288": "\u9a91", + "12289": "\u6c61", + "12290": "\u63d2", + "12291": "\u8fea", + "12292": "\u82f9", + "12293": "\u8bca", + "12294": "\u8d26", + "12295": "\u6682", + "12296": "\u7a23", + "12297": "\u9a7e", + "12298": "\u62fc", + "12299": "\u987f", + "12300": "\u9a82", + "12301": "\u8bfa", + "12302": "\u6c89", + "12303": "\u5582", + "12304": "\u5bbe", + "12305": "\u62ac", + "12306": "\u503a", + "12307": "\u51e4", + "12308": "\u8d8b", + "12309": "\u5385", + "12310": "\u7237", + "12311": "\u6865", + "12312": "\u6444", + "12313": "\u6269", + "12314": "\u9505", + "12315": "\u8ba2", + "12316": "\u9501", + "12317": "\u4e4c", + "12318": "\u4e30", + "12319": "\u9738", + "12320": "\u4f26", + "12321": "\u626b", + "12322": "\u8bda", + "12323": "\u9c9c", + "12324": "\u9057", + "12325": "\u9f50", + "12326": "\u6446", + "12327": "\u5434", + "12328": "\u9690", + "12329": "\u7840", + "12330": "\u5bbd", + "12331": "\u5706", + "12332": "\u78b0", + "12333": "\u60ef", + "12334": "\u4ecd", + "12335": "\u60ca", + "12336": "\u654c", + "12337": "\u997c", + "12338": "\u6325", + "12339": "\u6770", + "12340": "\u9c81", + "12341": "\u7ee9", + "12342": "\u62a2", + "12343": "\u8d3c", + "12344": "\u5e86", + "12345": "\u6c64", + "12346": "\u560e", + "12347": "\u8d1d", + "12348": "\u5f03", + "12349": "\u6316", + "12350": "\u955c", + "12351": "\u558a", + "12352": "\u5269", + "12353": "\u5077", + "12354": "\u9635", + "12355": "\u989c", + "12356": "\u8363", + "12357": "\u7f5a", + "12358": "\u54df", + "12359": "\u8f91", + "12360": "\u9634", + "12361": "\u7eaf", + "12362": "\u7b7e", + "12363": "\u6eda", + "12364": "\u84dd", + "12365": "\u7f18", + "12366": "\u8be2", + "12367": "\u6d89", + "12368": "\u9a97", + "12369": "\u7ade", + "12370": "\u8dcc", + "12371": "\u5761", + "12372": "\u8bbf", + "12373": "\u707e", + "12374": "\u95ed", + "12375": "\u9875", + "12376": "\u94a2", + "12377": "\u4f30", + "12378": "\u82cf", + "12379": "\u5e01", + "12380": "\u5251", + "12381": "\u5e93", + "12382": "\u706d", + "12383": "\u6302", + "12384": "\u8fdd", + "12385": "\u552e", + "12386": "\u5b81", + "12387": "\u6263", + "12388": "\u575a", + "12389": "\u6768", + "12390": "\u8d62", + "12391": "\u4e1d", + "12392": "\u55bd", + "12393": "\u67aa", + "12394": "\u8d5a", + "12395": "\u5708", + "12396": "\u7eb3", + "12397": "\u8d34", + "12398": "\u7597", + "12399": "\u5389", + "12400": "\u8f6f", + "12401": "\u6c9f", + "12402": "\u8bd7", + "12403": "\u8d5e", + "12404": "\u70df", + "12405": "\u8d25", + "12406": "\u8651", + "12407": "\u65c1", + "12408": "\u635f", + "12409": "\u54af", + "12410": "\u6742", + "12411": "\u7f3a", + "12412": "\u5976", + "12413": "\u5c9b", + "12414": "\u4e61", + "12415": "\u7ec7", + "12416": "\u70e7", + "12417": "\u989d", + "12418": "\u51c0", + "12419": "\u952e", + "12420": "\u9547", + "12421": "\u8138", + "12422": "\u7a33", + "12423": "\u6863", + "12424": "\u8f7d", + "12425": "\u5979", + "12426": "\u7a0d", + "12427": "\u8bf8", + "12428": "\u7f16", + "12429": "\u8d75", + "12430": "\u7334", + "12431": "\u6447", + "12432": "\u5170", + "12433": "\u54b1", + "12434": "\u4ec5", + "12435": "\u5218", + "12436": "\u8c0b", + "12437": "\u7adf", + "12438": "\u542f", + "12439": "\u68a6", + "12440": "\u4f1f", + "12441": "\u4e34", + "12442": "\u7edc", + "12443": "\u5b59", + "12444": "\u97e9", + "12445": "\u8f6e", + "12446": "\u6da8", + "12447": "\u5bfb", + "12448": "\u9500", + "12449": "\u8bef", + "12450": "\u5382", + "12451": "\u91ca", + "12452": "\u7ecd", + "12453": "\u4e8f", + "12454": "\u9636", + "12455": "\u8bad", + "12456": "\u8d2d", + "12457": "\u95ea", + "12458": "\u641c", + "12459": "\u9646", + "12460": "\u52b3", + "12461": "\u4e3d", + "12462": "\u5f39", + "12463": "\u6076", + "12464": "\u53bf", + "12465": "\u7801", + "12466": "\u4e22", + "12467": "\u5f02", + "12468": "\u8d27", + "12469": "\u6bd5", + "12470": "\u9891", + "12471": "\u8428", + "12472": "\u6293", + "12473": "\u5956", + "12474": "\u7b14", + "12475": "\u6000", + "12476": "\u8f93", + "12477": "\u6811", + "12478": "\u7eaa", + "12479": "\u996d", + "12480": "\u70e6", + "12481": "\u7eff", + "12482": "\u51b0", + "12483": "\u80dc", + "12484": "\u62e9", + "12485": "\u7238", + "12486": "\u51fb", + "12487": "\u95fb", + "12488": "\u574f", + "12489": "\u94c1", + "12490": "\u83b7", + "12491": "\u987e", + "12492": "\u56f4", + "12493": "\u8d23", + "12494": "\u60a8", + "12495": "\u9002", + "12496": "\u5f52", + "12497": "\u8bc4", + "12498": "\u76d8", + "12499": "\u9e21", + "12500": "\u5e7a", + "12501": "\u804c", + "12502": "\u79ef", + "12503": "\u827a", + "12504": "\u9488", + "12505": "\u8d76", + "12506": "\u8111", + "12507": "\u5174", + "12508": "\u8d22", + "12509": "\u519c", + "12510": "\u7d27", + "12511": "\u987a", + "12512": "\u56ed", + "12513": "\u6d4b", + "12514": "\u8baf", + "12515": "\u5f55", + "12516": "\u8d35", + "12517": "\u538b", + "12518": "\u94f6", + "12519": "\u8303", + "12520": "\u9648", + "12521": "\u5267", + "12522": "\u7ec3", + "12523": "\u76d1", + "12524": "\u534f", + "12525": "\u51cf", + "12526": "\u8bcd", + "12527": "\u5450", + "12528": "\u4f18", + "12529": "\u949f", + "12530": "\u5c81", + "12531": "\u4e25", + "12532": "\u7ec6", + "12533": "\u6c49", + "12534": "\u8d1f", + "12535": "\u76d6", + "12536": "\u836f", + "12537": "\u4e9a", + "12538": "\u9876", + "12539": "\u4f24", + "12540": "\u5c42", + "12541": "\u70ed", + "12542": "\u8f7b", + "12543": "\u68c0", + "12544": "\u5c14", + "12545": "\u7075", + "12546": "\u4ebf", + "12547": "\u7ef4", + "12548": "\u6781", + "12549": "\u8865", + "12550": "\u8425", + "12551": "\u54cd", + "12552": "\u9760", + "12553": "\u6548", + "12554": "\u6267", + "12555": "\u6740", + "12556": "\u663e", + "12557": "\u5ba1", + "12558": "\u8d28", + "12559": "\u987b", + "12560": "\u6784", + "12561": "\u5723", + "12562": "\u8c13", + "12563": "\u5356", + "12564": "\u54e5", + "12565": "\u4eb2", + "12566": "\u6d4e", + "12567": "\u7edd", + "12568": "\u9c7c", + "12569": "\u9669", + "12570": "\u8bfb", + "12571": "\u8bfe", + "12572": "\u7f57", + "12573": "\u867d", + "12574": "\u98de", + "12575": "\u5b69", + "12576": "\u5361", + "12577": "\u536b", + "12578": "\u503c", + "12579": "\u62a4", + "12580": "\u8c08", + "12581": "\u6015", + "12582": "\u9a8c", + "12583": "\u8d5b", + "12584": "\u620f", + "12585": "\u7ee7", + "12586": "\u8054", + "12587": "\u9633", + "12588": "\u5212", + "12589": "\u521b", + "12590": "\u665a", + "12591": "\u589e", + "12592": "\u8bc9", + "12593": "\u8bd5", + "12594": "\u8bc6", + "12595": "\u8dd1", + "12596": "\u9884", + "12597": "\u73af", + "12598": "\u8bb8", + "12599": "\u61c2", + "12600": "\u6001", + "12601": "\u9879", + "12602": "\u56e2", + "12603": "\u5bab", + "12604": "\u5907", + "12605": "\u79bb", + "12606": "\u9f99", + "12607": "\u8ba8", + "12608": "\u9645", + "12609": "\u7b80", + "12610": "\u517b", + "12611": "\u5bfc", + "12612": "\u4e3e", + "12613": "\u5757", + "12614": "\u961f", + "12615": "\u8fde", + "12616": "\u672f", + "12617": "\u5386", + "12618": "\u56fe", + "12619": "\u5219", + "12620": "\u8bc1", + "12621": "\u8bed", + "12622": "\u62dc", + "12623": "\u4e13", + "12624": "\u7ea2", + "12625": "\u6362", + "12626": "\u4f17", + "12627": "\u6b65", + "12628": "\u7ea7", + "12629": "\u6743", + "12630": "\u4e60", + "12631": "\u67e5", + "12632": "\u590d", + "12633": "\u513f", + "12634": "\u51b5", + "12635": "\u51b3", + "12636": "\u9886", + "12637": "\u8fbe", + "12638": "\u6807", + "12639": "\u6b22", + "12640": "\u7ec4", + "12641": "\u641e", + "12642": "\u7c7b", + "12643": "\u7eed", + "12644": "\u53e6", + "12645": "\u5988", + "12646": "\u5e7f", + "12647": "\u534e", + "12648": "\u4e50", + "12649": "\u89c4", + "12650": "\u4f20", + "12651": "\u786e", + "12652": "\u8282", + "12653": "\u4e49", + "12654": "\u561e", + "12655": "\u9519", + "12656": "\u7ea6", + "12657": "\u89c6", + "12658": "\u519b", + "12659": "\u54c7", + "12660": "\u6218", + "12661": "\u5f3a", + "12662": "\u8bae", + "12663": "\u6536", + "12664": "\u89c2", + "12665": "\u8c01", + "12666": "\u4ef7", + "12667": "\u8f6c", + "12668": "\u8fd0", + "12669": "\u62ff", + "12670": "\u52a1", + "12671": "\u6389", + "12672": "\u5e76", + "12673": "\u7f51", + "12674": "\u8fdc", + "12675": "\u6ee1", + "12676": "\u7ebf", + "12677": "\u96be", + "12678": "\u603b", + "12679": "\u94b1", + "12680": "\u7edf", + "12681": "\u5e2e", + "12682": "\u8ba1", + "12683": "\u98ce", + "12684": "\u95e8", + "12685": "\u7231", + "12686": "\u5f20", + "12687": "\u5440", + "12688": "\u9a6c", + "12689": "\u627e", + "12690": "\u6c14", + "12691": "\u529e", + "12692": "\u8bbe", + "12693": "\u5e26", + "12694": "\u4e70", + "12695": "\u5904", + "12696": "\u62a5", + "12697": "\u9009", + "12698": "\u8ba4", + "12699": "\u8bba", + "12700": "\u4e66", + "12701": "\u89c1", + "12702": "\u8f66", + "12703": "\u7ed3", + "12704": "\u5355", + "12705": "\u8bb0", + "12706": "\u6bcf", + "12707": "\u591f", + "12708": "\u8c03", + "12709": "\u4ea7", + "12710": "\u542c", + "12711": "\u5566", + "12712": "\u8c22", + "12713": "\u8bf6", + "12714": "\u5458", + "12715": "\u55ef", + "12716": "\u8f83", + "12717": "\u7535", + "12718": "\u8d44", + "12719": "\u53d8", + "12720": "\u65e0", + "12721": "\u522b", + "12722": "\u573a", + "12723": "\u54ce", + "12724": "\u5417", + "12725": "\u8ba9", + "12726": "\u8be5", + "12727": "\u4ece", + "12728": "\u5427", + "12729": "\u4e1a", + "12730": "\u9898", + "12731": "\u600e", + "12732": "\u95f4", + "12733": "\u4e1c", + "12734": "\u561b", + "12735": "\u5e94", + "12736": "\u957f", + "12737": "\u8fdb", + "12738": "\u521a", + "12739": "\u52a8", + "12740": "\u5173", + "12741": "\u8fb9", + "12742": "\u89c9", + "12743": "\u800c", + "12744": "\u53d1", + "12745": "\u7ecf", + "12746": "\u8bdd", + "12747": "\u79cd", + "12748": "\u8bb2", + "12749": "\u5f00", + "12750": "\u5b83", + "12751": "\u5b9e", + "12752": "\u7ed9", + "12753": "\u505a", + "12754": "\u8ddf", + "12755": "\u73b0", + "12756": "\u8fc7", + "12757": "\u5443", + "12758": "\u5f88", + "12759": "\u54e6", + "12760": "\u65f6", + "12761": "\u8fd8", + "12762": "\u5462", + "12763": "\u8bf4", + "12764": "\u4e3a", + "12765": "\u4e48", + "12766": "\u4eec", + "12767": "\u554a", + "12768": "\u4f60", + "12769": "\u8fd9", + "12770": "\u3da7", + "12771": "\u4f5a", + "12772": "\u4f5d", + "12773": "\u4fdf", + "12774": "\u5048", + "12775": "\u507b", + "12776": "\u52d0", + "12777": "\u530f", + "12778": "\u5372", + "12779": "\u540b", + "12780": "\u54d5", + "12781": "\u5533", + "12782": "\u5572", + "12783": "\u5576", + "12784": "\u55eb", + "12785": "\u55ec", + "12786": "\u560f", + "12787": "\u56d7", + "12788": "\u56eb", + "12789": "\u56ef", + "12790": "\u56f5", + "12791": "\u5704", + "12792": "\u576d", + "12793": "\u578c", + "12794": "\u5803", + "12795": "\u5914", + "12796": "\u5941", + "12797": "\u59aa", + "12798": "\u5a08", + "12799": "\u5ad2", + "12800": "\u5d5b", + "12801": "\u5e54", + "12802": "\u5fea", + "12803": "\u602b", + "12804": "\u60ad", + "12805": "\u61d1", + "12806": "\u620d", + "12807": "\u6217", + "12808": "\u6427", + "12809": "\u6555", + "12810": "\u65d6", + "12811": "\u661d", + "12812": "\u668c", + "12813": "\u66be", + "12814": "\u66c8", + "12815": "\u67a5", + "12816": "\u67c3", + "12817": "\u680c", + "12818": "\u6874", + "12819": "\u6877", + "12820": "\u6901", + "12821": "\u691f", + "12822": "\u6924", + "12823": "\u6934", + "12824": "\u6989", + "12825": "\u69ed", + "12826": "\u6a50", + "12827": "\u6a97", + "12828": "\u6b38", + "12829": "\u6b59", + "12830": "\u6b81", + "12831": "\u6b9a", + "12832": "\u6c1a", + "12833": "\u6c24", + "12834": "\u6c32", + "12835": "\u6cd6", + "12836": "\u6cfa", + "12837": "\u6d3a", + "12838": "\u6d50", + "12839": "\u6d91", + "12840": "\u6ef9", + "12841": "\u6f2d", + "12842": "\u6f46", + "12843": "\u6fa7", + "12844": "\u6fb6", + "12845": "\u70c0", + "12846": "\u71b5", + "12847": "\u7260", + "12848": "\u7301", + "12849": "\u7339", + "12850": "\u736d", + "12851": "\u7391", + "12852": "\u73e5", + "12853": "\u7622", + "12854": "\u765e", + "12855": "\u77cd", + "12856": "\u782d", + "12857": "\u7852", + "12858": "\u7856", + "12859": "\u78f4", + "12860": "\u7a39", + "12861": "\u7b38", + "12862": "\u7bfc", + "12863": "\u7ea1", + "12864": "\u7f1b", + "12865": "\u7f2c", + "12866": "\u7fa7", + "12867": "\u8004", + "12868": "\u800b", + "12869": "\u801c", + "12870": "\u802a", + "12871": "\u80b1", + "12872": "\u81ec", + "12873": "\u824b", + "12874": "\u827f", + "12875": "\u8297", + "12876": "\u82be", + "12877": "\u8333", + "12878": "\u833c", + "12879": "\u835b", + "12880": "\u8378", + "12881": "\u83d8", + "12882": "\u84af", + "12883": "\u84c1", + "12884": "\u85b7", + "12885": "\u85e0", + "12886": "\u86ac", + "12887": "\u86b4", + "12888": "\u86c9", + "12889": "\u877d", + "12890": "\u87c6", + "12891": "\u880a", + "12892": "\u89e5", + "12893": "\u8be4", + "12894": "\u8bf9", + "12895": "\u8c16", + "12896": "\u8c18", + "12897": "\u8c2e", + "12898": "\u8c36", + "12899": "\u8c47", + "12900": "\u8c62", + "12901": "\u8c89", + "12902": "\u8d32", + "12903": "\u8d49", + "12904": "\u8e41", + "12905": "\u8e49", + "12906": "\u8f8a", + "12907": "\u900b", + "12908": "\u9051", + "12909": "\u90c7", + "12910": "\u915e", + "12911": "\u9490", + "12912": "\u9492", + "12913": "\u94bc", + "12914": "\u94cb", + "12915": "\u94cd", + "12916": "\u94d1", + "12917": "\u9511", + "12918": "\u954f", + "12919": "\u9554", + "12920": "\u95fe", + "12921": "\u9649", + "12922": "\u972a", + "12923": "\u9751", + "12924": "\u97eb", + "12925": "\u98a6", + "12926": "\u990d", + "12927": "\u9974", + "12928": "\u9991", + "12929": "\u9a88", + "12930": "\u9a93", + "12931": "\u9aba", + "12932": "\u9acc", + "12933": "\u9aef", + "12934": "\u9b5f", + "12935": "\u9cbc", + "12936": "\u9cc3", + "12937": "\u9e29", + "12938": "\u9e2a", + "12939": "\u9e2b", + "12940": "\u9e41", + "12941": "\u9e67", + "12942": "\u9e73", + "12943": "\uff0c", + "12944": "", + "12945": "\u2581t", + "12946": "\u2581\u0111", + "12947": "nh", + "12948": "\u2581th", + "12949": "\u2581ch", + "12950": "\u2581nh", + "12951": "\u2581kh", + "12952": "\u2581ng", + "12953": "\u2581g", + "12954": "\u00f4ng", + "12955": "\u2581ph", + "12956": "\u2581r", + "12957": "\u2581gi", + "12958": "\u1eddi", + "12959": "\u00ean", + "12960": "\u2581c\u00e1", + "12961": "\u2581v\u00e0", + "12962": "\u2581c\u00f3", + "12963": "i\u1ec7", + "12964": "\u1ed9t", + "12965": "\u2581kh\u00f4ng", + "12966": "\u00f4i", + "12967": "i\u1ebf", + "12968": "\u2581m\u1ed9t", + "12969": "\u1edbi", + "12970": "\u1ee7a", + "12971": "\u2581c\u1ee7a", + "12972": "\u2581x", + "12973": "\u01b0\u1eddi", + "12974": "\u01b0\u1ee3", + "12975": "\u00ecnh", + "12976": "\u1ea5t", + "12977": "\u1ea1i", + "12978": "uy", + "12979": "\u00e0y", + "12980": "\u2581ng\u01b0\u1eddi", + "12981": "ong", + "12982": "anh", + "12983": "\u01b0\u1ee3c", + "12984": "i\u1ec1", + "12985": "\u2581\u0111\u01b0\u1ee3c", + "12986": "\u2581n\u00f3", + "12987": "\u1eefng", + "12988": "\u2581cho", + "12989": "\u1ea5y", + "12990": "\u2581nh\u01b0", + "12991": "\u2581ngh", + "12992": "\u2581m\u00e0", + "12993": "\u2581t\u00f4i", + "12994": "\u01b0\u01a1", + "12995": "\u1ea3i", + "12996": "\u2581nh\u1eefng", + "12997": "\u2581th\u00ec", + "12998": "\u00e2y", + "12999": "ao", + "13000": "\u2581\u0111\u00e3", + "13001": "\u1ea7n", + "13002": "\u2581c\u00e1i", + "13003": "\u2581\u0111\u00f3", + "13004": "\u2581\u0111i", + "13005": "\u2581v\u1edbi", + "13006": "\u01b0\u1edb", + "13007": "\u2581trong", + "13008": "\u2581c\u00e1c", + "13009": "i\u1ec1u", + "13010": "\u2581n\u00e0y", + "13011": "\u0169ng", + "13012": "\u00fang", + "13013": "\u0103m", + "13014": "\u1ed3i", + "13015": "\u1ea1n", + "13016": "\u2581anh", + "13017": "\u01b0", + "13018": "\u1ebf", + "13019": "\u1ea1", + "13020": "\u1ed9", + "13021": "\u1edd", + "13022": "\u1ea3", + "13023": "\u1ea5", + "13024": "\u1ed1", + "13025": "\u1edb", + "13026": "\u1ec7", + "13027": "\u1ec1", + "13028": "\u1ec3", + "13029": "\u01a1", + "13030": "\u1ee7", + "13031": "\u1ead", + "13032": "\u1ee3", + "13033": "\u1ea7", + "13034": "\u1ecb", + "13035": "\u1eef", + "13036": "\u1ee9", + "13037": "\u1ef1", + "13038": "\u1ecd", + "13039": "\u1ed3", + "13040": "\u1edf", + "13041": "\u1eaf", + "13042": "\u1eeb", + "13043": "\u1ee5", + "13044": "\u0169", + "13045": "\u1ed5", + "13046": "\u1eb7", + "13047": "\u1ebd", + "13048": "\u1eb1", + "13049": "\u0110", + "13050": "\u1ec9", + "13051": "\u1ecf", + "13052": "\u1eed", + "13053": "\u0129", + "13054": "\u1ed7", + "13055": "\u1eab", + "13056": "\u1eb9", + "13057": "\u1ea9", + "13058": "\u1ec5", + "13059": "\u1ebb", + "13060": "\u1eb3", + "13061": "\u1ef9", + "13062": "\u1ee1", + "13063": "\u1ef3", + "13064": "\u1ef7", + "13065": "\u1eb5", + "13066": "\u1ede", + "13067": "\u1ef5", + "13068": "\u1ea4", + "13069": "\u00dd", + "13070": "\u1eea", + "13071": "\u0102", + "13072": "\u1edc", + "13073": "\u1ea2", + "13074": "\u1ed2", + "13075": "\u01a0", + "13076": "\u01af", + "13077": "\u1ee8", + "13078": "\u1ed0", + "13079": "\u1eda", + "13080": "\u1ee6", + "13081": "\u1ea8", + "13082": "\u1eae", + "13083": "\u1ed4", + "13084": "\u1ef6", + "13085": "\u1ebe", + "13086": "\u1ef2" +} \ No newline at end of file diff --git a/multilingual/4480ms/decoder.mlmodelc/analytics/coremldata.bin b/multilingual/4480ms/decoder.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..646ff8b67c27f7e1035df022ec9e0691346d2780 --- /dev/null +++ b/multilingual/4480ms/decoder.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9c32520b84ded2c698000854a77228adf394db522b5a3c25f7737415aae7ed0d +size 243 diff --git a/multilingual/4480ms/decoder.mlmodelc/coremldata.bin b/multilingual/4480ms/decoder.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..cc88579eb4924d7e0d2bb8493d85adfc48562752 --- /dev/null +++ b/multilingual/4480ms/decoder.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e78d9de0dc336cbdb7d246bd958fc580c26519f6359a4164ad6cda2f0bfca964 +size 433 diff --git a/multilingual/4480ms/decoder.mlmodelc/model.mil b/multilingual/4480ms/decoder.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..697235107988a50dadcf7b2334d72723c3d73048 --- /dev/null +++ b/multilingual/4480ms/decoder.mlmodelc/model.mil @@ -0,0 +1,64 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.5.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] +{ + func main(tensor c_in, tensor h_in, tensor token, tensor token_length) { + int32 y_axis_0 = const()[name = string("y_axis_0"), val = int32(0)]; + int32 y_batch_dims_0 = const()[name = string("y_batch_dims_0"), val = int32(0)]; + bool y_validate_indices_0 = const()[name = string("y_validate_indices_0"), val = bool(false)]; + tensor module_prediction_embed_weight_to_fp16 = const()[name = string("module_prediction_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + string token_to_int16_dtype_0 = const()[name = string("token_to_int16_dtype_0"), val = string("int16")]; + tensor token_to_int16 = cast(dtype = token_to_int16_dtype_0, x = token)[name = string("cast_8")]; + tensor y_cast_fp16_cast_uint16 = gather(axis = y_axis_0, batch_dims = y_batch_dims_0, indices = token_to_int16, validate_indices = y_validate_indices_0, x = module_prediction_embed_weight_to_fp16)[name = string("y_cast_fp16_cast_uint16")]; + tensor input_3_perm_0 = const()[name = string("input_3_perm_0"), val = tensor([1, 0, 2])]; + int32 split_0_num_splits_0 = const()[name = string("split_0_num_splits_0"), val = int32(2)]; + int32 split_0_axis_0 = const()[name = string("split_0_axis_0"), val = int32(0)]; + string h_in_to_fp16_dtype_0 = const()[name = string("h_in_to_fp16_dtype_0"), val = string("fp16")]; + tensor h_in_to_fp16 = cast(dtype = h_in_to_fp16_dtype_0, x = h_in)[name = string("cast_7")]; + tensor split_0_cast_fp16_0, tensor split_0_cast_fp16_1 = split(axis = split_0_axis_0, num_splits = split_0_num_splits_0, x = h_in_to_fp16)[name = string("split_0_cast_fp16")]; + int32 split_1_num_splits_0 = const()[name = string("split_1_num_splits_0"), val = int32(2)]; + int32 split_1_axis_0 = const()[name = string("split_1_axis_0"), val = int32(0)]; + string c_in_to_fp16_dtype_0 = const()[name = string("c_in_to_fp16_dtype_0"), val = string("fp16")]; + tensor c_in_to_fp16 = cast(dtype = c_in_to_fp16_dtype_0, x = c_in)[name = string("cast_6")]; + tensor split_1_cast_fp16_0, tensor split_1_cast_fp16_1 = split(axis = split_1_axis_0, num_splits = split_1_num_splits_0, x = c_in_to_fp16)[name = string("split_1_cast_fp16")]; + tensor input_lstm_layer_0_lstm_h0_squeeze_axes_0 = const()[name = string("input_lstm_layer_0_lstm_h0_squeeze_axes_0"), val = tensor([0])]; + tensor input_lstm_layer_0_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_lstm_layer_0_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_0)[name = string("input_lstm_layer_0_lstm_h0_squeeze_cast_fp16")]; + tensor input_lstm_layer_0_lstm_c0_squeeze_axes_0 = const()[name = string("input_lstm_layer_0_lstm_c0_squeeze_axes_0"), val = tensor([0])]; + tensor input_lstm_layer_0_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_lstm_layer_0_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_0)[name = string("input_lstm_layer_0_lstm_c0_squeeze_cast_fp16")]; + string input_lstm_layer_0_direction_0 = const()[name = string("input_lstm_layer_0_direction_0"), val = string("forward")]; + bool input_lstm_layer_0_output_sequence_0 = const()[name = string("input_lstm_layer_0_output_sequence_0"), val = bool(true)]; + string input_lstm_layer_0_recurrent_activation_0 = const()[name = string("input_lstm_layer_0_recurrent_activation_0"), val = string("sigmoid")]; + string input_lstm_layer_0_cell_activation_0 = const()[name = string("input_lstm_layer_0_cell_activation_0"), val = string("tanh")]; + string input_lstm_layer_0_activation_0 = const()[name = string("input_lstm_layer_0_activation_0"), val = string("tanh")]; + tensor concat_1_to_fp16 = const()[name = string("concat_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16752768)))]; + tensor concat_2_to_fp16 = const()[name = string("concat_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20029632)))]; + tensor concat_0_to_fp16 = const()[name = string("concat_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23306496)))]; + tensor input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = y_cast_fp16_cast_uint16)[name = string("transpose_2")]; + tensor input_lstm_layer_0_cast_fp16_0, tensor input_lstm_layer_0_cast_fp16_1, tensor input_lstm_layer_0_cast_fp16_2 = lstm(activation = input_lstm_layer_0_activation_0, bias = concat_0_to_fp16, cell_activation = input_lstm_layer_0_cell_activation_0, direction = input_lstm_layer_0_direction_0, initial_c = input_lstm_layer_0_lstm_c0_squeeze_cast_fp16, initial_h = input_lstm_layer_0_lstm_h0_squeeze_cast_fp16, output_sequence = input_lstm_layer_0_output_sequence_0, recurrent_activation = input_lstm_layer_0_recurrent_activation_0, weight_hh = concat_2_to_fp16, weight_ih = concat_1_to_fp16, x = input_3_cast_fp16)[name = string("input_lstm_layer_0_cast_fp16")]; + tensor input_lstm_h0_squeeze_axes_0 = const()[name = string("input_lstm_h0_squeeze_axes_0"), val = tensor([0])]; + tensor input_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_1)[name = string("input_lstm_h0_squeeze_cast_fp16")]; + tensor input_lstm_c0_squeeze_axes_0 = const()[name = string("input_lstm_c0_squeeze_axes_0"), val = tensor([0])]; + tensor input_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_1)[name = string("input_lstm_c0_squeeze_cast_fp16")]; + string input_direction_0 = const()[name = string("input_direction_0"), val = string("forward")]; + bool input_output_sequence_0 = const()[name = string("input_output_sequence_0"), val = bool(true)]; + string input_recurrent_activation_0 = const()[name = string("input_recurrent_activation_0"), val = string("sigmoid")]; + string input_cell_activation_0 = const()[name = string("input_cell_activation_0"), val = string("tanh")]; + string input_activation_0 = const()[name = string("input_activation_0"), val = string("tanh")]; + tensor concat_4_to_fp16 = const()[name = string("concat_4_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23311680)))]; + tensor concat_5_to_fp16 = const()[name = string("concat_5_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26588544)))]; + tensor concat_3_to_fp16 = const()[name = string("concat_3_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29865408)))]; + tensor input_cast_fp16_0, tensor input_cast_fp16_1, tensor input_cast_fp16_2 = lstm(activation = input_activation_0, bias = concat_3_to_fp16, cell_activation = input_cell_activation_0, direction = input_direction_0, initial_c = input_lstm_c0_squeeze_cast_fp16, initial_h = input_lstm_h0_squeeze_cast_fp16, output_sequence = input_output_sequence_0, recurrent_activation = input_recurrent_activation_0, weight_hh = concat_5_to_fp16, weight_ih = concat_4_to_fp16, x = input_lstm_layer_0_cast_fp16_0)[name = string("input_cast_fp16")]; + int32 obj_3_axis_0 = const()[name = string("obj_3_axis_0"), val = int32(0)]; + tensor obj_3_cast_fp16 = stack(axis = obj_3_axis_0, values = (input_lstm_layer_0_cast_fp16_1, input_cast_fp16_1))[name = string("obj_3_cast_fp16")]; + string obj_3_cast_fp16_to_fp32_dtype_0 = const()[name = string("obj_3_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + int32 obj_axis_0 = const()[name = string("obj_axis_0"), val = int32(0)]; + tensor obj_cast_fp16 = stack(axis = obj_axis_0, values = (input_lstm_layer_0_cast_fp16_2, input_cast_fp16_2))[name = string("obj_cast_fp16")]; + string obj_cast_fp16_to_fp32_dtype_0 = const()[name = string("obj_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 2, 0])]; + string transpose_0_cast_fp16_to_fp32_dtype_0 = const()[name = string("transpose_0_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor transpose_0_cast_fp16 = transpose(perm = transpose_0_perm_0, x = input_cast_fp16_0)[name = string("transpose_1")]; + tensor decoder_out = cast(dtype = transpose_0_cast_fp16_to_fp32_dtype_0, x = transpose_0_cast_fp16)[name = string("cast_3")]; + tensor c_out = cast(dtype = obj_cast_fp16_to_fp32_dtype_0, x = obj_cast_fp16)[name = string("cast_4")]; + tensor h_out = cast(dtype = obj_3_cast_fp16_to_fp32_dtype_0, x = obj_3_cast_fp16)[name = string("cast_5")]; + tensor token_length_tmp = identity(x = token_length)[name = string("token_length_tmp")]; + } -> (decoder_out, h_out, c_out); +} \ No newline at end of file diff --git a/multilingual/4480ms/decoder.mlmodelc/weights/weight.bin b/multilingual/4480ms/decoder.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..8b094b9de3ad890a887d85c198a8ee88800323fa --- /dev/null +++ b/multilingual/4480ms/decoder.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2e49d029739602d265719f0040fcf5328c007a1ed62d0c6ea621e0b2aaeb9a64 +size 29870592 diff --git a/multilingual/4480ms/decoder.mlpackage/Data/com.apple.CoreML/model.mlmodel b/multilingual/4480ms/decoder.mlpackage/Data/com.apple.CoreML/model.mlmodel new file mode 100644 index 0000000000000000000000000000000000000000..3b86cf52f95837fc5e90f3e8cf6373bde60c5ad1 --- /dev/null +++ b/multilingual/4480ms/decoder.mlpackage/Data/com.apple.CoreML/model.mlmodel @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c26ee345b7763ed9f217561572b1386719956de5b58e9174f5586926b4ab85c5 +size 10360 diff --git a/multilingual/4480ms/decoder.mlpackage/Data/com.apple.CoreML/weights/weight.bin b/multilingual/4480ms/decoder.mlpackage/Data/com.apple.CoreML/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..8b094b9de3ad890a887d85c198a8ee88800323fa --- /dev/null +++ b/multilingual/4480ms/decoder.mlpackage/Data/com.apple.CoreML/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2e49d029739602d265719f0040fcf5328c007a1ed62d0c6ea621e0b2aaeb9a64 +size 29870592 diff --git a/multilingual/4480ms/decoder.mlpackage/Manifest.json b/multilingual/4480ms/decoder.mlpackage/Manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..1ac968d8fcfbdfdead1150534e3677d2f045db51 --- /dev/null +++ b/multilingual/4480ms/decoder.mlpackage/Manifest.json @@ -0,0 +1,18 @@ +{ + "fileFormatVersion": "1.0.0", + "itemInfoEntries": { + "542DC13B-08DF-47C7-AAAA-C2F9DE67BB37": { + "author": "com.apple.CoreML", + "description": "CoreML Model Weights", + "name": "weights", + "path": "com.apple.CoreML/weights" + }, + "8B23B00A-4F60-49E4-B460-719FB6B05887": { + "author": "com.apple.CoreML", + "description": "CoreML Model Specification", + "name": "model.mlmodel", + "path": "com.apple.CoreML/model.mlmodel" + } + }, + "rootModelIdentifier": "8B23B00A-4F60-49E4-B460-719FB6B05887" +} diff --git a/multilingual/4480ms/decoder_joint.mlmodelc/analytics/coremldata.bin b/multilingual/4480ms/decoder_joint.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..11215e473da8d2cc24d47de983947493613791d7 --- /dev/null +++ b/multilingual/4480ms/decoder_joint.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3b314763ced4d2bd27484b8ec2a9c60939b724f8ab60b32d29ad0c03f6192599 +size 243 diff --git a/multilingual/4480ms/decoder_joint.mlmodelc/coremldata.bin b/multilingual/4480ms/decoder_joint.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..e8bec280a33a1f48dd9567146f4fc504b5739527 --- /dev/null +++ b/multilingual/4480ms/decoder_joint.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9c71190b97b5654d6322193d7f3f6e22764afab15f3334a26c278c29ad06ae47 +size 454 diff --git a/multilingual/4480ms/decoder_joint.mlmodelc/model.mil b/multilingual/4480ms/decoder_joint.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..e0c611c93d86fefc0f5758164fe03174da291441 --- /dev/null +++ b/multilingual/4480ms/decoder_joint.mlmodelc/model.mil @@ -0,0 +1,83 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.5.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] +{ + func main(tensor c_in, tensor encoder, tensor h_in, tensor token, tensor token_length) { + int32 y_axis_0 = const()[name = string("y_axis_0"), val = int32(0)]; + int32 y_batch_dims_0 = const()[name = string("y_batch_dims_0"), val = int32(0)]; + bool y_validate_indices_0 = const()[name = string("y_validate_indices_0"), val = bool(false)]; + tensor decoder_module_prediction_embed_weight_to_fp16 = const()[name = string("decoder_module_prediction_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + string token_to_int16_dtype_0 = const()[name = string("token_to_int16_dtype_0"), val = string("int16")]; + tensor token_to_int16 = cast(dtype = token_to_int16_dtype_0, x = token)[name = string("cast_9")]; + tensor y_cast_fp16_cast_uint16 = gather(axis = y_axis_0, batch_dims = y_batch_dims_0, indices = token_to_int16, validate_indices = y_validate_indices_0, x = decoder_module_prediction_embed_weight_to_fp16)[name = string("y_cast_fp16_cast_uint16")]; + tensor input_3_perm_0 = const()[name = string("input_3_perm_0"), val = tensor([1, 0, 2])]; + int32 split_0_num_splits_0 = const()[name = string("split_0_num_splits_0"), val = int32(2)]; + int32 split_0_axis_0 = const()[name = string("split_0_axis_0"), val = int32(0)]; + string h_in_to_fp16_dtype_0 = const()[name = string("h_in_to_fp16_dtype_0"), val = string("fp16")]; + tensor h_in_to_fp16 = cast(dtype = h_in_to_fp16_dtype_0, x = h_in)[name = string("cast_8")]; + tensor split_0_cast_fp16_0, tensor split_0_cast_fp16_1 = split(axis = split_0_axis_0, num_splits = split_0_num_splits_0, x = h_in_to_fp16)[name = string("split_0_cast_fp16")]; + int32 split_1_num_splits_0 = const()[name = string("split_1_num_splits_0"), val = int32(2)]; + int32 split_1_axis_0 = const()[name = string("split_1_axis_0"), val = int32(0)]; + string c_in_to_fp16_dtype_0 = const()[name = string("c_in_to_fp16_dtype_0"), val = string("fp16")]; + tensor c_in_to_fp16 = cast(dtype = c_in_to_fp16_dtype_0, x = c_in)[name = string("cast_7")]; + tensor split_1_cast_fp16_0, tensor split_1_cast_fp16_1 = split(axis = split_1_axis_0, num_splits = split_1_num_splits_0, x = c_in_to_fp16)[name = string("split_1_cast_fp16")]; + tensor input_5_lstm_layer_0_lstm_h0_squeeze_axes_0 = const()[name = string("input_5_lstm_layer_0_lstm_h0_squeeze_axes_0"), val = tensor([0])]; + tensor input_5_lstm_layer_0_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_5_lstm_layer_0_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_0)[name = string("input_5_lstm_layer_0_lstm_h0_squeeze_cast_fp16")]; + tensor input_5_lstm_layer_0_lstm_c0_squeeze_axes_0 = const()[name = string("input_5_lstm_layer_0_lstm_c0_squeeze_axes_0"), val = tensor([0])]; + tensor input_5_lstm_layer_0_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_5_lstm_layer_0_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_0)[name = string("input_5_lstm_layer_0_lstm_c0_squeeze_cast_fp16")]; + string input_5_lstm_layer_0_direction_0 = const()[name = string("input_5_lstm_layer_0_direction_0"), val = string("forward")]; + bool input_5_lstm_layer_0_output_sequence_0 = const()[name = string("input_5_lstm_layer_0_output_sequence_0"), val = bool(true)]; + string input_5_lstm_layer_0_recurrent_activation_0 = const()[name = string("input_5_lstm_layer_0_recurrent_activation_0"), val = string("sigmoid")]; + string input_5_lstm_layer_0_cell_activation_0 = const()[name = string("input_5_lstm_layer_0_cell_activation_0"), val = string("tanh")]; + string input_5_lstm_layer_0_activation_0 = const()[name = string("input_5_lstm_layer_0_activation_0"), val = string("tanh")]; + tensor concat_1_to_fp16 = const()[name = string("concat_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16752768)))]; + tensor concat_2_to_fp16 = const()[name = string("concat_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20029632)))]; + tensor concat_0_to_fp16 = const()[name = string("concat_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23306496)))]; + tensor input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = y_cast_fp16_cast_uint16)[name = string("transpose_4")]; + tensor input_5_lstm_layer_0_cast_fp16_0, tensor input_5_lstm_layer_0_cast_fp16_1, tensor input_5_lstm_layer_0_cast_fp16_2 = lstm(activation = input_5_lstm_layer_0_activation_0, bias = concat_0_to_fp16, cell_activation = input_5_lstm_layer_0_cell_activation_0, direction = input_5_lstm_layer_0_direction_0, initial_c = input_5_lstm_layer_0_lstm_c0_squeeze_cast_fp16, initial_h = input_5_lstm_layer_0_lstm_h0_squeeze_cast_fp16, output_sequence = input_5_lstm_layer_0_output_sequence_0, recurrent_activation = input_5_lstm_layer_0_recurrent_activation_0, weight_hh = concat_2_to_fp16, weight_ih = concat_1_to_fp16, x = input_3_cast_fp16)[name = string("input_5_lstm_layer_0_cast_fp16")]; + tensor input_5_lstm_h0_squeeze_axes_0 = const()[name = string("input_5_lstm_h0_squeeze_axes_0"), val = tensor([0])]; + tensor input_5_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_5_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_1)[name = string("input_5_lstm_h0_squeeze_cast_fp16")]; + tensor input_5_lstm_c0_squeeze_axes_0 = const()[name = string("input_5_lstm_c0_squeeze_axes_0"), val = tensor([0])]; + tensor input_5_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_5_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_1)[name = string("input_5_lstm_c0_squeeze_cast_fp16")]; + string input_5_direction_0 = const()[name = string("input_5_direction_0"), val = string("forward")]; + bool input_5_output_sequence_0 = const()[name = string("input_5_output_sequence_0"), val = bool(true)]; + string input_5_recurrent_activation_0 = const()[name = string("input_5_recurrent_activation_0"), val = string("sigmoid")]; + string input_5_cell_activation_0 = const()[name = string("input_5_cell_activation_0"), val = string("tanh")]; + string input_5_activation_0 = const()[name = string("input_5_activation_0"), val = string("tanh")]; + tensor concat_4_to_fp16 = const()[name = string("concat_4_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23311680)))]; + tensor concat_5_to_fp16 = const()[name = string("concat_5_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26588544)))]; + tensor concat_3_to_fp16 = const()[name = string("concat_3_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29865408)))]; + tensor input_5_cast_fp16_0, tensor input_5_cast_fp16_1, tensor input_5_cast_fp16_2 = lstm(activation = input_5_activation_0, bias = concat_3_to_fp16, cell_activation = input_5_cell_activation_0, direction = input_5_direction_0, initial_c = input_5_lstm_c0_squeeze_cast_fp16, initial_h = input_5_lstm_h0_squeeze_cast_fp16, output_sequence = input_5_output_sequence_0, recurrent_activation = input_5_recurrent_activation_0, weight_hh = concat_5_to_fp16, weight_ih = concat_4_to_fp16, x = input_5_lstm_layer_0_cast_fp16_0)[name = string("input_5_cast_fp16")]; + int32 obj_3_axis_0 = const()[name = string("obj_3_axis_0"), val = int32(0)]; + tensor obj_3_cast_fp16 = stack(axis = obj_3_axis_0, values = (input_5_lstm_layer_0_cast_fp16_1, input_5_cast_fp16_1))[name = string("obj_3_cast_fp16")]; + string obj_3_cast_fp16_to_fp32_dtype_0 = const()[name = string("obj_3_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + int32 obj_axis_0 = const()[name = string("obj_axis_0"), val = int32(0)]; + tensor obj_cast_fp16 = stack(axis = obj_axis_0, values = (input_5_lstm_layer_0_cast_fp16_2, input_5_cast_fp16_2))[name = string("obj_cast_fp16")]; + string obj_cast_fp16_to_fp32_dtype_0 = const()[name = string("obj_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor transpose_1_perm_0 = const()[name = string("transpose_1_perm_0"), val = tensor([1, 0, 2])]; + tensor input_7_perm_0 = const()[name = string("input_7_perm_0"), val = tensor([0, 2, 1])]; + string encoder_to_fp16_dtype_0 = const()[name = string("encoder_to_fp16_dtype_0"), val = string("fp16")]; + tensor joint_module_enc_weight_to_fp16 = const()[name = string("joint_module_enc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29870592)))]; + tensor joint_module_enc_bias_to_fp16 = const()[name = string("joint_module_enc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31181376)))]; + tensor encoder_to_fp16 = cast(dtype = encoder_to_fp16_dtype_0, x = encoder)[name = string("cast_4")]; + tensor input_7_cast_fp16 = transpose(perm = input_7_perm_0, x = encoder_to_fp16)[name = string("transpose_2")]; + tensor linear_0_cast_fp16 = linear(bias = joint_module_enc_bias_to_fp16, weight = joint_module_enc_weight_to_fp16, x = input_7_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor joint_module_pred_weight_to_fp16 = const()[name = string("joint_module_pred_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31182720)))]; + tensor joint_module_pred_bias_to_fp16 = const()[name = string("joint_module_pred_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32001984)))]; + tensor transpose_1_cast_fp16 = transpose(perm = transpose_1_perm_0, x = input_5_cast_fp16_0)[name = string("transpose_3")]; + tensor linear_1_cast_fp16 = linear(bias = joint_module_pred_bias_to_fp16, weight = joint_module_pred_weight_to_fp16, x = transpose_1_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_79_axes_0 = const()[name = string("op_79_axes_0"), val = tensor([2])]; + tensor var_79_cast_fp16 = expand_dims(axes = var_79_axes_0, x = linear_0_cast_fp16)[name = string("op_79_cast_fp16")]; + tensor var_80_axes_0 = const()[name = string("op_80_axes_0"), val = tensor([1])]; + tensor var_80_cast_fp16 = expand_dims(axes = var_80_axes_0, x = linear_1_cast_fp16)[name = string("op_80_cast_fp16")]; + tensor input_11_cast_fp16 = add(x = var_79_cast_fp16, y = var_80_cast_fp16)[name = string("input_11_cast_fp16")]; + tensor input_13_cast_fp16 = relu(x = input_11_cast_fp16)[name = string("input_13_cast_fp16")]; + tensor joint_module_joint_net_2_weight_to_fp16 = const()[name = string("joint_module_joint_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32003328)))]; + tensor joint_module_joint_net_2_bias_to_fp16 = const()[name = string("joint_module_joint_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48756032)))]; + tensor linear_2_cast_fp16 = linear(bias = joint_module_joint_net_2_bias_to_fp16, weight = joint_module_joint_net_2_weight_to_fp16, x = input_13_cast_fp16)[name = string("linear_2_cast_fp16")]; + string linear_2_cast_fp16_to_fp32_dtype_0 = const()[name = string("linear_2_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor logits = cast(dtype = linear_2_cast_fp16_to_fp32_dtype_0, x = linear_2_cast_fp16)[name = string("cast_3")]; + tensor c_out = cast(dtype = obj_cast_fp16_to_fp32_dtype_0, x = obj_cast_fp16)[name = string("cast_5")]; + tensor h_out = cast(dtype = obj_3_cast_fp16_to_fp32_dtype_0, x = obj_3_cast_fp16)[name = string("cast_6")]; + tensor token_length_tmp = identity(x = token_length)[name = string("token_length_tmp")]; + } -> (logits, h_out, c_out); +} \ No newline at end of file diff --git a/multilingual/4480ms/decoder_joint.mlmodelc/weights/weight.bin b/multilingual/4480ms/decoder_joint.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..c9ff4a76fa3fff489b780debbcd78bdc261f1489 --- /dev/null +++ b/multilingual/4480ms/decoder_joint.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f283edec035d616e9e1372419dd9bfd8a2de2c92a70b23f6a0f5ed93366ebb03 +size 48782272 diff --git a/multilingual/4480ms/decoder_joint.mlpackage/Data/com.apple.CoreML/model.mlmodel b/multilingual/4480ms/decoder_joint.mlpackage/Data/com.apple.CoreML/model.mlmodel new file mode 100644 index 0000000000000000000000000000000000000000..abae2ff390063679ad4ac25d546acf5853a04d16 --- /dev/null +++ b/multilingual/4480ms/decoder_joint.mlpackage/Data/com.apple.CoreML/model.mlmodel @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:507c3a291a78a11f62b898c64e611016f518d1af658b2aa55c054e0a1029f7ea +size 13746 diff --git a/multilingual/4480ms/decoder_joint.mlpackage/Data/com.apple.CoreML/weights/weight.bin b/multilingual/4480ms/decoder_joint.mlpackage/Data/com.apple.CoreML/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..c9ff4a76fa3fff489b780debbcd78bdc261f1489 --- /dev/null +++ b/multilingual/4480ms/decoder_joint.mlpackage/Data/com.apple.CoreML/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f283edec035d616e9e1372419dd9bfd8a2de2c92a70b23f6a0f5ed93366ebb03 +size 48782272 diff --git a/multilingual/4480ms/decoder_joint.mlpackage/Manifest.json b/multilingual/4480ms/decoder_joint.mlpackage/Manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..baad94a8850f47a141e9357ae3ea3cee9981fcc9 --- /dev/null +++ b/multilingual/4480ms/decoder_joint.mlpackage/Manifest.json @@ -0,0 +1,18 @@ +{ + "fileFormatVersion": "1.0.0", + "itemInfoEntries": { + "627E3113-852A-47BD-981E-FAB26C6AB6D0": { + "author": "com.apple.CoreML", + "description": "CoreML Model Weights", + "name": "weights", + "path": "com.apple.CoreML/weights" + }, + "7EEB6F52-3184-47E3-98CD-28268604F7F1": { + "author": "com.apple.CoreML", + "description": "CoreML Model Specification", + "name": "model.mlmodel", + "path": "com.apple.CoreML/model.mlmodel" + } + }, + "rootModelIdentifier": "7EEB6F52-3184-47E3-98CD-28268604F7F1" +} diff --git a/multilingual/4480ms/decoder_joint_noencproj.mlmodelc/analytics/coremldata.bin b/multilingual/4480ms/decoder_joint_noencproj.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..ea284470a3fb674426cdea416f38e1e49cfb8994 --- /dev/null +++ b/multilingual/4480ms/decoder_joint_noencproj.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c4e3c25c06d72ba514e93ae2ea0dd313f057622a3fb70f65de3bee4b80b3946b +size 243 diff --git a/multilingual/4480ms/decoder_joint_noencproj.mlmodelc/coremldata.bin b/multilingual/4480ms/decoder_joint_noencproj.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..53860b2839faaa2e26e812d977488d6798ea5906 --- /dev/null +++ b/multilingual/4480ms/decoder_joint_noencproj.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:651f70cfc02dacdedb6c1db6049a648728141d4192a028c7db99c56357128446 +size 519 diff --git a/multilingual/4480ms/decoder_joint_noencproj.mlmodelc/model.mil b/multilingual/4480ms/decoder_joint_noencproj.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..0bb38a83354a8f578eda404248b0895118b55ad5 --- /dev/null +++ b/multilingual/4480ms/decoder_joint_noencproj.mlmodelc/model.mil @@ -0,0 +1,91 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.10.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] +{ + func main(tensor c_in, tensor encoder_proj, tensor h_in, tensor token, tensor token_length) { + int32 y_batch_dims_0 = const()[name = string("y_batch_dims_0"), val = int32(0)]; + bool y_validate_indices_0 = const()[name = string("y_validate_indices_0"), val = bool(false)]; + tensor decoder_module_prediction_embed_weight_to_fp16 = const()[name = string("decoder_module_prediction_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + string token_to_int16_dtype_0 = const()[name = string("token_to_int16_dtype_0"), val = string("int16")]; + string cast_1_dtype_0 = const()[name = string("cast_1_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor token_to_int16 = cast(dtype = token_to_int16_dtype_0, x = token)[name = string("cast_10")]; + tensor cast_1 = cast(dtype = cast_1_dtype_0, x = token_to_int16)[name = string("cast_9")]; + tensor greater_equal_0 = greater_equal(x = cast_1, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(13088)]; + tensor add_2 = add(x = cast_1, y = slice_by_index_0)[name = string("add_2")]; + tensor select_0 = select(a = cast_1, b = add_2, cond = greater_equal_0)[name = string("select_0")]; + int32 y_cast_fp16_cast_uint16_axis_0 = const()[name = string("y_cast_fp16_cast_uint16_axis_0"), val = int32(0)]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_8")]; + tensor y_cast_fp16_cast_uint16_cast_uint16 = gather(axis = y_cast_fp16_cast_uint16_axis_0, batch_dims = y_batch_dims_0, indices = select_0_to_int16, validate_indices = y_validate_indices_0, x = decoder_module_prediction_embed_weight_to_fp16)[name = string("y_cast_fp16_cast_uint16_cast_uint16")]; + tensor input_3_perm_0 = const()[name = string("input_3_perm_0"), val = tensor([1, 0, 2])]; + int32 split_0_num_splits_0 = const()[name = string("split_0_num_splits_0"), val = int32(2)]; + int32 split_0_axis_0 = const()[name = string("split_0_axis_0"), val = int32(0)]; + string h_in_to_fp16_dtype_0 = const()[name = string("h_in_to_fp16_dtype_0"), val = string("fp16")]; + tensor h_in_to_fp16 = cast(dtype = h_in_to_fp16_dtype_0, x = h_in)[name = string("cast_7")]; + tensor split_0_cast_fp16_0, tensor split_0_cast_fp16_1 = split(axis = split_0_axis_0, num_splits = split_0_num_splits_0, x = h_in_to_fp16)[name = string("split_0_cast_fp16")]; + int32 split_1_num_splits_0 = const()[name = string("split_1_num_splits_0"), val = int32(2)]; + int32 split_1_axis_0 = const()[name = string("split_1_axis_0"), val = int32(0)]; + string c_in_to_fp16_dtype_0 = const()[name = string("c_in_to_fp16_dtype_0"), val = string("fp16")]; + tensor c_in_to_fp16 = cast(dtype = c_in_to_fp16_dtype_0, x = c_in)[name = string("cast_6")]; + tensor split_1_cast_fp16_0, tensor split_1_cast_fp16_1 = split(axis = split_1_axis_0, num_splits = split_1_num_splits_0, x = c_in_to_fp16)[name = string("split_1_cast_fp16")]; + tensor input_5_lstm_layer_0_lstm_h0_squeeze_axes_0 = const()[name = string("input_5_lstm_layer_0_lstm_h0_squeeze_axes_0"), val = tensor([0])]; + tensor input_5_lstm_layer_0_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_5_lstm_layer_0_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_0)[name = string("input_5_lstm_layer_0_lstm_h0_squeeze_cast_fp16")]; + tensor input_5_lstm_layer_0_lstm_c0_squeeze_axes_0 = const()[name = string("input_5_lstm_layer_0_lstm_c0_squeeze_axes_0"), val = tensor([0])]; + tensor input_5_lstm_layer_0_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_5_lstm_layer_0_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_0)[name = string("input_5_lstm_layer_0_lstm_c0_squeeze_cast_fp16")]; + string input_5_lstm_layer_0_direction_0 = const()[name = string("input_5_lstm_layer_0_direction_0"), val = string("forward")]; + bool input_5_lstm_layer_0_output_sequence_0 = const()[name = string("input_5_lstm_layer_0_output_sequence_0"), val = bool(true)]; + string input_5_lstm_layer_0_recurrent_activation_0 = const()[name = string("input_5_lstm_layer_0_recurrent_activation_0"), val = string("sigmoid")]; + string input_5_lstm_layer_0_cell_activation_0 = const()[name = string("input_5_lstm_layer_0_cell_activation_0"), val = string("tanh")]; + string input_5_lstm_layer_0_activation_0 = const()[name = string("input_5_lstm_layer_0_activation_0"), val = string("tanh")]; + tensor concat_1_to_fp16 = const()[name = string("concat_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16752768)))]; + tensor concat_2_to_fp16 = const()[name = string("concat_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20029632)))]; + tensor concat_0_to_fp16 = const()[name = string("concat_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23306496)))]; + tensor input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = y_cast_fp16_cast_uint16_cast_uint16)[name = string("transpose_3")]; + tensor input_5_lstm_layer_0_cast_fp16_0, tensor input_5_lstm_layer_0_cast_fp16_1, tensor input_5_lstm_layer_0_cast_fp16_2 = lstm(activation = input_5_lstm_layer_0_activation_0, bias = concat_0_to_fp16, cell_activation = input_5_lstm_layer_0_cell_activation_0, direction = input_5_lstm_layer_0_direction_0, initial_c = input_5_lstm_layer_0_lstm_c0_squeeze_cast_fp16, initial_h = input_5_lstm_layer_0_lstm_h0_squeeze_cast_fp16, output_sequence = input_5_lstm_layer_0_output_sequence_0, recurrent_activation = input_5_lstm_layer_0_recurrent_activation_0, weight_hh = concat_2_to_fp16, weight_ih = concat_1_to_fp16, x = input_3_cast_fp16)[name = string("input_5_lstm_layer_0_cast_fp16")]; + tensor input_5_lstm_h0_squeeze_axes_0 = const()[name = string("input_5_lstm_h0_squeeze_axes_0"), val = tensor([0])]; + tensor input_5_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_5_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_1)[name = string("input_5_lstm_h0_squeeze_cast_fp16")]; + tensor input_5_lstm_c0_squeeze_axes_0 = const()[name = string("input_5_lstm_c0_squeeze_axes_0"), val = tensor([0])]; + tensor input_5_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_5_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_1)[name = string("input_5_lstm_c0_squeeze_cast_fp16")]; + string input_5_direction_0 = const()[name = string("input_5_direction_0"), val = string("forward")]; + bool input_5_output_sequence_0 = const()[name = string("input_5_output_sequence_0"), val = bool(true)]; + string input_5_recurrent_activation_0 = const()[name = string("input_5_recurrent_activation_0"), val = string("sigmoid")]; + string input_5_cell_activation_0 = const()[name = string("input_5_cell_activation_0"), val = string("tanh")]; + string input_5_activation_0 = const()[name = string("input_5_activation_0"), val = string("tanh")]; + tensor concat_4_to_fp16 = const()[name = string("concat_4_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23311680)))]; + tensor concat_5_to_fp16 = const()[name = string("concat_5_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26588544)))]; + tensor concat_3_to_fp16 = const()[name = string("concat_3_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29865408)))]; + tensor input_5_cast_fp16_0, tensor input_5_cast_fp16_1, tensor input_5_cast_fp16_2 = lstm(activation = input_5_activation_0, bias = concat_3_to_fp16, cell_activation = input_5_cell_activation_0, direction = input_5_direction_0, initial_c = input_5_lstm_c0_squeeze_cast_fp16, initial_h = input_5_lstm_h0_squeeze_cast_fp16, output_sequence = input_5_output_sequence_0, recurrent_activation = input_5_recurrent_activation_0, weight_hh = concat_5_to_fp16, weight_ih = concat_4_to_fp16, x = input_5_lstm_layer_0_cast_fp16_0)[name = string("input_5_cast_fp16")]; + int32 obj_3_axis_0 = const()[name = string("obj_3_axis_0"), val = int32(0)]; + tensor obj_3_cast_fp16 = stack(axis = obj_3_axis_0, values = (input_5_lstm_layer_0_cast_fp16_1, input_5_cast_fp16_1))[name = string("obj_3_cast_fp16")]; + string obj_3_cast_fp16_to_fp32_dtype_0 = const()[name = string("obj_3_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + int32 obj_axis_0 = const()[name = string("obj_axis_0"), val = int32(0)]; + tensor obj_cast_fp16 = stack(axis = obj_axis_0, values = (input_5_lstm_layer_0_cast_fp16_2, input_5_cast_fp16_2))[name = string("obj_cast_fp16")]; + string obj_cast_fp16_to_fp32_dtype_0 = const()[name = string("obj_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor transpose_1_perm_0 = const()[name = string("transpose_1_perm_0"), val = tensor([1, 0, 2])]; + tensor joint_module_pred_weight_to_fp16 = const()[name = string("joint_module_pred_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29870592)))]; + tensor joint_module_pred_bias_to_fp16 = const()[name = string("joint_module_pred_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30689856)))]; + tensor transpose_1_cast_fp16 = transpose(perm = transpose_1_perm_0, x = input_5_cast_fp16_0)[name = string("transpose_2")]; + tensor linear_0_cast_fp16 = linear(bias = joint_module_pred_bias_to_fp16, weight = joint_module_pred_weight_to_fp16, x = transpose_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor f_axes_0 = const()[name = string("f_axes_0"), val = tensor([2])]; + string encoder_proj_to_fp16_dtype_0 = const()[name = string("encoder_proj_to_fp16_dtype_0"), val = string("fp16")]; + tensor encoder_proj_to_fp16 = cast(dtype = encoder_proj_to_fp16_dtype_0, x = encoder_proj)[name = string("cast_3")]; + tensor f_cast_fp16 = expand_dims(axes = f_axes_0, x = encoder_proj_to_fp16)[name = string("f_cast_fp16")]; + tensor g_axes_0 = const()[name = string("g_axes_0"), val = tensor([1])]; + tensor g_cast_fp16 = expand_dims(axes = g_axes_0, x = linear_0_cast_fp16)[name = string("g_cast_fp16")]; + tensor input_9_cast_fp16 = add(x = f_cast_fp16, y = g_cast_fp16)[name = string("input_9_cast_fp16")]; + tensor input_11_cast_fp16 = relu(x = input_9_cast_fp16)[name = string("input_11_cast_fp16")]; + tensor joint_module_joint_net_2_weight_to_fp16 = const()[name = string("joint_module_joint_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30691200)))]; + tensor joint_module_joint_net_2_bias_to_fp16 = const()[name = string("joint_module_joint_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47443904)))]; + tensor linear_1_cast_fp16 = linear(bias = joint_module_joint_net_2_bias_to_fp16, weight = joint_module_joint_net_2_weight_to_fp16, x = input_11_cast_fp16)[name = string("linear_1_cast_fp16")]; + int32 var_83 = const()[name = string("op_83"), val = int32(-1)]; + tensor var_85_softmax_cast_fp16 = softmax(axis = var_83, x = linear_1_cast_fp16)[name = string("op_85_softmax_cast_fp16")]; + fp32 var_85_epsilon_0 = const()[name = string("op_85_epsilon_0"), val = fp32(0x1p-149)]; + tensor var_85_cast_fp16 = log(epsilon = var_85_epsilon_0, x = var_85_softmax_cast_fp16)[name = string("op_85_cast_fp16")]; + string var_85_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_85_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor logits = cast(dtype = var_85_cast_fp16_to_fp32_dtype_0, x = var_85_cast_fp16)[name = string("cast_2")]; + tensor c_out = cast(dtype = obj_cast_fp16_to_fp32_dtype_0, x = obj_cast_fp16)[name = string("cast_4")]; + tensor h_out = cast(dtype = obj_3_cast_fp16_to_fp32_dtype_0, x = obj_3_cast_fp16)[name = string("cast_5")]; + tensor token_length_tmp = identity(x = token_length)[name = string("token_length_tmp")]; + } -> (logits, h_out, c_out); +} \ No newline at end of file diff --git a/multilingual/4480ms/decoder_joint_noencproj.mlmodelc/weights/weight.bin b/multilingual/4480ms/decoder_joint_noencproj.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..a04dee90b1d0140bff13a5c28dd8d3b4054a8679 --- /dev/null +++ b/multilingual/4480ms/decoder_joint_noencproj.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c7ddfe0cb3be2e2896258d91a95483b898e8a274c49fee256e8effd86dc64dda +size 47470144 diff --git a/multilingual/4480ms/decoder_joint_noencproj.mlpackage/Data/com.apple.CoreML/model.mlmodel b/multilingual/4480ms/decoder_joint_noencproj.mlpackage/Data/com.apple.CoreML/model.mlmodel new file mode 100644 index 0000000000000000000000000000000000000000..79ca0efcf3d22f4ffa11f41012bd5933da668872 --- 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dict({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}})] +{ + func main(tensor cache_channel, tensor cache_len, tensor cache_time, tensor mel, tensor mel_length, tensor prompt_id) { + tensor value_3_perm_0 = const()[name = string("value_3_perm_0"), val = tensor([1, 0, 2, 3])]; + string cache_channel_to_fp16_dtype_0 = const()[name = string("cache_channel_to_fp16_dtype_0"), val = string("fp16")]; + tensor value_5_perm_0 = const()[name = string("value_5_perm_0"), val = tensor([1, 0, 2, 3])]; + string cache_time_to_fp16_dtype_0 = const()[name = string("cache_time_to_fp16_dtype_0"), val = string("fp16")]; + int32 var_60 = const()[name = string("op_60"), val = int32(-1)]; + int32 var_69 = const()[name = string("op_69"), val = int32(1)]; + tensor x_1_perm_0 = const()[name = string("x_1_perm_0"), val = tensor([0, 2, 1])]; + string mel_to_fp16_dtype_0 = const()[name = string("mel_to_fp16_dtype_0"), val = string("fp16")]; + tensor tensor_1_axes_0 = const()[name = string("tensor_1_axes_0"), val = tensor([1])]; + tensor mel_to_fp16 = cast(dtype = mel_to_fp16_dtype_0, x = mel)[name = string("cast_22")]; + tensor x_1_cast_fp16 = transpose(perm = x_1_perm_0, x = mel_to_fp16)[name = string("transpose_367")]; + tensor tensor_1_cast_fp16 = expand_dims(axes = tensor_1_axes_0, x = x_1_cast_fp16)[name = string("tensor_1_cast_fp16")]; + tensor expand_dims_0 = const()[name = string("expand_dims_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor var_138_axes_0 = const()[name = string("op_138_axes_0"), val = tensor([1])]; + tensor var_138 = expand_dims(axes = var_138_axes_0, x = mel_length)[name = string("op_138")]; + tensor time_mask_1 = less(x = expand_dims_0, y = var_138)[name = string("time_mask_1")]; + tensor var_140_axes_0 = const()[name = string("op_140_axes_0"), val = tensor([-1])]; + tensor var_140 = expand_dims(axes = var_140_axes_0, x = time_mask_1)[name = string("op_140")]; + tensor var_142_reps_0 = const()[name = string("op_142_reps_0"), val = tensor([1, 1, 128])]; + tensor var_142 = tile(reps = var_142_reps_0, x = var_140)[name = string("op_142")]; + tensor var_148_axes_0 = const()[name = string("op_148_axes_0"), val = tensor([1])]; + string mask_1_to_fp16_dtype_0 = const()[name = string("mask_1_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_142_to_fp16 = cast(dtype = mask_1_to_fp16_dtype_0, x = var_142)[name = string("cast_21")]; + tensor var_148_cast_fp16 = expand_dims(axes = var_148_axes_0, x = var_142_to_fp16)[name = string("op_148_cast_fp16")]; + tensor input_1_cast_fp16 = mul(x = tensor_1_cast_fp16, y = var_148_cast_fp16)[name = string("input_1_cast_fp16")]; + tensor input_3_pad_0 = const()[name = string("input_3_pad_0"), val = tensor([0, 0, 0, 0, 2, 1, 2, 1])]; + string input_3_mode_0 = const()[name = string("input_3_mode_0"), val = string("constant")]; + fp16 const_9_to_fp16 = const()[name = string("const_9_to_fp16"), val = fp16(0x0p+0)]; + tensor input_3_cast_fp16 = pad(constant_val = const_9_to_fp16, mode = input_3_mode_0, pad = input_3_pad_0, x = input_1_cast_fp16)[name = string("input_3_cast_fp16")]; + string tensor_3_pad_type_0 = const()[name = string("tensor_3_pad_type_0"), val = string("valid")]; + tensor tensor_3_strides_0 = const()[name = string("tensor_3_strides_0"), val = tensor([2, 2])]; + tensor tensor_3_pad_0 = const()[name = string("tensor_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor tensor_3_dilations_0 = const()[name = string("tensor_3_dilations_0"), val = tensor([1, 1])]; + int32 tensor_3_groups_0 = const()[name = string("tensor_3_groups_0"), val = int32(1)]; + tensor encoder_pre_encode_conv_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1984))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4352))))[name = string("encoder_pre_encode_conv_0_weight_to_fp16_quantized")]; + tensor encoder_pre_encode_conv_0_bias_to_fp16 = const()[name = string("encoder_pre_encode_conv_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4928)))]; + tensor tensor_3_cast_fp16 = conv(bias = encoder_pre_encode_conv_0_bias_to_fp16, dilations = tensor_3_dilations_0, groups = tensor_3_groups_0, pad = tensor_3_pad_0, pad_type = tensor_3_pad_type_0, strides = tensor_3_strides_0, weight = encoder_pre_encode_conv_0_weight_to_fp16_quantized, x = input_3_cast_fp16)[name = string("tensor_3_cast_fp16")]; + string current_lengths_1_to_fp16_dtype_0 = const()[name = string("current_lengths_1_to_fp16_dtype_0"), val = string("fp16")]; + fp16 var_161_promoted_to_fp16 = const()[name = string("op_161_promoted_to_fp16"), val = fp16(0x1p+1)]; + tensor mel_length_to_fp16 = cast(dtype = current_lengths_1_to_fp16_dtype_0, x = mel_length)[name = string("cast_20")]; + tensor var_162_cast_fp16 = add(x = mel_length_to_fp16, y = var_161_promoted_to_fp16)[name = string("op_162_cast_fp16")]; + fp16 var_163_promoted_to_fp16 = const()[name = string("op_163_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_164_cast_fp16 = add(x = var_162_cast_fp16, y = var_163_promoted_to_fp16)[name = string("op_164_cast_fp16")]; + fp16 var_165_promoted_to_fp16 = const()[name = string("op_165_promoted_to_fp16"), val = fp16(0x1.8p+1)]; + tensor var_166_cast_fp16 = sub(x = var_164_cast_fp16, y = var_165_promoted_to_fp16)[name = string("op_166_cast_fp16")]; + fp16 var_57_promoted_to_fp16 = const()[name = string("op_57_promoted_to_fp16"), val = fp16(0x1p+1)]; + tensor floor_div_0_cast_fp16 = floor_div(x = var_166_cast_fp16, y = var_57_promoted_to_fp16)[name = string("floor_div_0_cast_fp16")]; + fp16 var_168_promoted_to_fp16 = const()[name = string("op_168_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor current_lengths_3_cast_fp16 = add(x = floor_div_0_cast_fp16, y = var_168_promoted_to_fp16)[name = string("current_lengths_3_cast_fp16")]; + string lengths_19_dtype_0 = const()[name = string("lengths_19_dtype_0"), val = string("int32")]; + tensor expand_dims_1 = const()[name = string("expand_dims_1"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5504)))]; + tensor var_177_axes_0 = const()[name = string("op_177_axes_0"), val = tensor([1])]; + tensor current_lengths_3_cast_fp16_to_int32 = cast(dtype = lengths_19_dtype_0, x = current_lengths_3_cast_fp16)[name = string("cast_19")]; + tensor var_177 = expand_dims(axes = var_177_axes_0, x = current_lengths_3_cast_fp16_to_int32)[name = string("op_177")]; + tensor time_mask_3 = less(x = expand_dims_1, y = var_177)[name = string("time_mask_3")]; + tensor var_179_axes_0 = const()[name = string("op_179_axes_0"), val = tensor([-1])]; + tensor var_179 = expand_dims(axes = var_179_axes_0, x = time_mask_3)[name = string("op_179")]; + tensor var_181_reps_0 = const()[name = string("op_181_reps_0"), val = tensor([1, 1, 65])]; + tensor var_181 = tile(reps = var_181_reps_0, x = var_179)[name = string("op_181")]; + tensor var_187_axes_0 = const()[name = string("op_187_axes_0"), val = tensor([1])]; + string mask_3_to_fp16_dtype_0 = const()[name = string("mask_3_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_181_to_fp16 = cast(dtype = mask_3_to_fp16_dtype_0, x = var_181)[name = string("cast_18")]; + tensor var_187_cast_fp16 = expand_dims(axes = var_187_axes_0, x = var_181_to_fp16)[name = string("op_187_cast_fp16")]; + tensor expanded_mask_3_reps_0 = const()[name = string("expanded_mask_3_reps_0"), val = tensor([1, 256, 1, 1])]; + tensor expanded_mask_3_cast_fp16 = tile(reps = expanded_mask_3_reps_0, x = var_187_cast_fp16)[name = string("expanded_mask_3_cast_fp16")]; + tensor input_5_cast_fp16 = mul(x = tensor_3_cast_fp16, y = expanded_mask_3_cast_fp16)[name = string("input_5_cast_fp16")]; + tensor tensor_5_cast_fp16 = relu(x = input_5_cast_fp16)[name = string("tensor_5_cast_fp16")]; + tensor input_7_cast_fp16 = mul(x = tensor_5_cast_fp16, y = expanded_mask_3_cast_fp16)[name = string("input_7_cast_fp16")]; + tensor input_9_pad_0 = const()[name = string("input_9_pad_0"), val = tensor([0, 0, 0, 0, 2, 1, 2, 1])]; + string input_9_mode_0 = const()[name = string("input_9_mode_0"), val = string("constant")]; + fp16 const_23_to_fp16 = const()[name = string("const_23_to_fp16"), val = fp16(0x0p+0)]; + tensor input_9_cast_fp16 = pad(constant_val = const_23_to_fp16, mode = input_9_mode_0, pad = input_9_pad_0, x = input_7_cast_fp16)[name = string("input_9_cast_fp16")]; + string tensor_7_pad_type_0 = const()[name = string("tensor_7_pad_type_0"), val = string("valid")]; + tensor tensor_7_strides_0 = const()[name = string("tensor_7_strides_0"), val = tensor([2, 2])]; + int32 tensor_7_groups_0 = const()[name = string("tensor_7_groups_0"), val = int32(256)]; + tensor tensor_7_pad_0 = const()[name = string("tensor_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor tensor_7_dilations_0 = const()[name = string("tensor_7_dilations_0"), val = tensor([1, 1])]; + tensor encoder_pre_encode_conv_2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6528))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8896))))[name = string("encoder_pre_encode_conv_2_weight_to_fp16_quantized")]; + tensor encoder_pre_encode_conv_2_bias_to_fp16 = const()[name = string("encoder_pre_encode_conv_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9472)))]; + tensor tensor_7_cast_fp16 = conv(bias = encoder_pre_encode_conv_2_bias_to_fp16, dilations = tensor_7_dilations_0, groups = tensor_7_groups_0, pad = tensor_7_pad_0, pad_type = tensor_7_pad_type_0, strides = tensor_7_strides_0, weight = encoder_pre_encode_conv_2_weight_to_fp16_quantized, x = input_9_cast_fp16)[name = string("tensor_7_cast_fp16")]; + fp16 var_209_promoted_to_fp16 = const()[name = string("op_209_promoted_to_fp16"), val = fp16(0x1p+1)]; + tensor var_210_cast_fp16 = add(x = current_lengths_3_cast_fp16, y = var_209_promoted_to_fp16)[name = string("op_210_cast_fp16")]; + fp16 var_211_promoted_to_fp16 = const()[name = string("op_211_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_212_cast_fp16 = add(x = var_210_cast_fp16, y = var_211_promoted_to_fp16)[name = string("op_212_cast_fp16")]; + fp16 var_213_promoted_to_fp16 = const()[name = string("op_213_promoted_to_fp16"), val = fp16(0x1.8p+1)]; + tensor var_214_cast_fp16 = sub(x = var_212_cast_fp16, y = var_213_promoted_to_fp16)[name = string("op_214_cast_fp16")]; + fp16 var_57_promoted_1_to_fp16 = const()[name = string("op_57_promoted_1_to_fp16"), val = fp16(0x1p+1)]; + tensor floor_div_1_cast_fp16 = floor_div(x = var_214_cast_fp16, y = var_57_promoted_1_to_fp16)[name = string("floor_div_1_cast_fp16")]; + fp16 var_216_promoted_to_fp16 = const()[name = string("op_216_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor current_lengths_5_cast_fp16 = add(x = floor_div_1_cast_fp16, y = var_216_promoted_to_fp16)[name = string("current_lengths_5_cast_fp16")]; + string lengths_21_dtype_0 = const()[name = string("lengths_21_dtype_0"), val = string("int32")]; + tensor expand_dims_2 = const()[name = string("expand_dims_2"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10048)))]; + tensor var_225_axes_0 = const()[name = string("op_225_axes_0"), val = tensor([1])]; + tensor current_lengths_5_cast_fp16_to_int32 = cast(dtype = lengths_21_dtype_0, x = current_lengths_5_cast_fp16)[name = string("cast_17")]; + tensor var_225 = expand_dims(axes = var_225_axes_0, x = current_lengths_5_cast_fp16_to_int32)[name = string("op_225")]; + tensor time_mask_5 = less(x = expand_dims_2, y = var_225)[name = string("time_mask_5")]; + tensor var_227_axes_0 = const()[name = string("op_227_axes_0"), val = tensor([-1])]; + tensor var_227 = expand_dims(axes = var_227_axes_0, x = time_mask_5)[name = string("op_227")]; + tensor var_229_reps_0 = const()[name = string("op_229_reps_0"), val = tensor([1, 1, 33])]; + tensor var_229 = tile(reps = var_229_reps_0, x = var_227)[name = string("op_229")]; + tensor var_235_axes_0 = const()[name = string("op_235_axes_0"), val = tensor([1])]; + string mask_5_to_fp16_dtype_0 = const()[name = string("mask_5_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_229_to_fp16 = cast(dtype = mask_5_to_fp16_dtype_0, x = var_229)[name = string("cast_16")]; + tensor var_235_cast_fp16 = expand_dims(axes = var_235_axes_0, x = var_229_to_fp16)[name = string("op_235_cast_fp16")]; + tensor expanded_mask_7_reps_0 = const()[name = string("expanded_mask_7_reps_0"), val = tensor([1, 256, 1, 1])]; + tensor expanded_mask_7_cast_fp16 = tile(reps = expanded_mask_7_reps_0, x = var_235_cast_fp16)[name = string("expanded_mask_7_cast_fp16")]; + tensor input_11_cast_fp16 = mul(x = tensor_7_cast_fp16, y = expanded_mask_7_cast_fp16)[name = string("input_11_cast_fp16")]; + string tensor_9_pad_type_0 = const()[name = string("tensor_9_pad_type_0"), val = string("valid")]; + tensor tensor_9_strides_0 = const()[name = string("tensor_9_strides_0"), val = tensor([1, 1])]; + tensor tensor_9_pad_0 = const()[name = string("tensor_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor tensor_9_dilations_0 = const()[name = string("tensor_9_dilations_0"), val = tensor([1, 1])]; + int32 tensor_9_groups_0 = const()[name = string("tensor_9_groups_0"), val = int32(1)]; + tensor encoder_pre_encode_conv_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(76224))))[name = string("encoder_pre_encode_conv_3_weight_to_fp16_quantized")]; + tensor encoder_pre_encode_conv_3_bias_to_fp16 = const()[name = string("encoder_pre_encode_conv_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(76800)))]; + tensor tensor_9_cast_fp16 = conv(bias = encoder_pre_encode_conv_3_bias_to_fp16, dilations = tensor_9_dilations_0, groups = tensor_9_groups_0, pad = tensor_9_pad_0, pad_type = tensor_9_pad_type_0, strides = tensor_9_strides_0, weight = encoder_pre_encode_conv_3_weight_to_fp16_quantized, x = input_11_cast_fp16)[name = string("tensor_9_cast_fp16")]; + tensor input_13_cast_fp16 = mul(x = tensor_9_cast_fp16, y = expanded_mask_7_cast_fp16)[name = string("input_13_cast_fp16")]; + tensor tensor_11_cast_fp16 = relu(x = input_13_cast_fp16)[name = string("tensor_11_cast_fp16")]; + tensor input_15_cast_fp16 = mul(x = tensor_11_cast_fp16, y = expanded_mask_7_cast_fp16)[name = string("input_15_cast_fp16")]; + tensor input_17_pad_0 = const()[name = string("input_17_pad_0"), val = tensor([0, 0, 0, 0, 2, 1, 2, 1])]; + string input_17_mode_0 = const()[name = string("input_17_mode_0"), val = string("constant")]; + fp16 const_41_to_fp16 = const()[name = string("const_41_to_fp16"), val = fp16(0x0p+0)]; + tensor input_17_cast_fp16 = pad(constant_val = const_41_to_fp16, mode = input_17_mode_0, pad = input_17_pad_0, x = input_15_cast_fp16)[name = string("input_17_cast_fp16")]; + string tensor_13_pad_type_0 = const()[name = string("tensor_13_pad_type_0"), val = string("valid")]; + tensor tensor_13_strides_0 = const()[name = string("tensor_13_strides_0"), val = tensor([2, 2])]; + int32 tensor_13_groups_0 = const()[name = string("tensor_13_groups_0"), val = int32(256)]; + tensor tensor_13_pad_0 = const()[name = string("tensor_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor tensor_13_dilations_0 = const()[name = string("tensor_13_dilations_0"), val = tensor([1, 1])]; + tensor encoder_pre_encode_conv_5_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79744))))[name = string("encoder_pre_encode_conv_5_weight_to_fp16_quantized")]; + tensor encoder_pre_encode_conv_5_bias_to_fp16 = const()[name = string("encoder_pre_encode_conv_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80320)))]; + tensor tensor_13_cast_fp16 = conv(bias = encoder_pre_encode_conv_5_bias_to_fp16, dilations = tensor_13_dilations_0, groups = tensor_13_groups_0, pad = tensor_13_pad_0, pad_type = tensor_13_pad_type_0, strides = tensor_13_strides_0, weight = encoder_pre_encode_conv_5_weight_to_fp16_quantized, x = input_17_cast_fp16)[name = string("tensor_13_cast_fp16")]; + fp16 var_272_promoted_to_fp16 = const()[name = string("op_272_promoted_to_fp16"), val = fp16(0x1p+1)]; + tensor var_273_cast_fp16 = add(x = current_lengths_5_cast_fp16, y = var_272_promoted_to_fp16)[name = string("op_273_cast_fp16")]; + fp16 var_274_promoted_to_fp16 = const()[name = string("op_274_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_275_cast_fp16 = add(x = var_273_cast_fp16, y = var_274_promoted_to_fp16)[name = string("op_275_cast_fp16")]; + fp16 var_276_promoted_to_fp16 = const()[name = string("op_276_promoted_to_fp16"), val = fp16(0x1.8p+1)]; + tensor var_277_cast_fp16 = sub(x = var_275_cast_fp16, y = var_276_promoted_to_fp16)[name = string("op_277_cast_fp16")]; + fp16 var_57_promoted_2_to_fp16 = const()[name = string("op_57_promoted_2_to_fp16"), val = fp16(0x1p+1)]; + tensor floor_div_2_cast_fp16 = floor_div(x = var_277_cast_fp16, y = var_57_promoted_2_to_fp16)[name = string("floor_div_2_cast_fp16")]; + fp16 var_279_promoted_to_fp16 = const()[name = string("op_279_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor current_lengths_cast_fp16 = add(x = floor_div_2_cast_fp16, y = var_279_promoted_to_fp16)[name = string("current_lengths_cast_fp16")]; + string lengths_dtype_0 = const()[name = string("lengths_dtype_0"), val = string("int32")]; + tensor expand_dims_3 = const()[name = string("expand_dims_3"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80896)))]; + tensor var_288_axes_0 = const()[name = string("op_288_axes_0"), val = tensor([1])]; + tensor current_lengths_cast_fp16_to_int32 = cast(dtype = lengths_dtype_0, x = current_lengths_cast_fp16)[name = string("cast_15")]; + tensor var_288 = expand_dims(axes = var_288_axes_0, x = current_lengths_cast_fp16_to_int32)[name = string("op_288")]; + tensor time_mask = less(x = expand_dims_3, y = var_288)[name = string("time_mask")]; + tensor var_290_axes_0 = const()[name = string("op_290_axes_0"), val = tensor([-1])]; + tensor var_290 = expand_dims(axes = var_290_axes_0, x = time_mask)[name = string("op_290")]; + tensor var_292_reps_0 = const()[name = string("op_292_reps_0"), val = tensor([1, 1, 17])]; + tensor var_292 = tile(reps = var_292_reps_0, x = var_290)[name = string("op_292")]; + tensor var_298_axes_0 = const()[name = string("op_298_axes_0"), val = tensor([1])]; + string mask_7_to_fp16_dtype_0 = const()[name = string("mask_7_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_292_to_fp16 = cast(dtype = mask_7_to_fp16_dtype_0, x = var_292)[name = string("cast_14")]; + tensor var_298_cast_fp16 = expand_dims(axes = var_298_axes_0, x = var_292_to_fp16)[name = string("op_298_cast_fp16")]; + tensor expanded_mask_13_reps_0 = const()[name = string("expanded_mask_13_reps_0"), val = tensor([1, 256, 1, 1])]; + tensor expanded_mask_13_cast_fp16 = tile(reps = expanded_mask_13_reps_0, x = var_298_cast_fp16)[name = string("expanded_mask_13_cast_fp16")]; + tensor input_19_cast_fp16 = mul(x = tensor_13_cast_fp16, y = expanded_mask_13_cast_fp16)[name = string("input_19_cast_fp16")]; + string tensor_15_pad_type_0 = const()[name = string("tensor_15_pad_type_0"), val = string("valid")]; + tensor tensor_15_strides_0 = const()[name = string("tensor_15_strides_0"), val = tensor([1, 1])]; + tensor tensor_15_pad_0 = const()[name = string("tensor_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor tensor_15_dilations_0 = const()[name = string("tensor_15_dilations_0"), val = tensor([1, 1])]; + int32 tensor_15_groups_0 = const()[name = string("tensor_15_groups_0"), val = int32(1)]; + tensor encoder_pre_encode_conv_6_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146816))))[name = string("encoder_pre_encode_conv_6_weight_to_fp16_quantized")]; + tensor encoder_pre_encode_conv_6_bias_to_fp16 = const()[name = string("encoder_pre_encode_conv_6_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(147392)))]; + tensor tensor_15_cast_fp16 = conv(bias = encoder_pre_encode_conv_6_bias_to_fp16, dilations = tensor_15_dilations_0, groups = tensor_15_groups_0, pad = tensor_15_pad_0, pad_type = tensor_15_pad_type_0, strides = tensor_15_strides_0, weight = encoder_pre_encode_conv_6_weight_to_fp16_quantized, x = input_19_cast_fp16)[name = string("tensor_15_cast_fp16")]; + tensor input_21_cast_fp16 = mul(x = tensor_15_cast_fp16, y = expanded_mask_13_cast_fp16)[name = string("input_21_cast_fp16")]; + tensor tensor_cast_fp16 = relu(x = input_21_cast_fp16)[name = string("tensor_cast_fp16")]; + tensor x_3_cast_fp16 = mul(x = tensor_cast_fp16, y = expanded_mask_13_cast_fp16)[name = string("x_3_cast_fp16")]; + tensor var_332_perm_0 = const()[name = string("op_332_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_333 = const()[name = string("op_333"), val = tensor([1, 58, -1])]; + tensor var_332_cast_fp16 = transpose(perm = var_332_perm_0, x = x_3_cast_fp16)[name = string("transpose_366")]; + tensor input_23_cast_fp16 = reshape(shape = var_333, x = var_332_cast_fp16)[name = string("input_23_cast_fp16")]; + tensor encoder_pre_encode_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(147968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4604480))))[name = string("encoder_pre_encode_out_weight_to_fp16_quantized")]; + tensor encoder_pre_encode_out_bias_to_fp16 = const()[name = string("encoder_pre_encode_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4606592)))]; + tensor linear_0_cast_fp16 = linear(bias = encoder_pre_encode_out_bias_to_fp16, weight = encoder_pre_encode_out_weight_to_fp16_quantized, x = input_23_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_343_begin_0 = const()[name = string("op_343_begin_0"), val = tensor([0, 2, 0])]; + tensor var_343_end_0 = const()[name = string("op_343_end_0"), val = tensor([1, 58, 1024])]; + tensor var_343_end_mask_0 = const()[name = string("op_343_end_mask_0"), val = tensor([true, true, true])]; + tensor var_343_cast_fp16 = slice_by_index(begin = var_343_begin_0, end = var_343_end_0, end_mask = var_343_end_mask_0, x = linear_0_cast_fp16)[name = string("op_343_cast_fp16")]; + int32 var_345 = const()[name = string("op_345"), val = int32(2)]; + tensor var_346 = sub(x = current_lengths_cast_fp16_to_int32, y = var_345)[name = string("op_346")]; + string var_346_promoted_to_fp16_dtype_0 = const()[name = string("op_346_promoted_to_fp16_dtype_0"), val = string("fp16")]; + fp16 var_63_promoted_to_fp16 = const()[name = string("op_63_promoted_to_fp16"), val = fp16(0x0p+0)]; + fp16 const_61_to_fp16 = const()[name = string("const_61_to_fp16"), val = fp16(inf)]; + tensor var_346_to_fp16 = cast(dtype = var_346_promoted_to_fp16_dtype_0, x = var_346)[name = string("cast_13")]; + tensor clip_0_cast_fp16 = clip(alpha = var_63_promoted_to_fp16, beta = const_61_to_fp16, x = var_346_to_fp16)[name = string("clip_0_cast_fp16")]; + tensor max_audio_length_1 = const()[name = string("max_audio_length_1"), val = tensor([56])]; + fp16 var_362_promoted_to_fp16 = const()[name = string("op_362_promoted_to_fp16"), val = fp16(0x1.5p+5)]; + tensor padding_length_cast_fp16 = add(x = clip_0_cast_fp16, y = var_362_promoted_to_fp16)[name = string("padding_length_cast_fp16")]; + int32 const_63 = const()[name = string("const_63"), val = int32(-1)]; + tensor var_364 = mul(x = cache_len, y = const_63)[name = string("op_364")]; + int32 var_365 = const()[name = string("op_365"), val = int32(42)]; + tensor offset = add(x = var_364, y = var_365)[name = string("offset")]; + tensor var_405_axes_0 = const()[name = string("op_405_axes_0"), val = tensor([-1])]; + tensor var_405_cast_fp16 = expand_dims(axes = var_405_axes_0, x = padding_length_cast_fp16)[name = string("op_405_cast_fp16")]; + tensor var_404_promoted_to_fp16 = const()[name = string("op_404_promoted_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4608704)))]; + tensor pad_mask_1_cast_fp16 = less(x = var_404_promoted_to_fp16, y = var_405_cast_fp16)[name = string("pad_mask_1_cast_fp16")]; + tensor expand_dims_5 = const()[name = string("expand_dims_5"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4609024)))]; + tensor var_411_axes_0 = const()[name = string("op_411_axes_0"), val = tensor([-1])]; + tensor var_411 = expand_dims(axes = var_411_axes_0, x = offset)[name = string("op_411")]; + tensor pad_mask_off = greater_equal(x = expand_dims_5, y = var_411)[name = string("pad_mask_off")]; + tensor pad_mask_3 = logical_and(x = pad_mask_off, y = pad_mask_1_cast_fp16)[name = string("pad_mask_3")]; + tensor var_414_axes_0 = const()[name = string("op_414_axes_0"), val = tensor([1])]; + tensor var_414 = expand_dims(axes = var_414_axes_0, x = pad_mask_3)[name = string("op_414")]; + tensor var_415 = const()[name = string("op_415"), val = tensor([1, 98, 1])]; + tensor pad_mask_for_att_mask_1 = tile(reps = var_415, x = var_414)[name = string("pad_mask_for_att_mask_1")]; + tensor var_417_perm_0 = const()[name = string("op_417_perm_0"), val = tensor([0, 2, 1])]; + tensor var_417 = transpose(perm = var_417_perm_0, x = pad_mask_for_att_mask_1)[name = string("transpose_365")]; + tensor pad_mask_for_att_mask = logical_and(x = pad_mask_for_att_mask_1, y = var_417)[name = string("pad_mask_for_att_mask")]; + tensor const_71 = const()[name = string("const_71"), val = tensor([[[true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, 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true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, 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true, true, true, true, true, true, true, true, true, true, true, true], [false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true]]])]; + tensor att_mask_9 = logical_and(x = pad_mask_for_att_mask, y = const_71)[name = string("att_mask_9")]; + tensor att_mask = logical_not(x = att_mask_9)[name = string("att_mask")]; + tensor pad_mask_5 = logical_not(x = pad_mask_3)[name = string("pad_mask_5")]; + tensor pad_mask_begin_0 = const()[name = string("pad_mask_begin_0"), val = tensor([0, 42])]; + tensor pad_mask_end_0 = const()[name = string("pad_mask_end_0"), val = tensor([1, 98])]; + tensor pad_mask_end_mask_0 = const()[name = string("pad_mask_end_mask_0"), val = tensor([true, true])]; + tensor pad_mask = slice_by_index(begin = pad_mask_begin_0, end = pad_mask_end_0, end_mask = pad_mask_end_mask_0, x = pad_mask_5)[name = string("pad_mask")]; + tensor mask_9_begin_0 = const()[name = string("mask_9_begin_0"), val = tensor([0, 42, 0])]; + tensor mask_9_end_0 = const()[name = string("mask_9_end_0"), val = tensor([1, 98, 98])]; + tensor mask_9_end_mask_0 = const()[name = string("mask_9_end_mask_0"), val = tensor([true, true, true])]; + tensor mask_9 = slice_by_index(begin = mask_9_begin_0, end = mask_9_end_0, end_mask = mask_9_end_mask_0, x = att_mask)[name = string("mask_9")]; + tensor cache_1_begin_0 = const()[name = string("cache_1_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor cache_1_end_0 = const()[name = string("cache_1_end_0"), val = tensor([1, 1, 42, 1024])]; + tensor cache_1_end_mask_0 = const()[name = string("cache_1_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_1_squeeze_mask_0 = const()[name = string("cache_1_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_channel_to_fp16 = cast(dtype = cache_channel_to_fp16_dtype_0, x = cache_channel)[name = string("cast_12")]; + tensor value_3_cast_fp16 = transpose(perm = value_3_perm_0, x = cache_channel_to_fp16)[name = string("transpose_364")]; + tensor cache_1_cast_fp16 = slice_by_index(begin = cache_1_begin_0, end = cache_1_end_0, end_mask = cache_1_end_mask_0, squeeze_mask = cache_1_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_1_cast_fp16")]; + tensor cache_3_begin_0 = const()[name = string("cache_3_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor cache_3_end_0 = const()[name = string("cache_3_end_0"), val = tensor([1, 1, 1024, 8])]; + tensor cache_3_end_mask_0 = const()[name = string("cache_3_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_3_squeeze_mask_0 = const()[name = string("cache_3_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_time_to_fp16 = cast(dtype = cache_time_to_fp16_dtype_0, x = cache_time)[name = string("cast_11")]; + tensor value_5_cast_fp16 = transpose(perm = value_5_perm_0, x = cache_time_to_fp16)[name = string("transpose_363")]; + tensor cache_3_cast_fp16 = slice_by_index(begin = cache_3_begin_0, end = cache_3_end_0, end_mask = cache_3_end_mask_0, squeeze_mask = cache_3_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_3_cast_fp16")]; + tensor input_27_axes_0 = const()[name = string("input_27_axes_0"), val = tensor([-1])]; + tensor encoder_layers_0_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_0_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4609536)))]; + tensor encoder_layers_0_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_0_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4611648)))]; + fp16 var_43_to_fp16 = const()[name = string("op_43_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_27_cast_fp16 = layer_norm(axes = input_27_axes_0, beta = encoder_layers_0_norm_feed_forward1_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_0_norm_feed_forward1_weight_to_fp16, x = var_343_cast_fp16)[name = string("input_27_cast_fp16")]; + tensor encoder_layers_0_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4613760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8808128))))[name = string("encoder_layers_0_feed_forward1_linear1_weight_to_fp16_quantized")]; + tensor encoder_layers_0_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_0_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8816384)))]; + tensor linear_1_cast_fp16 = linear(bias = encoder_layers_0_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_0_feed_forward1_linear1_weight_to_fp16_quantized, x = input_27_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor input_31_cast_fp16 = silu(x = linear_1_cast_fp16)[name = string("input_31_cast_fp16")]; + tensor encoder_layers_0_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8824640))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13019008))))[name = string("encoder_layers_0_feed_forward1_linear2_weight_to_fp16_quantized")]; + tensor encoder_layers_0_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_0_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13021120)))]; + tensor linear_2_cast_fp16 = linear(bias = encoder_layers_0_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_0_feed_forward1_linear2_weight_to_fp16_quantized, x = input_31_cast_fp16)[name = string("linear_2_cast_fp16")]; + fp16 var_456_to_fp16 = const()[name = string("op_456_to_fp16"), val = fp16(0x1p-1)]; + tensor var_457_cast_fp16 = mul(x = linear_2_cast_fp16, y = var_456_to_fp16)[name = string("op_457_cast_fp16")]; + tensor input_37_cast_fp16 = add(x = var_343_cast_fp16, y = var_457_cast_fp16)[name = string("input_37_cast_fp16")]; + tensor key_1_axes_0 = const()[name = string("key_1_axes_0"), val = tensor([-1])]; + tensor encoder_layers_0_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_0_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13023232)))]; + tensor encoder_layers_0_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_0_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13025344)))]; + tensor key_1_cast_fp16 = layer_norm(axes = key_1_axes_0, beta = encoder_layers_0_norm_self_att_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_0_norm_self_att_weight_to_fp16, x = input_37_cast_fp16)[name = string("key_1_cast_fp16")]; + bool input_39_interleave_0 = const()[name = string("input_39_interleave_0"), val = bool(false)]; + tensor input_39_cast_fp16 = concat(axis = var_69, interleave = input_39_interleave_0, values = (cache_1_cast_fp16, key_1_cast_fp16))[name = string("input_39_cast_fp16")]; + bool var_485_interleave_0 = const()[name = string("op_485_interleave_0"), val = bool(false)]; + tensor var_485_cast_fp16 = concat(axis = var_69, interleave = var_485_interleave_0, values = key_1_cast_fp16)[name = string("op_485_cast_fp16")]; + tensor encoder_layers_0_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13027456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14076096))))[name = string("encoder_layers_0_self_attn_linear_q_weight_to_fp16_quantized")]; + tensor encoder_layers_0_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_0_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14078208)))]; + tensor linear_3_cast_fp16 = linear(bias = encoder_layers_0_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_0_self_attn_linear_q_weight_to_fp16_quantized, x = key_1_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_490 = const()[name = string("op_490"), val = tensor([1, -1, 8, 128])]; + tensor q_1_cast_fp16 = reshape(shape = var_490, x = linear_3_cast_fp16)[name = string("q_1_cast_fp16")]; + tensor encoder_layers_0_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14080320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15128960))))[name = string("encoder_layers_0_self_attn_linear_k_weight_to_fp16_quantized")]; + tensor encoder_layers_0_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_0_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15131072)))]; + tensor linear_4_cast_fp16 = linear(bias = encoder_layers_0_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_0_self_attn_linear_k_weight_to_fp16_quantized, x = input_39_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_495 = const()[name = string("op_495"), val = tensor([1, -1, 8, 128])]; + tensor k_1_cast_fp16 = reshape(shape = var_495, x = linear_4_cast_fp16)[name = string("k_1_cast_fp16")]; + tensor encoder_layers_0_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15133184))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16181824))))[name = string("encoder_layers_0_self_attn_linear_v_weight_to_fp16_quantized")]; + tensor encoder_layers_0_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_0_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16183936)))]; + tensor linear_5_cast_fp16 = linear(bias = encoder_layers_0_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_0_self_attn_linear_v_weight_to_fp16_quantized, x = input_39_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_500 = const()[name = string("op_500"), val = tensor([1, -1, 8, 128])]; + tensor v_1_cast_fp16 = reshape(shape = var_500, x = linear_5_cast_fp16)[name = string("v_1_cast_fp16")]; + tensor value_9_perm_0 = const()[name = string("value_9_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_0_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_0_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16186048)))]; + tensor var_513_cast_fp16 = add(x = q_1_cast_fp16, y = encoder_layers_0_self_attn_pos_bias_u_to_fp16)[name = string("op_513_cast_fp16")]; + tensor encoder_layers_0_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_0_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16188160)))]; + tensor var_515_cast_fp16 = add(x = q_1_cast_fp16, y = encoder_layers_0_self_attn_pos_bias_v_to_fp16)[name = string("op_515_cast_fp16")]; + tensor q_with_bias_v_1_perm_0 = const()[name = string("q_with_bias_v_1_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_7_transpose_x_0 = const()[name = string("x_7_transpose_x_0"), val = bool(false)]; + bool x_7_transpose_y_0 = const()[name = string("x_7_transpose_y_0"), val = bool(false)]; + tensor op_517_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16190272))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16390016))))[name = string("op_517_to_fp16_quantized")]; + tensor q_with_bias_v_1_cast_fp16 = transpose(perm = q_with_bias_v_1_perm_0, x = var_515_cast_fp16)[name = string("transpose_362")]; + tensor x_7_cast_fp16 = matmul(transpose_x = x_7_transpose_x_0, transpose_y = x_7_transpose_y_0, x = q_with_bias_v_1_cast_fp16, y = op_517_to_fp16_quantized)[name = string("x_7_cast_fp16")]; + tensor x_9_pad_0 = const()[name = string("x_9_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_9_mode_0 = const()[name = string("x_9_mode_0"), val = string("constant")]; + fp16 const_79_to_fp16 = const()[name = string("const_79_to_fp16"), val = fp16(0x0p+0)]; + tensor x_9_cast_fp16 = pad(constant_val = const_79_to_fp16, mode = x_9_mode_0, pad = x_9_pad_0, x = x_7_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor var_525 = const()[name = string("op_525"), val = tensor([1, 8, -1, 56])]; + tensor x_11_cast_fp16 = reshape(shape = var_525, x = x_9_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor var_529_begin_0 = const()[name = string("op_529_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_529_end_0 = const()[name = string("op_529_end_0"), val = tensor([1, 8, 196, 56])]; + tensor var_529_end_mask_0 = const()[name = string("op_529_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_529_cast_fp16 = slice_by_index(begin = var_529_begin_0, end = var_529_end_0, end_mask = var_529_end_mask_0, x = x_11_cast_fp16)[name = string("op_529_cast_fp16")]; + tensor var_530 = const()[name = string("op_530"), val = tensor([1, 8, 56, 195])]; + tensor matrix_bd_1_cast_fp16 = reshape(shape = var_530, x = var_529_cast_fp16)[name = string("matrix_bd_1_cast_fp16")]; + bool matrix_ac_1_transpose_x_0 = const()[name = string("matrix_ac_1_transpose_x_0"), val = bool(false)]; + bool matrix_ac_1_transpose_y_0 = const()[name = string("matrix_ac_1_transpose_y_0"), val = bool(false)]; + tensor transpose_96_perm_0 = const()[name = string("transpose_96_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_97_perm_0 = const()[name = string("transpose_97_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_97 = transpose(perm = transpose_97_perm_0, x = k_1_cast_fp16)[name = string("transpose_360")]; + tensor transpose_96 = transpose(perm = transpose_96_perm_0, x = var_513_cast_fp16)[name = string("transpose_361")]; + tensor matrix_ac_1_cast_fp16 = matmul(transpose_x = matrix_ac_1_transpose_x_0, transpose_y = matrix_ac_1_transpose_y_0, x = transpose_96, y = transpose_97)[name = string("matrix_ac_1_cast_fp16")]; + tensor matrix_bd_3_begin_0 = const()[name = string("matrix_bd_3_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_3_end_0 = const()[name = string("matrix_bd_3_end_0"), val = tensor([1, 8, 56, 98])]; + tensor matrix_bd_3_end_mask_0 = const()[name = string("matrix_bd_3_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_3_cast_fp16 = slice_by_index(begin = matrix_bd_3_begin_0, end = matrix_bd_3_end_0, end_mask = matrix_bd_3_end_mask_0, x = matrix_bd_1_cast_fp16)[name = string("matrix_bd_3_cast_fp16")]; + tensor var_539_cast_fp16 = add(x = matrix_ac_1_cast_fp16, y = matrix_bd_3_cast_fp16)[name = string("op_539_cast_fp16")]; + fp16 _inversed_scores_1_y_0_to_fp16 = const()[name = string("_inversed_scores_1_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_1_cast_fp16 = mul(x = var_539_cast_fp16, y = _inversed_scores_1_y_0_to_fp16)[name = string("_inversed_scores_1_cast_fp16")]; + tensor mask_11_axes_0 = const()[name = string("mask_11_axes_0"), val = tensor([1])]; + tensor mask_11 = expand_dims(axes = mask_11_axes_0, x = mask_9)[name = string("mask_11")]; + fp16 var_46_to_fp16 = const()[name = string("op_46_to_fp16"), val = fp16(-0x1.388p+13)]; + tensor scores_3_cast_fp16 = select(a = var_46_to_fp16, b = _inversed_scores_1_cast_fp16, cond = mask_11)[name = string("scores_3_cast_fp16")]; + tensor var_545_cast_fp16 = softmax(axis = var_60, x = scores_3_cast_fp16)[name = string("op_545_cast_fp16")]; + fp16 var_45_to_fp16 = const()[name = string("op_45_to_fp16"), val = fp16(0x0p+0)]; + tensor input_41_cast_fp16 = select(a = var_45_to_fp16, b = var_545_cast_fp16, cond = mask_11)[name = string("input_41_cast_fp16")]; + bool x_13_transpose_x_0 = const()[name = string("x_13_transpose_x_0"), val = bool(false)]; + bool x_13_transpose_y_0 = const()[name = string("x_13_transpose_y_0"), val = bool(false)]; + tensor value_9_cast_fp16 = transpose(perm = value_9_perm_0, x = v_1_cast_fp16)[name = string("transpose_359")]; + tensor x_13_cast_fp16 = matmul(transpose_x = x_13_transpose_x_0, transpose_y = x_13_transpose_y_0, x = input_41_cast_fp16, y = value_9_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor var_549_perm_0 = const()[name = string("op_549_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_550 = const()[name = string("op_550"), val = tensor([1, -1, 1024])]; + tensor var_549_cast_fp16 = transpose(perm = var_549_perm_0, x = x_13_cast_fp16)[name = string("transpose_358")]; + tensor input_43_cast_fp16 = reshape(shape = var_550, x = var_549_cast_fp16)[name = string("input_43_cast_fp16")]; + tensor encoder_layers_0_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16390528))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17439168))))[name = string("encoder_layers_0_self_attn_linear_out_weight_to_fp16_quantized")]; + tensor encoder_layers_0_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_0_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17441280)))]; + tensor linear_7_cast_fp16 = linear(bias = encoder_layers_0_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_0_self_attn_linear_out_weight_to_fp16_quantized, x = input_43_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor input_47_cast_fp16 = add(x = input_37_cast_fp16, y = linear_7_cast_fp16)[name = string("input_47_cast_fp16")]; + tensor x_17_axes_0 = const()[name = string("x_17_axes_0"), val = tensor([-1])]; + tensor encoder_layers_0_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_0_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17443392)))]; + tensor encoder_layers_0_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_0_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17445504)))]; + tensor x_17_cast_fp16 = layer_norm(axes = x_17_axes_0, beta = encoder_layers_0_norm_conv_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_0_norm_conv_weight_to_fp16, x = input_47_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor input_49_perm_0 = const()[name = string("input_49_perm_0"), val = tensor([0, 2, 1])]; + string input_51_pad_type_0 = const()[name = string("input_51_pad_type_0"), val = string("valid")]; + tensor input_51_strides_0 = const()[name = string("input_51_strides_0"), val = tensor([1])]; + tensor input_51_pad_0 = const()[name = string("input_51_pad_0"), val = tensor([0, 0])]; + tensor input_51_dilations_0 = const()[name = string("input_51_dilations_0"), val = tensor([1])]; + int32 input_51_groups_0 = const()[name = string("input_51_groups_0"), val = int32(1)]; + tensor encoder_layers_0_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17447616))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19544832))))[name = string("encoder_layers_0_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_49_cast_fp16 = transpose(perm = input_49_perm_0, x = x_17_cast_fp16)[name = string("transpose_357")]; + tensor input_51_cast_fp16 = conv(dilations = input_51_dilations_0, groups = input_51_groups_0, pad = input_51_pad_0, pad_type = input_51_pad_type_0, strides = input_51_strides_0, weight = encoder_layers_0_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_49_cast_fp16)[name = string("input_51_cast_fp16")]; + int32 x_19_split_num_splits_0 = const()[name = string("x_19_split_num_splits_0"), val = int32(2)]; + int32 x_19_split_axis_0 = const()[name = string("x_19_split_axis_0"), val = int32(1)]; + tensor x_19_split_cast_fp16_0, tensor x_19_split_cast_fp16_1 = split(axis = x_19_split_axis_0, num_splits = x_19_split_num_splits_0, x = input_51_cast_fp16)[name = string("x_19_split_cast_fp16")]; + tensor x_19_split_1_sigmoid_cast_fp16 = sigmoid(x = x_19_split_cast_fp16_1)[name = string("x_19_split_1_sigmoid_cast_fp16")]; + tensor x_19_cast_fp16 = mul(x = x_19_split_cast_fp16_0, y = x_19_split_1_sigmoid_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor var_576_axes_0 = const()[name = string("op_576_axes_0"), val = tensor([1])]; + tensor var_576 = expand_dims(axes = var_576_axes_0, x = pad_mask)[name = string("op_576")]; + tensor input_53_cast_fp16 = select(a = var_45_to_fp16, b = x_19_cast_fp16, cond = var_576)[name = string("input_53_cast_fp16")]; + bool new_x_3_interleave_0 = const()[name = string("new_x_3_interleave_0"), val = bool(false)]; + tensor new_x_3_cast_fp16 = concat(axis = var_60, interleave = new_x_3_interleave_0, values = (cache_3_cast_fp16, input_53_cast_fp16))[name = string("new_x_3_cast_fp16")]; + tensor var_589_begin_0 = const()[name = string("op_589_begin_0"), val = tensor([0, 0, 56])]; + tensor var_589_end_0 = const()[name = string("op_589_end_0"), val = tensor([1, 1024, 64])]; + tensor var_589_end_mask_0 = const()[name = string("op_589_end_mask_0"), val = tensor([true, true, true])]; + tensor var_589_cast_fp16 = slice_by_index(begin = var_589_begin_0, end = var_589_end_0, end_mask = var_589_end_mask_0, x = new_x_3_cast_fp16)[name = string("op_589_cast_fp16")]; + string x_21_pad_type_0 = const()[name = string("x_21_pad_type_0"), val = string("valid")]; + int32 x_21_groups_0 = const()[name = string("x_21_groups_0"), val = int32(1024)]; + tensor x_21_strides_0 = const()[name = string("x_21_strides_0"), val = tensor([1])]; + tensor x_21_pad_0 = const()[name = string("x_21_pad_0"), val = tensor([0, 0])]; + tensor x_21_dilations_0 = const()[name = string("x_21_dilations_0"), val = tensor([1])]; + tensor encoder_layers_0_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19548992))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19558272))))[name = string("encoder_layers_0_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_21_cast_fp16 = conv(dilations = x_21_dilations_0, groups = x_21_groups_0, pad = x_21_pad_0, pad_type = x_21_pad_type_0, strides = x_21_strides_0, weight = encoder_layers_0_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_3_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor input_55_perm_0 = const()[name = string("input_55_perm_0"), val = tensor([0, 2, 1])]; + tensor x_23_axes_0 = const()[name = string("x_23_axes_0"), val = tensor([-1])]; + tensor encoder_layers_0_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_0_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19560384)))]; + tensor encoder_layers_0_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_0_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19562496)))]; + tensor input_55_cast_fp16 = transpose(perm = input_55_perm_0, x = x_21_cast_fp16)[name = string("transpose_356")]; + tensor x_23_cast_fp16 = layer_norm(axes = x_23_axes_0, beta = encoder_layers_0_conv_batch_norm_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_0_conv_batch_norm_weight_to_fp16, x = input_55_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor input_57_perm_0 = const()[name = string("input_57_perm_0"), val = tensor([0, 2, 1])]; + tensor input_57_cast_fp16 = transpose(perm = input_57_perm_0, x = x_23_cast_fp16)[name = string("transpose_355")]; + tensor input_59_cast_fp16 = silu(x = input_57_cast_fp16)[name = string("input_59_cast_fp16")]; + string x_25_pad_type_0 = const()[name = string("x_25_pad_type_0"), val = string("valid")]; + tensor x_25_strides_0 = const()[name = string("x_25_strides_0"), val = tensor([1])]; + tensor x_25_pad_0 = const()[name = string("x_25_pad_0"), val = tensor([0, 0])]; + tensor x_25_dilations_0 = const()[name = string("x_25_dilations_0"), val = tensor([1])]; + int32 x_25_groups_0 = const()[name = string("x_25_groups_0"), val = int32(1)]; + tensor encoder_layers_0_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19564608))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20613248))))[name = string("encoder_layers_0_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_25_cast_fp16 = conv(dilations = x_25_dilations_0, groups = x_25_groups_0, pad = x_25_pad_0, pad_type = x_25_pad_type_0, strides = x_25_strides_0, weight = encoder_layers_0_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_59_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor input_61_perm_0 = const()[name = string("input_61_perm_0"), val = tensor([0, 2, 1])]; + tensor input_61_cast_fp16 = transpose(perm = input_61_perm_0, x = x_25_cast_fp16)[name = string("transpose_354")]; + tensor input_63_cast_fp16 = add(x = input_47_cast_fp16, y = input_61_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor input_65_axes_0 = const()[name = string("input_65_axes_0"), val = tensor([-1])]; + tensor encoder_layers_0_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_0_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20615360)))]; + tensor encoder_layers_0_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_0_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20617472)))]; + tensor input_65_cast_fp16 = layer_norm(axes = input_65_axes_0, beta = encoder_layers_0_norm_feed_forward2_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_0_norm_feed_forward2_weight_to_fp16, x = input_63_cast_fp16)[name = string("input_65_cast_fp16")]; + tensor encoder_layers_0_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20619584))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24813952))))[name = string("encoder_layers_0_feed_forward2_linear1_weight_to_fp16_quantized")]; + tensor encoder_layers_0_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_0_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24822208)))]; + tensor linear_8_cast_fp16 = linear(bias = encoder_layers_0_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_0_feed_forward2_linear1_weight_to_fp16_quantized, x = input_65_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor input_69_cast_fp16 = silu(x = linear_8_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor encoder_layers_0_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24830464))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29024832))))[name = string("encoder_layers_0_feed_forward2_linear2_weight_to_fp16_quantized")]; + tensor encoder_layers_0_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_0_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29026944)))]; + tensor linear_9_cast_fp16 = linear(bias = encoder_layers_0_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_0_feed_forward2_linear2_weight_to_fp16_quantized, x = input_69_cast_fp16)[name = string("linear_9_cast_fp16")]; + fp16 var_632_to_fp16 = const()[name = string("op_632_to_fp16"), val = fp16(0x1p-1)]; + tensor var_633_cast_fp16 = mul(x = linear_9_cast_fp16, y = var_632_to_fp16)[name = string("op_633_cast_fp16")]; + tensor input_75_cast_fp16 = add(x = input_63_cast_fp16, y = var_633_cast_fp16)[name = string("input_75_cast_fp16")]; + tensor input_77_axes_0 = const()[name = string("input_77_axes_0"), val = tensor([-1])]; + tensor encoder_layers_0_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_0_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29029056)))]; + tensor encoder_layers_0_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_0_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29031168)))]; + tensor input_77_cast_fp16 = layer_norm(axes = input_77_axes_0, beta = encoder_layers_0_norm_out_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_0_norm_out_weight_to_fp16, x = input_75_cast_fp16)[name = string("input_77_cast_fp16")]; + tensor cache_5_begin_0 = const()[name = string("cache_5_begin_0"), val = tensor([1, 0, 0, 0])]; + tensor cache_5_end_0 = const()[name = string("cache_5_end_0"), val = tensor([2, 1, 42, 1024])]; + tensor cache_5_end_mask_0 = const()[name = string("cache_5_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_5_squeeze_mask_0 = const()[name = string("cache_5_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_5_cast_fp16 = slice_by_index(begin = cache_5_begin_0, end = cache_5_end_0, end_mask = cache_5_end_mask_0, squeeze_mask = cache_5_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_5_cast_fp16")]; + tensor cache_7_begin_0 = const()[name = string("cache_7_begin_0"), val = tensor([1, 0, 0, 0])]; + tensor cache_7_end_0 = const()[name = string("cache_7_end_0"), val = tensor([2, 1, 1024, 8])]; + tensor cache_7_end_mask_0 = const()[name = string("cache_7_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_7_squeeze_mask_0 = const()[name = string("cache_7_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_7_cast_fp16 = slice_by_index(begin = cache_7_begin_0, end = cache_7_end_0, end_mask = cache_7_end_mask_0, squeeze_mask = cache_7_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_7_cast_fp16")]; + tensor input_79_axes_0 = const()[name = string("input_79_axes_0"), val = tensor([-1])]; + tensor encoder_layers_1_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_1_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29033280)))]; + tensor encoder_layers_1_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_1_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29035392)))]; + tensor input_79_cast_fp16 = layer_norm(axes = input_79_axes_0, beta = encoder_layers_1_norm_feed_forward1_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_1_norm_feed_forward1_weight_to_fp16, x = input_77_cast_fp16)[name = string("input_79_cast_fp16")]; + tensor encoder_layers_1_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29037504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33231872))))[name = string("encoder_layers_1_feed_forward1_linear1_weight_to_fp16_quantized")]; + tensor encoder_layers_1_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_1_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33240128)))]; + tensor linear_10_cast_fp16 = linear(bias = encoder_layers_1_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_1_feed_forward1_linear1_weight_to_fp16_quantized, x = input_79_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor input_83_cast_fp16 = silu(x = linear_10_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor encoder_layers_1_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33248384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37442752))))[name = string("encoder_layers_1_feed_forward1_linear2_weight_to_fp16_quantized")]; + tensor encoder_layers_1_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_1_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37444864)))]; + tensor linear_11_cast_fp16 = linear(bias = encoder_layers_1_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_1_feed_forward1_linear2_weight_to_fp16_quantized, x = input_83_cast_fp16)[name = string("linear_11_cast_fp16")]; + fp16 var_669_to_fp16 = const()[name = string("op_669_to_fp16"), val = fp16(0x1p-1)]; + tensor var_670_cast_fp16 = mul(x = linear_11_cast_fp16, y = var_669_to_fp16)[name = string("op_670_cast_fp16")]; + tensor input_89_cast_fp16 = add(x = input_77_cast_fp16, y = var_670_cast_fp16)[name = string("input_89_cast_fp16")]; + tensor key_3_axes_0 = const()[name = string("key_3_axes_0"), val = tensor([-1])]; + tensor encoder_layers_1_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_1_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37446976)))]; + tensor encoder_layers_1_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_1_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37449088)))]; + tensor key_3_cast_fp16 = layer_norm(axes = key_3_axes_0, beta = encoder_layers_1_norm_self_att_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_1_norm_self_att_weight_to_fp16, x = input_89_cast_fp16)[name = string("key_3_cast_fp16")]; + bool input_91_interleave_0 = const()[name = string("input_91_interleave_0"), val = bool(false)]; + tensor input_91_cast_fp16 = concat(axis = var_69, interleave = input_91_interleave_0, values = (cache_5_cast_fp16, key_3_cast_fp16))[name = string("input_91_cast_fp16")]; + bool var_698_interleave_0 = const()[name = string("op_698_interleave_0"), val = bool(false)]; + tensor var_698_cast_fp16 = concat(axis = var_69, interleave = var_698_interleave_0, values = key_3_cast_fp16)[name = string("op_698_cast_fp16")]; + tensor encoder_layers_1_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37451200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38499840))))[name = string("encoder_layers_1_self_attn_linear_q_weight_to_fp16_quantized")]; + tensor encoder_layers_1_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_1_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38501952)))]; + tensor linear_12_cast_fp16 = linear(bias = encoder_layers_1_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_1_self_attn_linear_q_weight_to_fp16_quantized, x = key_3_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_703 = const()[name = string("op_703"), val = tensor([1, -1, 8, 128])]; + tensor q_7_cast_fp16 = reshape(shape = var_703, x = linear_12_cast_fp16)[name = string("q_7_cast_fp16")]; + tensor encoder_layers_1_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38504064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39552704))))[name = string("encoder_layers_1_self_attn_linear_k_weight_to_fp16_quantized")]; + tensor encoder_layers_1_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_1_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39554816)))]; + tensor linear_13_cast_fp16 = linear(bias = encoder_layers_1_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_1_self_attn_linear_k_weight_to_fp16_quantized, x = input_91_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_708 = const()[name = string("op_708"), val = tensor([1, -1, 8, 128])]; + tensor k_5_cast_fp16 = reshape(shape = var_708, x = linear_13_cast_fp16)[name = string("k_5_cast_fp16")]; + tensor encoder_layers_1_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39556928))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40605568))))[name = string("encoder_layers_1_self_attn_linear_v_weight_to_fp16_quantized")]; + tensor encoder_layers_1_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_1_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40607680)))]; + tensor linear_14_cast_fp16 = linear(bias = encoder_layers_1_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_1_self_attn_linear_v_weight_to_fp16_quantized, x = input_91_cast_fp16)[name = string("linear_14_cast_fp16")]; + tensor var_713 = const()[name = string("op_713"), val = tensor([1, -1, 8, 128])]; + tensor v_3_cast_fp16 = reshape(shape = var_713, x = linear_14_cast_fp16)[name = string("v_3_cast_fp16")]; + tensor value_11_perm_0 = const()[name = string("value_11_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_1_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_1_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40609792)))]; + tensor var_726_cast_fp16 = add(x = q_7_cast_fp16, y = encoder_layers_1_self_attn_pos_bias_u_to_fp16)[name = string("op_726_cast_fp16")]; + tensor encoder_layers_1_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_1_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40611904)))]; + tensor var_728_cast_fp16 = add(x = q_7_cast_fp16, y = encoder_layers_1_self_attn_pos_bias_v_to_fp16)[name = string("op_728_cast_fp16")]; + tensor q_with_bias_v_3_perm_0 = const()[name = string("q_with_bias_v_3_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_33_transpose_x_0 = const()[name = string("x_33_transpose_x_0"), val = bool(false)]; + bool x_33_transpose_y_0 = const()[name = string("x_33_transpose_y_0"), val = bool(false)]; + tensor op_730_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40614016))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40813760))))[name = string("op_730_to_fp16_quantized")]; + tensor q_with_bias_v_3_cast_fp16 = transpose(perm = q_with_bias_v_3_perm_0, x = var_728_cast_fp16)[name = string("transpose_353")]; + tensor x_33_cast_fp16 = matmul(transpose_x = x_33_transpose_x_0, transpose_y = x_33_transpose_y_0, x = q_with_bias_v_3_cast_fp16, y = op_730_to_fp16_quantized)[name = string("x_33_cast_fp16")]; + tensor x_35_pad_0 = const()[name = string("x_35_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_35_mode_0 = const()[name = string("x_35_mode_0"), val = string("constant")]; + fp16 const_92_to_fp16 = const()[name = string("const_92_to_fp16"), val = fp16(0x0p+0)]; + tensor x_35_cast_fp16 = pad(constant_val = const_92_to_fp16, mode = x_35_mode_0, pad = x_35_pad_0, x = x_33_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor var_738 = const()[name = string("op_738"), val = tensor([1, 8, -1, 56])]; + tensor x_37_cast_fp16 = reshape(shape = var_738, x = x_35_cast_fp16)[name = string("x_37_cast_fp16")]; + tensor var_742_begin_0 = const()[name = string("op_742_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_742_end_0 = const()[name = string("op_742_end_0"), val = tensor([1, 8, 196, 56])]; + tensor var_742_end_mask_0 = const()[name = string("op_742_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_742_cast_fp16 = slice_by_index(begin = var_742_begin_0, end = var_742_end_0, end_mask = var_742_end_mask_0, x = x_37_cast_fp16)[name = string("op_742_cast_fp16")]; + tensor var_743 = const()[name = string("op_743"), val = tensor([1, 8, 56, 195])]; + tensor matrix_bd_5_cast_fp16 = reshape(shape = var_743, x = var_742_cast_fp16)[name = string("matrix_bd_5_cast_fp16")]; + bool matrix_ac_3_transpose_x_0 = const()[name = string("matrix_ac_3_transpose_x_0"), val = bool(false)]; + bool matrix_ac_3_transpose_y_0 = const()[name = string("matrix_ac_3_transpose_y_0"), val = bool(false)]; + tensor transpose_98_perm_0 = const()[name = string("transpose_98_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_99_perm_0 = const()[name = string("transpose_99_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_99 = transpose(perm = transpose_99_perm_0, x = k_5_cast_fp16)[name = string("transpose_351")]; + tensor transpose_98 = transpose(perm = transpose_98_perm_0, x = var_726_cast_fp16)[name = string("transpose_352")]; + tensor matrix_ac_3_cast_fp16 = matmul(transpose_x = matrix_ac_3_transpose_x_0, transpose_y = matrix_ac_3_transpose_y_0, x = transpose_98, y = transpose_99)[name = string("matrix_ac_3_cast_fp16")]; + tensor matrix_bd_7_begin_0 = const()[name = string("matrix_bd_7_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_7_end_0 = const()[name = string("matrix_bd_7_end_0"), val = tensor([1, 8, 56, 98])]; + tensor matrix_bd_7_end_mask_0 = const()[name = string("matrix_bd_7_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_7_cast_fp16 = slice_by_index(begin = matrix_bd_7_begin_0, end = matrix_bd_7_end_0, end_mask = matrix_bd_7_end_mask_0, x = matrix_bd_5_cast_fp16)[name = string("matrix_bd_7_cast_fp16")]; + tensor var_752_cast_fp16 = add(x = matrix_ac_3_cast_fp16, y = matrix_bd_7_cast_fp16)[name = string("op_752_cast_fp16")]; + fp16 _inversed_scores_5_y_0_to_fp16 = const()[name = string("_inversed_scores_5_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_5_cast_fp16 = mul(x = var_752_cast_fp16, y = _inversed_scores_5_y_0_to_fp16)[name = string("_inversed_scores_5_cast_fp16")]; + tensor scores_7_cast_fp16 = select(a = var_46_to_fp16, b = _inversed_scores_5_cast_fp16, cond = mask_11)[name = string("scores_7_cast_fp16")]; + tensor var_758_cast_fp16 = softmax(axis = var_60, x = scores_7_cast_fp16)[name = string("op_758_cast_fp16")]; + tensor input_93_cast_fp16 = select(a = var_45_to_fp16, b = var_758_cast_fp16, cond = mask_11)[name = string("input_93_cast_fp16")]; + bool x_39_transpose_x_0 = const()[name = string("x_39_transpose_x_0"), val = bool(false)]; + bool x_39_transpose_y_0 = const()[name = string("x_39_transpose_y_0"), val = bool(false)]; + tensor value_11_cast_fp16 = transpose(perm = value_11_perm_0, x = v_3_cast_fp16)[name = string("transpose_350")]; + tensor x_39_cast_fp16 = matmul(transpose_x = x_39_transpose_x_0, transpose_y = x_39_transpose_y_0, x = input_93_cast_fp16, y = value_11_cast_fp16)[name = string("x_39_cast_fp16")]; + tensor var_762_perm_0 = const()[name = string("op_762_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_763 = const()[name = string("op_763"), val = tensor([1, -1, 1024])]; + tensor var_762_cast_fp16 = transpose(perm = var_762_perm_0, x = x_39_cast_fp16)[name = string("transpose_349")]; + tensor input_95_cast_fp16 = reshape(shape = var_763, x = var_762_cast_fp16)[name = string("input_95_cast_fp16")]; + tensor encoder_layers_1_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40814272))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41862912))))[name = string("encoder_layers_1_self_attn_linear_out_weight_to_fp16_quantized")]; + tensor encoder_layers_1_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_1_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41865024)))]; + tensor linear_16_cast_fp16 = linear(bias = encoder_layers_1_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_1_self_attn_linear_out_weight_to_fp16_quantized, x = input_95_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor input_99_cast_fp16 = add(x = input_89_cast_fp16, y = linear_16_cast_fp16)[name = string("input_99_cast_fp16")]; + tensor x_43_axes_0 = const()[name = string("x_43_axes_0"), val = tensor([-1])]; + tensor encoder_layers_1_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_1_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41867136)))]; + tensor encoder_layers_1_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_1_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41869248)))]; + tensor x_43_cast_fp16 = layer_norm(axes = x_43_axes_0, beta = encoder_layers_1_norm_conv_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_1_norm_conv_weight_to_fp16, x = input_99_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor input_101_perm_0 = const()[name = string("input_101_perm_0"), val = tensor([0, 2, 1])]; + string input_103_pad_type_0 = const()[name = string("input_103_pad_type_0"), val = string("valid")]; + tensor input_103_strides_0 = const()[name = string("input_103_strides_0"), val = tensor([1])]; + tensor input_103_pad_0 = const()[name = string("input_103_pad_0"), val = tensor([0, 0])]; + tensor input_103_dilations_0 = const()[name = string("input_103_dilations_0"), val = tensor([1])]; + int32 input_103_groups_0 = const()[name = string("input_103_groups_0"), val = int32(1)]; + tensor encoder_layers_1_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41871360))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43968576))))[name = string("encoder_layers_1_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_101_cast_fp16 = transpose(perm = input_101_perm_0, x = x_43_cast_fp16)[name = string("transpose_348")]; + tensor input_103_cast_fp16 = conv(dilations = input_103_dilations_0, groups = input_103_groups_0, pad = input_103_pad_0, pad_type = input_103_pad_type_0, strides = input_103_strides_0, weight = encoder_layers_1_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_101_cast_fp16)[name = string("input_103_cast_fp16")]; + int32 x_45_split_num_splits_0 = const()[name = string("x_45_split_num_splits_0"), val = int32(2)]; + int32 x_45_split_axis_0 = const()[name = string("x_45_split_axis_0"), val = int32(1)]; + tensor x_45_split_cast_fp16_0, tensor x_45_split_cast_fp16_1 = split(axis = x_45_split_axis_0, num_splits = x_45_split_num_splits_0, x = input_103_cast_fp16)[name = string("x_45_split_cast_fp16")]; + tensor x_45_split_1_sigmoid_cast_fp16 = sigmoid(x = x_45_split_cast_fp16_1)[name = string("x_45_split_1_sigmoid_cast_fp16")]; + tensor x_45_cast_fp16 = mul(x = x_45_split_cast_fp16_0, y = x_45_split_1_sigmoid_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor input_105_cast_fp16 = select(a = var_45_to_fp16, b = x_45_cast_fp16, cond = var_576)[name = string("input_105_cast_fp16")]; + bool new_x_7_interleave_0 = const()[name = string("new_x_7_interleave_0"), val = bool(false)]; + tensor new_x_7_cast_fp16 = concat(axis = var_60, interleave = new_x_7_interleave_0, values = (cache_7_cast_fp16, input_105_cast_fp16))[name = string("new_x_7_cast_fp16")]; + tensor var_802_begin_0 = const()[name = string("op_802_begin_0"), val = tensor([0, 0, 56])]; + tensor var_802_end_0 = const()[name = string("op_802_end_0"), val = tensor([1, 1024, 64])]; + tensor var_802_end_mask_0 = const()[name = string("op_802_end_mask_0"), val = tensor([true, true, true])]; + tensor var_802_cast_fp16 = slice_by_index(begin = var_802_begin_0, end = var_802_end_0, end_mask = var_802_end_mask_0, x = new_x_7_cast_fp16)[name = string("op_802_cast_fp16")]; + string x_47_pad_type_0 = const()[name = string("x_47_pad_type_0"), val = string("valid")]; + int32 x_47_groups_0 = const()[name = string("x_47_groups_0"), val = int32(1024)]; + tensor x_47_strides_0 = const()[name = string("x_47_strides_0"), val = tensor([1])]; + tensor x_47_pad_0 = const()[name = string("x_47_pad_0"), val = tensor([0, 0])]; + tensor x_47_dilations_0 = const()[name = string("x_47_dilations_0"), val = tensor([1])]; + tensor encoder_layers_1_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43972736))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43982016))))[name = string("encoder_layers_1_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_47_cast_fp16 = conv(dilations = x_47_dilations_0, groups = x_47_groups_0, pad = x_47_pad_0, pad_type = x_47_pad_type_0, strides = x_47_strides_0, weight = encoder_layers_1_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_7_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor input_107_perm_0 = const()[name = string("input_107_perm_0"), val = tensor([0, 2, 1])]; + tensor x_49_axes_0 = const()[name = string("x_49_axes_0"), val = tensor([-1])]; + tensor encoder_layers_1_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_1_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43984128)))]; + tensor encoder_layers_1_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_1_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43986240)))]; + tensor input_107_cast_fp16 = transpose(perm = input_107_perm_0, x = x_47_cast_fp16)[name = string("transpose_347")]; + tensor x_49_cast_fp16 = layer_norm(axes = x_49_axes_0, beta = encoder_layers_1_conv_batch_norm_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_1_conv_batch_norm_weight_to_fp16, x = input_107_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor input_109_perm_0 = const()[name = string("input_109_perm_0"), val = tensor([0, 2, 1])]; + tensor input_109_cast_fp16 = transpose(perm = input_109_perm_0, x = x_49_cast_fp16)[name = string("transpose_346")]; + tensor input_111_cast_fp16 = silu(x = input_109_cast_fp16)[name = string("input_111_cast_fp16")]; + string x_51_pad_type_0 = const()[name = string("x_51_pad_type_0"), val = string("valid")]; + tensor x_51_strides_0 = const()[name = string("x_51_strides_0"), val = tensor([1])]; + tensor x_51_pad_0 = const()[name = string("x_51_pad_0"), val = tensor([0, 0])]; + tensor x_51_dilations_0 = const()[name = string("x_51_dilations_0"), val = tensor([1])]; + int32 x_51_groups_0 = const()[name = string("x_51_groups_0"), val = int32(1)]; + tensor encoder_layers_1_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43988352))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45036992))))[name = string("encoder_layers_1_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_51_cast_fp16 = conv(dilations = x_51_dilations_0, groups = x_51_groups_0, pad = x_51_pad_0, pad_type = x_51_pad_type_0, strides = x_51_strides_0, weight = encoder_layers_1_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_111_cast_fp16)[name = string("x_51_cast_fp16")]; + tensor input_113_perm_0 = const()[name = string("input_113_perm_0"), val = tensor([0, 2, 1])]; + tensor input_113_cast_fp16 = transpose(perm = input_113_perm_0, x = x_51_cast_fp16)[name = string("transpose_345")]; + tensor input_115_cast_fp16 = add(x = input_99_cast_fp16, y = input_113_cast_fp16)[name = string("input_115_cast_fp16")]; + tensor input_117_axes_0 = const()[name = string("input_117_axes_0"), val = tensor([-1])]; + tensor encoder_layers_1_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_1_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45039104)))]; + tensor encoder_layers_1_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_1_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45041216)))]; + tensor input_117_cast_fp16 = layer_norm(axes = input_117_axes_0, beta = encoder_layers_1_norm_feed_forward2_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_1_norm_feed_forward2_weight_to_fp16, x = input_115_cast_fp16)[name = string("input_117_cast_fp16")]; + tensor encoder_layers_1_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45043328))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49237696))))[name = string("encoder_layers_1_feed_forward2_linear1_weight_to_fp16_quantized")]; + tensor encoder_layers_1_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_1_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49245952)))]; + tensor linear_17_cast_fp16 = linear(bias = encoder_layers_1_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_1_feed_forward2_linear1_weight_to_fp16_quantized, x = input_117_cast_fp16)[name = string("linear_17_cast_fp16")]; + tensor input_121_cast_fp16 = silu(x = linear_17_cast_fp16)[name = string("input_121_cast_fp16")]; + tensor encoder_layers_1_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49254208))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53448576))))[name = string("encoder_layers_1_feed_forward2_linear2_weight_to_fp16_quantized")]; + tensor encoder_layers_1_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_1_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53450688)))]; + tensor linear_18_cast_fp16 = linear(bias = encoder_layers_1_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_1_feed_forward2_linear2_weight_to_fp16_quantized, x = input_121_cast_fp16)[name = string("linear_18_cast_fp16")]; + fp16 var_845_to_fp16 = const()[name = string("op_845_to_fp16"), val = fp16(0x1p-1)]; + tensor var_846_cast_fp16 = mul(x = linear_18_cast_fp16, y = var_845_to_fp16)[name = string("op_846_cast_fp16")]; + tensor input_127_cast_fp16 = add(x = input_115_cast_fp16, y = var_846_cast_fp16)[name = string("input_127_cast_fp16")]; + tensor input_129_axes_0 = const()[name = string("input_129_axes_0"), val = tensor([-1])]; + tensor encoder_layers_1_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_1_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53452800)))]; + tensor encoder_layers_1_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_1_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53454912)))]; + tensor input_129_cast_fp16 = layer_norm(axes = input_129_axes_0, beta = encoder_layers_1_norm_out_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_1_norm_out_weight_to_fp16, x = input_127_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor cache_9_begin_0 = const()[name = string("cache_9_begin_0"), val = tensor([2, 0, 0, 0])]; + tensor cache_9_end_0 = const()[name = string("cache_9_end_0"), val = tensor([3, 1, 42, 1024])]; + tensor cache_9_end_mask_0 = const()[name = string("cache_9_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_9_squeeze_mask_0 = const()[name = string("cache_9_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_9_cast_fp16 = slice_by_index(begin = cache_9_begin_0, end = cache_9_end_0, end_mask = cache_9_end_mask_0, squeeze_mask = cache_9_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_9_cast_fp16")]; + tensor cache_11_begin_0 = const()[name = string("cache_11_begin_0"), val = tensor([2, 0, 0, 0])]; + tensor cache_11_end_0 = const()[name = string("cache_11_end_0"), val = tensor([3, 1, 1024, 8])]; + tensor cache_11_end_mask_0 = const()[name = string("cache_11_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_11_squeeze_mask_0 = const()[name = string("cache_11_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_11_cast_fp16 = slice_by_index(begin = cache_11_begin_0, end = cache_11_end_0, end_mask = cache_11_end_mask_0, squeeze_mask = cache_11_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_11_cast_fp16")]; + tensor input_131_axes_0 = const()[name = string("input_131_axes_0"), val = tensor([-1])]; + tensor encoder_layers_2_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_2_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53457024)))]; + tensor encoder_layers_2_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_2_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53459136)))]; + tensor input_131_cast_fp16 = layer_norm(axes = input_131_axes_0, beta = encoder_layers_2_norm_feed_forward1_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_2_norm_feed_forward1_weight_to_fp16, x = input_129_cast_fp16)[name = string("input_131_cast_fp16")]; + tensor encoder_layers_2_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53461248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57655616))))[name = string("encoder_layers_2_feed_forward1_linear1_weight_to_fp16_quantized")]; + tensor encoder_layers_2_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_2_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57663872)))]; + tensor linear_19_cast_fp16 = linear(bias = encoder_layers_2_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_2_feed_forward1_linear1_weight_to_fp16_quantized, x = input_131_cast_fp16)[name = string("linear_19_cast_fp16")]; + tensor input_135_cast_fp16 = silu(x = linear_19_cast_fp16)[name = string("input_135_cast_fp16")]; + tensor encoder_layers_2_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57672128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61866496))))[name = string("encoder_layers_2_feed_forward1_linear2_weight_to_fp16_quantized")]; + tensor encoder_layers_2_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_2_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61868608)))]; + tensor linear_20_cast_fp16 = linear(bias = encoder_layers_2_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_2_feed_forward1_linear2_weight_to_fp16_quantized, x = input_135_cast_fp16)[name = string("linear_20_cast_fp16")]; + fp16 var_882_to_fp16 = const()[name = string("op_882_to_fp16"), val = fp16(0x1p-1)]; + tensor var_883_cast_fp16 = mul(x = linear_20_cast_fp16, y = var_882_to_fp16)[name = string("op_883_cast_fp16")]; + tensor input_141_cast_fp16 = add(x = input_129_cast_fp16, y = var_883_cast_fp16)[name = string("input_141_cast_fp16")]; + tensor key_5_axes_0 = const()[name = string("key_5_axes_0"), val = tensor([-1])]; + tensor encoder_layers_2_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_2_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61870720)))]; + tensor encoder_layers_2_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_2_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61872832)))]; + tensor key_5_cast_fp16 = layer_norm(axes = key_5_axes_0, beta = encoder_layers_2_norm_self_att_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_2_norm_self_att_weight_to_fp16, x = input_141_cast_fp16)[name = string("key_5_cast_fp16")]; + bool input_143_interleave_0 = const()[name = string("input_143_interleave_0"), val = bool(false)]; + tensor input_143_cast_fp16 = concat(axis = var_69, interleave = input_143_interleave_0, values = (cache_9_cast_fp16, key_5_cast_fp16))[name = string("input_143_cast_fp16")]; + bool var_911_interleave_0 = const()[name = string("op_911_interleave_0"), val = bool(false)]; + tensor var_911_cast_fp16 = concat(axis = var_69, interleave = var_911_interleave_0, values = key_5_cast_fp16)[name = string("op_911_cast_fp16")]; + tensor encoder_layers_2_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61874944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62923584))))[name = string("encoder_layers_2_self_attn_linear_q_weight_to_fp16_quantized")]; + tensor encoder_layers_2_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_2_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62925696)))]; + tensor linear_21_cast_fp16 = linear(bias = encoder_layers_2_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_2_self_attn_linear_q_weight_to_fp16_quantized, x = key_5_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_916 = const()[name = string("op_916"), val = tensor([1, -1, 8, 128])]; + tensor q_13_cast_fp16 = reshape(shape = var_916, x = linear_21_cast_fp16)[name = string("q_13_cast_fp16")]; + tensor encoder_layers_2_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62927808))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63976448))))[name = string("encoder_layers_2_self_attn_linear_k_weight_to_fp16_quantized")]; + tensor encoder_layers_2_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_2_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63978560)))]; + tensor linear_22_cast_fp16 = linear(bias = encoder_layers_2_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_2_self_attn_linear_k_weight_to_fp16_quantized, x = input_143_cast_fp16)[name = string("linear_22_cast_fp16")]; + tensor var_921 = const()[name = string("op_921"), val = tensor([1, -1, 8, 128])]; + tensor k_9_cast_fp16 = reshape(shape = var_921, x = linear_22_cast_fp16)[name = string("k_9_cast_fp16")]; + tensor encoder_layers_2_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63980672))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65029312))))[name = string("encoder_layers_2_self_attn_linear_v_weight_to_fp16_quantized")]; + tensor encoder_layers_2_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_2_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65031424)))]; + tensor linear_23_cast_fp16 = linear(bias = encoder_layers_2_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_2_self_attn_linear_v_weight_to_fp16_quantized, x = input_143_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor var_926 = const()[name = string("op_926"), val = tensor([1, -1, 8, 128])]; + tensor v_5_cast_fp16 = reshape(shape = var_926, x = linear_23_cast_fp16)[name = string("v_5_cast_fp16")]; + tensor value_13_perm_0 = const()[name = string("value_13_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_2_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_2_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65033536)))]; + tensor var_939_cast_fp16 = add(x = q_13_cast_fp16, y = encoder_layers_2_self_attn_pos_bias_u_to_fp16)[name = string("op_939_cast_fp16")]; + tensor encoder_layers_2_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_2_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65035648)))]; + tensor var_941_cast_fp16 = add(x = q_13_cast_fp16, y = encoder_layers_2_self_attn_pos_bias_v_to_fp16)[name = string("op_941_cast_fp16")]; + tensor q_with_bias_v_5_perm_0 = const()[name = string("q_with_bias_v_5_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_59_transpose_x_0 = const()[name = string("x_59_transpose_x_0"), val = bool(false)]; + bool x_59_transpose_y_0 = const()[name = string("x_59_transpose_y_0"), val = bool(false)]; + tensor op_943_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65037760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65237504))))[name = string("op_943_to_fp16_quantized")]; + tensor q_with_bias_v_5_cast_fp16 = transpose(perm = q_with_bias_v_5_perm_0, x = var_941_cast_fp16)[name = string("transpose_344")]; + tensor x_59_cast_fp16 = matmul(transpose_x = x_59_transpose_x_0, transpose_y = x_59_transpose_y_0, x = q_with_bias_v_5_cast_fp16, y = op_943_to_fp16_quantized)[name = string("x_59_cast_fp16")]; + tensor x_61_pad_0 = const()[name = string("x_61_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_61_mode_0 = const()[name = string("x_61_mode_0"), val = string("constant")]; + fp16 const_105_to_fp16 = const()[name = string("const_105_to_fp16"), val = fp16(0x0p+0)]; + tensor x_61_cast_fp16 = pad(constant_val = const_105_to_fp16, mode = x_61_mode_0, pad = x_61_pad_0, x = x_59_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor var_951 = const()[name = string("op_951"), val = tensor([1, 8, -1, 56])]; + tensor x_63_cast_fp16 = reshape(shape = var_951, x = x_61_cast_fp16)[name = string("x_63_cast_fp16")]; + tensor var_955_begin_0 = const()[name = string("op_955_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_955_end_0 = const()[name = string("op_955_end_0"), val = tensor([1, 8, 196, 56])]; + tensor var_955_end_mask_0 = const()[name = string("op_955_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_955_cast_fp16 = slice_by_index(begin = var_955_begin_0, end = var_955_end_0, end_mask = var_955_end_mask_0, x = x_63_cast_fp16)[name = string("op_955_cast_fp16")]; + tensor var_956 = const()[name = string("op_956"), val = tensor([1, 8, 56, 195])]; + tensor matrix_bd_9_cast_fp16 = reshape(shape = var_956, x = var_955_cast_fp16)[name = string("matrix_bd_9_cast_fp16")]; + bool matrix_ac_5_transpose_x_0 = const()[name = string("matrix_ac_5_transpose_x_0"), val = bool(false)]; + bool matrix_ac_5_transpose_y_0 = const()[name = string("matrix_ac_5_transpose_y_0"), val = bool(false)]; + tensor transpose_100_perm_0 = const()[name = string("transpose_100_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_101_perm_0 = const()[name = string("transpose_101_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_101 = transpose(perm = transpose_101_perm_0, x = k_9_cast_fp16)[name = string("transpose_342")]; + tensor transpose_100 = transpose(perm = transpose_100_perm_0, x = var_939_cast_fp16)[name = string("transpose_343")]; + tensor matrix_ac_5_cast_fp16 = matmul(transpose_x = matrix_ac_5_transpose_x_0, transpose_y = matrix_ac_5_transpose_y_0, x = transpose_100, y = transpose_101)[name = string("matrix_ac_5_cast_fp16")]; + tensor matrix_bd_11_begin_0 = const()[name = string("matrix_bd_11_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_11_end_0 = const()[name = string("matrix_bd_11_end_0"), val = tensor([1, 8, 56, 98])]; + tensor matrix_bd_11_end_mask_0 = const()[name = string("matrix_bd_11_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_11_cast_fp16 = slice_by_index(begin = matrix_bd_11_begin_0, end = matrix_bd_11_end_0, end_mask = matrix_bd_11_end_mask_0, x = matrix_bd_9_cast_fp16)[name = string("matrix_bd_11_cast_fp16")]; + tensor var_965_cast_fp16 = add(x = matrix_ac_5_cast_fp16, y = matrix_bd_11_cast_fp16)[name = string("op_965_cast_fp16")]; + fp16 _inversed_scores_9_y_0_to_fp16 = const()[name = string("_inversed_scores_9_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_9_cast_fp16 = mul(x = var_965_cast_fp16, y = _inversed_scores_9_y_0_to_fp16)[name = string("_inversed_scores_9_cast_fp16")]; + tensor scores_11_cast_fp16 = select(a = var_46_to_fp16, b = _inversed_scores_9_cast_fp16, cond = mask_11)[name = string("scores_11_cast_fp16")]; + tensor var_971_cast_fp16 = softmax(axis = var_60, x = scores_11_cast_fp16)[name = string("op_971_cast_fp16")]; + tensor input_145_cast_fp16 = select(a = var_45_to_fp16, b = var_971_cast_fp16, cond = mask_11)[name = string("input_145_cast_fp16")]; + bool x_65_transpose_x_0 = const()[name = string("x_65_transpose_x_0"), val = bool(false)]; + bool x_65_transpose_y_0 = const()[name = string("x_65_transpose_y_0"), val = bool(false)]; + tensor value_13_cast_fp16 = transpose(perm = value_13_perm_0, x = v_5_cast_fp16)[name = string("transpose_341")]; + tensor x_65_cast_fp16 = matmul(transpose_x = x_65_transpose_x_0, transpose_y = x_65_transpose_y_0, x = input_145_cast_fp16, y = value_13_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor var_975_perm_0 = const()[name = string("op_975_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_976 = const()[name = string("op_976"), val = tensor([1, -1, 1024])]; + tensor var_975_cast_fp16 = transpose(perm = var_975_perm_0, x = x_65_cast_fp16)[name = string("transpose_340")]; + tensor input_147_cast_fp16 = reshape(shape = var_976, x = var_975_cast_fp16)[name = string("input_147_cast_fp16")]; + tensor encoder_layers_2_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65238016))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66024512))))[name = string("encoder_layers_2_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_2_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_2_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66024704)))]; + tensor linear_25_cast_fp16 = linear(bias = encoder_layers_2_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_2_self_attn_linear_out_weight_to_fp16_palettized, x = input_147_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor input_151_cast_fp16 = add(x = input_141_cast_fp16, y = linear_25_cast_fp16)[name = string("input_151_cast_fp16")]; + tensor x_69_axes_0 = const()[name = string("x_69_axes_0"), val = tensor([-1])]; + tensor encoder_layers_2_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_2_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66026816)))]; + tensor encoder_layers_2_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_2_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66028928)))]; + tensor x_69_cast_fp16 = layer_norm(axes = x_69_axes_0, beta = encoder_layers_2_norm_conv_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_2_norm_conv_weight_to_fp16, x = input_151_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor input_153_perm_0 = const()[name = string("input_153_perm_0"), val = tensor([0, 2, 1])]; + string input_155_pad_type_0 = const()[name = string("input_155_pad_type_0"), val = string("valid")]; + tensor input_155_strides_0 = const()[name = string("input_155_strides_0"), val = tensor([1])]; + tensor input_155_pad_0 = const()[name = string("input_155_pad_0"), val = tensor([0, 0])]; + tensor input_155_dilations_0 = const()[name = string("input_155_dilations_0"), val = tensor([1])]; + int32 input_155_groups_0 = const()[name = string("input_155_groups_0"), val = int32(1)]; + tensor encoder_layers_2_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66031040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68128256))))[name = string("encoder_layers_2_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_153_cast_fp16 = transpose(perm = input_153_perm_0, x = x_69_cast_fp16)[name = string("transpose_339")]; + tensor input_155_cast_fp16 = conv(dilations = input_155_dilations_0, groups = input_155_groups_0, pad = input_155_pad_0, pad_type = input_155_pad_type_0, strides = input_155_strides_0, weight = encoder_layers_2_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_153_cast_fp16)[name = string("input_155_cast_fp16")]; + int32 x_71_split_num_splits_0 = const()[name = string("x_71_split_num_splits_0"), val = int32(2)]; + int32 x_71_split_axis_0 = const()[name = string("x_71_split_axis_0"), val = int32(1)]; + tensor x_71_split_cast_fp16_0, tensor x_71_split_cast_fp16_1 = split(axis = x_71_split_axis_0, num_splits = x_71_split_num_splits_0, x = input_155_cast_fp16)[name = string("x_71_split_cast_fp16")]; + tensor x_71_split_1_sigmoid_cast_fp16 = sigmoid(x = x_71_split_cast_fp16_1)[name = string("x_71_split_1_sigmoid_cast_fp16")]; + tensor x_71_cast_fp16 = mul(x = x_71_split_cast_fp16_0, y = x_71_split_1_sigmoid_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor input_157_cast_fp16 = select(a = var_45_to_fp16, b = x_71_cast_fp16, cond = var_576)[name = string("input_157_cast_fp16")]; + bool new_x_11_interleave_0 = const()[name = string("new_x_11_interleave_0"), val = bool(false)]; + tensor new_x_11_cast_fp16 = concat(axis = var_60, interleave = new_x_11_interleave_0, values = (cache_11_cast_fp16, input_157_cast_fp16))[name = string("new_x_11_cast_fp16")]; + tensor var_1015_begin_0 = const()[name = string("op_1015_begin_0"), val = tensor([0, 0, 56])]; + tensor var_1015_end_0 = const()[name = string("op_1015_end_0"), val = tensor([1, 1024, 64])]; + tensor var_1015_end_mask_0 = const()[name = string("op_1015_end_mask_0"), val = tensor([true, true, true])]; + tensor var_1015_cast_fp16 = slice_by_index(begin = var_1015_begin_0, end = var_1015_end_0, end_mask = var_1015_end_mask_0, x = new_x_11_cast_fp16)[name = string("op_1015_cast_fp16")]; + string x_73_pad_type_0 = const()[name = string("x_73_pad_type_0"), val = string("valid")]; + int32 x_73_groups_0 = const()[name = string("x_73_groups_0"), val = int32(1024)]; + tensor x_73_strides_0 = const()[name = string("x_73_strides_0"), val = tensor([1])]; + tensor x_73_pad_0 = const()[name = string("x_73_pad_0"), val = tensor([0, 0])]; + tensor x_73_dilations_0 = const()[name = string("x_73_dilations_0"), val = tensor([1])]; + tensor encoder_layers_2_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68132416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68141696))))[name = string("encoder_layers_2_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_73_cast_fp16 = conv(dilations = x_73_dilations_0, groups = x_73_groups_0, pad = x_73_pad_0, pad_type = x_73_pad_type_0, strides = x_73_strides_0, weight = encoder_layers_2_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_11_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor input_159_perm_0 = const()[name = string("input_159_perm_0"), val = tensor([0, 2, 1])]; + tensor x_75_axes_0 = const()[name = string("x_75_axes_0"), val = tensor([-1])]; + tensor encoder_layers_2_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_2_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68143808)))]; + tensor encoder_layers_2_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_2_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68145920)))]; + tensor input_159_cast_fp16 = transpose(perm = input_159_perm_0, x = x_73_cast_fp16)[name = string("transpose_338")]; + tensor x_75_cast_fp16 = layer_norm(axes = x_75_axes_0, beta = encoder_layers_2_conv_batch_norm_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_2_conv_batch_norm_weight_to_fp16, x = input_159_cast_fp16)[name = string("x_75_cast_fp16")]; + tensor input_161_perm_0 = const()[name = string("input_161_perm_0"), val = tensor([0, 2, 1])]; + tensor input_161_cast_fp16 = transpose(perm = input_161_perm_0, x = x_75_cast_fp16)[name = string("transpose_337")]; + tensor input_163_cast_fp16 = silu(x = input_161_cast_fp16)[name = string("input_163_cast_fp16")]; + string x_77_pad_type_0 = const()[name = string("x_77_pad_type_0"), val = string("valid")]; + tensor x_77_strides_0 = const()[name = string("x_77_strides_0"), val = tensor([1])]; + tensor x_77_pad_0 = const()[name = string("x_77_pad_0"), val = tensor([0, 0])]; + tensor x_77_dilations_0 = const()[name = string("x_77_dilations_0"), val = tensor([1])]; + int32 x_77_groups_0 = const()[name = string("x_77_groups_0"), val = int32(1)]; + tensor encoder_layers_2_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68148032))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69196672))))[name = string("encoder_layers_2_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_77_cast_fp16 = conv(dilations = x_77_dilations_0, groups = x_77_groups_0, pad = x_77_pad_0, pad_type = x_77_pad_type_0, strides = x_77_strides_0, weight = encoder_layers_2_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_163_cast_fp16)[name = string("x_77_cast_fp16")]; + tensor input_165_perm_0 = const()[name = string("input_165_perm_0"), val = tensor([0, 2, 1])]; + tensor input_165_cast_fp16 = transpose(perm = input_165_perm_0, x = x_77_cast_fp16)[name = string("transpose_336")]; + tensor input_167_cast_fp16 = add(x = input_151_cast_fp16, y = input_165_cast_fp16)[name = string("input_167_cast_fp16")]; + tensor input_169_axes_0 = const()[name = string("input_169_axes_0"), val = tensor([-1])]; + tensor encoder_layers_2_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_2_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69198784)))]; + tensor encoder_layers_2_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_2_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69200896)))]; + tensor input_169_cast_fp16 = layer_norm(axes = input_169_axes_0, beta = encoder_layers_2_norm_feed_forward2_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_2_norm_feed_forward2_weight_to_fp16, x = input_167_cast_fp16)[name = string("input_169_cast_fp16")]; + tensor encoder_layers_2_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69203008))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72348800))))[name = string("encoder_layers_2_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_2_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_2_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72348992)))]; + tensor linear_26_cast_fp16 = linear(bias = encoder_layers_2_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_2_feed_forward2_linear1_weight_to_fp16_palettized, x = input_169_cast_fp16)[name = string("linear_26_cast_fp16")]; + tensor input_173_cast_fp16 = silu(x = linear_26_cast_fp16)[name = string("input_173_cast_fp16")]; + tensor encoder_layers_2_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72357248))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75503040))))[name = string("encoder_layers_2_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_2_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_2_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75503232)))]; + tensor linear_27_cast_fp16 = linear(bias = encoder_layers_2_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_2_feed_forward2_linear2_weight_to_fp16_palettized, x = input_173_cast_fp16)[name = string("linear_27_cast_fp16")]; + fp16 var_1058_to_fp16 = const()[name = string("op_1058_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1059_cast_fp16 = mul(x = linear_27_cast_fp16, y = var_1058_to_fp16)[name = string("op_1059_cast_fp16")]; + tensor input_179_cast_fp16 = add(x = input_167_cast_fp16, y = var_1059_cast_fp16)[name = string("input_179_cast_fp16")]; + tensor input_181_axes_0 = const()[name = string("input_181_axes_0"), val = tensor([-1])]; + tensor encoder_layers_2_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_2_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75505344)))]; + tensor encoder_layers_2_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_2_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75507456)))]; + tensor input_181_cast_fp16 = layer_norm(axes = input_181_axes_0, beta = encoder_layers_2_norm_out_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_2_norm_out_weight_to_fp16, x = input_179_cast_fp16)[name = string("input_181_cast_fp16")]; + tensor cache_13_begin_0 = const()[name = string("cache_13_begin_0"), val = tensor([3, 0, 0, 0])]; + tensor cache_13_end_0 = const()[name = string("cache_13_end_0"), val = tensor([4, 1, 42, 1024])]; + tensor cache_13_end_mask_0 = const()[name = string("cache_13_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_13_squeeze_mask_0 = const()[name = string("cache_13_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_13_cast_fp16 = slice_by_index(begin = cache_13_begin_0, end = cache_13_end_0, end_mask = cache_13_end_mask_0, squeeze_mask = cache_13_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_13_cast_fp16")]; + tensor cache_15_begin_0 = const()[name = string("cache_15_begin_0"), val = tensor([3, 0, 0, 0])]; + tensor cache_15_end_0 = const()[name = string("cache_15_end_0"), val = tensor([4, 1, 1024, 8])]; + tensor cache_15_end_mask_0 = const()[name = string("cache_15_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_15_squeeze_mask_0 = const()[name = string("cache_15_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_15_cast_fp16 = slice_by_index(begin = cache_15_begin_0, end = cache_15_end_0, end_mask = cache_15_end_mask_0, squeeze_mask = cache_15_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_15_cast_fp16")]; + tensor input_183_axes_0 = const()[name = string("input_183_axes_0"), val = tensor([-1])]; + tensor encoder_layers_3_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_3_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75509568)))]; + tensor encoder_layers_3_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_3_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75511680)))]; + tensor input_183_cast_fp16 = layer_norm(axes = input_183_axes_0, beta = encoder_layers_3_norm_feed_forward1_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_3_norm_feed_forward1_weight_to_fp16, x = input_181_cast_fp16)[name = string("input_183_cast_fp16")]; + tensor encoder_layers_3_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75513792))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78659584))))[name = string("encoder_layers_3_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_3_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_3_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78659776)))]; + tensor linear_28_cast_fp16 = linear(bias = encoder_layers_3_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_3_feed_forward1_linear1_weight_to_fp16_palettized, x = input_183_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor input_187_cast_fp16 = silu(x = linear_28_cast_fp16)[name = string("input_187_cast_fp16")]; + tensor encoder_layers_3_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78668032))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81813824))))[name = string("encoder_layers_3_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_3_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_3_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81814016)))]; + tensor linear_29_cast_fp16 = linear(bias = encoder_layers_3_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_3_feed_forward1_linear2_weight_to_fp16_palettized, x = input_187_cast_fp16)[name = string("linear_29_cast_fp16")]; + fp16 var_1095_to_fp16 = const()[name = string("op_1095_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1096_cast_fp16 = mul(x = linear_29_cast_fp16, y = var_1095_to_fp16)[name = string("op_1096_cast_fp16")]; + tensor input_193_cast_fp16 = add(x = input_181_cast_fp16, y = var_1096_cast_fp16)[name = string("input_193_cast_fp16")]; + tensor key_7_axes_0 = const()[name = string("key_7_axes_0"), val = tensor([-1])]; + tensor encoder_layers_3_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_3_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81816128)))]; + tensor encoder_layers_3_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_3_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81818240)))]; + tensor key_7_cast_fp16 = layer_norm(axes = key_7_axes_0, beta = encoder_layers_3_norm_self_att_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_3_norm_self_att_weight_to_fp16, x = input_193_cast_fp16)[name = string("key_7_cast_fp16")]; + bool input_195_interleave_0 = const()[name = string("input_195_interleave_0"), val = bool(false)]; + tensor input_195_cast_fp16 = concat(axis = var_69, interleave = input_195_interleave_0, values = (cache_13_cast_fp16, key_7_cast_fp16))[name = string("input_195_cast_fp16")]; + bool var_1124_interleave_0 = const()[name = string("op_1124_interleave_0"), val = bool(false)]; + tensor var_1124_cast_fp16 = concat(axis = var_69, interleave = var_1124_interleave_0, values = key_7_cast_fp16)[name = string("op_1124_cast_fp16")]; + tensor encoder_layers_3_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81820352))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82606848))))[name = string("encoder_layers_3_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_3_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_3_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82607040)))]; + tensor linear_30_cast_fp16 = linear(bias = encoder_layers_3_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_3_self_attn_linear_q_weight_to_fp16_palettized, x = key_7_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor var_1129 = const()[name = string("op_1129"), val = tensor([1, -1, 8, 128])]; + tensor q_19_cast_fp16 = reshape(shape = var_1129, x = linear_30_cast_fp16)[name = string("q_19_cast_fp16")]; + tensor encoder_layers_3_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82609152))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83395648))))[name = string("encoder_layers_3_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_3_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_3_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83395840)))]; + tensor linear_31_cast_fp16 = linear(bias = encoder_layers_3_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_3_self_attn_linear_k_weight_to_fp16_palettized, x = input_195_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor var_1134 = const()[name = string("op_1134"), val = tensor([1, -1, 8, 128])]; + tensor k_13_cast_fp16 = reshape(shape = var_1134, x = linear_31_cast_fp16)[name = string("k_13_cast_fp16")]; + tensor encoder_layers_3_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83397952))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84184448))))[name = string("encoder_layers_3_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_3_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_3_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84184640)))]; + tensor linear_32_cast_fp16 = linear(bias = encoder_layers_3_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_3_self_attn_linear_v_weight_to_fp16_palettized, x = input_195_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor var_1139 = const()[name = string("op_1139"), val = tensor([1, -1, 8, 128])]; + tensor v_7_cast_fp16 = reshape(shape = var_1139, x = linear_32_cast_fp16)[name = string("v_7_cast_fp16")]; + tensor value_15_perm_0 = const()[name = string("value_15_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_3_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_3_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84186752)))]; + tensor var_1152_cast_fp16 = add(x = q_19_cast_fp16, y = encoder_layers_3_self_attn_pos_bias_u_to_fp16)[name = string("op_1152_cast_fp16")]; + tensor encoder_layers_3_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_3_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84188864)))]; + tensor var_1154_cast_fp16 = add(x = q_19_cast_fp16, y = encoder_layers_3_self_attn_pos_bias_v_to_fp16)[name = string("op_1154_cast_fp16")]; + tensor q_with_bias_v_7_perm_0 = const()[name = string("q_with_bias_v_7_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_85_transpose_x_0 = const()[name = string("x_85_transpose_x_0"), val = bool(false)]; + bool x_85_transpose_y_0 = const()[name = string("x_85_transpose_y_0"), val = bool(false)]; + tensor op_1156_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84190976))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84390720))))[name = string("op_1156_to_fp16_quantized")]; + tensor q_with_bias_v_7_cast_fp16 = transpose(perm = q_with_bias_v_7_perm_0, x = var_1154_cast_fp16)[name = string("transpose_335")]; + tensor x_85_cast_fp16 = matmul(transpose_x = x_85_transpose_x_0, transpose_y = x_85_transpose_y_0, x = q_with_bias_v_7_cast_fp16, y = op_1156_to_fp16_quantized)[name = string("x_85_cast_fp16")]; + tensor x_87_pad_0 = const()[name = string("x_87_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_87_mode_0 = const()[name = string("x_87_mode_0"), val = string("constant")]; + fp16 const_118_to_fp16 = const()[name = string("const_118_to_fp16"), val = fp16(0x0p+0)]; + tensor x_87_cast_fp16 = pad(constant_val = const_118_to_fp16, mode = x_87_mode_0, pad = x_87_pad_0, x = x_85_cast_fp16)[name = string("x_87_cast_fp16")]; + tensor var_1164 = const()[name = string("op_1164"), val = tensor([1, 8, -1, 56])]; + tensor x_89_cast_fp16 = reshape(shape = var_1164, x = x_87_cast_fp16)[name = string("x_89_cast_fp16")]; + tensor var_1168_begin_0 = const()[name = string("op_1168_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_1168_end_0 = const()[name = string("op_1168_end_0"), val = tensor([1, 8, 196, 56])]; + tensor var_1168_end_mask_0 = const()[name = string("op_1168_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1168_cast_fp16 = slice_by_index(begin = var_1168_begin_0, end = var_1168_end_0, end_mask = var_1168_end_mask_0, x = x_89_cast_fp16)[name = string("op_1168_cast_fp16")]; + tensor var_1169 = const()[name = string("op_1169"), val = tensor([1, 8, 56, 195])]; + tensor matrix_bd_13_cast_fp16 = reshape(shape = var_1169, x = var_1168_cast_fp16)[name = string("matrix_bd_13_cast_fp16")]; + bool matrix_ac_7_transpose_x_0 = const()[name = string("matrix_ac_7_transpose_x_0"), val = bool(false)]; + bool matrix_ac_7_transpose_y_0 = const()[name = string("matrix_ac_7_transpose_y_0"), val = bool(false)]; + tensor transpose_102_perm_0 = const()[name = string("transpose_102_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_103_perm_0 = const()[name = string("transpose_103_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_103 = transpose(perm = transpose_103_perm_0, x = k_13_cast_fp16)[name = string("transpose_333")]; + tensor transpose_102 = transpose(perm = transpose_102_perm_0, x = var_1152_cast_fp16)[name = string("transpose_334")]; + tensor matrix_ac_7_cast_fp16 = matmul(transpose_x = matrix_ac_7_transpose_x_0, transpose_y = matrix_ac_7_transpose_y_0, x = transpose_102, y = transpose_103)[name = string("matrix_ac_7_cast_fp16")]; + tensor matrix_bd_15_begin_0 = const()[name = string("matrix_bd_15_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_15_end_0 = const()[name = string("matrix_bd_15_end_0"), val = tensor([1, 8, 56, 98])]; + tensor matrix_bd_15_end_mask_0 = const()[name = string("matrix_bd_15_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_15_cast_fp16 = slice_by_index(begin = matrix_bd_15_begin_0, end = matrix_bd_15_end_0, end_mask = matrix_bd_15_end_mask_0, x = matrix_bd_13_cast_fp16)[name = string("matrix_bd_15_cast_fp16")]; + tensor var_1178_cast_fp16 = add(x = matrix_ac_7_cast_fp16, y = matrix_bd_15_cast_fp16)[name = string("op_1178_cast_fp16")]; + fp16 _inversed_scores_13_y_0_to_fp16 = const()[name = string("_inversed_scores_13_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_13_cast_fp16 = mul(x = var_1178_cast_fp16, y = _inversed_scores_13_y_0_to_fp16)[name = string("_inversed_scores_13_cast_fp16")]; + tensor scores_15_cast_fp16 = select(a = var_46_to_fp16, b = _inversed_scores_13_cast_fp16, cond = mask_11)[name = string("scores_15_cast_fp16")]; + tensor var_1184_cast_fp16 = softmax(axis = var_60, x = scores_15_cast_fp16)[name = string("op_1184_cast_fp16")]; + tensor input_197_cast_fp16 = select(a = var_45_to_fp16, b = var_1184_cast_fp16, cond = mask_11)[name = string("input_197_cast_fp16")]; + bool x_91_transpose_x_0 = const()[name = string("x_91_transpose_x_0"), val = bool(false)]; + bool x_91_transpose_y_0 = const()[name = string("x_91_transpose_y_0"), val = bool(false)]; + tensor value_15_cast_fp16 = transpose(perm = value_15_perm_0, x = v_7_cast_fp16)[name = string("transpose_332")]; + tensor x_91_cast_fp16 = matmul(transpose_x = x_91_transpose_x_0, transpose_y = x_91_transpose_y_0, x = input_197_cast_fp16, y = value_15_cast_fp16)[name = string("x_91_cast_fp16")]; + tensor var_1188_perm_0 = const()[name = string("op_1188_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1189 = const()[name = string("op_1189"), val = tensor([1, -1, 1024])]; + tensor var_1188_cast_fp16 = transpose(perm = var_1188_perm_0, x = x_91_cast_fp16)[name = string("transpose_331")]; + tensor input_199_cast_fp16 = reshape(shape = var_1189, x = var_1188_cast_fp16)[name = string("input_199_cast_fp16")]; + tensor encoder_layers_3_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84391232))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(85177728))))[name = string("encoder_layers_3_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_3_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_3_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(85177920)))]; + tensor linear_34_cast_fp16 = linear(bias = encoder_layers_3_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_3_self_attn_linear_out_weight_to_fp16_palettized, x = input_199_cast_fp16)[name = string("linear_34_cast_fp16")]; + tensor input_203_cast_fp16 = add(x = input_193_cast_fp16, y = linear_34_cast_fp16)[name = string("input_203_cast_fp16")]; + tensor x_95_axes_0 = const()[name = string("x_95_axes_0"), val = tensor([-1])]; + tensor encoder_layers_3_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_3_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(85180032)))]; + tensor encoder_layers_3_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_3_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(85182144)))]; + tensor x_95_cast_fp16 = layer_norm(axes = x_95_axes_0, beta = encoder_layers_3_norm_conv_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_3_norm_conv_weight_to_fp16, x = input_203_cast_fp16)[name = string("x_95_cast_fp16")]; + tensor input_205_perm_0 = const()[name = string("input_205_perm_0"), val = tensor([0, 2, 1])]; + string input_207_pad_type_0 = const()[name = string("input_207_pad_type_0"), val = string("valid")]; + tensor input_207_strides_0 = const()[name = string("input_207_strides_0"), val = tensor([1])]; + tensor input_207_pad_0 = const()[name = string("input_207_pad_0"), val = tensor([0, 0])]; + tensor input_207_dilations_0 = const()[name = string("input_207_dilations_0"), val = tensor([1])]; + int32 input_207_groups_0 = const()[name = string("input_207_groups_0"), val = int32(1)]; + tensor encoder_layers_3_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(85184256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(87281472))))[name = string("encoder_layers_3_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_205_cast_fp16 = transpose(perm = input_205_perm_0, x = x_95_cast_fp16)[name = string("transpose_330")]; + tensor input_207_cast_fp16 = conv(dilations = input_207_dilations_0, groups = input_207_groups_0, pad = input_207_pad_0, pad_type = input_207_pad_type_0, strides = input_207_strides_0, weight = encoder_layers_3_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_205_cast_fp16)[name = string("input_207_cast_fp16")]; + int32 x_97_split_num_splits_0 = const()[name = string("x_97_split_num_splits_0"), val = int32(2)]; + int32 x_97_split_axis_0 = const()[name = string("x_97_split_axis_0"), val = int32(1)]; + tensor x_97_split_cast_fp16_0, tensor x_97_split_cast_fp16_1 = split(axis = x_97_split_axis_0, num_splits = x_97_split_num_splits_0, x = input_207_cast_fp16)[name = string("x_97_split_cast_fp16")]; + tensor x_97_split_1_sigmoid_cast_fp16 = sigmoid(x = x_97_split_cast_fp16_1)[name = string("x_97_split_1_sigmoid_cast_fp16")]; + tensor x_97_cast_fp16 = mul(x = x_97_split_cast_fp16_0, y = x_97_split_1_sigmoid_cast_fp16)[name = string("x_97_cast_fp16")]; + tensor input_209_cast_fp16 = select(a = var_45_to_fp16, b = x_97_cast_fp16, cond = var_576)[name = string("input_209_cast_fp16")]; + bool new_x_15_interleave_0 = const()[name = string("new_x_15_interleave_0"), val = bool(false)]; + tensor new_x_15_cast_fp16 = concat(axis = var_60, interleave = new_x_15_interleave_0, values = (cache_15_cast_fp16, input_209_cast_fp16))[name = string("new_x_15_cast_fp16")]; + tensor var_1228_begin_0 = const()[name = string("op_1228_begin_0"), val = tensor([0, 0, 56])]; + tensor var_1228_end_0 = const()[name = string("op_1228_end_0"), val = tensor([1, 1024, 64])]; + tensor var_1228_end_mask_0 = const()[name = string("op_1228_end_mask_0"), val = tensor([true, true, true])]; + tensor var_1228_cast_fp16 = slice_by_index(begin = var_1228_begin_0, end = var_1228_end_0, end_mask = var_1228_end_mask_0, x = new_x_15_cast_fp16)[name = string("op_1228_cast_fp16")]; + string x_99_pad_type_0 = const()[name = string("x_99_pad_type_0"), val = string("valid")]; + int32 x_99_groups_0 = const()[name = string("x_99_groups_0"), val = int32(1024)]; + tensor x_99_strides_0 = const()[name = string("x_99_strides_0"), val = tensor([1])]; + tensor x_99_pad_0 = const()[name = string("x_99_pad_0"), val = tensor([0, 0])]; + tensor x_99_dilations_0 = const()[name = string("x_99_dilations_0"), val = tensor([1])]; + tensor encoder_layers_3_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(87285632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(87294912))))[name = string("encoder_layers_3_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_99_cast_fp16 = conv(dilations = x_99_dilations_0, groups = x_99_groups_0, pad = x_99_pad_0, pad_type = x_99_pad_type_0, strides = x_99_strides_0, weight = encoder_layers_3_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_15_cast_fp16)[name = string("x_99_cast_fp16")]; + tensor input_211_perm_0 = const()[name = string("input_211_perm_0"), val = tensor([0, 2, 1])]; + tensor x_101_axes_0 = const()[name = string("x_101_axes_0"), val = tensor([-1])]; + tensor encoder_layers_3_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_3_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(87297024)))]; + tensor encoder_layers_3_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_3_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(87299136)))]; + tensor input_211_cast_fp16 = transpose(perm = input_211_perm_0, x = x_99_cast_fp16)[name = string("transpose_329")]; + tensor x_101_cast_fp16 = layer_norm(axes = x_101_axes_0, beta = encoder_layers_3_conv_batch_norm_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_3_conv_batch_norm_weight_to_fp16, x = input_211_cast_fp16)[name = string("x_101_cast_fp16")]; + tensor input_213_perm_0 = const()[name = string("input_213_perm_0"), val = tensor([0, 2, 1])]; + tensor input_213_cast_fp16 = transpose(perm = input_213_perm_0, x = x_101_cast_fp16)[name = string("transpose_328")]; + tensor input_215_cast_fp16 = silu(x = input_213_cast_fp16)[name = string("input_215_cast_fp16")]; + string x_103_pad_type_0 = const()[name = string("x_103_pad_type_0"), val = string("valid")]; + tensor x_103_strides_0 = const()[name = string("x_103_strides_0"), val = tensor([1])]; + tensor x_103_pad_0 = const()[name = string("x_103_pad_0"), val = tensor([0, 0])]; + tensor x_103_dilations_0 = const()[name = string("x_103_dilations_0"), val = tensor([1])]; + int32 x_103_groups_0 = const()[name = string("x_103_groups_0"), val = int32(1)]; + tensor encoder_layers_3_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(87301248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88349888))))[name = string("encoder_layers_3_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_103_cast_fp16 = conv(dilations = x_103_dilations_0, groups = x_103_groups_0, pad = x_103_pad_0, pad_type = x_103_pad_type_0, strides = x_103_strides_0, weight = encoder_layers_3_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_215_cast_fp16)[name = string("x_103_cast_fp16")]; + tensor input_217_perm_0 = const()[name = string("input_217_perm_0"), val = tensor([0, 2, 1])]; + tensor input_217_cast_fp16 = transpose(perm = input_217_perm_0, x = x_103_cast_fp16)[name = string("transpose_327")]; + tensor input_219_cast_fp16 = add(x = input_203_cast_fp16, y = input_217_cast_fp16)[name = string("input_219_cast_fp16")]; + tensor input_221_axes_0 = const()[name = string("input_221_axes_0"), val = tensor([-1])]; + tensor encoder_layers_3_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_3_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88352000)))]; + tensor encoder_layers_3_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_3_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88354112)))]; + tensor input_221_cast_fp16 = layer_norm(axes = input_221_axes_0, beta = encoder_layers_3_norm_feed_forward2_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_3_norm_feed_forward2_weight_to_fp16, x = input_219_cast_fp16)[name = string("input_221_cast_fp16")]; + tensor encoder_layers_3_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88356224))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91502016))))[name = string("encoder_layers_3_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_3_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_3_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91502208)))]; + tensor linear_35_cast_fp16 = linear(bias = encoder_layers_3_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_3_feed_forward2_linear1_weight_to_fp16_palettized, x = input_221_cast_fp16)[name = string("linear_35_cast_fp16")]; + tensor input_225_cast_fp16 = silu(x = linear_35_cast_fp16)[name = string("input_225_cast_fp16")]; + tensor encoder_layers_3_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91510464))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94656256))))[name = string("encoder_layers_3_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_3_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_3_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94656448)))]; + tensor linear_36_cast_fp16 = linear(bias = encoder_layers_3_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_3_feed_forward2_linear2_weight_to_fp16_palettized, x = input_225_cast_fp16)[name = string("linear_36_cast_fp16")]; + fp16 var_1271_to_fp16 = const()[name = string("op_1271_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1272_cast_fp16 = mul(x = linear_36_cast_fp16, y = var_1271_to_fp16)[name = string("op_1272_cast_fp16")]; + tensor input_231_cast_fp16 = add(x = input_219_cast_fp16, y = var_1272_cast_fp16)[name = string("input_231_cast_fp16")]; + tensor input_233_axes_0 = const()[name = string("input_233_axes_0"), val = tensor([-1])]; + tensor encoder_layers_3_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_3_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94658560)))]; + tensor encoder_layers_3_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_3_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94660672)))]; + tensor input_233_cast_fp16 = layer_norm(axes = input_233_axes_0, beta = encoder_layers_3_norm_out_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_3_norm_out_weight_to_fp16, x = input_231_cast_fp16)[name = string("input_233_cast_fp16")]; + tensor cache_17_begin_0 = const()[name = string("cache_17_begin_0"), val = tensor([4, 0, 0, 0])]; + tensor cache_17_end_0 = const()[name = string("cache_17_end_0"), val = tensor([5, 1, 42, 1024])]; + tensor cache_17_end_mask_0 = const()[name = string("cache_17_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_17_squeeze_mask_0 = const()[name = string("cache_17_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_17_cast_fp16 = slice_by_index(begin = cache_17_begin_0, end = cache_17_end_0, end_mask = cache_17_end_mask_0, squeeze_mask = cache_17_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_17_cast_fp16")]; + tensor cache_19_begin_0 = const()[name = string("cache_19_begin_0"), val = tensor([4, 0, 0, 0])]; + tensor cache_19_end_0 = const()[name = string("cache_19_end_0"), val = tensor([5, 1, 1024, 8])]; + tensor cache_19_end_mask_0 = const()[name = string("cache_19_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_19_squeeze_mask_0 = const()[name = string("cache_19_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_19_cast_fp16 = slice_by_index(begin = cache_19_begin_0, end = cache_19_end_0, end_mask = cache_19_end_mask_0, squeeze_mask = cache_19_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_19_cast_fp16")]; + tensor input_235_axes_0 = const()[name = string("input_235_axes_0"), val = tensor([-1])]; + tensor encoder_layers_4_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_4_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94662784)))]; + tensor encoder_layers_4_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_4_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94664896)))]; + tensor input_235_cast_fp16 = layer_norm(axes = input_235_axes_0, beta = encoder_layers_4_norm_feed_forward1_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_4_norm_feed_forward1_weight_to_fp16, x = input_233_cast_fp16)[name = string("input_235_cast_fp16")]; + tensor encoder_layers_4_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94667008))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97812800))))[name = string("encoder_layers_4_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_4_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_4_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97812992)))]; + tensor linear_37_cast_fp16 = linear(bias = encoder_layers_4_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_4_feed_forward1_linear1_weight_to_fp16_palettized, x = input_235_cast_fp16)[name = string("linear_37_cast_fp16")]; + tensor input_239_cast_fp16 = silu(x = linear_37_cast_fp16)[name = string("input_239_cast_fp16")]; + tensor encoder_layers_4_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97821248))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100967040))))[name = string("encoder_layers_4_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_4_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_4_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100967232)))]; + tensor linear_38_cast_fp16 = linear(bias = encoder_layers_4_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_4_feed_forward1_linear2_weight_to_fp16_palettized, x = input_239_cast_fp16)[name = string("linear_38_cast_fp16")]; + fp16 var_1308_to_fp16 = const()[name = string("op_1308_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1309_cast_fp16 = mul(x = linear_38_cast_fp16, y = var_1308_to_fp16)[name = string("op_1309_cast_fp16")]; + tensor input_245_cast_fp16 = add(x = input_233_cast_fp16, y = var_1309_cast_fp16)[name = string("input_245_cast_fp16")]; + tensor key_9_axes_0 = const()[name = string("key_9_axes_0"), val = tensor([-1])]; + tensor encoder_layers_4_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_4_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100969344)))]; + tensor encoder_layers_4_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_4_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100971456)))]; + tensor key_9_cast_fp16 = layer_norm(axes = key_9_axes_0, beta = encoder_layers_4_norm_self_att_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_4_norm_self_att_weight_to_fp16, x = input_245_cast_fp16)[name = string("key_9_cast_fp16")]; + bool input_247_interleave_0 = const()[name = string("input_247_interleave_0"), val = bool(false)]; + tensor input_247_cast_fp16 = concat(axis = var_69, interleave = input_247_interleave_0, values = (cache_17_cast_fp16, key_9_cast_fp16))[name = string("input_247_cast_fp16")]; + bool var_1337_interleave_0 = const()[name = string("op_1337_interleave_0"), val = bool(false)]; + tensor var_1337_cast_fp16 = concat(axis = var_69, interleave = var_1337_interleave_0, values = key_9_cast_fp16)[name = string("op_1337_cast_fp16")]; + tensor encoder_layers_4_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100973568))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101760064))))[name = string("encoder_layers_4_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_4_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_4_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101760256)))]; + tensor linear_39_cast_fp16 = linear(bias = encoder_layers_4_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_4_self_attn_linear_q_weight_to_fp16_palettized, x = key_9_cast_fp16)[name = string("linear_39_cast_fp16")]; + tensor var_1342 = const()[name = string("op_1342"), val = tensor([1, -1, 8, 128])]; + tensor q_25_cast_fp16 = reshape(shape = var_1342, x = linear_39_cast_fp16)[name = string("q_25_cast_fp16")]; + tensor encoder_layers_4_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101762368))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102548864))))[name = string("encoder_layers_4_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_4_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_4_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102549056)))]; + tensor linear_40_cast_fp16 = linear(bias = encoder_layers_4_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_4_self_attn_linear_k_weight_to_fp16_palettized, x = input_247_cast_fp16)[name = string("linear_40_cast_fp16")]; + tensor var_1347 = const()[name = string("op_1347"), val = tensor([1, -1, 8, 128])]; + tensor k_17_cast_fp16 = reshape(shape = var_1347, x = linear_40_cast_fp16)[name = string("k_17_cast_fp16")]; + tensor encoder_layers_4_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102551168))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103337664))))[name = string("encoder_layers_4_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_4_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_4_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103337856)))]; + tensor linear_41_cast_fp16 = linear(bias = encoder_layers_4_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_4_self_attn_linear_v_weight_to_fp16_palettized, x = input_247_cast_fp16)[name = string("linear_41_cast_fp16")]; + tensor var_1352 = const()[name = string("op_1352"), val = tensor([1, -1, 8, 128])]; + tensor v_9_cast_fp16 = reshape(shape = var_1352, x = linear_41_cast_fp16)[name = string("v_9_cast_fp16")]; + tensor value_17_perm_0 = const()[name = string("value_17_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_4_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_4_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103339968)))]; + tensor var_1365_cast_fp16 = add(x = q_25_cast_fp16, y = encoder_layers_4_self_attn_pos_bias_u_to_fp16)[name = string("op_1365_cast_fp16")]; + tensor encoder_layers_4_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_4_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103342080)))]; + tensor var_1367_cast_fp16 = add(x = q_25_cast_fp16, y = encoder_layers_4_self_attn_pos_bias_v_to_fp16)[name = string("op_1367_cast_fp16")]; + tensor q_with_bias_v_9_perm_0 = const()[name = string("q_with_bias_v_9_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_111_transpose_x_0 = const()[name = string("x_111_transpose_x_0"), val = bool(false)]; + bool x_111_transpose_y_0 = const()[name = string("x_111_transpose_y_0"), val = bool(false)]; + tensor op_1369_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103344192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103543936))))[name = string("op_1369_to_fp16_quantized")]; + tensor q_with_bias_v_9_cast_fp16 = transpose(perm = q_with_bias_v_9_perm_0, x = var_1367_cast_fp16)[name = string("transpose_326")]; + tensor x_111_cast_fp16 = matmul(transpose_x = x_111_transpose_x_0, transpose_y = x_111_transpose_y_0, x = q_with_bias_v_9_cast_fp16, y = op_1369_to_fp16_quantized)[name = string("x_111_cast_fp16")]; + tensor x_113_pad_0 = const()[name = string("x_113_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_113_mode_0 = const()[name = string("x_113_mode_0"), val = string("constant")]; + fp16 const_131_to_fp16 = const()[name = string("const_131_to_fp16"), val = fp16(0x0p+0)]; + tensor x_113_cast_fp16 = pad(constant_val = const_131_to_fp16, mode = x_113_mode_0, pad = x_113_pad_0, x = x_111_cast_fp16)[name = string("x_113_cast_fp16")]; + tensor var_1377 = const()[name = string("op_1377"), val = tensor([1, 8, -1, 56])]; + tensor x_115_cast_fp16 = reshape(shape = var_1377, x = x_113_cast_fp16)[name = string("x_115_cast_fp16")]; + tensor var_1381_begin_0 = const()[name = string("op_1381_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_1381_end_0 = const()[name = string("op_1381_end_0"), val = tensor([1, 8, 196, 56])]; + tensor var_1381_end_mask_0 = const()[name = string("op_1381_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1381_cast_fp16 = slice_by_index(begin = var_1381_begin_0, end = var_1381_end_0, end_mask = var_1381_end_mask_0, x = x_115_cast_fp16)[name = string("op_1381_cast_fp16")]; + tensor var_1382 = const()[name = string("op_1382"), val = tensor([1, 8, 56, 195])]; + tensor matrix_bd_17_cast_fp16 = reshape(shape = var_1382, x = var_1381_cast_fp16)[name = string("matrix_bd_17_cast_fp16")]; + bool matrix_ac_9_transpose_x_0 = const()[name = string("matrix_ac_9_transpose_x_0"), val = bool(false)]; + bool matrix_ac_9_transpose_y_0 = const()[name = string("matrix_ac_9_transpose_y_0"), val = bool(false)]; + tensor transpose_104_perm_0 = const()[name = string("transpose_104_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_105_perm_0 = const()[name = string("transpose_105_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_105 = transpose(perm = transpose_105_perm_0, x = k_17_cast_fp16)[name = string("transpose_324")]; + tensor transpose_104 = transpose(perm = transpose_104_perm_0, x = var_1365_cast_fp16)[name = string("transpose_325")]; + tensor matrix_ac_9_cast_fp16 = matmul(transpose_x = matrix_ac_9_transpose_x_0, transpose_y = matrix_ac_9_transpose_y_0, x = transpose_104, y = transpose_105)[name = string("matrix_ac_9_cast_fp16")]; + tensor matrix_bd_19_begin_0 = const()[name = string("matrix_bd_19_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_19_end_0 = const()[name = string("matrix_bd_19_end_0"), val = tensor([1, 8, 56, 98])]; + tensor matrix_bd_19_end_mask_0 = const()[name = string("matrix_bd_19_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_19_cast_fp16 = slice_by_index(begin = matrix_bd_19_begin_0, end = matrix_bd_19_end_0, end_mask = matrix_bd_19_end_mask_0, x = matrix_bd_17_cast_fp16)[name = string("matrix_bd_19_cast_fp16")]; + tensor var_1391_cast_fp16 = add(x = matrix_ac_9_cast_fp16, y = matrix_bd_19_cast_fp16)[name = string("op_1391_cast_fp16")]; + fp16 _inversed_scores_17_y_0_to_fp16 = const()[name = string("_inversed_scores_17_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_17_cast_fp16 = mul(x = var_1391_cast_fp16, y = _inversed_scores_17_y_0_to_fp16)[name = string("_inversed_scores_17_cast_fp16")]; + tensor scores_19_cast_fp16 = select(a = var_46_to_fp16, b = _inversed_scores_17_cast_fp16, cond = mask_11)[name = string("scores_19_cast_fp16")]; + tensor var_1397_cast_fp16 = softmax(axis = var_60, x = scores_19_cast_fp16)[name = string("op_1397_cast_fp16")]; + tensor input_249_cast_fp16 = select(a = var_45_to_fp16, b = var_1397_cast_fp16, cond = mask_11)[name = string("input_249_cast_fp16")]; + bool x_117_transpose_x_0 = const()[name = string("x_117_transpose_x_0"), val = bool(false)]; + bool x_117_transpose_y_0 = const()[name = string("x_117_transpose_y_0"), val = bool(false)]; + tensor value_17_cast_fp16 = transpose(perm = value_17_perm_0, x = v_9_cast_fp16)[name = string("transpose_323")]; + tensor x_117_cast_fp16 = matmul(transpose_x = x_117_transpose_x_0, transpose_y = x_117_transpose_y_0, x = input_249_cast_fp16, y = value_17_cast_fp16)[name = string("x_117_cast_fp16")]; + tensor var_1401_perm_0 = const()[name = string("op_1401_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1402 = const()[name = string("op_1402"), val = tensor([1, -1, 1024])]; + tensor var_1401_cast_fp16 = transpose(perm = var_1401_perm_0, x = x_117_cast_fp16)[name = string("transpose_322")]; + tensor input_251_cast_fp16 = reshape(shape = var_1402, x = var_1401_cast_fp16)[name = string("input_251_cast_fp16")]; + tensor encoder_layers_4_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103544448))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(104330944))))[name = string("encoder_layers_4_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_4_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_4_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(104331136)))]; + tensor linear_43_cast_fp16 = linear(bias = encoder_layers_4_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_4_self_attn_linear_out_weight_to_fp16_palettized, x = input_251_cast_fp16)[name = string("linear_43_cast_fp16")]; + tensor input_255_cast_fp16 = add(x = input_245_cast_fp16, y = linear_43_cast_fp16)[name = string("input_255_cast_fp16")]; + tensor x_121_axes_0 = const()[name = string("x_121_axes_0"), val = tensor([-1])]; + tensor encoder_layers_4_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_4_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(104333248)))]; + tensor encoder_layers_4_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_4_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(104335360)))]; + tensor x_121_cast_fp16 = layer_norm(axes = x_121_axes_0, beta = encoder_layers_4_norm_conv_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_4_norm_conv_weight_to_fp16, x = input_255_cast_fp16)[name = string("x_121_cast_fp16")]; + tensor input_257_perm_0 = const()[name = string("input_257_perm_0"), val = tensor([0, 2, 1])]; + string input_259_pad_type_0 = const()[name = string("input_259_pad_type_0"), val = string("valid")]; + tensor input_259_strides_0 = const()[name = string("input_259_strides_0"), val = tensor([1])]; + tensor input_259_pad_0 = const()[name = string("input_259_pad_0"), val = tensor([0, 0])]; + tensor input_259_dilations_0 = const()[name = string("input_259_dilations_0"), val = tensor([1])]; + int32 input_259_groups_0 = const()[name = string("input_259_groups_0"), val = int32(1)]; + tensor encoder_layers_4_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(104337472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106434688))))[name = string("encoder_layers_4_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_257_cast_fp16 = transpose(perm = input_257_perm_0, x = x_121_cast_fp16)[name = string("transpose_321")]; + tensor input_259_cast_fp16 = conv(dilations = input_259_dilations_0, groups = input_259_groups_0, pad = input_259_pad_0, pad_type = input_259_pad_type_0, strides = input_259_strides_0, weight = encoder_layers_4_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_257_cast_fp16)[name = string("input_259_cast_fp16")]; + int32 x_123_split_num_splits_0 = const()[name = string("x_123_split_num_splits_0"), val = int32(2)]; + int32 x_123_split_axis_0 = const()[name = string("x_123_split_axis_0"), val = int32(1)]; + tensor x_123_split_cast_fp16_0, tensor x_123_split_cast_fp16_1 = split(axis = x_123_split_axis_0, num_splits = x_123_split_num_splits_0, x = input_259_cast_fp16)[name = string("x_123_split_cast_fp16")]; + tensor x_123_split_1_sigmoid_cast_fp16 = sigmoid(x = x_123_split_cast_fp16_1)[name = string("x_123_split_1_sigmoid_cast_fp16")]; + tensor x_123_cast_fp16 = mul(x = x_123_split_cast_fp16_0, y = x_123_split_1_sigmoid_cast_fp16)[name = string("x_123_cast_fp16")]; + tensor input_261_cast_fp16 = select(a = var_45_to_fp16, b = x_123_cast_fp16, cond = var_576)[name = string("input_261_cast_fp16")]; + bool new_x_19_interleave_0 = const()[name = string("new_x_19_interleave_0"), val = bool(false)]; + tensor new_x_19_cast_fp16 = concat(axis = var_60, interleave = new_x_19_interleave_0, values = (cache_19_cast_fp16, input_261_cast_fp16))[name = string("new_x_19_cast_fp16")]; + tensor var_1441_begin_0 = const()[name = string("op_1441_begin_0"), val = tensor([0, 0, 56])]; + tensor var_1441_end_0 = const()[name = string("op_1441_end_0"), val = tensor([1, 1024, 64])]; + tensor var_1441_end_mask_0 = const()[name = string("op_1441_end_mask_0"), val = tensor([true, true, true])]; + tensor var_1441_cast_fp16 = slice_by_index(begin = var_1441_begin_0, end = var_1441_end_0, end_mask = var_1441_end_mask_0, x = new_x_19_cast_fp16)[name = string("op_1441_cast_fp16")]; + string x_125_pad_type_0 = const()[name = string("x_125_pad_type_0"), val = string("valid")]; + int32 x_125_groups_0 = const()[name = string("x_125_groups_0"), val = int32(1024)]; + tensor x_125_strides_0 = const()[name = string("x_125_strides_0"), val = tensor([1])]; + tensor x_125_pad_0 = const()[name = string("x_125_pad_0"), val = tensor([0, 0])]; + tensor x_125_dilations_0 = const()[name = string("x_125_dilations_0"), val = tensor([1])]; + tensor encoder_layers_4_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106438848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106448128))))[name = string("encoder_layers_4_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_125_cast_fp16 = conv(dilations = x_125_dilations_0, groups = x_125_groups_0, pad = x_125_pad_0, pad_type = x_125_pad_type_0, strides = x_125_strides_0, weight = encoder_layers_4_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_19_cast_fp16)[name = string("x_125_cast_fp16")]; + tensor input_263_perm_0 = const()[name = string("input_263_perm_0"), val = tensor([0, 2, 1])]; + tensor x_127_axes_0 = const()[name = string("x_127_axes_0"), val = tensor([-1])]; + tensor encoder_layers_4_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_4_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106450240)))]; + tensor encoder_layers_4_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_4_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106452352)))]; + tensor input_263_cast_fp16 = transpose(perm = input_263_perm_0, x = x_125_cast_fp16)[name = string("transpose_320")]; + tensor x_127_cast_fp16 = layer_norm(axes = x_127_axes_0, beta = encoder_layers_4_conv_batch_norm_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_4_conv_batch_norm_weight_to_fp16, x = input_263_cast_fp16)[name = string("x_127_cast_fp16")]; + tensor input_265_perm_0 = const()[name = string("input_265_perm_0"), val = tensor([0, 2, 1])]; + tensor input_265_cast_fp16 = transpose(perm = input_265_perm_0, x = x_127_cast_fp16)[name = string("transpose_319")]; + tensor input_267_cast_fp16 = silu(x = input_265_cast_fp16)[name = string("input_267_cast_fp16")]; + string x_129_pad_type_0 = const()[name = string("x_129_pad_type_0"), val = string("valid")]; + tensor x_129_strides_0 = const()[name = string("x_129_strides_0"), val = tensor([1])]; + tensor x_129_pad_0 = const()[name = string("x_129_pad_0"), val = tensor([0, 0])]; + tensor x_129_dilations_0 = const()[name = string("x_129_dilations_0"), val = tensor([1])]; + int32 x_129_groups_0 = const()[name = string("x_129_groups_0"), val = int32(1)]; + tensor encoder_layers_4_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106454464))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(107503104))))[name = string("encoder_layers_4_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_129_cast_fp16 = conv(dilations = x_129_dilations_0, groups = x_129_groups_0, pad = x_129_pad_0, pad_type = x_129_pad_type_0, strides = x_129_strides_0, weight = encoder_layers_4_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_267_cast_fp16)[name = string("x_129_cast_fp16")]; + tensor input_269_perm_0 = const()[name = string("input_269_perm_0"), val = tensor([0, 2, 1])]; + tensor input_269_cast_fp16 = transpose(perm = input_269_perm_0, x = x_129_cast_fp16)[name = string("transpose_318")]; + tensor input_271_cast_fp16 = add(x = input_255_cast_fp16, y = input_269_cast_fp16)[name = string("input_271_cast_fp16")]; + tensor input_273_axes_0 = const()[name = string("input_273_axes_0"), val = tensor([-1])]; + tensor encoder_layers_4_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_4_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(107505216)))]; + tensor encoder_layers_4_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_4_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(107507328)))]; + tensor input_273_cast_fp16 = layer_norm(axes = input_273_axes_0, beta = encoder_layers_4_norm_feed_forward2_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_4_norm_feed_forward2_weight_to_fp16, x = input_271_cast_fp16)[name = string("input_273_cast_fp16")]; + tensor encoder_layers_4_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(107509440))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110655232))))[name = string("encoder_layers_4_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_4_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_4_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110655424)))]; + tensor linear_44_cast_fp16 = linear(bias = encoder_layers_4_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_4_feed_forward2_linear1_weight_to_fp16_palettized, x = input_273_cast_fp16)[name = string("linear_44_cast_fp16")]; + tensor input_277_cast_fp16 = silu(x = linear_44_cast_fp16)[name = string("input_277_cast_fp16")]; + tensor encoder_layers_4_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110663680))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113809472))))[name = string("encoder_layers_4_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_4_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_4_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113809664)))]; + tensor linear_45_cast_fp16 = linear(bias = encoder_layers_4_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_4_feed_forward2_linear2_weight_to_fp16_palettized, x = input_277_cast_fp16)[name = string("linear_45_cast_fp16")]; + fp16 var_1484_to_fp16 = const()[name = string("op_1484_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1485_cast_fp16 = mul(x = linear_45_cast_fp16, y = var_1484_to_fp16)[name = string("op_1485_cast_fp16")]; + tensor input_283_cast_fp16 = add(x = input_271_cast_fp16, y = var_1485_cast_fp16)[name = string("input_283_cast_fp16")]; + tensor input_285_axes_0 = const()[name = string("input_285_axes_0"), val = tensor([-1])]; + tensor encoder_layers_4_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_4_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113811776)))]; + tensor encoder_layers_4_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_4_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113813888)))]; + tensor input_285_cast_fp16 = layer_norm(axes = input_285_axes_0, beta = encoder_layers_4_norm_out_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_4_norm_out_weight_to_fp16, x = input_283_cast_fp16)[name = string("input_285_cast_fp16")]; + tensor cache_21_begin_0 = const()[name = string("cache_21_begin_0"), val = tensor([5, 0, 0, 0])]; + tensor cache_21_end_0 = const()[name = string("cache_21_end_0"), val = tensor([6, 1, 42, 1024])]; + tensor cache_21_end_mask_0 = const()[name = string("cache_21_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_21_squeeze_mask_0 = const()[name = string("cache_21_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_21_cast_fp16 = slice_by_index(begin = cache_21_begin_0, end = cache_21_end_0, end_mask = cache_21_end_mask_0, squeeze_mask = cache_21_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_21_cast_fp16")]; + tensor cache_23_begin_0 = const()[name = string("cache_23_begin_0"), val = tensor([5, 0, 0, 0])]; + tensor cache_23_end_0 = const()[name = string("cache_23_end_0"), val = tensor([6, 1, 1024, 8])]; + tensor cache_23_end_mask_0 = const()[name = string("cache_23_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_23_squeeze_mask_0 = const()[name = string("cache_23_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_23_cast_fp16 = slice_by_index(begin = cache_23_begin_0, end = cache_23_end_0, end_mask = cache_23_end_mask_0, squeeze_mask = cache_23_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_23_cast_fp16")]; + tensor input_287_axes_0 = const()[name = string("input_287_axes_0"), val = tensor([-1])]; + tensor encoder_layers_5_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_5_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113816000)))]; + tensor encoder_layers_5_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_5_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113818112)))]; + tensor input_287_cast_fp16 = layer_norm(axes = input_287_axes_0, beta = encoder_layers_5_norm_feed_forward1_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_5_norm_feed_forward1_weight_to_fp16, x = input_285_cast_fp16)[name = string("input_287_cast_fp16")]; + tensor encoder_layers_5_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113820224))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116966016))))[name = string("encoder_layers_5_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_5_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_5_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116966208)))]; + tensor linear_46_cast_fp16 = linear(bias = encoder_layers_5_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_5_feed_forward1_linear1_weight_to_fp16_palettized, x = input_287_cast_fp16)[name = string("linear_46_cast_fp16")]; + tensor input_291_cast_fp16 = silu(x = linear_46_cast_fp16)[name = string("input_291_cast_fp16")]; + tensor encoder_layers_5_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116974464))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120120256))))[name = string("encoder_layers_5_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_5_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_5_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120120448)))]; + tensor linear_47_cast_fp16 = linear(bias = encoder_layers_5_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_5_feed_forward1_linear2_weight_to_fp16_palettized, x = input_291_cast_fp16)[name = string("linear_47_cast_fp16")]; + fp16 var_1521_to_fp16 = const()[name = string("op_1521_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1522_cast_fp16 = mul(x = linear_47_cast_fp16, y = var_1521_to_fp16)[name = string("op_1522_cast_fp16")]; + tensor input_297_cast_fp16 = add(x = input_285_cast_fp16, y = var_1522_cast_fp16)[name = string("input_297_cast_fp16")]; + tensor key_11_axes_0 = const()[name = string("key_11_axes_0"), val = tensor([-1])]; + tensor encoder_layers_5_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_5_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120122560)))]; + tensor encoder_layers_5_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_5_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120124672)))]; + tensor key_11_cast_fp16 = layer_norm(axes = key_11_axes_0, beta = encoder_layers_5_norm_self_att_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_5_norm_self_att_weight_to_fp16, x = input_297_cast_fp16)[name = string("key_11_cast_fp16")]; + bool input_299_interleave_0 = const()[name = string("input_299_interleave_0"), val = bool(false)]; + tensor input_299_cast_fp16 = concat(axis = var_69, interleave = input_299_interleave_0, values = (cache_21_cast_fp16, key_11_cast_fp16))[name = string("input_299_cast_fp16")]; + bool var_1550_interleave_0 = const()[name = string("op_1550_interleave_0"), val = bool(false)]; + tensor var_1550_cast_fp16 = concat(axis = var_69, interleave = var_1550_interleave_0, values = key_11_cast_fp16)[name = string("op_1550_cast_fp16")]; + tensor encoder_layers_5_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120126784))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120913280))))[name = string("encoder_layers_5_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_5_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_5_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120913472)))]; + tensor linear_48_cast_fp16 = linear(bias = encoder_layers_5_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_5_self_attn_linear_q_weight_to_fp16_palettized, x = key_11_cast_fp16)[name = string("linear_48_cast_fp16")]; + tensor var_1555 = const()[name = string("op_1555"), val = tensor([1, -1, 8, 128])]; + tensor q_31_cast_fp16 = reshape(shape = var_1555, x = linear_48_cast_fp16)[name = string("q_31_cast_fp16")]; + tensor encoder_layers_5_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120915584))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121702080))))[name = string("encoder_layers_5_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_5_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_5_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121702272)))]; + tensor linear_49_cast_fp16 = linear(bias = encoder_layers_5_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_5_self_attn_linear_k_weight_to_fp16_palettized, x = input_299_cast_fp16)[name = string("linear_49_cast_fp16")]; + tensor var_1560 = const()[name = string("op_1560"), val = tensor([1, -1, 8, 128])]; + tensor k_21_cast_fp16 = reshape(shape = var_1560, x = linear_49_cast_fp16)[name = string("k_21_cast_fp16")]; + tensor encoder_layers_5_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121704384))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122490880))))[name = string("encoder_layers_5_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_5_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_5_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122491072)))]; + tensor linear_50_cast_fp16 = linear(bias = encoder_layers_5_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_5_self_attn_linear_v_weight_to_fp16_palettized, x = input_299_cast_fp16)[name = string("linear_50_cast_fp16")]; + tensor var_1565 = const()[name = string("op_1565"), val = tensor([1, -1, 8, 128])]; + tensor v_11_cast_fp16 = reshape(shape = var_1565, x = linear_50_cast_fp16)[name = string("v_11_cast_fp16")]; + tensor value_19_perm_0 = const()[name = string("value_19_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_5_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_5_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122493184)))]; + tensor var_1578_cast_fp16 = add(x = q_31_cast_fp16, y = encoder_layers_5_self_attn_pos_bias_u_to_fp16)[name = string("op_1578_cast_fp16")]; + tensor encoder_layers_5_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_5_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122495296)))]; + tensor var_1580_cast_fp16 = add(x = q_31_cast_fp16, y = encoder_layers_5_self_attn_pos_bias_v_to_fp16)[name = string("op_1580_cast_fp16")]; + tensor q_with_bias_v_11_perm_0 = const()[name = string("q_with_bias_v_11_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_137_transpose_x_0 = const()[name = string("x_137_transpose_x_0"), val = bool(false)]; + bool x_137_transpose_y_0 = const()[name = string("x_137_transpose_y_0"), val = bool(false)]; + tensor op_1582_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122497408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122697152))))[name = string("op_1582_to_fp16_quantized")]; + tensor q_with_bias_v_11_cast_fp16 = transpose(perm = q_with_bias_v_11_perm_0, x = var_1580_cast_fp16)[name = string("transpose_317")]; + tensor x_137_cast_fp16 = matmul(transpose_x = x_137_transpose_x_0, transpose_y = x_137_transpose_y_0, x = q_with_bias_v_11_cast_fp16, y = op_1582_to_fp16_quantized)[name = string("x_137_cast_fp16")]; + tensor x_139_pad_0 = const()[name = string("x_139_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_139_mode_0 = const()[name = string("x_139_mode_0"), val = string("constant")]; + fp16 const_144_to_fp16 = const()[name = string("const_144_to_fp16"), val = fp16(0x0p+0)]; + tensor x_139_cast_fp16 = pad(constant_val = const_144_to_fp16, mode = x_139_mode_0, pad = x_139_pad_0, x = x_137_cast_fp16)[name = string("x_139_cast_fp16")]; + tensor var_1590 = const()[name = string("op_1590"), val = tensor([1, 8, -1, 56])]; + tensor x_141_cast_fp16 = reshape(shape = var_1590, x = x_139_cast_fp16)[name = string("x_141_cast_fp16")]; + tensor var_1594_begin_0 = const()[name = string("op_1594_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_1594_end_0 = const()[name = string("op_1594_end_0"), val = tensor([1, 8, 196, 56])]; + tensor var_1594_end_mask_0 = const()[name = string("op_1594_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1594_cast_fp16 = slice_by_index(begin = var_1594_begin_0, end = var_1594_end_0, end_mask = var_1594_end_mask_0, x = x_141_cast_fp16)[name = string("op_1594_cast_fp16")]; + tensor var_1595 = const()[name = string("op_1595"), val = tensor([1, 8, 56, 195])]; + tensor matrix_bd_21_cast_fp16 = reshape(shape = var_1595, x = var_1594_cast_fp16)[name = string("matrix_bd_21_cast_fp16")]; + bool matrix_ac_11_transpose_x_0 = const()[name = string("matrix_ac_11_transpose_x_0"), val = bool(false)]; + bool matrix_ac_11_transpose_y_0 = const()[name = string("matrix_ac_11_transpose_y_0"), val = bool(false)]; + tensor transpose_106_perm_0 = const()[name = string("transpose_106_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_107_perm_0 = const()[name = string("transpose_107_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_107 = transpose(perm = transpose_107_perm_0, x = k_21_cast_fp16)[name = string("transpose_315")]; + tensor transpose_106 = transpose(perm = transpose_106_perm_0, x = var_1578_cast_fp16)[name = string("transpose_316")]; + tensor matrix_ac_11_cast_fp16 = matmul(transpose_x = matrix_ac_11_transpose_x_0, transpose_y = matrix_ac_11_transpose_y_0, x = transpose_106, y = transpose_107)[name = string("matrix_ac_11_cast_fp16")]; + tensor matrix_bd_23_begin_0 = const()[name = string("matrix_bd_23_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_23_end_0 = const()[name = string("matrix_bd_23_end_0"), val = tensor([1, 8, 56, 98])]; + tensor matrix_bd_23_end_mask_0 = const()[name = string("matrix_bd_23_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_23_cast_fp16 = slice_by_index(begin = matrix_bd_23_begin_0, end = matrix_bd_23_end_0, end_mask = matrix_bd_23_end_mask_0, x = matrix_bd_21_cast_fp16)[name = string("matrix_bd_23_cast_fp16")]; + tensor var_1604_cast_fp16 = add(x = matrix_ac_11_cast_fp16, y = matrix_bd_23_cast_fp16)[name = string("op_1604_cast_fp16")]; + fp16 _inversed_scores_21_y_0_to_fp16 = const()[name = string("_inversed_scores_21_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_21_cast_fp16 = mul(x = var_1604_cast_fp16, y = _inversed_scores_21_y_0_to_fp16)[name = string("_inversed_scores_21_cast_fp16")]; + tensor scores_23_cast_fp16 = select(a = var_46_to_fp16, b = _inversed_scores_21_cast_fp16, cond = mask_11)[name = string("scores_23_cast_fp16")]; + tensor var_1610_cast_fp16 = softmax(axis = var_60, x = scores_23_cast_fp16)[name = string("op_1610_cast_fp16")]; + tensor input_301_cast_fp16 = select(a = var_45_to_fp16, b = var_1610_cast_fp16, cond = mask_11)[name = string("input_301_cast_fp16")]; + bool x_143_transpose_x_0 = const()[name = string("x_143_transpose_x_0"), val = bool(false)]; + bool x_143_transpose_y_0 = const()[name = string("x_143_transpose_y_0"), val = bool(false)]; + tensor value_19_cast_fp16 = transpose(perm = value_19_perm_0, x = v_11_cast_fp16)[name = string("transpose_314")]; + tensor x_143_cast_fp16 = matmul(transpose_x = x_143_transpose_x_0, transpose_y = x_143_transpose_y_0, x = input_301_cast_fp16, y = value_19_cast_fp16)[name = string("x_143_cast_fp16")]; + tensor var_1614_perm_0 = const()[name = string("op_1614_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1615 = const()[name = string("op_1615"), val = tensor([1, -1, 1024])]; + tensor var_1614_cast_fp16 = transpose(perm = var_1614_perm_0, x = x_143_cast_fp16)[name = string("transpose_313")]; + tensor input_303_cast_fp16 = reshape(shape = var_1615, x = var_1614_cast_fp16)[name = string("input_303_cast_fp16")]; + tensor encoder_layers_5_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122697664))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123484160))))[name = string("encoder_layers_5_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_5_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_5_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123484352)))]; + tensor linear_52_cast_fp16 = linear(bias = encoder_layers_5_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_5_self_attn_linear_out_weight_to_fp16_palettized, x = input_303_cast_fp16)[name = string("linear_52_cast_fp16")]; + tensor input_307_cast_fp16 = add(x = input_297_cast_fp16, y = linear_52_cast_fp16)[name = string("input_307_cast_fp16")]; + tensor x_147_axes_0 = const()[name = string("x_147_axes_0"), val = tensor([-1])]; + tensor encoder_layers_5_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_5_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123486464)))]; + tensor encoder_layers_5_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_5_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123488576)))]; + tensor x_147_cast_fp16 = layer_norm(axes = x_147_axes_0, beta = encoder_layers_5_norm_conv_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_5_norm_conv_weight_to_fp16, x = input_307_cast_fp16)[name = string("x_147_cast_fp16")]; + tensor input_309_perm_0 = const()[name = string("input_309_perm_0"), val = tensor([0, 2, 1])]; + string input_311_pad_type_0 = const()[name = string("input_311_pad_type_0"), val = string("valid")]; + tensor input_311_strides_0 = const()[name = string("input_311_strides_0"), val = tensor([1])]; + tensor input_311_pad_0 = const()[name = string("input_311_pad_0"), val = tensor([0, 0])]; + tensor input_311_dilations_0 = const()[name = string("input_311_dilations_0"), val = tensor([1])]; + int32 input_311_groups_0 = const()[name = string("input_311_groups_0"), val = int32(1)]; + tensor encoder_layers_5_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123490688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125587904))))[name = string("encoder_layers_5_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_309_cast_fp16 = transpose(perm = input_309_perm_0, x = x_147_cast_fp16)[name = string("transpose_312")]; + tensor input_311_cast_fp16 = conv(dilations = input_311_dilations_0, groups = input_311_groups_0, pad = input_311_pad_0, pad_type = input_311_pad_type_0, strides = input_311_strides_0, weight = encoder_layers_5_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_309_cast_fp16)[name = string("input_311_cast_fp16")]; + int32 x_149_split_num_splits_0 = const()[name = string("x_149_split_num_splits_0"), val = int32(2)]; + int32 x_149_split_axis_0 = const()[name = string("x_149_split_axis_0"), val = int32(1)]; + tensor x_149_split_cast_fp16_0, tensor x_149_split_cast_fp16_1 = split(axis = x_149_split_axis_0, num_splits = x_149_split_num_splits_0, x = input_311_cast_fp16)[name = string("x_149_split_cast_fp16")]; + tensor x_149_split_1_sigmoid_cast_fp16 = sigmoid(x = x_149_split_cast_fp16_1)[name = string("x_149_split_1_sigmoid_cast_fp16")]; + tensor x_149_cast_fp16 = mul(x = x_149_split_cast_fp16_0, y = x_149_split_1_sigmoid_cast_fp16)[name = string("x_149_cast_fp16")]; + tensor input_313_cast_fp16 = select(a = var_45_to_fp16, b = x_149_cast_fp16, cond = var_576)[name = string("input_313_cast_fp16")]; + bool new_x_23_interleave_0 = const()[name = string("new_x_23_interleave_0"), val = bool(false)]; + tensor new_x_23_cast_fp16 = concat(axis = var_60, interleave = new_x_23_interleave_0, values = (cache_23_cast_fp16, input_313_cast_fp16))[name = string("new_x_23_cast_fp16")]; + tensor var_1654_begin_0 = const()[name = string("op_1654_begin_0"), val = tensor([0, 0, 56])]; + tensor var_1654_end_0 = const()[name = string("op_1654_end_0"), val = tensor([1, 1024, 64])]; + tensor var_1654_end_mask_0 = const()[name = string("op_1654_end_mask_0"), val = tensor([true, true, true])]; + tensor var_1654_cast_fp16 = slice_by_index(begin = var_1654_begin_0, end = var_1654_end_0, end_mask = var_1654_end_mask_0, x = new_x_23_cast_fp16)[name = string("op_1654_cast_fp16")]; + string x_151_pad_type_0 = const()[name = string("x_151_pad_type_0"), val = string("valid")]; + int32 x_151_groups_0 = const()[name = string("x_151_groups_0"), val = int32(1024)]; + tensor x_151_strides_0 = const()[name = string("x_151_strides_0"), val = tensor([1])]; + tensor x_151_pad_0 = const()[name = string("x_151_pad_0"), val = tensor([0, 0])]; + tensor x_151_dilations_0 = const()[name = string("x_151_dilations_0"), val = tensor([1])]; + tensor encoder_layers_5_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125592064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125601344))))[name = string("encoder_layers_5_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_151_cast_fp16 = conv(dilations = x_151_dilations_0, groups = x_151_groups_0, pad = x_151_pad_0, pad_type = x_151_pad_type_0, strides = x_151_strides_0, weight = encoder_layers_5_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_23_cast_fp16)[name = string("x_151_cast_fp16")]; + tensor input_315_perm_0 = const()[name = string("input_315_perm_0"), val = tensor([0, 2, 1])]; + tensor x_153_axes_0 = const()[name = string("x_153_axes_0"), val = tensor([-1])]; + tensor encoder_layers_5_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_5_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125603456)))]; + tensor encoder_layers_5_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_5_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125605568)))]; + tensor input_315_cast_fp16 = transpose(perm = input_315_perm_0, x = x_151_cast_fp16)[name = string("transpose_311")]; + tensor x_153_cast_fp16 = layer_norm(axes = x_153_axes_0, beta = encoder_layers_5_conv_batch_norm_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_5_conv_batch_norm_weight_to_fp16, x = input_315_cast_fp16)[name = string("x_153_cast_fp16")]; + tensor input_317_perm_0 = const()[name = string("input_317_perm_0"), val = tensor([0, 2, 1])]; + tensor input_317_cast_fp16 = transpose(perm = input_317_perm_0, x = x_153_cast_fp16)[name = string("transpose_310")]; + tensor input_319_cast_fp16 = silu(x = input_317_cast_fp16)[name = string("input_319_cast_fp16")]; + string x_155_pad_type_0 = const()[name = string("x_155_pad_type_0"), val = string("valid")]; + tensor x_155_strides_0 = const()[name = string("x_155_strides_0"), val = tensor([1])]; + tensor x_155_pad_0 = const()[name = string("x_155_pad_0"), val = tensor([0, 0])]; + tensor x_155_dilations_0 = const()[name = string("x_155_dilations_0"), val = tensor([1])]; + int32 x_155_groups_0 = const()[name = string("x_155_groups_0"), val = int32(1)]; + tensor encoder_layers_5_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125607680))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(126656320))))[name = string("encoder_layers_5_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_155_cast_fp16 = conv(dilations = x_155_dilations_0, groups = x_155_groups_0, pad = x_155_pad_0, pad_type = x_155_pad_type_0, strides = x_155_strides_0, weight = encoder_layers_5_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_319_cast_fp16)[name = string("x_155_cast_fp16")]; + tensor input_321_perm_0 = const()[name = string("input_321_perm_0"), val = tensor([0, 2, 1])]; + tensor input_321_cast_fp16 = transpose(perm = input_321_perm_0, x = x_155_cast_fp16)[name = string("transpose_309")]; + tensor input_323_cast_fp16 = add(x = input_307_cast_fp16, y = input_321_cast_fp16)[name = string("input_323_cast_fp16")]; + tensor input_325_axes_0 = const()[name = string("input_325_axes_0"), val = tensor([-1])]; + tensor encoder_layers_5_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_5_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(126658432)))]; + tensor encoder_layers_5_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_5_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(126660544)))]; + tensor input_325_cast_fp16 = layer_norm(axes = input_325_axes_0, beta = encoder_layers_5_norm_feed_forward2_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_5_norm_feed_forward2_weight_to_fp16, x = input_323_cast_fp16)[name = string("input_325_cast_fp16")]; + tensor encoder_layers_5_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(126662656))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129808448))))[name = string("encoder_layers_5_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_5_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_5_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129808640)))]; + tensor linear_53_cast_fp16 = linear(bias = encoder_layers_5_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_5_feed_forward2_linear1_weight_to_fp16_palettized, x = input_325_cast_fp16)[name = string("linear_53_cast_fp16")]; + tensor input_329_cast_fp16 = silu(x = linear_53_cast_fp16)[name = string("input_329_cast_fp16")]; + tensor encoder_layers_5_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129816896))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132962688))))[name = string("encoder_layers_5_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_5_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_5_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132962880)))]; + tensor linear_54_cast_fp16 = linear(bias = encoder_layers_5_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_5_feed_forward2_linear2_weight_to_fp16_palettized, x = input_329_cast_fp16)[name = string("linear_54_cast_fp16")]; + fp16 var_1697_to_fp16 = const()[name = string("op_1697_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1698_cast_fp16 = mul(x = linear_54_cast_fp16, y = var_1697_to_fp16)[name = string("op_1698_cast_fp16")]; + tensor input_335_cast_fp16 = add(x = input_323_cast_fp16, y = var_1698_cast_fp16)[name = string("input_335_cast_fp16")]; + tensor input_337_axes_0 = const()[name = string("input_337_axes_0"), val = tensor([-1])]; + tensor encoder_layers_5_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_5_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132964992)))]; + tensor encoder_layers_5_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_5_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132967104)))]; + tensor input_337_cast_fp16 = layer_norm(axes = input_337_axes_0, beta = encoder_layers_5_norm_out_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_5_norm_out_weight_to_fp16, x = input_335_cast_fp16)[name = string("input_337_cast_fp16")]; + tensor cache_25_begin_0 = const()[name = string("cache_25_begin_0"), val = tensor([6, 0, 0, 0])]; + tensor cache_25_end_0 = const()[name = string("cache_25_end_0"), val = tensor([7, 1, 42, 1024])]; + tensor cache_25_end_mask_0 = const()[name = string("cache_25_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_25_squeeze_mask_0 = const()[name = string("cache_25_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_25_cast_fp16 = slice_by_index(begin = cache_25_begin_0, end = cache_25_end_0, end_mask = cache_25_end_mask_0, squeeze_mask = cache_25_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_25_cast_fp16")]; + tensor cache_27_begin_0 = const()[name = string("cache_27_begin_0"), val = tensor([6, 0, 0, 0])]; + tensor cache_27_end_0 = const()[name = string("cache_27_end_0"), val = tensor([7, 1, 1024, 8])]; + tensor cache_27_end_mask_0 = const()[name = string("cache_27_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_27_squeeze_mask_0 = const()[name = string("cache_27_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_27_cast_fp16 = slice_by_index(begin = cache_27_begin_0, end = cache_27_end_0, end_mask = cache_27_end_mask_0, squeeze_mask = cache_27_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_27_cast_fp16")]; + tensor input_339_axes_0 = const()[name = string("input_339_axes_0"), val = tensor([-1])]; + tensor encoder_layers_6_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_6_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132969216)))]; + tensor encoder_layers_6_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_6_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132971328)))]; + tensor input_339_cast_fp16 = layer_norm(axes = input_339_axes_0, beta = encoder_layers_6_norm_feed_forward1_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_6_norm_feed_forward1_weight_to_fp16, x = input_337_cast_fp16)[name = string("input_339_cast_fp16")]; + tensor encoder_layers_6_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132973440))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(136119232))))[name = string("encoder_layers_6_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_6_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_6_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(136119424)))]; + tensor linear_55_cast_fp16 = linear(bias = encoder_layers_6_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_6_feed_forward1_linear1_weight_to_fp16_palettized, x = input_339_cast_fp16)[name = string("linear_55_cast_fp16")]; + tensor input_343_cast_fp16 = silu(x = linear_55_cast_fp16)[name = string("input_343_cast_fp16")]; + tensor encoder_layers_6_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(136127680))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(139273472))))[name = string("encoder_layers_6_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_6_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_6_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(139273664)))]; + tensor linear_56_cast_fp16 = linear(bias = encoder_layers_6_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_6_feed_forward1_linear2_weight_to_fp16_palettized, x = input_343_cast_fp16)[name = string("linear_56_cast_fp16")]; + fp16 var_1734_to_fp16 = const()[name = string("op_1734_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1735_cast_fp16 = mul(x = linear_56_cast_fp16, y = var_1734_to_fp16)[name = string("op_1735_cast_fp16")]; + tensor input_349_cast_fp16 = add(x = input_337_cast_fp16, y = var_1735_cast_fp16)[name = string("input_349_cast_fp16")]; + tensor key_13_axes_0 = const()[name = string("key_13_axes_0"), val = tensor([-1])]; + tensor encoder_layers_6_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_6_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(139275776)))]; + tensor encoder_layers_6_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_6_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(139277888)))]; + tensor key_13_cast_fp16 = layer_norm(axes = key_13_axes_0, beta = encoder_layers_6_norm_self_att_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_6_norm_self_att_weight_to_fp16, x = input_349_cast_fp16)[name = string("key_13_cast_fp16")]; + bool input_351_interleave_0 = const()[name = string("input_351_interleave_0"), val = bool(false)]; + tensor input_351_cast_fp16 = concat(axis = var_69, interleave = input_351_interleave_0, values = (cache_25_cast_fp16, key_13_cast_fp16))[name = string("input_351_cast_fp16")]; + bool var_1763_interleave_0 = const()[name = string("op_1763_interleave_0"), val = bool(false)]; + tensor var_1763_cast_fp16 = concat(axis = var_69, interleave = var_1763_interleave_0, values = key_13_cast_fp16)[name = string("op_1763_cast_fp16")]; + tensor encoder_layers_6_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(139280000))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140066496))))[name = string("encoder_layers_6_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_6_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_6_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140066688)))]; + tensor linear_57_cast_fp16 = linear(bias = encoder_layers_6_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_6_self_attn_linear_q_weight_to_fp16_palettized, x = key_13_cast_fp16)[name = string("linear_57_cast_fp16")]; + tensor var_1768 = const()[name = string("op_1768"), val = tensor([1, -1, 8, 128])]; + tensor q_37_cast_fp16 = reshape(shape = var_1768, x = linear_57_cast_fp16)[name = string("q_37_cast_fp16")]; + tensor encoder_layers_6_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140068800))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140855296))))[name = string("encoder_layers_6_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_6_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_6_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140855488)))]; + tensor linear_58_cast_fp16 = linear(bias = encoder_layers_6_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_6_self_attn_linear_k_weight_to_fp16_palettized, x = input_351_cast_fp16)[name = string("linear_58_cast_fp16")]; + tensor var_1773 = const()[name = string("op_1773"), val = tensor([1, -1, 8, 128])]; + tensor k_25_cast_fp16 = reshape(shape = var_1773, x = linear_58_cast_fp16)[name = string("k_25_cast_fp16")]; + tensor encoder_layers_6_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140857600))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141644096))))[name = string("encoder_layers_6_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_6_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_6_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141644288)))]; + tensor linear_59_cast_fp16 = linear(bias = encoder_layers_6_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_6_self_attn_linear_v_weight_to_fp16_palettized, x = input_351_cast_fp16)[name = string("linear_59_cast_fp16")]; + tensor var_1778 = const()[name = string("op_1778"), val = tensor([1, -1, 8, 128])]; + tensor v_13_cast_fp16 = reshape(shape = var_1778, x = linear_59_cast_fp16)[name = string("v_13_cast_fp16")]; + tensor value_21_perm_0 = const()[name = string("value_21_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_6_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_6_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141646400)))]; + tensor var_1791_cast_fp16 = add(x = q_37_cast_fp16, y = encoder_layers_6_self_attn_pos_bias_u_to_fp16)[name = string("op_1791_cast_fp16")]; + tensor encoder_layers_6_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_6_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141648512)))]; + tensor var_1793_cast_fp16 = add(x = q_37_cast_fp16, y = encoder_layers_6_self_attn_pos_bias_v_to_fp16)[name = string("op_1793_cast_fp16")]; + tensor q_with_bias_v_13_perm_0 = const()[name = string("q_with_bias_v_13_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_163_transpose_x_0 = const()[name = string("x_163_transpose_x_0"), val = bool(false)]; + bool x_163_transpose_y_0 = const()[name = string("x_163_transpose_y_0"), val = bool(false)]; + tensor op_1795_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141650624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141850368))))[name = string("op_1795_to_fp16_quantized")]; + tensor q_with_bias_v_13_cast_fp16 = transpose(perm = q_with_bias_v_13_perm_0, x = var_1793_cast_fp16)[name = string("transpose_308")]; + tensor x_163_cast_fp16 = matmul(transpose_x = x_163_transpose_x_0, transpose_y = x_163_transpose_y_0, x = q_with_bias_v_13_cast_fp16, y = op_1795_to_fp16_quantized)[name = string("x_163_cast_fp16")]; + tensor x_165_pad_0 = const()[name = string("x_165_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_165_mode_0 = const()[name = string("x_165_mode_0"), val = string("constant")]; + fp16 const_157_to_fp16 = const()[name = string("const_157_to_fp16"), val = fp16(0x0p+0)]; + tensor x_165_cast_fp16 = pad(constant_val = const_157_to_fp16, mode = x_165_mode_0, pad = x_165_pad_0, x = x_163_cast_fp16)[name = string("x_165_cast_fp16")]; + tensor var_1803 = const()[name = string("op_1803"), val = tensor([1, 8, -1, 56])]; + tensor x_167_cast_fp16 = reshape(shape = var_1803, x = x_165_cast_fp16)[name = string("x_167_cast_fp16")]; + tensor var_1807_begin_0 = const()[name = string("op_1807_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_1807_end_0 = const()[name = string("op_1807_end_0"), val = tensor([1, 8, 196, 56])]; + tensor var_1807_end_mask_0 = const()[name = string("op_1807_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1807_cast_fp16 = slice_by_index(begin = var_1807_begin_0, end = var_1807_end_0, end_mask = var_1807_end_mask_0, x = x_167_cast_fp16)[name = string("op_1807_cast_fp16")]; + tensor var_1808 = const()[name = string("op_1808"), val = tensor([1, 8, 56, 195])]; + tensor matrix_bd_25_cast_fp16 = reshape(shape = var_1808, x = var_1807_cast_fp16)[name = string("matrix_bd_25_cast_fp16")]; + bool matrix_ac_13_transpose_x_0 = const()[name = string("matrix_ac_13_transpose_x_0"), val = bool(false)]; + bool matrix_ac_13_transpose_y_0 = const()[name = string("matrix_ac_13_transpose_y_0"), val = bool(false)]; + tensor transpose_108_perm_0 = const()[name = string("transpose_108_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_109_perm_0 = const()[name = string("transpose_109_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_109 = transpose(perm = transpose_109_perm_0, x = k_25_cast_fp16)[name = string("transpose_306")]; + tensor transpose_108 = transpose(perm = transpose_108_perm_0, x = var_1791_cast_fp16)[name = string("transpose_307")]; + tensor matrix_ac_13_cast_fp16 = matmul(transpose_x = matrix_ac_13_transpose_x_0, transpose_y = matrix_ac_13_transpose_y_0, x = transpose_108, y = transpose_109)[name = string("matrix_ac_13_cast_fp16")]; + tensor matrix_bd_27_begin_0 = const()[name = string("matrix_bd_27_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_27_end_0 = const()[name = string("matrix_bd_27_end_0"), val = tensor([1, 8, 56, 98])]; + tensor matrix_bd_27_end_mask_0 = const()[name = string("matrix_bd_27_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_27_cast_fp16 = slice_by_index(begin = matrix_bd_27_begin_0, end = matrix_bd_27_end_0, end_mask = matrix_bd_27_end_mask_0, x = matrix_bd_25_cast_fp16)[name = string("matrix_bd_27_cast_fp16")]; + tensor var_1817_cast_fp16 = add(x = matrix_ac_13_cast_fp16, y = matrix_bd_27_cast_fp16)[name = string("op_1817_cast_fp16")]; + fp16 _inversed_scores_25_y_0_to_fp16 = const()[name = string("_inversed_scores_25_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_25_cast_fp16 = mul(x = var_1817_cast_fp16, y = _inversed_scores_25_y_0_to_fp16)[name = string("_inversed_scores_25_cast_fp16")]; + tensor scores_27_cast_fp16 = select(a = var_46_to_fp16, b = _inversed_scores_25_cast_fp16, cond = mask_11)[name = string("scores_27_cast_fp16")]; + tensor var_1823_cast_fp16 = softmax(axis = var_60, x = scores_27_cast_fp16)[name = string("op_1823_cast_fp16")]; + tensor input_353_cast_fp16 = select(a = var_45_to_fp16, b = var_1823_cast_fp16, cond = mask_11)[name = string("input_353_cast_fp16")]; + bool x_169_transpose_x_0 = const()[name = string("x_169_transpose_x_0"), val = bool(false)]; + bool x_169_transpose_y_0 = const()[name = string("x_169_transpose_y_0"), val = bool(false)]; + tensor value_21_cast_fp16 = transpose(perm = value_21_perm_0, x = v_13_cast_fp16)[name = string("transpose_305")]; + tensor x_169_cast_fp16 = matmul(transpose_x = x_169_transpose_x_0, transpose_y = x_169_transpose_y_0, x = input_353_cast_fp16, y = value_21_cast_fp16)[name = string("x_169_cast_fp16")]; + tensor var_1827_perm_0 = const()[name = string("op_1827_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1828 = const()[name = string("op_1828"), val = tensor([1, -1, 1024])]; + tensor var_1827_cast_fp16 = transpose(perm = var_1827_perm_0, x = x_169_cast_fp16)[name = string("transpose_304")]; + tensor input_355_cast_fp16 = reshape(shape = var_1828, x = var_1827_cast_fp16)[name = string("input_355_cast_fp16")]; + tensor encoder_layers_6_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141850880))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142637376))))[name = string("encoder_layers_6_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_6_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_6_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142637568)))]; + tensor linear_61_cast_fp16 = linear(bias = encoder_layers_6_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_6_self_attn_linear_out_weight_to_fp16_palettized, x = input_355_cast_fp16)[name = string("linear_61_cast_fp16")]; + tensor input_359_cast_fp16 = add(x = input_349_cast_fp16, y = linear_61_cast_fp16)[name = string("input_359_cast_fp16")]; + tensor x_173_axes_0 = const()[name = string("x_173_axes_0"), val = tensor([-1])]; + tensor encoder_layers_6_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_6_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142639680)))]; + tensor encoder_layers_6_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_6_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142641792)))]; + tensor x_173_cast_fp16 = layer_norm(axes = x_173_axes_0, beta = encoder_layers_6_norm_conv_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_6_norm_conv_weight_to_fp16, x = input_359_cast_fp16)[name = string("x_173_cast_fp16")]; + tensor input_361_perm_0 = const()[name = string("input_361_perm_0"), val = tensor([0, 2, 1])]; + string input_363_pad_type_0 = const()[name = string("input_363_pad_type_0"), val = string("valid")]; + tensor input_363_strides_0 = const()[name = string("input_363_strides_0"), val = tensor([1])]; + tensor input_363_pad_0 = const()[name = string("input_363_pad_0"), val = tensor([0, 0])]; + tensor input_363_dilations_0 = const()[name = string("input_363_dilations_0"), val = tensor([1])]; + int32 input_363_groups_0 = const()[name = string("input_363_groups_0"), val = int32(1)]; + tensor encoder_layers_6_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142643904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144741120))))[name = string("encoder_layers_6_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_361_cast_fp16 = transpose(perm = input_361_perm_0, x = x_173_cast_fp16)[name = string("transpose_303")]; + tensor input_363_cast_fp16 = conv(dilations = input_363_dilations_0, groups = input_363_groups_0, pad = input_363_pad_0, pad_type = input_363_pad_type_0, strides = input_363_strides_0, weight = encoder_layers_6_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_361_cast_fp16)[name = string("input_363_cast_fp16")]; + int32 x_175_split_num_splits_0 = const()[name = string("x_175_split_num_splits_0"), val = int32(2)]; + int32 x_175_split_axis_0 = const()[name = string("x_175_split_axis_0"), val = int32(1)]; + tensor x_175_split_cast_fp16_0, tensor x_175_split_cast_fp16_1 = split(axis = x_175_split_axis_0, num_splits = x_175_split_num_splits_0, x = input_363_cast_fp16)[name = string("x_175_split_cast_fp16")]; + tensor x_175_split_1_sigmoid_cast_fp16 = sigmoid(x = x_175_split_cast_fp16_1)[name = string("x_175_split_1_sigmoid_cast_fp16")]; + tensor x_175_cast_fp16 = mul(x = x_175_split_cast_fp16_0, y = x_175_split_1_sigmoid_cast_fp16)[name = string("x_175_cast_fp16")]; + tensor input_365_cast_fp16 = select(a = var_45_to_fp16, b = x_175_cast_fp16, cond = var_576)[name = string("input_365_cast_fp16")]; + bool new_x_27_interleave_0 = const()[name = string("new_x_27_interleave_0"), val = bool(false)]; + tensor new_x_27_cast_fp16 = concat(axis = var_60, interleave = new_x_27_interleave_0, values = (cache_27_cast_fp16, input_365_cast_fp16))[name = string("new_x_27_cast_fp16")]; + tensor var_1867_begin_0 = const()[name = string("op_1867_begin_0"), val = tensor([0, 0, 56])]; + tensor var_1867_end_0 = const()[name = string("op_1867_end_0"), val = tensor([1, 1024, 64])]; + tensor var_1867_end_mask_0 = const()[name = string("op_1867_end_mask_0"), val = tensor([true, true, true])]; + tensor var_1867_cast_fp16 = slice_by_index(begin = var_1867_begin_0, end = var_1867_end_0, end_mask = var_1867_end_mask_0, x = new_x_27_cast_fp16)[name = string("op_1867_cast_fp16")]; + string x_177_pad_type_0 = const()[name = string("x_177_pad_type_0"), val = string("valid")]; + int32 x_177_groups_0 = const()[name = string("x_177_groups_0"), val = int32(1024)]; + tensor x_177_strides_0 = const()[name = string("x_177_strides_0"), val = tensor([1])]; + tensor x_177_pad_0 = const()[name = string("x_177_pad_0"), val = tensor([0, 0])]; + tensor x_177_dilations_0 = const()[name = string("x_177_dilations_0"), val = tensor([1])]; + tensor encoder_layers_6_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144745280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144754560))))[name = string("encoder_layers_6_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_177_cast_fp16 = conv(dilations = x_177_dilations_0, groups = x_177_groups_0, pad = x_177_pad_0, pad_type = x_177_pad_type_0, strides = x_177_strides_0, weight = encoder_layers_6_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_27_cast_fp16)[name = string("x_177_cast_fp16")]; + tensor input_367_perm_0 = const()[name = string("input_367_perm_0"), val = tensor([0, 2, 1])]; + tensor x_179_axes_0 = const()[name = string("x_179_axes_0"), val = tensor([-1])]; + tensor encoder_layers_6_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_6_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144756672)))]; + tensor encoder_layers_6_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_6_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144758784)))]; + tensor input_367_cast_fp16 = transpose(perm = input_367_perm_0, x = x_177_cast_fp16)[name = string("transpose_302")]; + tensor x_179_cast_fp16 = layer_norm(axes = x_179_axes_0, beta = encoder_layers_6_conv_batch_norm_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_6_conv_batch_norm_weight_to_fp16, x = input_367_cast_fp16)[name = string("x_179_cast_fp16")]; + tensor input_369_perm_0 = const()[name = string("input_369_perm_0"), val = tensor([0, 2, 1])]; + tensor input_369_cast_fp16 = transpose(perm = input_369_perm_0, x = x_179_cast_fp16)[name = string("transpose_301")]; + tensor input_371_cast_fp16 = silu(x = input_369_cast_fp16)[name = string("input_371_cast_fp16")]; + string x_181_pad_type_0 = const()[name = string("x_181_pad_type_0"), val = string("valid")]; + tensor x_181_strides_0 = const()[name = string("x_181_strides_0"), val = tensor([1])]; + tensor x_181_pad_0 = const()[name = string("x_181_pad_0"), val = tensor([0, 0])]; + tensor x_181_dilations_0 = const()[name = string("x_181_dilations_0"), val = tensor([1])]; + int32 x_181_groups_0 = const()[name = string("x_181_groups_0"), val = int32(1)]; + tensor encoder_layers_6_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144760896))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145809536))))[name = string("encoder_layers_6_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_181_cast_fp16 = conv(dilations = x_181_dilations_0, groups = x_181_groups_0, pad = x_181_pad_0, pad_type = x_181_pad_type_0, strides = x_181_strides_0, weight = encoder_layers_6_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_371_cast_fp16)[name = string("x_181_cast_fp16")]; + tensor input_373_perm_0 = const()[name = string("input_373_perm_0"), val = tensor([0, 2, 1])]; + tensor input_373_cast_fp16 = transpose(perm = input_373_perm_0, x = x_181_cast_fp16)[name = string("transpose_300")]; + tensor input_375_cast_fp16 = add(x = input_359_cast_fp16, y = input_373_cast_fp16)[name = string("input_375_cast_fp16")]; + tensor input_377_axes_0 = const()[name = string("input_377_axes_0"), val = tensor([-1])]; + tensor encoder_layers_6_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_6_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145811648)))]; + tensor encoder_layers_6_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_6_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145813760)))]; + tensor input_377_cast_fp16 = layer_norm(axes = input_377_axes_0, beta = encoder_layers_6_norm_feed_forward2_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_6_norm_feed_forward2_weight_to_fp16, x = input_375_cast_fp16)[name = string("input_377_cast_fp16")]; + tensor encoder_layers_6_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145815872))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(148961664))))[name = string("encoder_layers_6_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_6_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_6_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(148961856)))]; + tensor linear_62_cast_fp16 = linear(bias = encoder_layers_6_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_6_feed_forward2_linear1_weight_to_fp16_palettized, x = input_377_cast_fp16)[name = string("linear_62_cast_fp16")]; + tensor input_381_cast_fp16 = silu(x = linear_62_cast_fp16)[name = string("input_381_cast_fp16")]; + tensor encoder_layers_6_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(148970112))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(152115904))))[name = string("encoder_layers_6_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_6_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_6_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(152116096)))]; + tensor linear_63_cast_fp16 = linear(bias = encoder_layers_6_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_6_feed_forward2_linear2_weight_to_fp16_palettized, x = input_381_cast_fp16)[name = string("linear_63_cast_fp16")]; + fp16 var_1910_to_fp16 = const()[name = string("op_1910_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1911_cast_fp16 = mul(x = linear_63_cast_fp16, y = var_1910_to_fp16)[name = string("op_1911_cast_fp16")]; + tensor input_387_cast_fp16 = add(x = input_375_cast_fp16, y = var_1911_cast_fp16)[name = string("input_387_cast_fp16")]; + tensor input_389_axes_0 = const()[name = string("input_389_axes_0"), val = tensor([-1])]; + tensor encoder_layers_6_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_6_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(152118208)))]; + tensor encoder_layers_6_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_6_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(152120320)))]; + tensor input_389_cast_fp16 = layer_norm(axes = input_389_axes_0, beta = encoder_layers_6_norm_out_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_6_norm_out_weight_to_fp16, x = input_387_cast_fp16)[name = string("input_389_cast_fp16")]; + tensor cache_29_begin_0 = const()[name = string("cache_29_begin_0"), val = tensor([7, 0, 0, 0])]; + tensor cache_29_end_0 = const()[name = string("cache_29_end_0"), val = tensor([8, 1, 42, 1024])]; + tensor cache_29_end_mask_0 = const()[name = string("cache_29_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_29_squeeze_mask_0 = const()[name = string("cache_29_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_29_cast_fp16 = slice_by_index(begin = cache_29_begin_0, end = cache_29_end_0, end_mask = cache_29_end_mask_0, squeeze_mask = cache_29_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_29_cast_fp16")]; + tensor cache_31_begin_0 = const()[name = string("cache_31_begin_0"), val = tensor([7, 0, 0, 0])]; + tensor cache_31_end_0 = const()[name = string("cache_31_end_0"), val = tensor([8, 1, 1024, 8])]; + tensor cache_31_end_mask_0 = const()[name = string("cache_31_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_31_squeeze_mask_0 = const()[name = string("cache_31_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_31_cast_fp16 = slice_by_index(begin = cache_31_begin_0, end = cache_31_end_0, end_mask = cache_31_end_mask_0, squeeze_mask = cache_31_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_31_cast_fp16")]; + tensor input_391_axes_0 = const()[name = string("input_391_axes_0"), val = tensor([-1])]; + tensor encoder_layers_7_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_7_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(152122432)))]; + tensor encoder_layers_7_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_7_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(152124544)))]; + tensor input_391_cast_fp16 = layer_norm(axes = input_391_axes_0, beta = encoder_layers_7_norm_feed_forward1_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_7_norm_feed_forward1_weight_to_fp16, x = input_389_cast_fp16)[name = string("input_391_cast_fp16")]; + tensor encoder_layers_7_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(152126656))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(155272448))))[name = string("encoder_layers_7_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_7_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_7_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(155272640)))]; + tensor linear_64_cast_fp16 = linear(bias = encoder_layers_7_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_7_feed_forward1_linear1_weight_to_fp16_palettized, x = input_391_cast_fp16)[name = string("linear_64_cast_fp16")]; + tensor input_395_cast_fp16 = silu(x = linear_64_cast_fp16)[name = string("input_395_cast_fp16")]; + tensor encoder_layers_7_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(155280896))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158426688))))[name = string("encoder_layers_7_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_7_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_7_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158426880)))]; + tensor linear_65_cast_fp16 = linear(bias = encoder_layers_7_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_7_feed_forward1_linear2_weight_to_fp16_palettized, x = input_395_cast_fp16)[name = string("linear_65_cast_fp16")]; + fp16 var_1947_to_fp16 = const()[name = string("op_1947_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1948_cast_fp16 = mul(x = linear_65_cast_fp16, y = var_1947_to_fp16)[name = string("op_1948_cast_fp16")]; + tensor input_401_cast_fp16 = add(x = input_389_cast_fp16, y = var_1948_cast_fp16)[name = string("input_401_cast_fp16")]; + tensor key_15_axes_0 = const()[name = string("key_15_axes_0"), val = tensor([-1])]; + tensor encoder_layers_7_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_7_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158428992)))]; + tensor encoder_layers_7_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_7_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158431104)))]; + tensor key_15_cast_fp16 = layer_norm(axes = key_15_axes_0, beta = encoder_layers_7_norm_self_att_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_7_norm_self_att_weight_to_fp16, x = input_401_cast_fp16)[name = string("key_15_cast_fp16")]; + bool input_403_interleave_0 = const()[name = string("input_403_interleave_0"), val = bool(false)]; + tensor input_403_cast_fp16 = concat(axis = var_69, interleave = input_403_interleave_0, values = (cache_29_cast_fp16, key_15_cast_fp16))[name = string("input_403_cast_fp16")]; + bool var_1976_interleave_0 = const()[name = string("op_1976_interleave_0"), val = bool(false)]; + tensor var_1976_cast_fp16 = concat(axis = var_69, interleave = var_1976_interleave_0, values = key_15_cast_fp16)[name = string("op_1976_cast_fp16")]; + tensor encoder_layers_7_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158433216))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(159219712))))[name = string("encoder_layers_7_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_7_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_7_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(159219904)))]; + tensor linear_66_cast_fp16 = linear(bias = encoder_layers_7_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_7_self_attn_linear_q_weight_to_fp16_palettized, x = key_15_cast_fp16)[name = string("linear_66_cast_fp16")]; + tensor var_1981 = const()[name = string("op_1981"), val = tensor([1, -1, 8, 128])]; + tensor q_43_cast_fp16 = reshape(shape = var_1981, x = linear_66_cast_fp16)[name = string("q_43_cast_fp16")]; + tensor encoder_layers_7_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(159222016))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160008512))))[name = string("encoder_layers_7_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_7_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_7_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160008704)))]; + tensor linear_67_cast_fp16 = linear(bias = encoder_layers_7_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_7_self_attn_linear_k_weight_to_fp16_palettized, x = input_403_cast_fp16)[name = string("linear_67_cast_fp16")]; + tensor var_1986 = const()[name = string("op_1986"), val = tensor([1, -1, 8, 128])]; + tensor k_29_cast_fp16 = reshape(shape = var_1986, x = linear_67_cast_fp16)[name = string("k_29_cast_fp16")]; + tensor encoder_layers_7_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160010816))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160797312))))[name = string("encoder_layers_7_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_7_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_7_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160797504)))]; + tensor linear_68_cast_fp16 = linear(bias = encoder_layers_7_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_7_self_attn_linear_v_weight_to_fp16_palettized, x = input_403_cast_fp16)[name = string("linear_68_cast_fp16")]; + tensor var_1991 = const()[name = string("op_1991"), val = tensor([1, -1, 8, 128])]; + tensor v_15_cast_fp16 = reshape(shape = var_1991, x = linear_68_cast_fp16)[name = string("v_15_cast_fp16")]; + tensor value_23_perm_0 = const()[name = string("value_23_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_7_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_7_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160799616)))]; + tensor var_2004_cast_fp16 = add(x = q_43_cast_fp16, y = encoder_layers_7_self_attn_pos_bias_u_to_fp16)[name = string("op_2004_cast_fp16")]; + tensor encoder_layers_7_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_7_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160801728)))]; + tensor var_2006_cast_fp16 = add(x = q_43_cast_fp16, y = encoder_layers_7_self_attn_pos_bias_v_to_fp16)[name = string("op_2006_cast_fp16")]; + tensor q_with_bias_v_15_perm_0 = const()[name = string("q_with_bias_v_15_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_189_transpose_x_0 = const()[name = string("x_189_transpose_x_0"), val = bool(false)]; + bool x_189_transpose_y_0 = const()[name = string("x_189_transpose_y_0"), val = bool(false)]; + tensor op_2008_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160803840))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(161003584))))[name = string("op_2008_to_fp16_quantized")]; + tensor q_with_bias_v_15_cast_fp16 = transpose(perm = q_with_bias_v_15_perm_0, x = var_2006_cast_fp16)[name = string("transpose_299")]; + tensor x_189_cast_fp16 = matmul(transpose_x = x_189_transpose_x_0, transpose_y = x_189_transpose_y_0, x = q_with_bias_v_15_cast_fp16, y = op_2008_to_fp16_quantized)[name = string("x_189_cast_fp16")]; + tensor x_191_pad_0 = const()[name = string("x_191_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_191_mode_0 = const()[name = string("x_191_mode_0"), val = string("constant")]; + fp16 const_170_to_fp16 = const()[name = string("const_170_to_fp16"), val = fp16(0x0p+0)]; + tensor x_191_cast_fp16 = pad(constant_val = const_170_to_fp16, mode = x_191_mode_0, pad = x_191_pad_0, x = x_189_cast_fp16)[name = string("x_191_cast_fp16")]; + tensor var_2016 = const()[name = string("op_2016"), val = tensor([1, 8, -1, 56])]; + tensor x_193_cast_fp16 = reshape(shape = var_2016, x = x_191_cast_fp16)[name = string("x_193_cast_fp16")]; + tensor var_2020_begin_0 = const()[name = string("op_2020_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_2020_end_0 = const()[name = string("op_2020_end_0"), val = tensor([1, 8, 196, 56])]; + tensor var_2020_end_mask_0 = const()[name = string("op_2020_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_2020_cast_fp16 = slice_by_index(begin = var_2020_begin_0, end = var_2020_end_0, end_mask = var_2020_end_mask_0, x = x_193_cast_fp16)[name = string("op_2020_cast_fp16")]; + tensor var_2021 = const()[name = string("op_2021"), val = tensor([1, 8, 56, 195])]; + tensor matrix_bd_29_cast_fp16 = reshape(shape = var_2021, x = var_2020_cast_fp16)[name = string("matrix_bd_29_cast_fp16")]; + bool matrix_ac_15_transpose_x_0 = const()[name = string("matrix_ac_15_transpose_x_0"), val = bool(false)]; + bool matrix_ac_15_transpose_y_0 = const()[name = string("matrix_ac_15_transpose_y_0"), val = bool(false)]; + tensor transpose_110_perm_0 = const()[name = string("transpose_110_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_111_perm_0 = const()[name = string("transpose_111_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_111 = transpose(perm = transpose_111_perm_0, x = k_29_cast_fp16)[name = string("transpose_297")]; + tensor transpose_110 = transpose(perm = transpose_110_perm_0, x = var_2004_cast_fp16)[name = string("transpose_298")]; + tensor matrix_ac_15_cast_fp16 = matmul(transpose_x = matrix_ac_15_transpose_x_0, transpose_y = matrix_ac_15_transpose_y_0, x = transpose_110, y = transpose_111)[name = string("matrix_ac_15_cast_fp16")]; + tensor matrix_bd_31_begin_0 = const()[name = string("matrix_bd_31_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_31_end_0 = const()[name = string("matrix_bd_31_end_0"), val = tensor([1, 8, 56, 98])]; + tensor matrix_bd_31_end_mask_0 = const()[name = string("matrix_bd_31_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_31_cast_fp16 = slice_by_index(begin = matrix_bd_31_begin_0, end = matrix_bd_31_end_0, end_mask = matrix_bd_31_end_mask_0, x = matrix_bd_29_cast_fp16)[name = string("matrix_bd_31_cast_fp16")]; + tensor var_2030_cast_fp16 = add(x = matrix_ac_15_cast_fp16, y = matrix_bd_31_cast_fp16)[name = string("op_2030_cast_fp16")]; + fp16 _inversed_scores_29_y_0_to_fp16 = const()[name = string("_inversed_scores_29_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_29_cast_fp16 = mul(x = var_2030_cast_fp16, y = _inversed_scores_29_y_0_to_fp16)[name = string("_inversed_scores_29_cast_fp16")]; + tensor scores_31_cast_fp16 = select(a = var_46_to_fp16, b = _inversed_scores_29_cast_fp16, cond = mask_11)[name = string("scores_31_cast_fp16")]; + tensor var_2036_cast_fp16 = softmax(axis = var_60, x = scores_31_cast_fp16)[name = string("op_2036_cast_fp16")]; + tensor input_405_cast_fp16 = select(a = var_45_to_fp16, b = var_2036_cast_fp16, cond = mask_11)[name = string("input_405_cast_fp16")]; + bool x_195_transpose_x_0 = const()[name = string("x_195_transpose_x_0"), val = bool(false)]; + bool x_195_transpose_y_0 = const()[name = string("x_195_transpose_y_0"), val = bool(false)]; + tensor value_23_cast_fp16 = transpose(perm = value_23_perm_0, x = v_15_cast_fp16)[name = string("transpose_296")]; + tensor x_195_cast_fp16 = matmul(transpose_x = x_195_transpose_x_0, transpose_y = x_195_transpose_y_0, x = input_405_cast_fp16, y = value_23_cast_fp16)[name = string("x_195_cast_fp16")]; + tensor var_2040_perm_0 = const()[name = string("op_2040_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2041 = const()[name = string("op_2041"), val = tensor([1, -1, 1024])]; + tensor var_2040_cast_fp16 = transpose(perm = var_2040_perm_0, x = x_195_cast_fp16)[name = string("transpose_295")]; + tensor input_407_cast_fp16 = reshape(shape = var_2041, x = var_2040_cast_fp16)[name = string("input_407_cast_fp16")]; + tensor encoder_layers_7_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(161004096))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(161790592))))[name = string("encoder_layers_7_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_7_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_7_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(161790784)))]; + tensor linear_70_cast_fp16 = linear(bias = encoder_layers_7_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_7_self_attn_linear_out_weight_to_fp16_palettized, x = input_407_cast_fp16)[name = string("linear_70_cast_fp16")]; + tensor input_411_cast_fp16 = add(x = input_401_cast_fp16, y = linear_70_cast_fp16)[name = string("input_411_cast_fp16")]; + tensor x_199_axes_0 = const()[name = string("x_199_axes_0"), val = tensor([-1])]; + tensor encoder_layers_7_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_7_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(161792896)))]; + tensor encoder_layers_7_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_7_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(161795008)))]; + tensor x_199_cast_fp16 = layer_norm(axes = x_199_axes_0, beta = encoder_layers_7_norm_conv_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_7_norm_conv_weight_to_fp16, x = input_411_cast_fp16)[name = string("x_199_cast_fp16")]; + tensor input_413_perm_0 = const()[name = string("input_413_perm_0"), val = tensor([0, 2, 1])]; + string input_415_pad_type_0 = const()[name = string("input_415_pad_type_0"), val = string("valid")]; + tensor input_415_strides_0 = const()[name = string("input_415_strides_0"), val = tensor([1])]; + tensor input_415_pad_0 = const()[name = string("input_415_pad_0"), val = tensor([0, 0])]; + tensor input_415_dilations_0 = const()[name = string("input_415_dilations_0"), val = tensor([1])]; + int32 input_415_groups_0 = const()[name = string("input_415_groups_0"), val = int32(1)]; + tensor encoder_layers_7_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(161797120))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163894336))))[name = string("encoder_layers_7_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_413_cast_fp16 = transpose(perm = input_413_perm_0, x = x_199_cast_fp16)[name = string("transpose_294")]; + tensor input_415_cast_fp16 = conv(dilations = input_415_dilations_0, groups = input_415_groups_0, pad = input_415_pad_0, pad_type = input_415_pad_type_0, strides = input_415_strides_0, weight = encoder_layers_7_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_413_cast_fp16)[name = string("input_415_cast_fp16")]; + int32 x_201_split_num_splits_0 = const()[name = string("x_201_split_num_splits_0"), val = int32(2)]; + int32 x_201_split_axis_0 = const()[name = string("x_201_split_axis_0"), val = int32(1)]; + tensor x_201_split_cast_fp16_0, tensor x_201_split_cast_fp16_1 = split(axis = x_201_split_axis_0, num_splits = x_201_split_num_splits_0, x = input_415_cast_fp16)[name = string("x_201_split_cast_fp16")]; + tensor x_201_split_1_sigmoid_cast_fp16 = sigmoid(x = x_201_split_cast_fp16_1)[name = string("x_201_split_1_sigmoid_cast_fp16")]; + tensor x_201_cast_fp16 = mul(x = x_201_split_cast_fp16_0, y = x_201_split_1_sigmoid_cast_fp16)[name = string("x_201_cast_fp16")]; + tensor input_417_cast_fp16 = select(a = var_45_to_fp16, b = x_201_cast_fp16, cond = var_576)[name = string("input_417_cast_fp16")]; + bool new_x_31_interleave_0 = const()[name = string("new_x_31_interleave_0"), val = bool(false)]; + tensor new_x_31_cast_fp16 = concat(axis = var_60, interleave = new_x_31_interleave_0, values = (cache_31_cast_fp16, input_417_cast_fp16))[name = string("new_x_31_cast_fp16")]; + tensor var_2080_begin_0 = const()[name = string("op_2080_begin_0"), val = tensor([0, 0, 56])]; + tensor var_2080_end_0 = const()[name = string("op_2080_end_0"), val = tensor([1, 1024, 64])]; + tensor var_2080_end_mask_0 = const()[name = string("op_2080_end_mask_0"), val = tensor([true, true, true])]; + tensor var_2080_cast_fp16 = slice_by_index(begin = var_2080_begin_0, end = var_2080_end_0, end_mask = var_2080_end_mask_0, x = new_x_31_cast_fp16)[name = string("op_2080_cast_fp16")]; + string x_203_pad_type_0 = const()[name = string("x_203_pad_type_0"), val = string("valid")]; + int32 x_203_groups_0 = const()[name = string("x_203_groups_0"), val = int32(1024)]; + tensor x_203_strides_0 = const()[name = string("x_203_strides_0"), val = tensor([1])]; + tensor x_203_pad_0 = const()[name = string("x_203_pad_0"), val = tensor([0, 0])]; + tensor x_203_dilations_0 = const()[name = string("x_203_dilations_0"), val = tensor([1])]; + tensor encoder_layers_7_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163898496))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163907776))))[name = string("encoder_layers_7_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_203_cast_fp16 = conv(dilations = x_203_dilations_0, groups = x_203_groups_0, pad = x_203_pad_0, pad_type = x_203_pad_type_0, strides = x_203_strides_0, weight = encoder_layers_7_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_31_cast_fp16)[name = string("x_203_cast_fp16")]; + tensor input_419_perm_0 = const()[name = string("input_419_perm_0"), val = tensor([0, 2, 1])]; + tensor x_205_axes_0 = const()[name = string("x_205_axes_0"), val = tensor([-1])]; + tensor encoder_layers_7_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_7_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163909888)))]; + tensor encoder_layers_7_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_7_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163912000)))]; + tensor input_419_cast_fp16 = transpose(perm = input_419_perm_0, x = x_203_cast_fp16)[name = string("transpose_293")]; + tensor x_205_cast_fp16 = layer_norm(axes = x_205_axes_0, beta = encoder_layers_7_conv_batch_norm_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_7_conv_batch_norm_weight_to_fp16, x = input_419_cast_fp16)[name = string("x_205_cast_fp16")]; + tensor input_421_perm_0 = const()[name = string("input_421_perm_0"), val = tensor([0, 2, 1])]; + tensor input_421_cast_fp16 = transpose(perm = input_421_perm_0, x = x_205_cast_fp16)[name = string("transpose_292")]; + tensor input_423_cast_fp16 = silu(x = input_421_cast_fp16)[name = string("input_423_cast_fp16")]; + string x_207_pad_type_0 = const()[name = string("x_207_pad_type_0"), val = string("valid")]; + tensor x_207_strides_0 = const()[name = string("x_207_strides_0"), val = tensor([1])]; + tensor x_207_pad_0 = const()[name = string("x_207_pad_0"), val = tensor([0, 0])]; + tensor x_207_dilations_0 = const()[name = string("x_207_dilations_0"), val = tensor([1])]; + int32 x_207_groups_0 = const()[name = string("x_207_groups_0"), val = int32(1)]; + tensor encoder_layers_7_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163914112))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164962752))))[name = string("encoder_layers_7_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_207_cast_fp16 = conv(dilations = x_207_dilations_0, groups = x_207_groups_0, pad = x_207_pad_0, pad_type = x_207_pad_type_0, strides = x_207_strides_0, weight = encoder_layers_7_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_423_cast_fp16)[name = string("x_207_cast_fp16")]; + tensor input_425_perm_0 = const()[name = string("input_425_perm_0"), val = tensor([0, 2, 1])]; + tensor input_425_cast_fp16 = transpose(perm = input_425_perm_0, x = x_207_cast_fp16)[name = string("transpose_291")]; + tensor input_427_cast_fp16 = add(x = input_411_cast_fp16, y = input_425_cast_fp16)[name = string("input_427_cast_fp16")]; + tensor input_429_axes_0 = const()[name = string("input_429_axes_0"), val = tensor([-1])]; + tensor encoder_layers_7_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_7_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164964864)))]; + tensor encoder_layers_7_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_7_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164966976)))]; + tensor input_429_cast_fp16 = layer_norm(axes = input_429_axes_0, beta = encoder_layers_7_norm_feed_forward2_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_7_norm_feed_forward2_weight_to_fp16, x = input_427_cast_fp16)[name = string("input_429_cast_fp16")]; + tensor encoder_layers_7_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164969088))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(168114880))))[name = string("encoder_layers_7_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_7_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_7_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(168115072)))]; + tensor linear_71_cast_fp16 = linear(bias = encoder_layers_7_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_7_feed_forward2_linear1_weight_to_fp16_palettized, x = input_429_cast_fp16)[name = string("linear_71_cast_fp16")]; + tensor input_433_cast_fp16 = silu(x = linear_71_cast_fp16)[name = string("input_433_cast_fp16")]; + tensor encoder_layers_7_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(168123328))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(171269120))))[name = string("encoder_layers_7_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_7_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_7_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(171269312)))]; + tensor linear_72_cast_fp16 = linear(bias = encoder_layers_7_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_7_feed_forward2_linear2_weight_to_fp16_palettized, x = input_433_cast_fp16)[name = string("linear_72_cast_fp16")]; + fp16 var_2123_to_fp16 = const()[name = string("op_2123_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2124_cast_fp16 = mul(x = linear_72_cast_fp16, y = var_2123_to_fp16)[name = string("op_2124_cast_fp16")]; + tensor input_439_cast_fp16 = add(x = input_427_cast_fp16, y = var_2124_cast_fp16)[name = string("input_439_cast_fp16")]; + tensor input_441_axes_0 = const()[name = string("input_441_axes_0"), val = tensor([-1])]; + tensor encoder_layers_7_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_7_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(171271424)))]; + tensor encoder_layers_7_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_7_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(171273536)))]; + tensor input_441_cast_fp16 = layer_norm(axes = input_441_axes_0, beta = encoder_layers_7_norm_out_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_7_norm_out_weight_to_fp16, x = input_439_cast_fp16)[name = string("input_441_cast_fp16")]; + tensor cache_33_begin_0 = const()[name = string("cache_33_begin_0"), val = tensor([8, 0, 0, 0])]; + tensor cache_33_end_0 = const()[name = string("cache_33_end_0"), val = tensor([9, 1, 42, 1024])]; + tensor cache_33_end_mask_0 = const()[name = string("cache_33_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_33_squeeze_mask_0 = const()[name = string("cache_33_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_33_cast_fp16 = slice_by_index(begin = cache_33_begin_0, end = cache_33_end_0, end_mask = cache_33_end_mask_0, squeeze_mask = cache_33_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_33_cast_fp16")]; + tensor cache_35_begin_0 = const()[name = string("cache_35_begin_0"), val = tensor([8, 0, 0, 0])]; + tensor cache_35_end_0 = const()[name = string("cache_35_end_0"), val = tensor([9, 1, 1024, 8])]; + tensor cache_35_end_mask_0 = const()[name = string("cache_35_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_35_squeeze_mask_0 = const()[name = string("cache_35_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_35_cast_fp16 = slice_by_index(begin = cache_35_begin_0, end = cache_35_end_0, end_mask = cache_35_end_mask_0, squeeze_mask = cache_35_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_35_cast_fp16")]; + tensor input_443_axes_0 = const()[name = string("input_443_axes_0"), val = tensor([-1])]; + tensor encoder_layers_8_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_8_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(171275648)))]; + tensor encoder_layers_8_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_8_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(171277760)))]; + tensor input_443_cast_fp16 = layer_norm(axes = input_443_axes_0, beta = encoder_layers_8_norm_feed_forward1_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_8_norm_feed_forward1_weight_to_fp16, x = input_441_cast_fp16)[name = string("input_443_cast_fp16")]; + tensor encoder_layers_8_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(171279872))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174425664))))[name = string("encoder_layers_8_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_8_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_8_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174425856)))]; + tensor linear_73_cast_fp16 = linear(bias = encoder_layers_8_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_8_feed_forward1_linear1_weight_to_fp16_palettized, x = input_443_cast_fp16)[name = string("linear_73_cast_fp16")]; + tensor input_447_cast_fp16 = silu(x = linear_73_cast_fp16)[name = string("input_447_cast_fp16")]; + tensor encoder_layers_8_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174434112))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177579904))))[name = string("encoder_layers_8_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_8_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_8_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177580096)))]; + tensor linear_74_cast_fp16 = linear(bias = encoder_layers_8_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_8_feed_forward1_linear2_weight_to_fp16_palettized, x = input_447_cast_fp16)[name = string("linear_74_cast_fp16")]; + fp16 var_2160_to_fp16 = const()[name = string("op_2160_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2161_cast_fp16 = mul(x = linear_74_cast_fp16, y = var_2160_to_fp16)[name = string("op_2161_cast_fp16")]; + tensor input_453_cast_fp16 = add(x = input_441_cast_fp16, y = var_2161_cast_fp16)[name = string("input_453_cast_fp16")]; + tensor key_17_axes_0 = const()[name = string("key_17_axes_0"), val = tensor([-1])]; + tensor encoder_layers_8_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_8_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177582208)))]; + tensor encoder_layers_8_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_8_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177584320)))]; + tensor key_17_cast_fp16 = layer_norm(axes = key_17_axes_0, beta = encoder_layers_8_norm_self_att_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_8_norm_self_att_weight_to_fp16, x = input_453_cast_fp16)[name = string("key_17_cast_fp16")]; + bool input_455_interleave_0 = const()[name = string("input_455_interleave_0"), val = bool(false)]; + tensor input_455_cast_fp16 = concat(axis = var_69, interleave = input_455_interleave_0, values = (cache_33_cast_fp16, key_17_cast_fp16))[name = string("input_455_cast_fp16")]; + bool var_2189_interleave_0 = const()[name = string("op_2189_interleave_0"), val = bool(false)]; + tensor var_2189_cast_fp16 = concat(axis = var_69, interleave = var_2189_interleave_0, values = key_17_cast_fp16)[name = string("op_2189_cast_fp16")]; + tensor encoder_layers_8_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177586432))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(178372928))))[name = string("encoder_layers_8_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_8_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_8_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(178373120)))]; + tensor linear_75_cast_fp16 = linear(bias = encoder_layers_8_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_8_self_attn_linear_q_weight_to_fp16_palettized, x = key_17_cast_fp16)[name = string("linear_75_cast_fp16")]; + tensor var_2194 = const()[name = string("op_2194"), val = tensor([1, -1, 8, 128])]; + tensor q_49_cast_fp16 = reshape(shape = var_2194, x = linear_75_cast_fp16)[name = string("q_49_cast_fp16")]; + tensor encoder_layers_8_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(178375232))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179161728))))[name = string("encoder_layers_8_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_8_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_8_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179161920)))]; + tensor linear_76_cast_fp16 = linear(bias = encoder_layers_8_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_8_self_attn_linear_k_weight_to_fp16_palettized, x = input_455_cast_fp16)[name = string("linear_76_cast_fp16")]; + tensor var_2199 = const()[name = string("op_2199"), val = tensor([1, -1, 8, 128])]; + tensor k_33_cast_fp16 = reshape(shape = var_2199, x = linear_76_cast_fp16)[name = string("k_33_cast_fp16")]; + tensor encoder_layers_8_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179164032))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179950528))))[name = string("encoder_layers_8_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_8_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_8_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179950720)))]; + tensor linear_77_cast_fp16 = linear(bias = encoder_layers_8_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_8_self_attn_linear_v_weight_to_fp16_palettized, x = input_455_cast_fp16)[name = string("linear_77_cast_fp16")]; + tensor var_2204 = const()[name = string("op_2204"), val = tensor([1, -1, 8, 128])]; + tensor v_17_cast_fp16 = reshape(shape = var_2204, x = linear_77_cast_fp16)[name = string("v_17_cast_fp16")]; + tensor value_25_perm_0 = const()[name = string("value_25_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_8_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_8_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179952832)))]; + tensor var_2217_cast_fp16 = add(x = q_49_cast_fp16, y = encoder_layers_8_self_attn_pos_bias_u_to_fp16)[name = string("op_2217_cast_fp16")]; + tensor encoder_layers_8_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_8_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179954944)))]; + tensor var_2219_cast_fp16 = add(x = q_49_cast_fp16, y = encoder_layers_8_self_attn_pos_bias_v_to_fp16)[name = string("op_2219_cast_fp16")]; + tensor q_with_bias_v_17_perm_0 = const()[name = string("q_with_bias_v_17_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_215_transpose_x_0 = const()[name = string("x_215_transpose_x_0"), val = bool(false)]; + bool x_215_transpose_y_0 = const()[name = string("x_215_transpose_y_0"), val = bool(false)]; + tensor op_2221_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179957056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180156800))))[name = string("op_2221_to_fp16_quantized")]; + tensor q_with_bias_v_17_cast_fp16 = transpose(perm = q_with_bias_v_17_perm_0, x = var_2219_cast_fp16)[name = string("transpose_290")]; + tensor x_215_cast_fp16 = matmul(transpose_x = x_215_transpose_x_0, transpose_y = x_215_transpose_y_0, x = q_with_bias_v_17_cast_fp16, y = op_2221_to_fp16_quantized)[name = string("x_215_cast_fp16")]; + tensor x_217_pad_0 = const()[name = string("x_217_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_217_mode_0 = const()[name = string("x_217_mode_0"), val = string("constant")]; + fp16 const_183_to_fp16 = const()[name = string("const_183_to_fp16"), val = fp16(0x0p+0)]; + tensor x_217_cast_fp16 = pad(constant_val = const_183_to_fp16, mode = x_217_mode_0, pad = x_217_pad_0, x = x_215_cast_fp16)[name = string("x_217_cast_fp16")]; + tensor var_2229 = const()[name = string("op_2229"), val = tensor([1, 8, -1, 56])]; + tensor x_219_cast_fp16 = reshape(shape = var_2229, x = x_217_cast_fp16)[name = string("x_219_cast_fp16")]; + tensor var_2233_begin_0 = const()[name = string("op_2233_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_2233_end_0 = const()[name = string("op_2233_end_0"), val = tensor([1, 8, 196, 56])]; + tensor var_2233_end_mask_0 = const()[name = string("op_2233_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_2233_cast_fp16 = slice_by_index(begin = var_2233_begin_0, end = var_2233_end_0, end_mask = var_2233_end_mask_0, x = x_219_cast_fp16)[name = string("op_2233_cast_fp16")]; + tensor var_2234 = const()[name = string("op_2234"), val = tensor([1, 8, 56, 195])]; + tensor matrix_bd_33_cast_fp16 = reshape(shape = var_2234, x = var_2233_cast_fp16)[name = string("matrix_bd_33_cast_fp16")]; + bool matrix_ac_17_transpose_x_0 = const()[name = string("matrix_ac_17_transpose_x_0"), val = bool(false)]; + bool matrix_ac_17_transpose_y_0 = const()[name = string("matrix_ac_17_transpose_y_0"), val = bool(false)]; + tensor transpose_112_perm_0 = const()[name = string("transpose_112_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_113_perm_0 = const()[name = string("transpose_113_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_113 = transpose(perm = transpose_113_perm_0, x = k_33_cast_fp16)[name = string("transpose_288")]; + tensor transpose_112 = transpose(perm = transpose_112_perm_0, x = var_2217_cast_fp16)[name = string("transpose_289")]; + tensor matrix_ac_17_cast_fp16 = matmul(transpose_x = matrix_ac_17_transpose_x_0, transpose_y = matrix_ac_17_transpose_y_0, x = transpose_112, y = transpose_113)[name = string("matrix_ac_17_cast_fp16")]; + tensor matrix_bd_35_begin_0 = const()[name = string("matrix_bd_35_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_35_end_0 = const()[name = string("matrix_bd_35_end_0"), val = tensor([1, 8, 56, 98])]; + tensor matrix_bd_35_end_mask_0 = const()[name = string("matrix_bd_35_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_35_cast_fp16 = slice_by_index(begin = matrix_bd_35_begin_0, end = matrix_bd_35_end_0, end_mask = matrix_bd_35_end_mask_0, x = matrix_bd_33_cast_fp16)[name = string("matrix_bd_35_cast_fp16")]; + tensor var_2243_cast_fp16 = add(x = matrix_ac_17_cast_fp16, y = matrix_bd_35_cast_fp16)[name = string("op_2243_cast_fp16")]; + fp16 _inversed_scores_33_y_0_to_fp16 = const()[name = string("_inversed_scores_33_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_33_cast_fp16 = mul(x = var_2243_cast_fp16, y = _inversed_scores_33_y_0_to_fp16)[name = string("_inversed_scores_33_cast_fp16")]; + tensor scores_35_cast_fp16 = select(a = var_46_to_fp16, b = _inversed_scores_33_cast_fp16, cond = mask_11)[name = string("scores_35_cast_fp16")]; + tensor var_2249_cast_fp16 = softmax(axis = var_60, x = scores_35_cast_fp16)[name = string("op_2249_cast_fp16")]; + tensor input_457_cast_fp16 = select(a = var_45_to_fp16, b = var_2249_cast_fp16, cond = mask_11)[name = string("input_457_cast_fp16")]; + bool x_221_transpose_x_0 = const()[name = string("x_221_transpose_x_0"), val = bool(false)]; + bool x_221_transpose_y_0 = const()[name = string("x_221_transpose_y_0"), val = bool(false)]; + tensor value_25_cast_fp16 = transpose(perm = value_25_perm_0, x = v_17_cast_fp16)[name = string("transpose_287")]; + tensor x_221_cast_fp16 = matmul(transpose_x = x_221_transpose_x_0, transpose_y = x_221_transpose_y_0, x = input_457_cast_fp16, y = value_25_cast_fp16)[name = string("x_221_cast_fp16")]; + tensor var_2253_perm_0 = const()[name = string("op_2253_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2254 = const()[name = string("op_2254"), val = tensor([1, -1, 1024])]; + tensor var_2253_cast_fp16 = transpose(perm = var_2253_perm_0, x = x_221_cast_fp16)[name = string("transpose_286")]; + tensor input_459_cast_fp16 = reshape(shape = var_2254, x = var_2253_cast_fp16)[name = string("input_459_cast_fp16")]; + tensor encoder_layers_8_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180157312))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180943808))))[name = string("encoder_layers_8_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_8_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_8_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180944000)))]; + tensor linear_79_cast_fp16 = linear(bias = encoder_layers_8_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_8_self_attn_linear_out_weight_to_fp16_palettized, x = input_459_cast_fp16)[name = string("linear_79_cast_fp16")]; + tensor input_463_cast_fp16 = add(x = input_453_cast_fp16, y = linear_79_cast_fp16)[name = string("input_463_cast_fp16")]; + tensor x_225_axes_0 = const()[name = string("x_225_axes_0"), val = tensor([-1])]; + tensor encoder_layers_8_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_8_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180946112)))]; + tensor encoder_layers_8_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_8_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180948224)))]; + tensor x_225_cast_fp16 = layer_norm(axes = x_225_axes_0, beta = encoder_layers_8_norm_conv_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_8_norm_conv_weight_to_fp16, x = input_463_cast_fp16)[name = string("x_225_cast_fp16")]; + tensor input_465_perm_0 = const()[name = string("input_465_perm_0"), val = tensor([0, 2, 1])]; + string input_467_pad_type_0 = const()[name = string("input_467_pad_type_0"), val = string("valid")]; + tensor input_467_strides_0 = const()[name = string("input_467_strides_0"), val = tensor([1])]; + tensor input_467_pad_0 = const()[name = string("input_467_pad_0"), val = tensor([0, 0])]; + tensor input_467_dilations_0 = const()[name = string("input_467_dilations_0"), val = tensor([1])]; + int32 input_467_groups_0 = const()[name = string("input_467_groups_0"), val = int32(1)]; + tensor encoder_layers_8_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180950336))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(183047552))))[name = string("encoder_layers_8_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_465_cast_fp16 = transpose(perm = input_465_perm_0, x = x_225_cast_fp16)[name = string("transpose_285")]; + tensor input_467_cast_fp16 = conv(dilations = input_467_dilations_0, groups = input_467_groups_0, pad = input_467_pad_0, pad_type = input_467_pad_type_0, strides = input_467_strides_0, weight = encoder_layers_8_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_465_cast_fp16)[name = string("input_467_cast_fp16")]; + int32 x_227_split_num_splits_0 = const()[name = string("x_227_split_num_splits_0"), val = int32(2)]; + int32 x_227_split_axis_0 = const()[name = string("x_227_split_axis_0"), val = int32(1)]; + tensor x_227_split_cast_fp16_0, tensor x_227_split_cast_fp16_1 = split(axis = x_227_split_axis_0, num_splits = x_227_split_num_splits_0, x = input_467_cast_fp16)[name = string("x_227_split_cast_fp16")]; + tensor x_227_split_1_sigmoid_cast_fp16 = sigmoid(x = x_227_split_cast_fp16_1)[name = string("x_227_split_1_sigmoid_cast_fp16")]; + tensor x_227_cast_fp16 = mul(x = x_227_split_cast_fp16_0, y = x_227_split_1_sigmoid_cast_fp16)[name = string("x_227_cast_fp16")]; + tensor input_469_cast_fp16 = select(a = var_45_to_fp16, b = x_227_cast_fp16, cond = var_576)[name = string("input_469_cast_fp16")]; + bool new_x_35_interleave_0 = const()[name = string("new_x_35_interleave_0"), val = bool(false)]; + tensor new_x_35_cast_fp16 = concat(axis = var_60, interleave = new_x_35_interleave_0, values = (cache_35_cast_fp16, input_469_cast_fp16))[name = string("new_x_35_cast_fp16")]; + tensor var_2293_begin_0 = const()[name = string("op_2293_begin_0"), val = tensor([0, 0, 56])]; + tensor var_2293_end_0 = const()[name = string("op_2293_end_0"), val = tensor([1, 1024, 64])]; + tensor var_2293_end_mask_0 = const()[name = string("op_2293_end_mask_0"), val = tensor([true, true, true])]; + tensor var_2293_cast_fp16 = slice_by_index(begin = var_2293_begin_0, end = var_2293_end_0, end_mask = var_2293_end_mask_0, x = new_x_35_cast_fp16)[name = string("op_2293_cast_fp16")]; + string x_229_pad_type_0 = const()[name = string("x_229_pad_type_0"), val = string("valid")]; + int32 x_229_groups_0 = const()[name = string("x_229_groups_0"), val = int32(1024)]; + tensor x_229_strides_0 = const()[name = string("x_229_strides_0"), val = tensor([1])]; + tensor x_229_pad_0 = const()[name = string("x_229_pad_0"), val = tensor([0, 0])]; + tensor x_229_dilations_0 = const()[name = string("x_229_dilations_0"), val = tensor([1])]; + tensor encoder_layers_8_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(183051712))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(183060992))))[name = string("encoder_layers_8_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_229_cast_fp16 = conv(dilations = x_229_dilations_0, groups = x_229_groups_0, pad = x_229_pad_0, pad_type = x_229_pad_type_0, strides = x_229_strides_0, weight = encoder_layers_8_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_35_cast_fp16)[name = string("x_229_cast_fp16")]; + tensor input_471_perm_0 = const()[name = string("input_471_perm_0"), val = tensor([0, 2, 1])]; + tensor x_231_axes_0 = const()[name = string("x_231_axes_0"), val = tensor([-1])]; + tensor encoder_layers_8_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_8_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(183063104)))]; + tensor encoder_layers_8_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_8_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(183065216)))]; + tensor input_471_cast_fp16 = transpose(perm = input_471_perm_0, x = x_229_cast_fp16)[name = string("transpose_284")]; + tensor x_231_cast_fp16 = layer_norm(axes = x_231_axes_0, beta = encoder_layers_8_conv_batch_norm_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_8_conv_batch_norm_weight_to_fp16, x = input_471_cast_fp16)[name = string("x_231_cast_fp16")]; + tensor input_473_perm_0 = const()[name = string("input_473_perm_0"), val = tensor([0, 2, 1])]; + tensor input_473_cast_fp16 = transpose(perm = input_473_perm_0, x = x_231_cast_fp16)[name = string("transpose_283")]; + tensor input_475_cast_fp16 = silu(x = input_473_cast_fp16)[name = string("input_475_cast_fp16")]; + string x_233_pad_type_0 = const()[name = string("x_233_pad_type_0"), val = string("valid")]; + tensor x_233_strides_0 = const()[name = string("x_233_strides_0"), val = tensor([1])]; + tensor x_233_pad_0 = const()[name = string("x_233_pad_0"), val = tensor([0, 0])]; + tensor x_233_dilations_0 = const()[name = string("x_233_dilations_0"), val = tensor([1])]; + int32 x_233_groups_0 = const()[name = string("x_233_groups_0"), val = int32(1)]; + tensor encoder_layers_8_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(183067328))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(184115968))))[name = string("encoder_layers_8_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_233_cast_fp16 = conv(dilations = x_233_dilations_0, groups = x_233_groups_0, pad = x_233_pad_0, pad_type = x_233_pad_type_0, strides = x_233_strides_0, weight = encoder_layers_8_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_475_cast_fp16)[name = string("x_233_cast_fp16")]; + tensor input_477_perm_0 = const()[name = string("input_477_perm_0"), val = tensor([0, 2, 1])]; + tensor input_477_cast_fp16 = transpose(perm = input_477_perm_0, x = x_233_cast_fp16)[name = string("transpose_282")]; + tensor input_479_cast_fp16 = add(x = input_463_cast_fp16, y = input_477_cast_fp16)[name = string("input_479_cast_fp16")]; + tensor input_481_axes_0 = const()[name = string("input_481_axes_0"), val = tensor([-1])]; + tensor encoder_layers_8_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_8_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(184118080)))]; + tensor encoder_layers_8_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_8_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(184120192)))]; + tensor input_481_cast_fp16 = layer_norm(axes = input_481_axes_0, beta = encoder_layers_8_norm_feed_forward2_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_8_norm_feed_forward2_weight_to_fp16, x = input_479_cast_fp16)[name = string("input_481_cast_fp16")]; + tensor encoder_layers_8_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(184122304))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187268096))))[name = string("encoder_layers_8_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_8_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_8_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187268288)))]; + tensor linear_80_cast_fp16 = linear(bias = encoder_layers_8_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_8_feed_forward2_linear1_weight_to_fp16_palettized, x = input_481_cast_fp16)[name = string("linear_80_cast_fp16")]; + tensor input_485_cast_fp16 = silu(x = linear_80_cast_fp16)[name = string("input_485_cast_fp16")]; + tensor encoder_layers_8_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187276544))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(190422336))))[name = string("encoder_layers_8_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_8_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_8_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(190422528)))]; + tensor linear_81_cast_fp16 = linear(bias = encoder_layers_8_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_8_feed_forward2_linear2_weight_to_fp16_palettized, x = input_485_cast_fp16)[name = string("linear_81_cast_fp16")]; + fp16 var_2336_to_fp16 = const()[name = string("op_2336_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2337_cast_fp16 = mul(x = linear_81_cast_fp16, y = var_2336_to_fp16)[name = string("op_2337_cast_fp16")]; + tensor input_491_cast_fp16 = add(x = input_479_cast_fp16, y = var_2337_cast_fp16)[name = string("input_491_cast_fp16")]; + tensor input_493_axes_0 = const()[name = string("input_493_axes_0"), val = tensor([-1])]; + tensor encoder_layers_8_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_8_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(190424640)))]; + tensor encoder_layers_8_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_8_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(190426752)))]; + tensor input_493_cast_fp16 = layer_norm(axes = input_493_axes_0, beta = encoder_layers_8_norm_out_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_8_norm_out_weight_to_fp16, x = input_491_cast_fp16)[name = string("input_493_cast_fp16")]; + tensor cache_37_begin_0 = const()[name = string("cache_37_begin_0"), val = tensor([9, 0, 0, 0])]; + tensor cache_37_end_0 = const()[name = string("cache_37_end_0"), val = tensor([10, 1, 42, 1024])]; + tensor cache_37_end_mask_0 = const()[name = string("cache_37_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_37_squeeze_mask_0 = const()[name = string("cache_37_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_37_cast_fp16 = slice_by_index(begin = cache_37_begin_0, end = cache_37_end_0, end_mask = cache_37_end_mask_0, squeeze_mask = cache_37_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_37_cast_fp16")]; + tensor cache_39_begin_0 = const()[name = string("cache_39_begin_0"), val = tensor([9, 0, 0, 0])]; + tensor cache_39_end_0 = const()[name = string("cache_39_end_0"), val = tensor([10, 1, 1024, 8])]; + tensor cache_39_end_mask_0 = const()[name = string("cache_39_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_39_squeeze_mask_0 = const()[name = string("cache_39_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_39_cast_fp16 = slice_by_index(begin = cache_39_begin_0, end = cache_39_end_0, end_mask = cache_39_end_mask_0, squeeze_mask = cache_39_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_39_cast_fp16")]; + tensor input_495_axes_0 = const()[name = string("input_495_axes_0"), val = tensor([-1])]; + tensor encoder_layers_9_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_9_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(190428864)))]; + tensor encoder_layers_9_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_9_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(190430976)))]; + tensor input_495_cast_fp16 = layer_norm(axes = input_495_axes_0, beta = encoder_layers_9_norm_feed_forward1_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_9_norm_feed_forward1_weight_to_fp16, x = input_493_cast_fp16)[name = string("input_495_cast_fp16")]; + tensor encoder_layers_9_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(190433088))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193578880))))[name = string("encoder_layers_9_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_9_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_9_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193579072)))]; + tensor linear_82_cast_fp16 = linear(bias = encoder_layers_9_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_9_feed_forward1_linear1_weight_to_fp16_palettized, x = input_495_cast_fp16)[name = string("linear_82_cast_fp16")]; + tensor input_499_cast_fp16 = silu(x = linear_82_cast_fp16)[name = string("input_499_cast_fp16")]; + tensor encoder_layers_9_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193587328))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196733120))))[name = string("encoder_layers_9_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_9_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_9_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196733312)))]; + tensor linear_83_cast_fp16 = linear(bias = encoder_layers_9_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_9_feed_forward1_linear2_weight_to_fp16_palettized, x = input_499_cast_fp16)[name = string("linear_83_cast_fp16")]; + fp16 var_2373_to_fp16 = const()[name = string("op_2373_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2374_cast_fp16 = mul(x = linear_83_cast_fp16, y = var_2373_to_fp16)[name = string("op_2374_cast_fp16")]; + tensor input_505_cast_fp16 = add(x = input_493_cast_fp16, y = var_2374_cast_fp16)[name = string("input_505_cast_fp16")]; + tensor key_19_axes_0 = const()[name = string("key_19_axes_0"), val = tensor([-1])]; + tensor encoder_layers_9_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_9_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196735424)))]; + tensor encoder_layers_9_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_9_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196737536)))]; + tensor key_19_cast_fp16 = layer_norm(axes = key_19_axes_0, beta = encoder_layers_9_norm_self_att_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_9_norm_self_att_weight_to_fp16, x = input_505_cast_fp16)[name = string("key_19_cast_fp16")]; + bool input_507_interleave_0 = const()[name = string("input_507_interleave_0"), val = bool(false)]; + tensor input_507_cast_fp16 = concat(axis = var_69, interleave = input_507_interleave_0, values = (cache_37_cast_fp16, key_19_cast_fp16))[name = string("input_507_cast_fp16")]; + bool var_2402_interleave_0 = const()[name = string("op_2402_interleave_0"), val = bool(false)]; + tensor var_2402_cast_fp16 = concat(axis = var_69, interleave = var_2402_interleave_0, values = key_19_cast_fp16)[name = string("op_2402_cast_fp16")]; + tensor encoder_layers_9_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196739648))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197526144))))[name = string("encoder_layers_9_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_9_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_9_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197526336)))]; + tensor linear_84_cast_fp16 = linear(bias = encoder_layers_9_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_9_self_attn_linear_q_weight_to_fp16_palettized, x = key_19_cast_fp16)[name = string("linear_84_cast_fp16")]; + tensor var_2407 = const()[name = string("op_2407"), val = tensor([1, -1, 8, 128])]; + tensor q_55_cast_fp16 = reshape(shape = var_2407, x = linear_84_cast_fp16)[name = string("q_55_cast_fp16")]; + tensor encoder_layers_9_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197528448))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198314944))))[name = string("encoder_layers_9_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_9_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_9_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198315136)))]; + tensor linear_85_cast_fp16 = linear(bias = encoder_layers_9_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_9_self_attn_linear_k_weight_to_fp16_palettized, x = input_507_cast_fp16)[name = string("linear_85_cast_fp16")]; + tensor var_2412 = const()[name = string("op_2412"), val = tensor([1, -1, 8, 128])]; + tensor k_37_cast_fp16 = reshape(shape = var_2412, x = linear_85_cast_fp16)[name = string("k_37_cast_fp16")]; + tensor encoder_layers_9_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198317248))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199103744))))[name = string("encoder_layers_9_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_9_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_9_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199103936)))]; + tensor linear_86_cast_fp16 = linear(bias = encoder_layers_9_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_9_self_attn_linear_v_weight_to_fp16_palettized, x = input_507_cast_fp16)[name = string("linear_86_cast_fp16")]; + tensor var_2417 = const()[name = string("op_2417"), val = tensor([1, -1, 8, 128])]; + tensor v_19_cast_fp16 = reshape(shape = var_2417, x = linear_86_cast_fp16)[name = string("v_19_cast_fp16")]; + tensor value_27_perm_0 = const()[name = string("value_27_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_9_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_9_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199106048)))]; + tensor var_2430_cast_fp16 = add(x = q_55_cast_fp16, y = encoder_layers_9_self_attn_pos_bias_u_to_fp16)[name = string("op_2430_cast_fp16")]; + tensor encoder_layers_9_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_9_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199108160)))]; + tensor var_2432_cast_fp16 = add(x = q_55_cast_fp16, y = encoder_layers_9_self_attn_pos_bias_v_to_fp16)[name = string("op_2432_cast_fp16")]; + tensor q_with_bias_v_19_perm_0 = const()[name = string("q_with_bias_v_19_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_241_transpose_x_0 = const()[name = string("x_241_transpose_x_0"), val = bool(false)]; + bool x_241_transpose_y_0 = const()[name = string("x_241_transpose_y_0"), val = bool(false)]; + tensor op_2434_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199110272))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199310016))))[name = string("op_2434_to_fp16_quantized")]; + tensor q_with_bias_v_19_cast_fp16 = transpose(perm = q_with_bias_v_19_perm_0, x = var_2432_cast_fp16)[name = string("transpose_281")]; + tensor x_241_cast_fp16 = matmul(transpose_x = x_241_transpose_x_0, transpose_y = x_241_transpose_y_0, x = q_with_bias_v_19_cast_fp16, y = op_2434_to_fp16_quantized)[name = string("x_241_cast_fp16")]; + tensor x_243_pad_0 = const()[name = string("x_243_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_243_mode_0 = const()[name = string("x_243_mode_0"), val = string("constant")]; + fp16 const_196_to_fp16 = const()[name = string("const_196_to_fp16"), val = fp16(0x0p+0)]; + tensor x_243_cast_fp16 = pad(constant_val = const_196_to_fp16, mode = x_243_mode_0, pad = x_243_pad_0, x = x_241_cast_fp16)[name = string("x_243_cast_fp16")]; + tensor var_2442 = const()[name = string("op_2442"), val = tensor([1, 8, -1, 56])]; + tensor x_245_cast_fp16 = reshape(shape = var_2442, x = x_243_cast_fp16)[name = string("x_245_cast_fp16")]; + tensor var_2446_begin_0 = const()[name = string("op_2446_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_2446_end_0 = const()[name = string("op_2446_end_0"), val = tensor([1, 8, 196, 56])]; + tensor var_2446_end_mask_0 = const()[name = string("op_2446_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_2446_cast_fp16 = slice_by_index(begin = var_2446_begin_0, end = var_2446_end_0, end_mask = var_2446_end_mask_0, x = x_245_cast_fp16)[name = string("op_2446_cast_fp16")]; + tensor var_2447 = const()[name = string("op_2447"), val = tensor([1, 8, 56, 195])]; + tensor matrix_bd_37_cast_fp16 = reshape(shape = var_2447, x = var_2446_cast_fp16)[name = string("matrix_bd_37_cast_fp16")]; + bool matrix_ac_19_transpose_x_0 = const()[name = string("matrix_ac_19_transpose_x_0"), val = bool(false)]; + bool matrix_ac_19_transpose_y_0 = const()[name = string("matrix_ac_19_transpose_y_0"), val = bool(false)]; + tensor transpose_114_perm_0 = const()[name = string("transpose_114_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_115_perm_0 = const()[name = string("transpose_115_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_115 = transpose(perm = transpose_115_perm_0, x = k_37_cast_fp16)[name = string("transpose_279")]; + tensor transpose_114 = transpose(perm = transpose_114_perm_0, x = var_2430_cast_fp16)[name = string("transpose_280")]; + tensor matrix_ac_19_cast_fp16 = matmul(transpose_x = matrix_ac_19_transpose_x_0, transpose_y = matrix_ac_19_transpose_y_0, x = transpose_114, y = transpose_115)[name = string("matrix_ac_19_cast_fp16")]; + tensor matrix_bd_39_begin_0 = const()[name = string("matrix_bd_39_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_39_end_0 = const()[name = string("matrix_bd_39_end_0"), val = tensor([1, 8, 56, 98])]; + tensor matrix_bd_39_end_mask_0 = const()[name = string("matrix_bd_39_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_39_cast_fp16 = slice_by_index(begin = matrix_bd_39_begin_0, end = matrix_bd_39_end_0, end_mask = matrix_bd_39_end_mask_0, x = matrix_bd_37_cast_fp16)[name = string("matrix_bd_39_cast_fp16")]; + tensor var_2456_cast_fp16 = add(x = matrix_ac_19_cast_fp16, y = matrix_bd_39_cast_fp16)[name = string("op_2456_cast_fp16")]; + fp16 _inversed_scores_37_y_0_to_fp16 = const()[name = string("_inversed_scores_37_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_37_cast_fp16 = mul(x = var_2456_cast_fp16, y = _inversed_scores_37_y_0_to_fp16)[name = string("_inversed_scores_37_cast_fp16")]; + tensor scores_39_cast_fp16 = select(a = var_46_to_fp16, b = _inversed_scores_37_cast_fp16, cond = mask_11)[name = string("scores_39_cast_fp16")]; + tensor var_2462_cast_fp16 = softmax(axis = var_60, x = scores_39_cast_fp16)[name = string("op_2462_cast_fp16")]; + tensor input_509_cast_fp16 = select(a = var_45_to_fp16, b = var_2462_cast_fp16, cond = mask_11)[name = string("input_509_cast_fp16")]; + bool x_247_transpose_x_0 = const()[name = string("x_247_transpose_x_0"), val = bool(false)]; + bool x_247_transpose_y_0 = const()[name = string("x_247_transpose_y_0"), val = bool(false)]; + tensor value_27_cast_fp16 = transpose(perm = value_27_perm_0, x = v_19_cast_fp16)[name = string("transpose_278")]; + tensor x_247_cast_fp16 = matmul(transpose_x = x_247_transpose_x_0, transpose_y = x_247_transpose_y_0, x = input_509_cast_fp16, y = value_27_cast_fp16)[name = string("x_247_cast_fp16")]; + tensor var_2466_perm_0 = const()[name = string("op_2466_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2467 = const()[name = string("op_2467"), val = tensor([1, -1, 1024])]; + tensor var_2466_cast_fp16 = transpose(perm = var_2466_perm_0, x = x_247_cast_fp16)[name = string("transpose_277")]; + tensor input_511_cast_fp16 = reshape(shape = var_2467, x = var_2466_cast_fp16)[name = string("input_511_cast_fp16")]; + tensor encoder_layers_9_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199310528))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(200097024))))[name = string("encoder_layers_9_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_9_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_9_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(200097216)))]; + tensor linear_88_cast_fp16 = linear(bias = encoder_layers_9_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_9_self_attn_linear_out_weight_to_fp16_palettized, x = input_511_cast_fp16)[name = string("linear_88_cast_fp16")]; + tensor input_515_cast_fp16 = add(x = input_505_cast_fp16, y = linear_88_cast_fp16)[name = string("input_515_cast_fp16")]; + tensor x_251_axes_0 = const()[name = string("x_251_axes_0"), val = tensor([-1])]; + tensor encoder_layers_9_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_9_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(200099328)))]; + tensor encoder_layers_9_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_9_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(200101440)))]; + tensor x_251_cast_fp16 = layer_norm(axes = x_251_axes_0, beta = encoder_layers_9_norm_conv_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_9_norm_conv_weight_to_fp16, x = input_515_cast_fp16)[name = string("x_251_cast_fp16")]; + tensor input_517_perm_0 = const()[name = string("input_517_perm_0"), val = tensor([0, 2, 1])]; + string input_519_pad_type_0 = const()[name = string("input_519_pad_type_0"), val = string("valid")]; + tensor input_519_strides_0 = const()[name = string("input_519_strides_0"), val = tensor([1])]; + tensor input_519_pad_0 = const()[name = string("input_519_pad_0"), val = tensor([0, 0])]; + tensor input_519_dilations_0 = const()[name = string("input_519_dilations_0"), val = tensor([1])]; + int32 input_519_groups_0 = const()[name = string("input_519_groups_0"), val = int32(1)]; + tensor encoder_layers_9_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(200103552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(202200768))))[name = string("encoder_layers_9_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_517_cast_fp16 = transpose(perm = input_517_perm_0, x = x_251_cast_fp16)[name = string("transpose_276")]; + tensor input_519_cast_fp16 = conv(dilations = input_519_dilations_0, groups = input_519_groups_0, pad = input_519_pad_0, pad_type = input_519_pad_type_0, strides = input_519_strides_0, weight = encoder_layers_9_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_517_cast_fp16)[name = string("input_519_cast_fp16")]; + int32 x_253_split_num_splits_0 = const()[name = string("x_253_split_num_splits_0"), val = int32(2)]; + int32 x_253_split_axis_0 = const()[name = string("x_253_split_axis_0"), val = int32(1)]; + tensor x_253_split_cast_fp16_0, tensor x_253_split_cast_fp16_1 = split(axis = x_253_split_axis_0, num_splits = x_253_split_num_splits_0, x = input_519_cast_fp16)[name = string("x_253_split_cast_fp16")]; + tensor x_253_split_1_sigmoid_cast_fp16 = sigmoid(x = x_253_split_cast_fp16_1)[name = string("x_253_split_1_sigmoid_cast_fp16")]; + tensor x_253_cast_fp16 = mul(x = x_253_split_cast_fp16_0, y = x_253_split_1_sigmoid_cast_fp16)[name = string("x_253_cast_fp16")]; + tensor input_521_cast_fp16 = select(a = var_45_to_fp16, b = x_253_cast_fp16, cond = var_576)[name = string("input_521_cast_fp16")]; + bool new_x_39_interleave_0 = const()[name = string("new_x_39_interleave_0"), val = bool(false)]; + tensor new_x_39_cast_fp16 = concat(axis = var_60, interleave = new_x_39_interleave_0, values = (cache_39_cast_fp16, input_521_cast_fp16))[name = string("new_x_39_cast_fp16")]; + tensor var_2506_begin_0 = const()[name = string("op_2506_begin_0"), val = tensor([0, 0, 56])]; + tensor var_2506_end_0 = const()[name = string("op_2506_end_0"), val = tensor([1, 1024, 64])]; + tensor var_2506_end_mask_0 = const()[name = string("op_2506_end_mask_0"), val = tensor([true, true, true])]; + tensor var_2506_cast_fp16 = slice_by_index(begin = var_2506_begin_0, end = var_2506_end_0, end_mask = var_2506_end_mask_0, x = new_x_39_cast_fp16)[name = string("op_2506_cast_fp16")]; + string x_255_pad_type_0 = const()[name = string("x_255_pad_type_0"), val = string("valid")]; + int32 x_255_groups_0 = const()[name = string("x_255_groups_0"), val = int32(1024)]; + tensor x_255_strides_0 = const()[name = string("x_255_strides_0"), val = tensor([1])]; + tensor x_255_pad_0 = const()[name = string("x_255_pad_0"), val = tensor([0, 0])]; + tensor x_255_dilations_0 = const()[name = string("x_255_dilations_0"), val = tensor([1])]; + tensor encoder_layers_9_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(202204928))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(202214208))))[name = string("encoder_layers_9_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_255_cast_fp16 = conv(dilations = x_255_dilations_0, groups = x_255_groups_0, pad = x_255_pad_0, pad_type = x_255_pad_type_0, strides = x_255_strides_0, weight = encoder_layers_9_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_39_cast_fp16)[name = string("x_255_cast_fp16")]; + tensor input_523_perm_0 = const()[name = string("input_523_perm_0"), val = tensor([0, 2, 1])]; + tensor x_257_axes_0 = const()[name = string("x_257_axes_0"), val = tensor([-1])]; + tensor encoder_layers_9_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_9_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(202216320)))]; + tensor encoder_layers_9_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_9_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(202218432)))]; + tensor input_523_cast_fp16 = transpose(perm = input_523_perm_0, x = x_255_cast_fp16)[name = string("transpose_275")]; + tensor x_257_cast_fp16 = layer_norm(axes = x_257_axes_0, beta = encoder_layers_9_conv_batch_norm_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_9_conv_batch_norm_weight_to_fp16, x = input_523_cast_fp16)[name = string("x_257_cast_fp16")]; + tensor input_525_perm_0 = const()[name = string("input_525_perm_0"), val = tensor([0, 2, 1])]; + tensor input_525_cast_fp16 = transpose(perm = input_525_perm_0, x = x_257_cast_fp16)[name = string("transpose_274")]; + tensor input_527_cast_fp16 = silu(x = input_525_cast_fp16)[name = string("input_527_cast_fp16")]; + string x_259_pad_type_0 = const()[name = string("x_259_pad_type_0"), val = string("valid")]; + tensor x_259_strides_0 = const()[name = string("x_259_strides_0"), val = tensor([1])]; + tensor x_259_pad_0 = const()[name = string("x_259_pad_0"), val = tensor([0, 0])]; + tensor x_259_dilations_0 = const()[name = string("x_259_dilations_0"), val = tensor([1])]; + int32 x_259_groups_0 = const()[name = string("x_259_groups_0"), val = int32(1)]; + tensor encoder_layers_9_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(202220544))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203269184))))[name = string("encoder_layers_9_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_259_cast_fp16 = conv(dilations = x_259_dilations_0, groups = x_259_groups_0, pad = x_259_pad_0, pad_type = x_259_pad_type_0, strides = x_259_strides_0, weight = encoder_layers_9_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_527_cast_fp16)[name = string("x_259_cast_fp16")]; + tensor input_529_perm_0 = const()[name = string("input_529_perm_0"), val = tensor([0, 2, 1])]; + tensor input_529_cast_fp16 = transpose(perm = input_529_perm_0, x = x_259_cast_fp16)[name = string("transpose_273")]; + tensor input_531_cast_fp16 = add(x = input_515_cast_fp16, y = input_529_cast_fp16)[name = string("input_531_cast_fp16")]; + tensor input_533_axes_0 = const()[name = string("input_533_axes_0"), val = tensor([-1])]; + tensor encoder_layers_9_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_9_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203271296)))]; + tensor encoder_layers_9_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_9_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203273408)))]; + tensor input_533_cast_fp16 = layer_norm(axes = input_533_axes_0, beta = encoder_layers_9_norm_feed_forward2_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_9_norm_feed_forward2_weight_to_fp16, x = input_531_cast_fp16)[name = string("input_533_cast_fp16")]; + tensor encoder_layers_9_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203275520))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(206421312))))[name = string("encoder_layers_9_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_9_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_9_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(206421504)))]; + tensor linear_89_cast_fp16 = linear(bias = encoder_layers_9_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_9_feed_forward2_linear1_weight_to_fp16_palettized, x = input_533_cast_fp16)[name = string("linear_89_cast_fp16")]; + tensor input_537_cast_fp16 = silu(x = linear_89_cast_fp16)[name = string("input_537_cast_fp16")]; + tensor encoder_layers_9_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(206429760))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209575552))))[name = string("encoder_layers_9_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_9_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_9_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209575744)))]; + tensor linear_90_cast_fp16 = linear(bias = encoder_layers_9_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_9_feed_forward2_linear2_weight_to_fp16_palettized, x = input_537_cast_fp16)[name = string("linear_90_cast_fp16")]; + fp16 var_2549_to_fp16 = const()[name = string("op_2549_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2550_cast_fp16 = mul(x = linear_90_cast_fp16, y = var_2549_to_fp16)[name = string("op_2550_cast_fp16")]; + tensor input_543_cast_fp16 = add(x = input_531_cast_fp16, y = var_2550_cast_fp16)[name = string("input_543_cast_fp16")]; + tensor input_545_axes_0 = const()[name = string("input_545_axes_0"), val = tensor([-1])]; + tensor encoder_layers_9_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_9_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209577856)))]; + tensor encoder_layers_9_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_9_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209579968)))]; + tensor input_545_cast_fp16 = layer_norm(axes = input_545_axes_0, beta = encoder_layers_9_norm_out_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_9_norm_out_weight_to_fp16, x = input_543_cast_fp16)[name = string("input_545_cast_fp16")]; + tensor cache_41_begin_0 = const()[name = string("cache_41_begin_0"), val = tensor([10, 0, 0, 0])]; + tensor cache_41_end_0 = const()[name = string("cache_41_end_0"), val = tensor([11, 1, 42, 1024])]; + tensor cache_41_end_mask_0 = const()[name = string("cache_41_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_41_squeeze_mask_0 = const()[name = string("cache_41_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_41_cast_fp16 = slice_by_index(begin = cache_41_begin_0, end = cache_41_end_0, end_mask = cache_41_end_mask_0, squeeze_mask = cache_41_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_41_cast_fp16")]; + tensor cache_43_begin_0 = const()[name = string("cache_43_begin_0"), val = tensor([10, 0, 0, 0])]; + tensor cache_43_end_0 = const()[name = string("cache_43_end_0"), val = tensor([11, 1, 1024, 8])]; + tensor cache_43_end_mask_0 = const()[name = string("cache_43_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_43_squeeze_mask_0 = const()[name = string("cache_43_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_43_cast_fp16 = slice_by_index(begin = cache_43_begin_0, end = cache_43_end_0, end_mask = cache_43_end_mask_0, squeeze_mask = cache_43_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_43_cast_fp16")]; + tensor input_547_axes_0 = const()[name = string("input_547_axes_0"), val = tensor([-1])]; + tensor encoder_layers_10_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_10_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209582080)))]; + tensor encoder_layers_10_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_10_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209584192)))]; + tensor input_547_cast_fp16 = layer_norm(axes = input_547_axes_0, beta = encoder_layers_10_norm_feed_forward1_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_10_norm_feed_forward1_weight_to_fp16, x = input_545_cast_fp16)[name = string("input_547_cast_fp16")]; + tensor encoder_layers_10_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209586304))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(212732096))))[name = string("encoder_layers_10_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_10_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_10_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(212732288)))]; + tensor linear_91_cast_fp16 = linear(bias = encoder_layers_10_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_10_feed_forward1_linear1_weight_to_fp16_palettized, x = input_547_cast_fp16)[name = string("linear_91_cast_fp16")]; + tensor input_551_cast_fp16 = silu(x = linear_91_cast_fp16)[name = string("input_551_cast_fp16")]; + tensor encoder_layers_10_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(212740544))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215886336))))[name = string("encoder_layers_10_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_10_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_10_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215886528)))]; + tensor linear_92_cast_fp16 = linear(bias = encoder_layers_10_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_10_feed_forward1_linear2_weight_to_fp16_palettized, x = input_551_cast_fp16)[name = string("linear_92_cast_fp16")]; + fp16 var_2586_to_fp16 = const()[name = string("op_2586_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2587_cast_fp16 = mul(x = linear_92_cast_fp16, y = var_2586_to_fp16)[name = string("op_2587_cast_fp16")]; + tensor input_557_cast_fp16 = add(x = input_545_cast_fp16, y = var_2587_cast_fp16)[name = string("input_557_cast_fp16")]; + tensor key_21_axes_0 = const()[name = string("key_21_axes_0"), val = tensor([-1])]; + tensor encoder_layers_10_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_10_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215888640)))]; + tensor encoder_layers_10_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_10_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215890752)))]; + tensor key_21_cast_fp16 = layer_norm(axes = key_21_axes_0, beta = encoder_layers_10_norm_self_att_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_10_norm_self_att_weight_to_fp16, x = input_557_cast_fp16)[name = string("key_21_cast_fp16")]; + bool input_559_interleave_0 = const()[name = string("input_559_interleave_0"), val = bool(false)]; + tensor input_559_cast_fp16 = concat(axis = var_69, interleave = input_559_interleave_0, values = (cache_41_cast_fp16, key_21_cast_fp16))[name = string("input_559_cast_fp16")]; + bool var_2615_interleave_0 = const()[name = string("op_2615_interleave_0"), val = bool(false)]; + tensor var_2615_cast_fp16 = concat(axis = var_69, interleave = var_2615_interleave_0, values = key_21_cast_fp16)[name = string("op_2615_cast_fp16")]; + tensor encoder_layers_10_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215892864))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216679360))))[name = string("encoder_layers_10_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_10_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_10_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216679552)))]; + tensor linear_93_cast_fp16 = linear(bias = encoder_layers_10_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_10_self_attn_linear_q_weight_to_fp16_palettized, x = key_21_cast_fp16)[name = string("linear_93_cast_fp16")]; + tensor var_2620 = const()[name = string("op_2620"), val = tensor([1, -1, 8, 128])]; + tensor q_61_cast_fp16 = reshape(shape = var_2620, x = linear_93_cast_fp16)[name = string("q_61_cast_fp16")]; + tensor encoder_layers_10_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216681664))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217468160))))[name = string("encoder_layers_10_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_10_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_10_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217468352)))]; + tensor linear_94_cast_fp16 = linear(bias = encoder_layers_10_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_10_self_attn_linear_k_weight_to_fp16_palettized, x = input_559_cast_fp16)[name = string("linear_94_cast_fp16")]; + tensor var_2625 = const()[name = string("op_2625"), val = tensor([1, -1, 8, 128])]; + tensor k_41_cast_fp16 = reshape(shape = var_2625, x = linear_94_cast_fp16)[name = string("k_41_cast_fp16")]; + tensor encoder_layers_10_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217470464))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218256960))))[name = string("encoder_layers_10_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_10_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_10_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218257152)))]; + tensor linear_95_cast_fp16 = linear(bias = encoder_layers_10_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_10_self_attn_linear_v_weight_to_fp16_palettized, x = input_559_cast_fp16)[name = string("linear_95_cast_fp16")]; + tensor var_2630 = const()[name = string("op_2630"), val = tensor([1, -1, 8, 128])]; + tensor v_21_cast_fp16 = reshape(shape = var_2630, x = linear_95_cast_fp16)[name = string("v_21_cast_fp16")]; + tensor value_29_perm_0 = const()[name = string("value_29_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_10_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_10_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218259264)))]; + tensor var_2643_cast_fp16 = add(x = q_61_cast_fp16, y = encoder_layers_10_self_attn_pos_bias_u_to_fp16)[name = string("op_2643_cast_fp16")]; + tensor encoder_layers_10_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_10_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218261376)))]; + tensor var_2645_cast_fp16 = add(x = q_61_cast_fp16, y = encoder_layers_10_self_attn_pos_bias_v_to_fp16)[name = string("op_2645_cast_fp16")]; + tensor q_with_bias_v_21_perm_0 = const()[name = string("q_with_bias_v_21_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_267_transpose_x_0 = const()[name = string("x_267_transpose_x_0"), val = bool(false)]; + bool x_267_transpose_y_0 = const()[name = string("x_267_transpose_y_0"), val = bool(false)]; + tensor op_2647_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218263488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218463232))))[name = string("op_2647_to_fp16_quantized")]; + tensor q_with_bias_v_21_cast_fp16 = transpose(perm = q_with_bias_v_21_perm_0, x = var_2645_cast_fp16)[name = string("transpose_272")]; + tensor x_267_cast_fp16 = matmul(transpose_x = x_267_transpose_x_0, transpose_y = x_267_transpose_y_0, x = q_with_bias_v_21_cast_fp16, y = op_2647_to_fp16_quantized)[name = string("x_267_cast_fp16")]; + tensor x_269_pad_0 = const()[name = string("x_269_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_269_mode_0 = const()[name = string("x_269_mode_0"), val = string("constant")]; + fp16 const_209_to_fp16 = const()[name = string("const_209_to_fp16"), val = fp16(0x0p+0)]; + tensor x_269_cast_fp16 = pad(constant_val = const_209_to_fp16, mode = x_269_mode_0, pad = x_269_pad_0, x = x_267_cast_fp16)[name = string("x_269_cast_fp16")]; + tensor var_2655 = const()[name = string("op_2655"), val = tensor([1, 8, -1, 56])]; + tensor x_271_cast_fp16 = reshape(shape = var_2655, x = x_269_cast_fp16)[name = string("x_271_cast_fp16")]; + tensor var_2659_begin_0 = const()[name = string("op_2659_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_2659_end_0 = const()[name = string("op_2659_end_0"), val = tensor([1, 8, 196, 56])]; + tensor var_2659_end_mask_0 = const()[name = string("op_2659_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_2659_cast_fp16 = slice_by_index(begin = var_2659_begin_0, end = var_2659_end_0, end_mask = var_2659_end_mask_0, x = x_271_cast_fp16)[name = string("op_2659_cast_fp16")]; + tensor var_2660 = const()[name = string("op_2660"), val = tensor([1, 8, 56, 195])]; + tensor matrix_bd_41_cast_fp16 = reshape(shape = var_2660, x = var_2659_cast_fp16)[name = string("matrix_bd_41_cast_fp16")]; + bool matrix_ac_21_transpose_x_0 = const()[name = string("matrix_ac_21_transpose_x_0"), val = bool(false)]; + bool matrix_ac_21_transpose_y_0 = const()[name = string("matrix_ac_21_transpose_y_0"), val = bool(false)]; + tensor transpose_116_perm_0 = const()[name = string("transpose_116_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_117_perm_0 = const()[name = string("transpose_117_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_117 = transpose(perm = transpose_117_perm_0, x = k_41_cast_fp16)[name = string("transpose_270")]; + tensor transpose_116 = transpose(perm = transpose_116_perm_0, x = var_2643_cast_fp16)[name = string("transpose_271")]; + tensor matrix_ac_21_cast_fp16 = matmul(transpose_x = matrix_ac_21_transpose_x_0, transpose_y = matrix_ac_21_transpose_y_0, x = transpose_116, y = transpose_117)[name = string("matrix_ac_21_cast_fp16")]; + tensor matrix_bd_43_begin_0 = const()[name = string("matrix_bd_43_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_43_end_0 = const()[name = string("matrix_bd_43_end_0"), val = tensor([1, 8, 56, 98])]; + tensor matrix_bd_43_end_mask_0 = const()[name = string("matrix_bd_43_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_43_cast_fp16 = slice_by_index(begin = matrix_bd_43_begin_0, end = matrix_bd_43_end_0, end_mask = matrix_bd_43_end_mask_0, x = matrix_bd_41_cast_fp16)[name = string("matrix_bd_43_cast_fp16")]; + tensor var_2669_cast_fp16 = add(x = matrix_ac_21_cast_fp16, y = matrix_bd_43_cast_fp16)[name = string("op_2669_cast_fp16")]; + fp16 _inversed_scores_41_y_0_to_fp16 = const()[name = string("_inversed_scores_41_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_41_cast_fp16 = mul(x = var_2669_cast_fp16, y = _inversed_scores_41_y_0_to_fp16)[name = string("_inversed_scores_41_cast_fp16")]; + tensor scores_43_cast_fp16 = select(a = var_46_to_fp16, b = _inversed_scores_41_cast_fp16, cond = mask_11)[name = string("scores_43_cast_fp16")]; + tensor var_2675_cast_fp16 = softmax(axis = var_60, x = scores_43_cast_fp16)[name = string("op_2675_cast_fp16")]; + tensor input_561_cast_fp16 = select(a = var_45_to_fp16, b = var_2675_cast_fp16, cond = mask_11)[name = string("input_561_cast_fp16")]; + bool x_273_transpose_x_0 = const()[name = string("x_273_transpose_x_0"), val = bool(false)]; + bool x_273_transpose_y_0 = const()[name = string("x_273_transpose_y_0"), val = bool(false)]; + tensor value_29_cast_fp16 = transpose(perm = value_29_perm_0, x = v_21_cast_fp16)[name = string("transpose_269")]; + tensor x_273_cast_fp16 = matmul(transpose_x = x_273_transpose_x_0, transpose_y = x_273_transpose_y_0, x = input_561_cast_fp16, y = value_29_cast_fp16)[name = string("x_273_cast_fp16")]; + tensor var_2679_perm_0 = const()[name = string("op_2679_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2680 = const()[name = string("op_2680"), val = tensor([1, -1, 1024])]; + tensor var_2679_cast_fp16 = transpose(perm = var_2679_perm_0, x = x_273_cast_fp16)[name = string("transpose_268")]; + tensor input_563_cast_fp16 = reshape(shape = var_2680, x = var_2679_cast_fp16)[name = string("input_563_cast_fp16")]; + tensor encoder_layers_10_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218463744))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(219250240))))[name = string("encoder_layers_10_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_10_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_10_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(219250432)))]; + tensor linear_97_cast_fp16 = linear(bias = encoder_layers_10_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_10_self_attn_linear_out_weight_to_fp16_palettized, x = input_563_cast_fp16)[name = string("linear_97_cast_fp16")]; + tensor input_567_cast_fp16 = add(x = input_557_cast_fp16, y = linear_97_cast_fp16)[name = string("input_567_cast_fp16")]; + tensor x_277_axes_0 = const()[name = string("x_277_axes_0"), val = tensor([-1])]; + tensor encoder_layers_10_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_10_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(219252544)))]; + tensor encoder_layers_10_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_10_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(219254656)))]; + tensor x_277_cast_fp16 = layer_norm(axes = x_277_axes_0, beta = encoder_layers_10_norm_conv_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_10_norm_conv_weight_to_fp16, x = input_567_cast_fp16)[name = string("x_277_cast_fp16")]; + tensor input_569_perm_0 = const()[name = string("input_569_perm_0"), val = tensor([0, 2, 1])]; + string input_571_pad_type_0 = const()[name = string("input_571_pad_type_0"), val = string("valid")]; + tensor input_571_strides_0 = const()[name = string("input_571_strides_0"), val = tensor([1])]; + tensor input_571_pad_0 = const()[name = string("input_571_pad_0"), val = tensor([0, 0])]; + tensor input_571_dilations_0 = const()[name = string("input_571_dilations_0"), val = tensor([1])]; + int32 input_571_groups_0 = const()[name = string("input_571_groups_0"), val = int32(1)]; + tensor encoder_layers_10_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(219256768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(221353984))))[name = string("encoder_layers_10_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_569_cast_fp16 = transpose(perm = input_569_perm_0, x = x_277_cast_fp16)[name = string("transpose_267")]; + tensor input_571_cast_fp16 = conv(dilations = input_571_dilations_0, groups = input_571_groups_0, pad = input_571_pad_0, pad_type = input_571_pad_type_0, strides = input_571_strides_0, weight = encoder_layers_10_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_569_cast_fp16)[name = string("input_571_cast_fp16")]; + int32 x_279_split_num_splits_0 = const()[name = string("x_279_split_num_splits_0"), val = int32(2)]; + int32 x_279_split_axis_0 = const()[name = string("x_279_split_axis_0"), val = int32(1)]; + tensor x_279_split_cast_fp16_0, tensor x_279_split_cast_fp16_1 = split(axis = x_279_split_axis_0, num_splits = x_279_split_num_splits_0, x = input_571_cast_fp16)[name = string("x_279_split_cast_fp16")]; + tensor x_279_split_1_sigmoid_cast_fp16 = sigmoid(x = x_279_split_cast_fp16_1)[name = string("x_279_split_1_sigmoid_cast_fp16")]; + tensor x_279_cast_fp16 = mul(x = x_279_split_cast_fp16_0, y = x_279_split_1_sigmoid_cast_fp16)[name = string("x_279_cast_fp16")]; + tensor input_573_cast_fp16 = select(a = var_45_to_fp16, b = x_279_cast_fp16, cond = var_576)[name = string("input_573_cast_fp16")]; + bool new_x_43_interleave_0 = const()[name = string("new_x_43_interleave_0"), val = bool(false)]; + tensor new_x_43_cast_fp16 = concat(axis = var_60, interleave = new_x_43_interleave_0, values = (cache_43_cast_fp16, input_573_cast_fp16))[name = string("new_x_43_cast_fp16")]; + tensor var_2719_begin_0 = const()[name = string("op_2719_begin_0"), val = tensor([0, 0, 56])]; + tensor var_2719_end_0 = const()[name = string("op_2719_end_0"), val = tensor([1, 1024, 64])]; + tensor var_2719_end_mask_0 = const()[name = string("op_2719_end_mask_0"), val = tensor([true, true, true])]; + tensor var_2719_cast_fp16 = slice_by_index(begin = var_2719_begin_0, end = var_2719_end_0, end_mask = var_2719_end_mask_0, x = new_x_43_cast_fp16)[name = string("op_2719_cast_fp16")]; + string x_281_pad_type_0 = const()[name = string("x_281_pad_type_0"), val = string("valid")]; + int32 x_281_groups_0 = const()[name = string("x_281_groups_0"), val = int32(1024)]; + tensor x_281_strides_0 = const()[name = string("x_281_strides_0"), val = tensor([1])]; + tensor x_281_pad_0 = const()[name = string("x_281_pad_0"), val = tensor([0, 0])]; + tensor x_281_dilations_0 = const()[name = string("x_281_dilations_0"), val = tensor([1])]; + tensor encoder_layers_10_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(221358144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(221367424))))[name = string("encoder_layers_10_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_281_cast_fp16 = conv(dilations = x_281_dilations_0, groups = x_281_groups_0, pad = x_281_pad_0, pad_type = x_281_pad_type_0, strides = x_281_strides_0, weight = encoder_layers_10_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_43_cast_fp16)[name = string("x_281_cast_fp16")]; + tensor input_575_perm_0 = const()[name = string("input_575_perm_0"), val = tensor([0, 2, 1])]; + tensor x_283_axes_0 = const()[name = string("x_283_axes_0"), val = tensor([-1])]; + tensor encoder_layers_10_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_10_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(221369536)))]; + tensor encoder_layers_10_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_10_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(221371648)))]; + tensor input_575_cast_fp16 = transpose(perm = input_575_perm_0, x = x_281_cast_fp16)[name = string("transpose_266")]; + tensor x_283_cast_fp16 = layer_norm(axes = x_283_axes_0, beta = encoder_layers_10_conv_batch_norm_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_10_conv_batch_norm_weight_to_fp16, x = input_575_cast_fp16)[name = string("x_283_cast_fp16")]; + tensor input_577_perm_0 = const()[name = string("input_577_perm_0"), val = tensor([0, 2, 1])]; + tensor input_577_cast_fp16 = transpose(perm = input_577_perm_0, x = x_283_cast_fp16)[name = string("transpose_265")]; + tensor input_579_cast_fp16 = silu(x = input_577_cast_fp16)[name = string("input_579_cast_fp16")]; + string x_285_pad_type_0 = const()[name = string("x_285_pad_type_0"), val = string("valid")]; + tensor x_285_strides_0 = const()[name = string("x_285_strides_0"), val = tensor([1])]; + tensor x_285_pad_0 = const()[name = string("x_285_pad_0"), val = tensor([0, 0])]; + tensor x_285_dilations_0 = const()[name = string("x_285_dilations_0"), val = tensor([1])]; + int32 x_285_groups_0 = const()[name = string("x_285_groups_0"), val = int32(1)]; + tensor encoder_layers_10_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(221373760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(222422400))))[name = string("encoder_layers_10_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_285_cast_fp16 = conv(dilations = x_285_dilations_0, groups = x_285_groups_0, pad = x_285_pad_0, pad_type = x_285_pad_type_0, strides = x_285_strides_0, weight = encoder_layers_10_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_579_cast_fp16)[name = string("x_285_cast_fp16")]; + tensor input_581_perm_0 = const()[name = string("input_581_perm_0"), val = tensor([0, 2, 1])]; + tensor input_581_cast_fp16 = transpose(perm = input_581_perm_0, x = x_285_cast_fp16)[name = string("transpose_264")]; + tensor input_583_cast_fp16 = add(x = input_567_cast_fp16, y = input_581_cast_fp16)[name = string("input_583_cast_fp16")]; + tensor input_585_axes_0 = const()[name = string("input_585_axes_0"), val = tensor([-1])]; + tensor encoder_layers_10_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_10_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(222424512)))]; + tensor encoder_layers_10_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_10_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(222426624)))]; + tensor input_585_cast_fp16 = layer_norm(axes = input_585_axes_0, beta = encoder_layers_10_norm_feed_forward2_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_10_norm_feed_forward2_weight_to_fp16, x = input_583_cast_fp16)[name = string("input_585_cast_fp16")]; + tensor encoder_layers_10_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(222428736))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(225574528))))[name = string("encoder_layers_10_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_10_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_10_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(225574720)))]; + tensor linear_98_cast_fp16 = linear(bias = encoder_layers_10_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_10_feed_forward2_linear1_weight_to_fp16_palettized, x = input_585_cast_fp16)[name = string("linear_98_cast_fp16")]; + tensor input_589_cast_fp16 = silu(x = linear_98_cast_fp16)[name = string("input_589_cast_fp16")]; + tensor encoder_layers_10_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(225582976))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(228728768))))[name = string("encoder_layers_10_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_10_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_10_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(228728960)))]; + tensor linear_99_cast_fp16 = linear(bias = encoder_layers_10_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_10_feed_forward2_linear2_weight_to_fp16_palettized, x = input_589_cast_fp16)[name = string("linear_99_cast_fp16")]; + fp16 var_2762_to_fp16 = const()[name = string("op_2762_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2763_cast_fp16 = mul(x = linear_99_cast_fp16, y = var_2762_to_fp16)[name = string("op_2763_cast_fp16")]; + tensor input_595_cast_fp16 = add(x = input_583_cast_fp16, y = var_2763_cast_fp16)[name = string("input_595_cast_fp16")]; + tensor input_597_axes_0 = const()[name = string("input_597_axes_0"), val = tensor([-1])]; + tensor encoder_layers_10_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_10_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(228731072)))]; + tensor encoder_layers_10_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_10_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(228733184)))]; + tensor input_597_cast_fp16 = layer_norm(axes = input_597_axes_0, beta = encoder_layers_10_norm_out_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_10_norm_out_weight_to_fp16, x = input_595_cast_fp16)[name = string("input_597_cast_fp16")]; + tensor cache_45_begin_0 = const()[name = string("cache_45_begin_0"), val = tensor([11, 0, 0, 0])]; + tensor cache_45_end_0 = const()[name = string("cache_45_end_0"), val = tensor([12, 1, 42, 1024])]; + tensor cache_45_end_mask_0 = const()[name = string("cache_45_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_45_squeeze_mask_0 = const()[name = string("cache_45_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_45_cast_fp16 = slice_by_index(begin = cache_45_begin_0, end = cache_45_end_0, end_mask = cache_45_end_mask_0, squeeze_mask = cache_45_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_45_cast_fp16")]; + tensor cache_47_begin_0 = const()[name = string("cache_47_begin_0"), val = tensor([11, 0, 0, 0])]; + tensor cache_47_end_0 = const()[name = string("cache_47_end_0"), val = tensor([12, 1, 1024, 8])]; + tensor cache_47_end_mask_0 = const()[name = string("cache_47_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_47_squeeze_mask_0 = const()[name = string("cache_47_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_47_cast_fp16 = slice_by_index(begin = cache_47_begin_0, end = cache_47_end_0, end_mask = cache_47_end_mask_0, squeeze_mask = cache_47_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_47_cast_fp16")]; + tensor input_599_axes_0 = const()[name = string("input_599_axes_0"), val = tensor([-1])]; + tensor encoder_layers_11_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_11_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(228735296)))]; + tensor encoder_layers_11_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_11_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(228737408)))]; + tensor input_599_cast_fp16 = layer_norm(axes = input_599_axes_0, beta = encoder_layers_11_norm_feed_forward1_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_11_norm_feed_forward1_weight_to_fp16, x = input_597_cast_fp16)[name = string("input_599_cast_fp16")]; + tensor encoder_layers_11_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(228739520))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231885312))))[name = string("encoder_layers_11_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_11_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_11_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231885504)))]; + tensor linear_100_cast_fp16 = linear(bias = encoder_layers_11_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_11_feed_forward1_linear1_weight_to_fp16_palettized, x = input_599_cast_fp16)[name = string("linear_100_cast_fp16")]; + tensor input_603_cast_fp16 = silu(x = linear_100_cast_fp16)[name = string("input_603_cast_fp16")]; + tensor encoder_layers_11_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231893760))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235039552))))[name = string("encoder_layers_11_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_11_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_11_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235039744)))]; + tensor linear_101_cast_fp16 = linear(bias = encoder_layers_11_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_11_feed_forward1_linear2_weight_to_fp16_palettized, x = input_603_cast_fp16)[name = string("linear_101_cast_fp16")]; + fp16 var_2799_to_fp16 = const()[name = string("op_2799_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2800_cast_fp16 = mul(x = linear_101_cast_fp16, y = var_2799_to_fp16)[name = string("op_2800_cast_fp16")]; + tensor input_609_cast_fp16 = add(x = input_597_cast_fp16, y = var_2800_cast_fp16)[name = string("input_609_cast_fp16")]; + tensor key_23_axes_0 = const()[name = string("key_23_axes_0"), val = tensor([-1])]; + tensor encoder_layers_11_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_11_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235041856)))]; + tensor encoder_layers_11_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_11_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235043968)))]; + tensor key_23_cast_fp16 = layer_norm(axes = key_23_axes_0, beta = encoder_layers_11_norm_self_att_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_11_norm_self_att_weight_to_fp16, x = input_609_cast_fp16)[name = string("key_23_cast_fp16")]; + bool input_611_interleave_0 = const()[name = string("input_611_interleave_0"), val = bool(false)]; + tensor input_611_cast_fp16 = concat(axis = var_69, interleave = input_611_interleave_0, values = (cache_45_cast_fp16, key_23_cast_fp16))[name = string("input_611_cast_fp16")]; + bool var_2828_interleave_0 = const()[name = string("op_2828_interleave_0"), val = bool(false)]; + tensor var_2828_cast_fp16 = concat(axis = var_69, interleave = var_2828_interleave_0, values = key_23_cast_fp16)[name = string("op_2828_cast_fp16")]; + tensor encoder_layers_11_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235046080))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235832576))))[name = string("encoder_layers_11_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_11_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_11_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235832768)))]; + tensor linear_102_cast_fp16 = linear(bias = encoder_layers_11_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_11_self_attn_linear_q_weight_to_fp16_palettized, x = key_23_cast_fp16)[name = string("linear_102_cast_fp16")]; + tensor var_2833 = const()[name = string("op_2833"), val = tensor([1, -1, 8, 128])]; + tensor q_67_cast_fp16 = reshape(shape = var_2833, x = linear_102_cast_fp16)[name = string("q_67_cast_fp16")]; + tensor encoder_layers_11_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235834880))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236621376))))[name = string("encoder_layers_11_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_11_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_11_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236621568)))]; + tensor linear_103_cast_fp16 = linear(bias = encoder_layers_11_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_11_self_attn_linear_k_weight_to_fp16_palettized, x = input_611_cast_fp16)[name = string("linear_103_cast_fp16")]; + tensor var_2838 = const()[name = string("op_2838"), val = tensor([1, -1, 8, 128])]; + tensor k_45_cast_fp16 = reshape(shape = var_2838, x = linear_103_cast_fp16)[name = string("k_45_cast_fp16")]; + tensor encoder_layers_11_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236623680))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(237410176))))[name = string("encoder_layers_11_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_11_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_11_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(237410368)))]; + tensor linear_104_cast_fp16 = linear(bias = encoder_layers_11_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_11_self_attn_linear_v_weight_to_fp16_palettized, x = input_611_cast_fp16)[name = string("linear_104_cast_fp16")]; + tensor var_2843 = const()[name = string("op_2843"), val = tensor([1, -1, 8, 128])]; + tensor v_23_cast_fp16 = reshape(shape = var_2843, x = linear_104_cast_fp16)[name = string("v_23_cast_fp16")]; + tensor value_31_perm_0 = const()[name = string("value_31_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_11_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_11_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(237412480)))]; + tensor var_2856_cast_fp16 = add(x = q_67_cast_fp16, y = encoder_layers_11_self_attn_pos_bias_u_to_fp16)[name = string("op_2856_cast_fp16")]; + tensor encoder_layers_11_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_11_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(237414592)))]; + tensor var_2858_cast_fp16 = add(x = q_67_cast_fp16, y = encoder_layers_11_self_attn_pos_bias_v_to_fp16)[name = string("op_2858_cast_fp16")]; + tensor q_with_bias_v_23_perm_0 = const()[name = string("q_with_bias_v_23_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_293_transpose_x_0 = const()[name = string("x_293_transpose_x_0"), val = bool(false)]; + bool x_293_transpose_y_0 = const()[name = string("x_293_transpose_y_0"), val = bool(false)]; + tensor op_2860_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(237416704))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(237616448))))[name = string("op_2860_to_fp16_quantized")]; + tensor q_with_bias_v_23_cast_fp16 = transpose(perm = q_with_bias_v_23_perm_0, x = var_2858_cast_fp16)[name = string("transpose_263")]; + tensor x_293_cast_fp16 = matmul(transpose_x = x_293_transpose_x_0, transpose_y = x_293_transpose_y_0, x = q_with_bias_v_23_cast_fp16, y = op_2860_to_fp16_quantized)[name = string("x_293_cast_fp16")]; + tensor x_295_pad_0 = const()[name = string("x_295_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_295_mode_0 = const()[name = string("x_295_mode_0"), val = string("constant")]; + fp16 const_222_to_fp16 = const()[name = string("const_222_to_fp16"), val = fp16(0x0p+0)]; + tensor x_295_cast_fp16 = pad(constant_val = const_222_to_fp16, mode = x_295_mode_0, pad = x_295_pad_0, x = x_293_cast_fp16)[name = string("x_295_cast_fp16")]; + tensor var_2868 = const()[name = string("op_2868"), val = tensor([1, 8, -1, 56])]; + tensor x_297_cast_fp16 = reshape(shape = var_2868, x = x_295_cast_fp16)[name = string("x_297_cast_fp16")]; + tensor var_2872_begin_0 = const()[name = string("op_2872_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_2872_end_0 = const()[name = string("op_2872_end_0"), val = tensor([1, 8, 196, 56])]; + tensor var_2872_end_mask_0 = const()[name = string("op_2872_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_2872_cast_fp16 = slice_by_index(begin = var_2872_begin_0, end = var_2872_end_0, end_mask = var_2872_end_mask_0, x = x_297_cast_fp16)[name = string("op_2872_cast_fp16")]; + tensor var_2873 = const()[name = string("op_2873"), val = tensor([1, 8, 56, 195])]; + tensor matrix_bd_45_cast_fp16 = reshape(shape = var_2873, x = var_2872_cast_fp16)[name = string("matrix_bd_45_cast_fp16")]; + bool matrix_ac_23_transpose_x_0 = const()[name = string("matrix_ac_23_transpose_x_0"), val = bool(false)]; + bool matrix_ac_23_transpose_y_0 = const()[name = string("matrix_ac_23_transpose_y_0"), val = bool(false)]; + tensor transpose_118_perm_0 = const()[name = string("transpose_118_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_119_perm_0 = const()[name = string("transpose_119_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_119 = transpose(perm = transpose_119_perm_0, x = k_45_cast_fp16)[name = string("transpose_261")]; + tensor transpose_118 = transpose(perm = transpose_118_perm_0, x = var_2856_cast_fp16)[name = string("transpose_262")]; + tensor matrix_ac_23_cast_fp16 = matmul(transpose_x = matrix_ac_23_transpose_x_0, transpose_y = matrix_ac_23_transpose_y_0, x = transpose_118, y = transpose_119)[name = string("matrix_ac_23_cast_fp16")]; + tensor matrix_bd_47_begin_0 = const()[name = string("matrix_bd_47_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_47_end_0 = const()[name = string("matrix_bd_47_end_0"), val = tensor([1, 8, 56, 98])]; + tensor matrix_bd_47_end_mask_0 = const()[name = string("matrix_bd_47_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_47_cast_fp16 = slice_by_index(begin = matrix_bd_47_begin_0, end = matrix_bd_47_end_0, end_mask = matrix_bd_47_end_mask_0, x = matrix_bd_45_cast_fp16)[name = string("matrix_bd_47_cast_fp16")]; + tensor var_2882_cast_fp16 = add(x = matrix_ac_23_cast_fp16, y = matrix_bd_47_cast_fp16)[name = string("op_2882_cast_fp16")]; + fp16 _inversed_scores_45_y_0_to_fp16 = const()[name = string("_inversed_scores_45_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_45_cast_fp16 = mul(x = var_2882_cast_fp16, y = _inversed_scores_45_y_0_to_fp16)[name = string("_inversed_scores_45_cast_fp16")]; + tensor scores_47_cast_fp16 = select(a = var_46_to_fp16, b = _inversed_scores_45_cast_fp16, cond = mask_11)[name = string("scores_47_cast_fp16")]; + tensor var_2888_cast_fp16 = softmax(axis = var_60, x = scores_47_cast_fp16)[name = string("op_2888_cast_fp16")]; + tensor input_613_cast_fp16 = select(a = var_45_to_fp16, b = var_2888_cast_fp16, cond = mask_11)[name = string("input_613_cast_fp16")]; + bool x_299_transpose_x_0 = const()[name = string("x_299_transpose_x_0"), val = bool(false)]; + bool x_299_transpose_y_0 = const()[name = string("x_299_transpose_y_0"), val = bool(false)]; + tensor value_31_cast_fp16 = transpose(perm = value_31_perm_0, x = v_23_cast_fp16)[name = string("transpose_260")]; + tensor x_299_cast_fp16 = matmul(transpose_x = x_299_transpose_x_0, transpose_y = x_299_transpose_y_0, x = input_613_cast_fp16, y = value_31_cast_fp16)[name = string("x_299_cast_fp16")]; + tensor var_2892_perm_0 = const()[name = string("op_2892_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2893 = const()[name = string("op_2893"), val = tensor([1, -1, 1024])]; + tensor var_2892_cast_fp16 = transpose(perm = var_2892_perm_0, x = x_299_cast_fp16)[name = string("transpose_259")]; + tensor input_615_cast_fp16 = reshape(shape = var_2893, x = var_2892_cast_fp16)[name = string("input_615_cast_fp16")]; + tensor encoder_layers_11_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(237616960))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(238403456))))[name = string("encoder_layers_11_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_11_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_11_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(238403648)))]; + tensor linear_106_cast_fp16 = linear(bias = encoder_layers_11_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_11_self_attn_linear_out_weight_to_fp16_palettized, x = input_615_cast_fp16)[name = string("linear_106_cast_fp16")]; + tensor input_619_cast_fp16 = add(x = input_609_cast_fp16, y = linear_106_cast_fp16)[name = string("input_619_cast_fp16")]; + tensor x_303_axes_0 = const()[name = string("x_303_axes_0"), val = tensor([-1])]; + tensor encoder_layers_11_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_11_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(238405760)))]; + tensor encoder_layers_11_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_11_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(238407872)))]; + tensor x_303_cast_fp16 = layer_norm(axes = x_303_axes_0, beta = encoder_layers_11_norm_conv_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_11_norm_conv_weight_to_fp16, x = input_619_cast_fp16)[name = string("x_303_cast_fp16")]; + tensor input_621_perm_0 = const()[name = string("input_621_perm_0"), val = tensor([0, 2, 1])]; + string input_623_pad_type_0 = const()[name = string("input_623_pad_type_0"), val = string("valid")]; + tensor input_623_strides_0 = const()[name = string("input_623_strides_0"), val = tensor([1])]; + tensor input_623_pad_0 = const()[name = string("input_623_pad_0"), val = tensor([0, 0])]; + tensor input_623_dilations_0 = const()[name = string("input_623_dilations_0"), val = tensor([1])]; + int32 input_623_groups_0 = const()[name = string("input_623_groups_0"), val = int32(1)]; + tensor encoder_layers_11_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(238409984))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240507200))))[name = string("encoder_layers_11_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_621_cast_fp16 = transpose(perm = input_621_perm_0, x = x_303_cast_fp16)[name = string("transpose_258")]; + tensor input_623_cast_fp16 = conv(dilations = input_623_dilations_0, groups = input_623_groups_0, pad = input_623_pad_0, pad_type = input_623_pad_type_0, strides = input_623_strides_0, weight = encoder_layers_11_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_621_cast_fp16)[name = string("input_623_cast_fp16")]; + int32 x_305_split_num_splits_0 = const()[name = string("x_305_split_num_splits_0"), val = int32(2)]; + int32 x_305_split_axis_0 = const()[name = string("x_305_split_axis_0"), val = int32(1)]; + tensor x_305_split_cast_fp16_0, tensor x_305_split_cast_fp16_1 = split(axis = x_305_split_axis_0, num_splits = x_305_split_num_splits_0, x = input_623_cast_fp16)[name = string("x_305_split_cast_fp16")]; + tensor x_305_split_1_sigmoid_cast_fp16 = sigmoid(x = x_305_split_cast_fp16_1)[name = string("x_305_split_1_sigmoid_cast_fp16")]; + tensor x_305_cast_fp16 = mul(x = x_305_split_cast_fp16_0, y = x_305_split_1_sigmoid_cast_fp16)[name = string("x_305_cast_fp16")]; + tensor input_625_cast_fp16 = select(a = var_45_to_fp16, b = x_305_cast_fp16, cond = var_576)[name = string("input_625_cast_fp16")]; + bool new_x_47_interleave_0 = const()[name = string("new_x_47_interleave_0"), val = bool(false)]; + tensor new_x_47_cast_fp16 = concat(axis = var_60, interleave = new_x_47_interleave_0, values = (cache_47_cast_fp16, input_625_cast_fp16))[name = string("new_x_47_cast_fp16")]; + tensor var_2932_begin_0 = const()[name = string("op_2932_begin_0"), val = tensor([0, 0, 56])]; + tensor var_2932_end_0 = const()[name = string("op_2932_end_0"), val = tensor([1, 1024, 64])]; + tensor var_2932_end_mask_0 = const()[name = string("op_2932_end_mask_0"), val = tensor([true, true, true])]; + tensor var_2932_cast_fp16 = slice_by_index(begin = var_2932_begin_0, end = var_2932_end_0, end_mask = var_2932_end_mask_0, x = new_x_47_cast_fp16)[name = string("op_2932_cast_fp16")]; + string x_307_pad_type_0 = const()[name = string("x_307_pad_type_0"), val = string("valid")]; + int32 x_307_groups_0 = const()[name = string("x_307_groups_0"), val = int32(1024)]; + tensor x_307_strides_0 = const()[name = string("x_307_strides_0"), val = tensor([1])]; + tensor x_307_pad_0 = const()[name = string("x_307_pad_0"), val = tensor([0, 0])]; + tensor x_307_dilations_0 = const()[name = string("x_307_dilations_0"), val = tensor([1])]; + tensor encoder_layers_11_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240511360))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240520640))))[name = string("encoder_layers_11_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_307_cast_fp16 = conv(dilations = x_307_dilations_0, groups = x_307_groups_0, pad = x_307_pad_0, pad_type = x_307_pad_type_0, strides = x_307_strides_0, weight = encoder_layers_11_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_47_cast_fp16)[name = string("x_307_cast_fp16")]; + tensor input_627_perm_0 = const()[name = string("input_627_perm_0"), val = tensor([0, 2, 1])]; + tensor x_309_axes_0 = const()[name = string("x_309_axes_0"), val = tensor([-1])]; + tensor encoder_layers_11_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_11_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240522752)))]; + tensor encoder_layers_11_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_11_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240524864)))]; + tensor input_627_cast_fp16 = transpose(perm = input_627_perm_0, x = x_307_cast_fp16)[name = string("transpose_257")]; + tensor x_309_cast_fp16 = layer_norm(axes = x_309_axes_0, beta = encoder_layers_11_conv_batch_norm_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_11_conv_batch_norm_weight_to_fp16, x = input_627_cast_fp16)[name = string("x_309_cast_fp16")]; + tensor input_629_perm_0 = const()[name = string("input_629_perm_0"), val = tensor([0, 2, 1])]; + tensor input_629_cast_fp16 = transpose(perm = input_629_perm_0, x = x_309_cast_fp16)[name = string("transpose_256")]; + tensor input_631_cast_fp16 = silu(x = input_629_cast_fp16)[name = string("input_631_cast_fp16")]; + string x_311_pad_type_0 = const()[name = string("x_311_pad_type_0"), val = string("valid")]; + tensor x_311_strides_0 = const()[name = string("x_311_strides_0"), val = tensor([1])]; + tensor x_311_pad_0 = const()[name = string("x_311_pad_0"), val = tensor([0, 0])]; + tensor x_311_dilations_0 = const()[name = string("x_311_dilations_0"), val = tensor([1])]; + int32 x_311_groups_0 = const()[name = string("x_311_groups_0"), val = int32(1)]; + tensor encoder_layers_11_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240526976))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(241575616))))[name = string("encoder_layers_11_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_311_cast_fp16 = conv(dilations = x_311_dilations_0, groups = x_311_groups_0, pad = x_311_pad_0, pad_type = x_311_pad_type_0, strides = x_311_strides_0, weight = encoder_layers_11_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_631_cast_fp16)[name = string("x_311_cast_fp16")]; + tensor input_633_perm_0 = const()[name = string("input_633_perm_0"), val = tensor([0, 2, 1])]; + tensor input_633_cast_fp16 = transpose(perm = input_633_perm_0, x = x_311_cast_fp16)[name = string("transpose_255")]; + tensor input_635_cast_fp16 = add(x = input_619_cast_fp16, y = input_633_cast_fp16)[name = string("input_635_cast_fp16")]; + tensor input_637_axes_0 = const()[name = string("input_637_axes_0"), val = tensor([-1])]; + tensor encoder_layers_11_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_11_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(241577728)))]; + tensor encoder_layers_11_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_11_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(241579840)))]; + tensor input_637_cast_fp16 = layer_norm(axes = input_637_axes_0, beta = encoder_layers_11_norm_feed_forward2_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_11_norm_feed_forward2_weight_to_fp16, x = input_635_cast_fp16)[name = string("input_637_cast_fp16")]; + tensor encoder_layers_11_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(241581952))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(244727744))))[name = string("encoder_layers_11_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_11_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_11_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(244727936)))]; + tensor linear_107_cast_fp16 = linear(bias = encoder_layers_11_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_11_feed_forward2_linear1_weight_to_fp16_palettized, x = input_637_cast_fp16)[name = string("linear_107_cast_fp16")]; + tensor input_641_cast_fp16 = silu(x = linear_107_cast_fp16)[name = string("input_641_cast_fp16")]; + tensor encoder_layers_11_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(244736192))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247881984))))[name = string("encoder_layers_11_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_11_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_11_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247882176)))]; + tensor linear_108_cast_fp16 = linear(bias = encoder_layers_11_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_11_feed_forward2_linear2_weight_to_fp16_palettized, x = input_641_cast_fp16)[name = string("linear_108_cast_fp16")]; + fp16 var_2975_to_fp16 = const()[name = string("op_2975_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2976_cast_fp16 = mul(x = linear_108_cast_fp16, y = var_2975_to_fp16)[name = string("op_2976_cast_fp16")]; + tensor input_647_cast_fp16 = add(x = input_635_cast_fp16, y = var_2976_cast_fp16)[name = string("input_647_cast_fp16")]; + tensor input_649_axes_0 = const()[name = string("input_649_axes_0"), val = tensor([-1])]; + tensor encoder_layers_11_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_11_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247884288)))]; + tensor encoder_layers_11_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_11_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247886400)))]; + tensor input_649_cast_fp16 = layer_norm(axes = input_649_axes_0, beta = encoder_layers_11_norm_out_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_11_norm_out_weight_to_fp16, x = input_647_cast_fp16)[name = string("input_649_cast_fp16")]; + tensor cache_49_begin_0 = const()[name = string("cache_49_begin_0"), val = tensor([12, 0, 0, 0])]; + tensor cache_49_end_0 = const()[name = string("cache_49_end_0"), val = tensor([13, 1, 42, 1024])]; + tensor cache_49_end_mask_0 = const()[name = string("cache_49_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_49_squeeze_mask_0 = const()[name = string("cache_49_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_49_cast_fp16 = slice_by_index(begin = cache_49_begin_0, end = cache_49_end_0, end_mask = cache_49_end_mask_0, squeeze_mask = cache_49_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_49_cast_fp16")]; + tensor cache_51_begin_0 = const()[name = string("cache_51_begin_0"), val = tensor([12, 0, 0, 0])]; + tensor cache_51_end_0 = const()[name = string("cache_51_end_0"), val = tensor([13, 1, 1024, 8])]; + tensor cache_51_end_mask_0 = const()[name = string("cache_51_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_51_squeeze_mask_0 = const()[name = string("cache_51_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_51_cast_fp16 = slice_by_index(begin = cache_51_begin_0, end = cache_51_end_0, end_mask = cache_51_end_mask_0, squeeze_mask = cache_51_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_51_cast_fp16")]; + tensor input_651_axes_0 = const()[name = string("input_651_axes_0"), val = tensor([-1])]; + tensor encoder_layers_12_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_12_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247888512)))]; + tensor encoder_layers_12_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_12_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247890624)))]; + tensor input_651_cast_fp16 = layer_norm(axes = input_651_axes_0, beta = encoder_layers_12_norm_feed_forward1_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_12_norm_feed_forward1_weight_to_fp16, x = input_649_cast_fp16)[name = string("input_651_cast_fp16")]; + tensor encoder_layers_12_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247892736))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(251038528))))[name = string("encoder_layers_12_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_12_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_12_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(251038720)))]; + tensor linear_109_cast_fp16 = linear(bias = encoder_layers_12_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_12_feed_forward1_linear1_weight_to_fp16_palettized, x = input_651_cast_fp16)[name = string("linear_109_cast_fp16")]; + tensor input_655_cast_fp16 = silu(x = linear_109_cast_fp16)[name = string("input_655_cast_fp16")]; + tensor encoder_layers_12_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(251046976))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(254192768))))[name = string("encoder_layers_12_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_12_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_12_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(254192960)))]; + tensor linear_110_cast_fp16 = linear(bias = encoder_layers_12_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_12_feed_forward1_linear2_weight_to_fp16_palettized, x = input_655_cast_fp16)[name = string("linear_110_cast_fp16")]; + fp16 var_3012_to_fp16 = const()[name = string("op_3012_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3013_cast_fp16 = mul(x = linear_110_cast_fp16, y = var_3012_to_fp16)[name = string("op_3013_cast_fp16")]; + tensor input_661_cast_fp16 = add(x = input_649_cast_fp16, y = var_3013_cast_fp16)[name = string("input_661_cast_fp16")]; + tensor key_25_axes_0 = const()[name = string("key_25_axes_0"), val = tensor([-1])]; + tensor encoder_layers_12_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_12_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(254195072)))]; + tensor encoder_layers_12_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_12_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(254197184)))]; + tensor key_25_cast_fp16 = layer_norm(axes = key_25_axes_0, beta = encoder_layers_12_norm_self_att_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_12_norm_self_att_weight_to_fp16, x = input_661_cast_fp16)[name = string("key_25_cast_fp16")]; + bool input_663_interleave_0 = const()[name = string("input_663_interleave_0"), val = bool(false)]; + tensor input_663_cast_fp16 = concat(axis = var_69, interleave = input_663_interleave_0, values = (cache_49_cast_fp16, key_25_cast_fp16))[name = string("input_663_cast_fp16")]; + bool var_3041_interleave_0 = const()[name = string("op_3041_interleave_0"), val = bool(false)]; + tensor var_3041_cast_fp16 = concat(axis = var_69, interleave = var_3041_interleave_0, values = key_25_cast_fp16)[name = string("op_3041_cast_fp16")]; + tensor encoder_layers_12_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(254199296))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(254985792))))[name = string("encoder_layers_12_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_12_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_12_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(254985984)))]; + tensor linear_111_cast_fp16 = linear(bias = encoder_layers_12_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_12_self_attn_linear_q_weight_to_fp16_palettized, x = key_25_cast_fp16)[name = string("linear_111_cast_fp16")]; + tensor var_3046 = const()[name = string("op_3046"), val = tensor([1, -1, 8, 128])]; + tensor q_73_cast_fp16 = reshape(shape = var_3046, x = linear_111_cast_fp16)[name = string("q_73_cast_fp16")]; + tensor encoder_layers_12_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(254988096))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(255774592))))[name = string("encoder_layers_12_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_12_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_12_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(255774784)))]; + tensor linear_112_cast_fp16 = linear(bias = encoder_layers_12_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_12_self_attn_linear_k_weight_to_fp16_palettized, x = input_663_cast_fp16)[name = string("linear_112_cast_fp16")]; + tensor var_3051 = const()[name = string("op_3051"), val = tensor([1, -1, 8, 128])]; + tensor k_49_cast_fp16 = reshape(shape = var_3051, x = linear_112_cast_fp16)[name = string("k_49_cast_fp16")]; + tensor encoder_layers_12_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(255776896))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(256563392))))[name = string("encoder_layers_12_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_12_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_12_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(256563584)))]; + tensor linear_113_cast_fp16 = linear(bias = encoder_layers_12_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_12_self_attn_linear_v_weight_to_fp16_palettized, x = input_663_cast_fp16)[name = string("linear_113_cast_fp16")]; + tensor var_3056 = const()[name = string("op_3056"), val = tensor([1, -1, 8, 128])]; + tensor v_25_cast_fp16 = reshape(shape = var_3056, x = linear_113_cast_fp16)[name = string("v_25_cast_fp16")]; + tensor value_33_perm_0 = const()[name = string("value_33_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_12_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_12_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(256565696)))]; + tensor var_3069_cast_fp16 = add(x = q_73_cast_fp16, y = encoder_layers_12_self_attn_pos_bias_u_to_fp16)[name = string("op_3069_cast_fp16")]; + tensor encoder_layers_12_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_12_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(256567808)))]; + tensor var_3071_cast_fp16 = add(x = q_73_cast_fp16, y = encoder_layers_12_self_attn_pos_bias_v_to_fp16)[name = string("op_3071_cast_fp16")]; + tensor q_with_bias_v_25_perm_0 = const()[name = string("q_with_bias_v_25_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_319_transpose_x_0 = const()[name = string("x_319_transpose_x_0"), val = bool(false)]; + bool x_319_transpose_y_0 = const()[name = string("x_319_transpose_y_0"), val = bool(false)]; + tensor op_3073_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(256569920))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(256769664))))[name = string("op_3073_to_fp16_quantized")]; + tensor q_with_bias_v_25_cast_fp16 = transpose(perm = q_with_bias_v_25_perm_0, x = var_3071_cast_fp16)[name = string("transpose_254")]; + tensor x_319_cast_fp16 = matmul(transpose_x = x_319_transpose_x_0, transpose_y = x_319_transpose_y_0, x = q_with_bias_v_25_cast_fp16, y = op_3073_to_fp16_quantized)[name = string("x_319_cast_fp16")]; + tensor x_321_pad_0 = const()[name = string("x_321_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_321_mode_0 = const()[name = string("x_321_mode_0"), val = string("constant")]; + fp16 const_235_to_fp16 = const()[name = string("const_235_to_fp16"), val = fp16(0x0p+0)]; + tensor x_321_cast_fp16 = pad(constant_val = const_235_to_fp16, mode = x_321_mode_0, pad = x_321_pad_0, x = x_319_cast_fp16)[name = string("x_321_cast_fp16")]; + tensor var_3081 = const()[name = string("op_3081"), val = tensor([1, 8, -1, 56])]; + tensor x_323_cast_fp16 = reshape(shape = var_3081, x = x_321_cast_fp16)[name = string("x_323_cast_fp16")]; + tensor var_3085_begin_0 = const()[name = string("op_3085_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_3085_end_0 = const()[name = string("op_3085_end_0"), val = tensor([1, 8, 196, 56])]; + tensor var_3085_end_mask_0 = const()[name = string("op_3085_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_3085_cast_fp16 = slice_by_index(begin = var_3085_begin_0, end = var_3085_end_0, end_mask = var_3085_end_mask_0, x = x_323_cast_fp16)[name = string("op_3085_cast_fp16")]; + tensor var_3086 = const()[name = string("op_3086"), val = tensor([1, 8, 56, 195])]; + tensor matrix_bd_49_cast_fp16 = reshape(shape = var_3086, x = var_3085_cast_fp16)[name = string("matrix_bd_49_cast_fp16")]; + bool matrix_ac_25_transpose_x_0 = const()[name = string("matrix_ac_25_transpose_x_0"), val = bool(false)]; + bool matrix_ac_25_transpose_y_0 = const()[name = string("matrix_ac_25_transpose_y_0"), val = bool(false)]; + tensor transpose_120_perm_0 = const()[name = string("transpose_120_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_121_perm_0 = const()[name = string("transpose_121_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_121 = transpose(perm = transpose_121_perm_0, x = k_49_cast_fp16)[name = string("transpose_252")]; + tensor transpose_120 = transpose(perm = transpose_120_perm_0, x = var_3069_cast_fp16)[name = string("transpose_253")]; + tensor matrix_ac_25_cast_fp16 = matmul(transpose_x = matrix_ac_25_transpose_x_0, transpose_y = matrix_ac_25_transpose_y_0, x = transpose_120, y = transpose_121)[name = string("matrix_ac_25_cast_fp16")]; + tensor matrix_bd_51_begin_0 = const()[name = string("matrix_bd_51_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_51_end_0 = const()[name = string("matrix_bd_51_end_0"), val = tensor([1, 8, 56, 98])]; + tensor matrix_bd_51_end_mask_0 = const()[name = string("matrix_bd_51_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_51_cast_fp16 = slice_by_index(begin = matrix_bd_51_begin_0, end = matrix_bd_51_end_0, end_mask = matrix_bd_51_end_mask_0, x = matrix_bd_49_cast_fp16)[name = string("matrix_bd_51_cast_fp16")]; + tensor var_3095_cast_fp16 = add(x = matrix_ac_25_cast_fp16, y = matrix_bd_51_cast_fp16)[name = string("op_3095_cast_fp16")]; + fp16 _inversed_scores_49_y_0_to_fp16 = const()[name = string("_inversed_scores_49_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_49_cast_fp16 = mul(x = var_3095_cast_fp16, y = _inversed_scores_49_y_0_to_fp16)[name = string("_inversed_scores_49_cast_fp16")]; + tensor scores_51_cast_fp16 = select(a = var_46_to_fp16, b = _inversed_scores_49_cast_fp16, cond = mask_11)[name = string("scores_51_cast_fp16")]; + tensor var_3101_cast_fp16 = softmax(axis = var_60, x = scores_51_cast_fp16)[name = string("op_3101_cast_fp16")]; + tensor input_665_cast_fp16 = select(a = var_45_to_fp16, b = var_3101_cast_fp16, cond = mask_11)[name = string("input_665_cast_fp16")]; + bool x_325_transpose_x_0 = const()[name = string("x_325_transpose_x_0"), val = bool(false)]; + bool x_325_transpose_y_0 = const()[name = string("x_325_transpose_y_0"), val = bool(false)]; + tensor value_33_cast_fp16 = transpose(perm = value_33_perm_0, x = v_25_cast_fp16)[name = string("transpose_251")]; + tensor x_325_cast_fp16 = matmul(transpose_x = x_325_transpose_x_0, transpose_y = x_325_transpose_y_0, x = input_665_cast_fp16, y = value_33_cast_fp16)[name = string("x_325_cast_fp16")]; + tensor var_3105_perm_0 = const()[name = string("op_3105_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3106 = const()[name = string("op_3106"), val = tensor([1, -1, 1024])]; + tensor var_3105_cast_fp16 = transpose(perm = var_3105_perm_0, x = x_325_cast_fp16)[name = string("transpose_250")]; + tensor input_667_cast_fp16 = reshape(shape = var_3106, x = var_3105_cast_fp16)[name = string("input_667_cast_fp16")]; + tensor encoder_layers_12_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(256770176))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(257556672))))[name = string("encoder_layers_12_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_12_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_12_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(257556864)))]; + tensor linear_115_cast_fp16 = linear(bias = encoder_layers_12_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_12_self_attn_linear_out_weight_to_fp16_palettized, x = input_667_cast_fp16)[name = string("linear_115_cast_fp16")]; + tensor input_671_cast_fp16 = add(x = input_661_cast_fp16, y = linear_115_cast_fp16)[name = string("input_671_cast_fp16")]; + tensor x_329_axes_0 = const()[name = string("x_329_axes_0"), val = tensor([-1])]; + tensor encoder_layers_12_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_12_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(257558976)))]; + tensor encoder_layers_12_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_12_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(257561088)))]; + tensor x_329_cast_fp16 = layer_norm(axes = x_329_axes_0, beta = encoder_layers_12_norm_conv_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_12_norm_conv_weight_to_fp16, x = input_671_cast_fp16)[name = string("x_329_cast_fp16")]; + tensor input_673_perm_0 = const()[name = string("input_673_perm_0"), val = tensor([0, 2, 1])]; + string input_675_pad_type_0 = const()[name = string("input_675_pad_type_0"), val = string("valid")]; + tensor input_675_strides_0 = const()[name = string("input_675_strides_0"), val = tensor([1])]; + tensor input_675_pad_0 = const()[name = string("input_675_pad_0"), val = tensor([0, 0])]; + tensor input_675_dilations_0 = const()[name = string("input_675_dilations_0"), val = tensor([1])]; + int32 input_675_groups_0 = const()[name = string("input_675_groups_0"), val = int32(1)]; + tensor encoder_layers_12_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(257563200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259660416))))[name = string("encoder_layers_12_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_673_cast_fp16 = transpose(perm = input_673_perm_0, x = x_329_cast_fp16)[name = string("transpose_249")]; + tensor input_675_cast_fp16 = conv(dilations = input_675_dilations_0, groups = input_675_groups_0, pad = input_675_pad_0, pad_type = input_675_pad_type_0, strides = input_675_strides_0, weight = encoder_layers_12_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_673_cast_fp16)[name = string("input_675_cast_fp16")]; + int32 x_331_split_num_splits_0 = const()[name = string("x_331_split_num_splits_0"), val = int32(2)]; + int32 x_331_split_axis_0 = const()[name = string("x_331_split_axis_0"), val = int32(1)]; + tensor x_331_split_cast_fp16_0, tensor x_331_split_cast_fp16_1 = split(axis = x_331_split_axis_0, num_splits = x_331_split_num_splits_0, x = input_675_cast_fp16)[name = string("x_331_split_cast_fp16")]; + tensor x_331_split_1_sigmoid_cast_fp16 = sigmoid(x = x_331_split_cast_fp16_1)[name = string("x_331_split_1_sigmoid_cast_fp16")]; + tensor x_331_cast_fp16 = mul(x = x_331_split_cast_fp16_0, y = x_331_split_1_sigmoid_cast_fp16)[name = string("x_331_cast_fp16")]; + tensor input_677_cast_fp16 = select(a = var_45_to_fp16, b = x_331_cast_fp16, cond = var_576)[name = string("input_677_cast_fp16")]; + bool new_x_51_interleave_0 = const()[name = string("new_x_51_interleave_0"), val = bool(false)]; + tensor new_x_51_cast_fp16 = concat(axis = var_60, interleave = new_x_51_interleave_0, values = (cache_51_cast_fp16, input_677_cast_fp16))[name = string("new_x_51_cast_fp16")]; + tensor var_3145_begin_0 = const()[name = string("op_3145_begin_0"), val = tensor([0, 0, 56])]; + tensor var_3145_end_0 = const()[name = string("op_3145_end_0"), val = tensor([1, 1024, 64])]; + tensor var_3145_end_mask_0 = const()[name = string("op_3145_end_mask_0"), val = tensor([true, true, true])]; + tensor var_3145_cast_fp16 = slice_by_index(begin = var_3145_begin_0, end = var_3145_end_0, end_mask = var_3145_end_mask_0, x = new_x_51_cast_fp16)[name = string("op_3145_cast_fp16")]; + string x_333_pad_type_0 = const()[name = string("x_333_pad_type_0"), val = string("valid")]; + int32 x_333_groups_0 = const()[name = string("x_333_groups_0"), val = int32(1024)]; + tensor x_333_strides_0 = const()[name = string("x_333_strides_0"), val = tensor([1])]; + tensor x_333_pad_0 = const()[name = string("x_333_pad_0"), val = tensor([0, 0])]; + tensor x_333_dilations_0 = const()[name = string("x_333_dilations_0"), val = tensor([1])]; + tensor encoder_layers_12_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259664576))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259673856))))[name = string("encoder_layers_12_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_333_cast_fp16 = conv(dilations = x_333_dilations_0, groups = x_333_groups_0, pad = x_333_pad_0, pad_type = x_333_pad_type_0, strides = x_333_strides_0, weight = encoder_layers_12_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_51_cast_fp16)[name = string("x_333_cast_fp16")]; + tensor input_679_perm_0 = const()[name = string("input_679_perm_0"), val = tensor([0, 2, 1])]; + tensor x_335_axes_0 = const()[name = string("x_335_axes_0"), val = tensor([-1])]; + tensor encoder_layers_12_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_12_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259675968)))]; + tensor encoder_layers_12_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_12_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259678080)))]; + tensor input_679_cast_fp16 = transpose(perm = input_679_perm_0, x = x_333_cast_fp16)[name = string("transpose_248")]; + tensor x_335_cast_fp16 = layer_norm(axes = x_335_axes_0, beta = encoder_layers_12_conv_batch_norm_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_12_conv_batch_norm_weight_to_fp16, x = input_679_cast_fp16)[name = string("x_335_cast_fp16")]; + tensor input_681_perm_0 = const()[name = string("input_681_perm_0"), val = tensor([0, 2, 1])]; + tensor input_681_cast_fp16 = transpose(perm = input_681_perm_0, x = x_335_cast_fp16)[name = string("transpose_247")]; + tensor input_683_cast_fp16 = silu(x = input_681_cast_fp16)[name = string("input_683_cast_fp16")]; + string x_337_pad_type_0 = const()[name = string("x_337_pad_type_0"), val = string("valid")]; + tensor x_337_strides_0 = const()[name = string("x_337_strides_0"), val = tensor([1])]; + tensor x_337_pad_0 = const()[name = string("x_337_pad_0"), val = tensor([0, 0])]; + tensor x_337_dilations_0 = const()[name = string("x_337_dilations_0"), val = tensor([1])]; + int32 x_337_groups_0 = const()[name = string("x_337_groups_0"), val = int32(1)]; + tensor encoder_layers_12_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259680192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(260728832))))[name = string("encoder_layers_12_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_337_cast_fp16 = conv(dilations = x_337_dilations_0, groups = x_337_groups_0, pad = x_337_pad_0, pad_type = x_337_pad_type_0, strides = x_337_strides_0, weight = encoder_layers_12_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_683_cast_fp16)[name = string("x_337_cast_fp16")]; + tensor input_685_perm_0 = const()[name = string("input_685_perm_0"), val = tensor([0, 2, 1])]; + tensor input_685_cast_fp16 = transpose(perm = input_685_perm_0, x = x_337_cast_fp16)[name = string("transpose_246")]; + tensor input_687_cast_fp16 = add(x = input_671_cast_fp16, y = input_685_cast_fp16)[name = string("input_687_cast_fp16")]; + tensor input_689_axes_0 = const()[name = string("input_689_axes_0"), val = tensor([-1])]; + tensor encoder_layers_12_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_12_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(260730944)))]; + tensor encoder_layers_12_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_12_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(260733056)))]; + tensor input_689_cast_fp16 = layer_norm(axes = input_689_axes_0, beta = encoder_layers_12_norm_feed_forward2_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_12_norm_feed_forward2_weight_to_fp16, x = input_687_cast_fp16)[name = string("input_689_cast_fp16")]; + tensor encoder_layers_12_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(260735168))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263880960))))[name = string("encoder_layers_12_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_12_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_12_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263881152)))]; + tensor linear_116_cast_fp16 = linear(bias = encoder_layers_12_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_12_feed_forward2_linear1_weight_to_fp16_palettized, x = input_689_cast_fp16)[name = string("linear_116_cast_fp16")]; + tensor input_693_cast_fp16 = silu(x = linear_116_cast_fp16)[name = string("input_693_cast_fp16")]; + tensor encoder_layers_12_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263889408))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(267035200))))[name = string("encoder_layers_12_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_12_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_12_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(267035392)))]; + tensor linear_117_cast_fp16 = linear(bias = encoder_layers_12_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_12_feed_forward2_linear2_weight_to_fp16_palettized, x = input_693_cast_fp16)[name = string("linear_117_cast_fp16")]; + fp16 var_3188_to_fp16 = const()[name = string("op_3188_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3189_cast_fp16 = mul(x = linear_117_cast_fp16, y = var_3188_to_fp16)[name = string("op_3189_cast_fp16")]; + tensor input_699_cast_fp16 = add(x = input_687_cast_fp16, y = var_3189_cast_fp16)[name = string("input_699_cast_fp16")]; + tensor input_701_axes_0 = const()[name = string("input_701_axes_0"), val = tensor([-1])]; + tensor encoder_layers_12_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_12_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(267037504)))]; + tensor encoder_layers_12_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_12_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(267039616)))]; + tensor input_701_cast_fp16 = layer_norm(axes = input_701_axes_0, beta = encoder_layers_12_norm_out_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_12_norm_out_weight_to_fp16, x = input_699_cast_fp16)[name = string("input_701_cast_fp16")]; + tensor cache_53_begin_0 = const()[name = string("cache_53_begin_0"), val = tensor([13, 0, 0, 0])]; + tensor cache_53_end_0 = const()[name = string("cache_53_end_0"), val = tensor([14, 1, 42, 1024])]; + tensor cache_53_end_mask_0 = const()[name = string("cache_53_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_53_squeeze_mask_0 = const()[name = string("cache_53_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_53_cast_fp16 = slice_by_index(begin = cache_53_begin_0, end = cache_53_end_0, end_mask = cache_53_end_mask_0, squeeze_mask = cache_53_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_53_cast_fp16")]; + tensor cache_55_begin_0 = const()[name = string("cache_55_begin_0"), val = tensor([13, 0, 0, 0])]; + tensor cache_55_end_0 = const()[name = string("cache_55_end_0"), val = tensor([14, 1, 1024, 8])]; + tensor cache_55_end_mask_0 = const()[name = string("cache_55_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_55_squeeze_mask_0 = const()[name = string("cache_55_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_55_cast_fp16 = slice_by_index(begin = cache_55_begin_0, end = cache_55_end_0, end_mask = cache_55_end_mask_0, squeeze_mask = cache_55_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_55_cast_fp16")]; + tensor input_703_axes_0 = const()[name = string("input_703_axes_0"), val = tensor([-1])]; + tensor encoder_layers_13_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_13_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(267041728)))]; + tensor encoder_layers_13_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_13_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(267043840)))]; + tensor input_703_cast_fp16 = layer_norm(axes = input_703_axes_0, beta = encoder_layers_13_norm_feed_forward1_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_13_norm_feed_forward1_weight_to_fp16, x = input_701_cast_fp16)[name = string("input_703_cast_fp16")]; + tensor encoder_layers_13_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(267045952))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(270191744))))[name = string("encoder_layers_13_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_13_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_13_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(270191936)))]; + tensor linear_118_cast_fp16 = linear(bias = encoder_layers_13_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_13_feed_forward1_linear1_weight_to_fp16_palettized, x = input_703_cast_fp16)[name = string("linear_118_cast_fp16")]; + tensor input_707_cast_fp16 = silu(x = linear_118_cast_fp16)[name = string("input_707_cast_fp16")]; + tensor encoder_layers_13_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(270200192))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(273345984))))[name = string("encoder_layers_13_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_13_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_13_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(273346176)))]; + tensor linear_119_cast_fp16 = linear(bias = encoder_layers_13_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_13_feed_forward1_linear2_weight_to_fp16_palettized, x = input_707_cast_fp16)[name = string("linear_119_cast_fp16")]; + fp16 var_3225_to_fp16 = const()[name = string("op_3225_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3226_cast_fp16 = mul(x = linear_119_cast_fp16, y = var_3225_to_fp16)[name = string("op_3226_cast_fp16")]; + tensor input_713_cast_fp16 = add(x = input_701_cast_fp16, y = var_3226_cast_fp16)[name = string("input_713_cast_fp16")]; + tensor key_27_axes_0 = const()[name = string("key_27_axes_0"), val = tensor([-1])]; + tensor encoder_layers_13_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_13_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(273348288)))]; + tensor encoder_layers_13_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_13_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(273350400)))]; + tensor key_27_cast_fp16 = layer_norm(axes = key_27_axes_0, beta = encoder_layers_13_norm_self_att_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_13_norm_self_att_weight_to_fp16, x = input_713_cast_fp16)[name = string("key_27_cast_fp16")]; + bool input_715_interleave_0 = const()[name = string("input_715_interleave_0"), val = bool(false)]; + tensor input_715_cast_fp16 = concat(axis = var_69, interleave = input_715_interleave_0, values = (cache_53_cast_fp16, key_27_cast_fp16))[name = string("input_715_cast_fp16")]; + bool var_3254_interleave_0 = const()[name = string("op_3254_interleave_0"), val = bool(false)]; + tensor var_3254_cast_fp16 = concat(axis = var_69, interleave = var_3254_interleave_0, values = key_27_cast_fp16)[name = string("op_3254_cast_fp16")]; + tensor encoder_layers_13_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(273352512))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(274139008))))[name = string("encoder_layers_13_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_13_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_13_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(274139200)))]; + tensor linear_120_cast_fp16 = linear(bias = encoder_layers_13_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_13_self_attn_linear_q_weight_to_fp16_palettized, x = key_27_cast_fp16)[name = string("linear_120_cast_fp16")]; + tensor var_3259 = const()[name = string("op_3259"), val = tensor([1, -1, 8, 128])]; + tensor q_79_cast_fp16 = reshape(shape = var_3259, x = linear_120_cast_fp16)[name = string("q_79_cast_fp16")]; + tensor encoder_layers_13_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(274141312))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(274927808))))[name = string("encoder_layers_13_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_13_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_13_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(274928000)))]; + tensor linear_121_cast_fp16 = linear(bias = encoder_layers_13_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_13_self_attn_linear_k_weight_to_fp16_palettized, x = input_715_cast_fp16)[name = string("linear_121_cast_fp16")]; + tensor var_3264 = const()[name = string("op_3264"), val = tensor([1, -1, 8, 128])]; + tensor k_53_cast_fp16 = reshape(shape = var_3264, x = linear_121_cast_fp16)[name = string("k_53_cast_fp16")]; + tensor encoder_layers_13_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(274930112))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275716608))))[name = string("encoder_layers_13_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_13_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_13_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275716800)))]; + tensor linear_122_cast_fp16 = linear(bias = encoder_layers_13_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_13_self_attn_linear_v_weight_to_fp16_palettized, x = input_715_cast_fp16)[name = string("linear_122_cast_fp16")]; + tensor var_3269 = const()[name = string("op_3269"), val = tensor([1, -1, 8, 128])]; + tensor v_27_cast_fp16 = reshape(shape = var_3269, x = linear_122_cast_fp16)[name = string("v_27_cast_fp16")]; + tensor value_35_perm_0 = const()[name = string("value_35_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_13_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_13_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275718912)))]; + tensor var_3282_cast_fp16 = add(x = q_79_cast_fp16, y = encoder_layers_13_self_attn_pos_bias_u_to_fp16)[name = string("op_3282_cast_fp16")]; + tensor encoder_layers_13_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_13_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275721024)))]; + tensor var_3284_cast_fp16 = add(x = q_79_cast_fp16, y = encoder_layers_13_self_attn_pos_bias_v_to_fp16)[name = string("op_3284_cast_fp16")]; + tensor q_with_bias_v_27_perm_0 = const()[name = string("q_with_bias_v_27_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_345_transpose_x_0 = const()[name = string("x_345_transpose_x_0"), val = bool(false)]; + bool x_345_transpose_y_0 = const()[name = string("x_345_transpose_y_0"), val = bool(false)]; + tensor op_3286_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275723136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275922880))))[name = string("op_3286_to_fp16_quantized")]; + tensor q_with_bias_v_27_cast_fp16 = transpose(perm = q_with_bias_v_27_perm_0, x = var_3284_cast_fp16)[name = string("transpose_245")]; + tensor x_345_cast_fp16 = matmul(transpose_x = x_345_transpose_x_0, transpose_y = x_345_transpose_y_0, x = q_with_bias_v_27_cast_fp16, y = op_3286_to_fp16_quantized)[name = string("x_345_cast_fp16")]; + tensor x_347_pad_0 = const()[name = string("x_347_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_347_mode_0 = const()[name = string("x_347_mode_0"), val = string("constant")]; + fp16 const_248_to_fp16 = const()[name = string("const_248_to_fp16"), val = fp16(0x0p+0)]; + tensor x_347_cast_fp16 = pad(constant_val = const_248_to_fp16, mode = x_347_mode_0, pad = x_347_pad_0, x = x_345_cast_fp16)[name = string("x_347_cast_fp16")]; + tensor var_3294 = const()[name = string("op_3294"), val = tensor([1, 8, -1, 56])]; + tensor x_349_cast_fp16 = reshape(shape = var_3294, x = x_347_cast_fp16)[name = string("x_349_cast_fp16")]; + tensor var_3298_begin_0 = const()[name = string("op_3298_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_3298_end_0 = const()[name = string("op_3298_end_0"), val = tensor([1, 8, 196, 56])]; + tensor var_3298_end_mask_0 = const()[name = string("op_3298_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_3298_cast_fp16 = slice_by_index(begin = var_3298_begin_0, end = var_3298_end_0, end_mask = var_3298_end_mask_0, x = x_349_cast_fp16)[name = string("op_3298_cast_fp16")]; + tensor var_3299 = const()[name = string("op_3299"), val = tensor([1, 8, 56, 195])]; + tensor matrix_bd_53_cast_fp16 = reshape(shape = var_3299, x = var_3298_cast_fp16)[name = string("matrix_bd_53_cast_fp16")]; + bool matrix_ac_27_transpose_x_0 = const()[name = string("matrix_ac_27_transpose_x_0"), val = bool(false)]; + bool matrix_ac_27_transpose_y_0 = const()[name = string("matrix_ac_27_transpose_y_0"), val = bool(false)]; + tensor transpose_122_perm_0 = const()[name = string("transpose_122_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_123_perm_0 = const()[name = string("transpose_123_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_123 = transpose(perm = transpose_123_perm_0, x = k_53_cast_fp16)[name = string("transpose_243")]; + tensor transpose_122 = transpose(perm = transpose_122_perm_0, x = var_3282_cast_fp16)[name = string("transpose_244")]; + tensor matrix_ac_27_cast_fp16 = matmul(transpose_x = matrix_ac_27_transpose_x_0, transpose_y = matrix_ac_27_transpose_y_0, x = transpose_122, y = transpose_123)[name = string("matrix_ac_27_cast_fp16")]; + tensor matrix_bd_55_begin_0 = const()[name = string("matrix_bd_55_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_55_end_0 = const()[name = string("matrix_bd_55_end_0"), val = tensor([1, 8, 56, 98])]; + tensor matrix_bd_55_end_mask_0 = const()[name = string("matrix_bd_55_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_55_cast_fp16 = slice_by_index(begin = matrix_bd_55_begin_0, end = matrix_bd_55_end_0, end_mask = matrix_bd_55_end_mask_0, x = matrix_bd_53_cast_fp16)[name = string("matrix_bd_55_cast_fp16")]; + tensor var_3308_cast_fp16 = add(x = matrix_ac_27_cast_fp16, y = matrix_bd_55_cast_fp16)[name = string("op_3308_cast_fp16")]; + fp16 _inversed_scores_53_y_0_to_fp16 = const()[name = string("_inversed_scores_53_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_53_cast_fp16 = mul(x = var_3308_cast_fp16, y = _inversed_scores_53_y_0_to_fp16)[name = string("_inversed_scores_53_cast_fp16")]; + tensor scores_55_cast_fp16 = select(a = var_46_to_fp16, b = _inversed_scores_53_cast_fp16, cond = mask_11)[name = string("scores_55_cast_fp16")]; + tensor var_3314_cast_fp16 = softmax(axis = var_60, x = scores_55_cast_fp16)[name = string("op_3314_cast_fp16")]; + tensor input_717_cast_fp16 = select(a = var_45_to_fp16, b = var_3314_cast_fp16, cond = mask_11)[name = string("input_717_cast_fp16")]; + bool x_351_transpose_x_0 = const()[name = string("x_351_transpose_x_0"), val = bool(false)]; + bool x_351_transpose_y_0 = const()[name = string("x_351_transpose_y_0"), val = bool(false)]; + tensor value_35_cast_fp16 = transpose(perm = value_35_perm_0, x = v_27_cast_fp16)[name = string("transpose_242")]; + tensor x_351_cast_fp16 = matmul(transpose_x = x_351_transpose_x_0, transpose_y = x_351_transpose_y_0, x = input_717_cast_fp16, y = value_35_cast_fp16)[name = string("x_351_cast_fp16")]; + tensor var_3318_perm_0 = const()[name = string("op_3318_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3319 = const()[name = string("op_3319"), val = tensor([1, -1, 1024])]; + tensor var_3318_cast_fp16 = transpose(perm = var_3318_perm_0, x = x_351_cast_fp16)[name = string("transpose_241")]; + tensor input_719_cast_fp16 = reshape(shape = var_3319, x = var_3318_cast_fp16)[name = string("input_719_cast_fp16")]; + tensor encoder_layers_13_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275923392))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(276709888))))[name = string("encoder_layers_13_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_13_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_13_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(276710080)))]; + tensor linear_124_cast_fp16 = linear(bias = encoder_layers_13_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_13_self_attn_linear_out_weight_to_fp16_palettized, x = input_719_cast_fp16)[name = string("linear_124_cast_fp16")]; + tensor input_723_cast_fp16 = add(x = input_713_cast_fp16, y = linear_124_cast_fp16)[name = string("input_723_cast_fp16")]; + tensor x_355_axes_0 = const()[name = string("x_355_axes_0"), val = tensor([-1])]; + tensor encoder_layers_13_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_13_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(276712192)))]; + tensor encoder_layers_13_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_13_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(276714304)))]; + tensor x_355_cast_fp16 = layer_norm(axes = x_355_axes_0, beta = encoder_layers_13_norm_conv_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_13_norm_conv_weight_to_fp16, x = input_723_cast_fp16)[name = string("x_355_cast_fp16")]; + tensor input_725_perm_0 = const()[name = string("input_725_perm_0"), val = tensor([0, 2, 1])]; + string input_727_pad_type_0 = const()[name = string("input_727_pad_type_0"), val = string("valid")]; + tensor input_727_strides_0 = const()[name = string("input_727_strides_0"), val = tensor([1])]; + tensor input_727_pad_0 = const()[name = string("input_727_pad_0"), val = tensor([0, 0])]; + tensor input_727_dilations_0 = const()[name = string("input_727_dilations_0"), val = tensor([1])]; + int32 input_727_groups_0 = const()[name = string("input_727_groups_0"), val = int32(1)]; + tensor encoder_layers_13_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(276716416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278813632))))[name = string("encoder_layers_13_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_725_cast_fp16 = transpose(perm = input_725_perm_0, x = x_355_cast_fp16)[name = string("transpose_240")]; + tensor input_727_cast_fp16 = conv(dilations = input_727_dilations_0, groups = input_727_groups_0, pad = input_727_pad_0, pad_type = input_727_pad_type_0, strides = input_727_strides_0, weight = encoder_layers_13_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_725_cast_fp16)[name = string("input_727_cast_fp16")]; + int32 x_357_split_num_splits_0 = const()[name = string("x_357_split_num_splits_0"), val = int32(2)]; + int32 x_357_split_axis_0 = const()[name = string("x_357_split_axis_0"), val = int32(1)]; + tensor x_357_split_cast_fp16_0, tensor x_357_split_cast_fp16_1 = split(axis = x_357_split_axis_0, num_splits = x_357_split_num_splits_0, x = input_727_cast_fp16)[name = string("x_357_split_cast_fp16")]; + tensor x_357_split_1_sigmoid_cast_fp16 = sigmoid(x = x_357_split_cast_fp16_1)[name = string("x_357_split_1_sigmoid_cast_fp16")]; + tensor x_357_cast_fp16 = mul(x = x_357_split_cast_fp16_0, y = x_357_split_1_sigmoid_cast_fp16)[name = string("x_357_cast_fp16")]; + tensor input_729_cast_fp16 = select(a = var_45_to_fp16, b = x_357_cast_fp16, cond = var_576)[name = string("input_729_cast_fp16")]; + bool new_x_55_interleave_0 = const()[name = string("new_x_55_interleave_0"), val = bool(false)]; + tensor new_x_55_cast_fp16 = concat(axis = var_60, interleave = new_x_55_interleave_0, values = (cache_55_cast_fp16, input_729_cast_fp16))[name = string("new_x_55_cast_fp16")]; + tensor var_3358_begin_0 = const()[name = string("op_3358_begin_0"), val = tensor([0, 0, 56])]; + tensor var_3358_end_0 = const()[name = string("op_3358_end_0"), val = tensor([1, 1024, 64])]; + tensor var_3358_end_mask_0 = const()[name = string("op_3358_end_mask_0"), val = tensor([true, true, true])]; + tensor var_3358_cast_fp16 = slice_by_index(begin = var_3358_begin_0, end = var_3358_end_0, end_mask = var_3358_end_mask_0, x = new_x_55_cast_fp16)[name = string("op_3358_cast_fp16")]; + string x_359_pad_type_0 = const()[name = string("x_359_pad_type_0"), val = string("valid")]; + int32 x_359_groups_0 = const()[name = string("x_359_groups_0"), val = int32(1024)]; + tensor x_359_strides_0 = const()[name = string("x_359_strides_0"), val = tensor([1])]; + tensor x_359_pad_0 = const()[name = string("x_359_pad_0"), val = tensor([0, 0])]; + tensor x_359_dilations_0 = const()[name = string("x_359_dilations_0"), val = tensor([1])]; + tensor encoder_layers_13_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278817792))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278827072))))[name = string("encoder_layers_13_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_359_cast_fp16 = conv(dilations = x_359_dilations_0, groups = x_359_groups_0, pad = x_359_pad_0, pad_type = x_359_pad_type_0, strides = x_359_strides_0, weight = encoder_layers_13_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_55_cast_fp16)[name = string("x_359_cast_fp16")]; + tensor input_731_perm_0 = const()[name = string("input_731_perm_0"), val = tensor([0, 2, 1])]; + tensor x_361_axes_0 = const()[name = string("x_361_axes_0"), val = tensor([-1])]; + tensor encoder_layers_13_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_13_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278829184)))]; + tensor encoder_layers_13_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_13_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278831296)))]; + tensor input_731_cast_fp16 = transpose(perm = input_731_perm_0, x = x_359_cast_fp16)[name = string("transpose_239")]; + tensor x_361_cast_fp16 = layer_norm(axes = x_361_axes_0, beta = encoder_layers_13_conv_batch_norm_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_13_conv_batch_norm_weight_to_fp16, x = input_731_cast_fp16)[name = string("x_361_cast_fp16")]; + tensor input_733_perm_0 = const()[name = string("input_733_perm_0"), val = tensor([0, 2, 1])]; + tensor input_733_cast_fp16 = transpose(perm = input_733_perm_0, x = x_361_cast_fp16)[name = string("transpose_238")]; + tensor input_735_cast_fp16 = silu(x = input_733_cast_fp16)[name = string("input_735_cast_fp16")]; + string x_363_pad_type_0 = const()[name = string("x_363_pad_type_0"), val = string("valid")]; + tensor x_363_strides_0 = const()[name = string("x_363_strides_0"), val = tensor([1])]; + tensor x_363_pad_0 = const()[name = string("x_363_pad_0"), val = tensor([0, 0])]; + tensor x_363_dilations_0 = const()[name = string("x_363_dilations_0"), val = tensor([1])]; + int32 x_363_groups_0 = const()[name = string("x_363_groups_0"), val = int32(1)]; + tensor encoder_layers_13_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278833408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(279882048))))[name = string("encoder_layers_13_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_363_cast_fp16 = conv(dilations = x_363_dilations_0, groups = x_363_groups_0, pad = x_363_pad_0, pad_type = x_363_pad_type_0, strides = x_363_strides_0, weight = encoder_layers_13_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_735_cast_fp16)[name = string("x_363_cast_fp16")]; + tensor input_737_perm_0 = const()[name = string("input_737_perm_0"), val = tensor([0, 2, 1])]; + tensor input_737_cast_fp16 = transpose(perm = input_737_perm_0, x = x_363_cast_fp16)[name = string("transpose_237")]; + tensor input_739_cast_fp16 = add(x = input_723_cast_fp16, y = input_737_cast_fp16)[name = string("input_739_cast_fp16")]; + tensor input_741_axes_0 = const()[name = string("input_741_axes_0"), val = tensor([-1])]; + tensor encoder_layers_13_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_13_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(279884160)))]; + tensor encoder_layers_13_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_13_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(279886272)))]; + tensor input_741_cast_fp16 = layer_norm(axes = input_741_axes_0, beta = encoder_layers_13_norm_feed_forward2_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_13_norm_feed_forward2_weight_to_fp16, x = input_739_cast_fp16)[name = string("input_741_cast_fp16")]; + tensor encoder_layers_13_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(279888384))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(283034176))))[name = string("encoder_layers_13_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_13_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_13_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(283034368)))]; + tensor linear_125_cast_fp16 = linear(bias = encoder_layers_13_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_13_feed_forward2_linear1_weight_to_fp16_palettized, x = input_741_cast_fp16)[name = string("linear_125_cast_fp16")]; + tensor input_745_cast_fp16 = silu(x = linear_125_cast_fp16)[name = string("input_745_cast_fp16")]; + tensor encoder_layers_13_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(283042624))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(286188416))))[name = string("encoder_layers_13_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_13_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_13_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(286188608)))]; + tensor linear_126_cast_fp16 = linear(bias = encoder_layers_13_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_13_feed_forward2_linear2_weight_to_fp16_palettized, x = input_745_cast_fp16)[name = string("linear_126_cast_fp16")]; + fp16 var_3401_to_fp16 = const()[name = string("op_3401_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3402_cast_fp16 = mul(x = linear_126_cast_fp16, y = var_3401_to_fp16)[name = string("op_3402_cast_fp16")]; + tensor input_751_cast_fp16 = add(x = input_739_cast_fp16, y = var_3402_cast_fp16)[name = string("input_751_cast_fp16")]; + tensor input_753_axes_0 = const()[name = string("input_753_axes_0"), val = tensor([-1])]; + tensor encoder_layers_13_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_13_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(286190720)))]; + tensor encoder_layers_13_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_13_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(286192832)))]; + tensor input_753_cast_fp16 = layer_norm(axes = input_753_axes_0, beta = encoder_layers_13_norm_out_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_13_norm_out_weight_to_fp16, x = input_751_cast_fp16)[name = string("input_753_cast_fp16")]; + tensor cache_57_begin_0 = const()[name = string("cache_57_begin_0"), val = tensor([14, 0, 0, 0])]; + tensor cache_57_end_0 = const()[name = string("cache_57_end_0"), val = tensor([15, 1, 42, 1024])]; + tensor cache_57_end_mask_0 = const()[name = string("cache_57_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_57_squeeze_mask_0 = const()[name = string("cache_57_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_57_cast_fp16 = slice_by_index(begin = cache_57_begin_0, end = cache_57_end_0, end_mask = cache_57_end_mask_0, squeeze_mask = cache_57_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_57_cast_fp16")]; + tensor cache_59_begin_0 = const()[name = string("cache_59_begin_0"), val = tensor([14, 0, 0, 0])]; + tensor cache_59_end_0 = const()[name = string("cache_59_end_0"), val = tensor([15, 1, 1024, 8])]; + tensor cache_59_end_mask_0 = const()[name = string("cache_59_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_59_squeeze_mask_0 = const()[name = string("cache_59_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_59_cast_fp16 = slice_by_index(begin = cache_59_begin_0, end = cache_59_end_0, end_mask = cache_59_end_mask_0, squeeze_mask = cache_59_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_59_cast_fp16")]; + tensor input_755_axes_0 = const()[name = string("input_755_axes_0"), val = tensor([-1])]; + tensor encoder_layers_14_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_14_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(286194944)))]; + tensor encoder_layers_14_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_14_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(286197056)))]; + tensor input_755_cast_fp16 = layer_norm(axes = input_755_axes_0, beta = encoder_layers_14_norm_feed_forward1_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_14_norm_feed_forward1_weight_to_fp16, x = input_753_cast_fp16)[name = string("input_755_cast_fp16")]; + tensor encoder_layers_14_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(286199168))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(289344960))))[name = string("encoder_layers_14_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_14_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_14_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(289345152)))]; + tensor linear_127_cast_fp16 = linear(bias = encoder_layers_14_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_14_feed_forward1_linear1_weight_to_fp16_palettized, x = input_755_cast_fp16)[name = string("linear_127_cast_fp16")]; + tensor input_759_cast_fp16 = silu(x = linear_127_cast_fp16)[name = string("input_759_cast_fp16")]; + tensor encoder_layers_14_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(289353408))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292499200))))[name = string("encoder_layers_14_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_14_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_14_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292499392)))]; + tensor linear_128_cast_fp16 = linear(bias = encoder_layers_14_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_14_feed_forward1_linear2_weight_to_fp16_palettized, x = input_759_cast_fp16)[name = string("linear_128_cast_fp16")]; + fp16 var_3438_to_fp16 = const()[name = string("op_3438_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3439_cast_fp16 = mul(x = linear_128_cast_fp16, y = var_3438_to_fp16)[name = string("op_3439_cast_fp16")]; + tensor input_765_cast_fp16 = add(x = input_753_cast_fp16, y = var_3439_cast_fp16)[name = string("input_765_cast_fp16")]; + tensor key_29_axes_0 = const()[name = string("key_29_axes_0"), val = tensor([-1])]; + tensor encoder_layers_14_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_14_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292501504)))]; + tensor encoder_layers_14_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_14_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292503616)))]; + tensor key_29_cast_fp16 = layer_norm(axes = key_29_axes_0, beta = encoder_layers_14_norm_self_att_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_14_norm_self_att_weight_to_fp16, x = input_765_cast_fp16)[name = string("key_29_cast_fp16")]; + bool input_767_interleave_0 = const()[name = string("input_767_interleave_0"), val = bool(false)]; + tensor input_767_cast_fp16 = concat(axis = var_69, interleave = input_767_interleave_0, values = (cache_57_cast_fp16, key_29_cast_fp16))[name = string("input_767_cast_fp16")]; + bool var_3467_interleave_0 = const()[name = string("op_3467_interleave_0"), val = bool(false)]; + tensor var_3467_cast_fp16 = concat(axis = var_69, interleave = var_3467_interleave_0, values = key_29_cast_fp16)[name = string("op_3467_cast_fp16")]; + tensor encoder_layers_14_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292505728))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(293292224))))[name = string("encoder_layers_14_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_14_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_14_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(293292416)))]; + tensor linear_129_cast_fp16 = linear(bias = encoder_layers_14_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_14_self_attn_linear_q_weight_to_fp16_palettized, x = key_29_cast_fp16)[name = string("linear_129_cast_fp16")]; + tensor var_3472 = const()[name = string("op_3472"), val = tensor([1, -1, 8, 128])]; + tensor q_85_cast_fp16 = reshape(shape = var_3472, x = linear_129_cast_fp16)[name = string("q_85_cast_fp16")]; + tensor encoder_layers_14_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(293294528))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(294081024))))[name = string("encoder_layers_14_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_14_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_14_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(294081216)))]; + tensor linear_130_cast_fp16 = linear(bias = encoder_layers_14_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_14_self_attn_linear_k_weight_to_fp16_palettized, x = input_767_cast_fp16)[name = string("linear_130_cast_fp16")]; + tensor var_3477 = const()[name = string("op_3477"), val = tensor([1, -1, 8, 128])]; + tensor k_57_cast_fp16 = reshape(shape = var_3477, x = linear_130_cast_fp16)[name = string("k_57_cast_fp16")]; + tensor encoder_layers_14_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(294083328))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(294869824))))[name = string("encoder_layers_14_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_14_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_14_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(294870016)))]; + tensor linear_131_cast_fp16 = linear(bias = encoder_layers_14_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_14_self_attn_linear_v_weight_to_fp16_palettized, x = input_767_cast_fp16)[name = string("linear_131_cast_fp16")]; + tensor var_3482 = const()[name = string("op_3482"), val = tensor([1, -1, 8, 128])]; + tensor v_29_cast_fp16 = reshape(shape = var_3482, x = linear_131_cast_fp16)[name = string("v_29_cast_fp16")]; + tensor value_37_perm_0 = const()[name = string("value_37_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_14_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_14_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(294872128)))]; + tensor var_3495_cast_fp16 = add(x = q_85_cast_fp16, y = encoder_layers_14_self_attn_pos_bias_u_to_fp16)[name = string("op_3495_cast_fp16")]; + tensor encoder_layers_14_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_14_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(294874240)))]; + tensor var_3497_cast_fp16 = add(x = q_85_cast_fp16, y = encoder_layers_14_self_attn_pos_bias_v_to_fp16)[name = string("op_3497_cast_fp16")]; + tensor q_with_bias_v_29_perm_0 = const()[name = string("q_with_bias_v_29_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_371_transpose_x_0 = const()[name = string("x_371_transpose_x_0"), val = bool(false)]; + bool x_371_transpose_y_0 = const()[name = string("x_371_transpose_y_0"), val = bool(false)]; + tensor op_3499_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(294876352))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(295076096))))[name = string("op_3499_to_fp16_quantized")]; + tensor q_with_bias_v_29_cast_fp16 = transpose(perm = q_with_bias_v_29_perm_0, x = var_3497_cast_fp16)[name = string("transpose_236")]; + tensor x_371_cast_fp16 = matmul(transpose_x = x_371_transpose_x_0, transpose_y = x_371_transpose_y_0, x = q_with_bias_v_29_cast_fp16, y = op_3499_to_fp16_quantized)[name = string("x_371_cast_fp16")]; + tensor x_373_pad_0 = const()[name = string("x_373_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_373_mode_0 = const()[name = string("x_373_mode_0"), val = string("constant")]; + fp16 const_261_to_fp16 = const()[name = string("const_261_to_fp16"), val = fp16(0x0p+0)]; + tensor x_373_cast_fp16 = pad(constant_val = const_261_to_fp16, mode = x_373_mode_0, pad = x_373_pad_0, x = x_371_cast_fp16)[name = string("x_373_cast_fp16")]; + tensor var_3507 = const()[name = string("op_3507"), val = tensor([1, 8, -1, 56])]; + tensor x_375_cast_fp16 = reshape(shape = var_3507, x = x_373_cast_fp16)[name = string("x_375_cast_fp16")]; + tensor var_3511_begin_0 = const()[name = string("op_3511_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_3511_end_0 = const()[name = string("op_3511_end_0"), val = tensor([1, 8, 196, 56])]; + tensor var_3511_end_mask_0 = const()[name = string("op_3511_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_3511_cast_fp16 = slice_by_index(begin = var_3511_begin_0, end = var_3511_end_0, end_mask = var_3511_end_mask_0, x = x_375_cast_fp16)[name = string("op_3511_cast_fp16")]; + tensor var_3512 = const()[name = string("op_3512"), val = tensor([1, 8, 56, 195])]; + tensor matrix_bd_57_cast_fp16 = reshape(shape = var_3512, x = var_3511_cast_fp16)[name = string("matrix_bd_57_cast_fp16")]; + bool matrix_ac_29_transpose_x_0 = const()[name = string("matrix_ac_29_transpose_x_0"), val = bool(false)]; + bool matrix_ac_29_transpose_y_0 = const()[name = string("matrix_ac_29_transpose_y_0"), val = bool(false)]; + tensor transpose_124_perm_0 = const()[name = string("transpose_124_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_125_perm_0 = const()[name = string("transpose_125_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_125 = transpose(perm = transpose_125_perm_0, x = k_57_cast_fp16)[name = string("transpose_234")]; + tensor transpose_124 = transpose(perm = transpose_124_perm_0, x = var_3495_cast_fp16)[name = string("transpose_235")]; + tensor matrix_ac_29_cast_fp16 = matmul(transpose_x = matrix_ac_29_transpose_x_0, transpose_y = matrix_ac_29_transpose_y_0, x = transpose_124, y = transpose_125)[name = string("matrix_ac_29_cast_fp16")]; + tensor matrix_bd_59_begin_0 = const()[name = string("matrix_bd_59_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_59_end_0 = const()[name = string("matrix_bd_59_end_0"), val = tensor([1, 8, 56, 98])]; + tensor matrix_bd_59_end_mask_0 = const()[name = string("matrix_bd_59_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_59_cast_fp16 = slice_by_index(begin = matrix_bd_59_begin_0, end = matrix_bd_59_end_0, end_mask = matrix_bd_59_end_mask_0, x = matrix_bd_57_cast_fp16)[name = string("matrix_bd_59_cast_fp16")]; + tensor var_3521_cast_fp16 = add(x = matrix_ac_29_cast_fp16, y = matrix_bd_59_cast_fp16)[name = string("op_3521_cast_fp16")]; + fp16 _inversed_scores_57_y_0_to_fp16 = const()[name = string("_inversed_scores_57_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_57_cast_fp16 = mul(x = var_3521_cast_fp16, y = _inversed_scores_57_y_0_to_fp16)[name = string("_inversed_scores_57_cast_fp16")]; + tensor scores_59_cast_fp16 = select(a = var_46_to_fp16, b = _inversed_scores_57_cast_fp16, cond = mask_11)[name = string("scores_59_cast_fp16")]; + tensor var_3527_cast_fp16 = softmax(axis = var_60, x = scores_59_cast_fp16)[name = string("op_3527_cast_fp16")]; + tensor input_769_cast_fp16 = select(a = var_45_to_fp16, b = var_3527_cast_fp16, cond = mask_11)[name = string("input_769_cast_fp16")]; + bool x_377_transpose_x_0 = const()[name = string("x_377_transpose_x_0"), val = bool(false)]; + bool x_377_transpose_y_0 = const()[name = string("x_377_transpose_y_0"), val = bool(false)]; + tensor value_37_cast_fp16 = transpose(perm = value_37_perm_0, x = v_29_cast_fp16)[name = string("transpose_233")]; + tensor x_377_cast_fp16 = matmul(transpose_x = x_377_transpose_x_0, transpose_y = x_377_transpose_y_0, x = input_769_cast_fp16, y = value_37_cast_fp16)[name = string("x_377_cast_fp16")]; + tensor var_3531_perm_0 = const()[name = string("op_3531_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3532 = const()[name = string("op_3532"), val = tensor([1, -1, 1024])]; + tensor var_3531_cast_fp16 = transpose(perm = var_3531_perm_0, x = x_377_cast_fp16)[name = string("transpose_232")]; + tensor input_771_cast_fp16 = reshape(shape = var_3532, x = var_3531_cast_fp16)[name = string("input_771_cast_fp16")]; + tensor encoder_layers_14_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(295076608))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(295863104))))[name = string("encoder_layers_14_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_14_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_14_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(295863296)))]; + tensor linear_133_cast_fp16 = linear(bias = encoder_layers_14_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_14_self_attn_linear_out_weight_to_fp16_palettized, x = input_771_cast_fp16)[name = string("linear_133_cast_fp16")]; + tensor input_775_cast_fp16 = add(x = input_765_cast_fp16, y = linear_133_cast_fp16)[name = string("input_775_cast_fp16")]; + tensor x_381_axes_0 = const()[name = string("x_381_axes_0"), val = tensor([-1])]; + tensor encoder_layers_14_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_14_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(295865408)))]; + tensor encoder_layers_14_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_14_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(295867520)))]; + tensor x_381_cast_fp16 = layer_norm(axes = x_381_axes_0, beta = encoder_layers_14_norm_conv_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_14_norm_conv_weight_to_fp16, x = input_775_cast_fp16)[name = string("x_381_cast_fp16")]; + tensor input_777_perm_0 = const()[name = string("input_777_perm_0"), val = tensor([0, 2, 1])]; + string input_779_pad_type_0 = const()[name = string("input_779_pad_type_0"), val = string("valid")]; + tensor input_779_strides_0 = const()[name = string("input_779_strides_0"), val = tensor([1])]; + tensor input_779_pad_0 = const()[name = string("input_779_pad_0"), val = tensor([0, 0])]; + tensor input_779_dilations_0 = const()[name = string("input_779_dilations_0"), val = tensor([1])]; + int32 input_779_groups_0 = const()[name = string("input_779_groups_0"), val = int32(1)]; + tensor encoder_layers_14_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(295869632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(297966848))))[name = string("encoder_layers_14_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_777_cast_fp16 = transpose(perm = input_777_perm_0, x = x_381_cast_fp16)[name = string("transpose_231")]; + tensor input_779_cast_fp16 = conv(dilations = input_779_dilations_0, groups = input_779_groups_0, pad = input_779_pad_0, pad_type = input_779_pad_type_0, strides = input_779_strides_0, weight = encoder_layers_14_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_777_cast_fp16)[name = string("input_779_cast_fp16")]; + int32 x_383_split_num_splits_0 = const()[name = string("x_383_split_num_splits_0"), val = int32(2)]; + int32 x_383_split_axis_0 = const()[name = string("x_383_split_axis_0"), val = int32(1)]; + tensor x_383_split_cast_fp16_0, tensor x_383_split_cast_fp16_1 = split(axis = x_383_split_axis_0, num_splits = x_383_split_num_splits_0, x = input_779_cast_fp16)[name = string("x_383_split_cast_fp16")]; + tensor x_383_split_1_sigmoid_cast_fp16 = sigmoid(x = x_383_split_cast_fp16_1)[name = string("x_383_split_1_sigmoid_cast_fp16")]; + tensor x_383_cast_fp16 = mul(x = x_383_split_cast_fp16_0, y = x_383_split_1_sigmoid_cast_fp16)[name = string("x_383_cast_fp16")]; + tensor input_781_cast_fp16 = select(a = var_45_to_fp16, b = x_383_cast_fp16, cond = var_576)[name = string("input_781_cast_fp16")]; + bool new_x_59_interleave_0 = const()[name = string("new_x_59_interleave_0"), val = bool(false)]; + tensor new_x_59_cast_fp16 = concat(axis = var_60, interleave = new_x_59_interleave_0, values = (cache_59_cast_fp16, input_781_cast_fp16))[name = string("new_x_59_cast_fp16")]; + tensor var_3571_begin_0 = const()[name = string("op_3571_begin_0"), val = tensor([0, 0, 56])]; + tensor var_3571_end_0 = const()[name = string("op_3571_end_0"), val = tensor([1, 1024, 64])]; + tensor var_3571_end_mask_0 = const()[name = string("op_3571_end_mask_0"), val = tensor([true, true, true])]; + tensor var_3571_cast_fp16 = slice_by_index(begin = var_3571_begin_0, end = var_3571_end_0, end_mask = var_3571_end_mask_0, x = new_x_59_cast_fp16)[name = string("op_3571_cast_fp16")]; + string x_385_pad_type_0 = const()[name = string("x_385_pad_type_0"), val = string("valid")]; + int32 x_385_groups_0 = const()[name = string("x_385_groups_0"), val = int32(1024)]; + tensor x_385_strides_0 = const()[name = string("x_385_strides_0"), val = tensor([1])]; + tensor x_385_pad_0 = const()[name = string("x_385_pad_0"), val = tensor([0, 0])]; + tensor x_385_dilations_0 = const()[name = string("x_385_dilations_0"), val = tensor([1])]; + tensor encoder_layers_14_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(297971008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(297980288))))[name = string("encoder_layers_14_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_385_cast_fp16 = conv(dilations = x_385_dilations_0, groups = x_385_groups_0, pad = x_385_pad_0, pad_type = x_385_pad_type_0, strides = x_385_strides_0, weight = encoder_layers_14_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_59_cast_fp16)[name = string("x_385_cast_fp16")]; + tensor input_783_perm_0 = const()[name = string("input_783_perm_0"), val = tensor([0, 2, 1])]; + tensor x_387_axes_0 = const()[name = string("x_387_axes_0"), val = tensor([-1])]; + tensor encoder_layers_14_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_14_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(297982400)))]; + tensor encoder_layers_14_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_14_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(297984512)))]; + tensor input_783_cast_fp16 = transpose(perm = input_783_perm_0, x = x_385_cast_fp16)[name = string("transpose_230")]; + tensor x_387_cast_fp16 = layer_norm(axes = x_387_axes_0, beta = encoder_layers_14_conv_batch_norm_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_14_conv_batch_norm_weight_to_fp16, x = input_783_cast_fp16)[name = string("x_387_cast_fp16")]; + tensor input_785_perm_0 = const()[name = string("input_785_perm_0"), val = tensor([0, 2, 1])]; + tensor input_785_cast_fp16 = transpose(perm = input_785_perm_0, x = x_387_cast_fp16)[name = string("transpose_229")]; + tensor input_787_cast_fp16 = silu(x = input_785_cast_fp16)[name = string("input_787_cast_fp16")]; + string x_389_pad_type_0 = const()[name = string("x_389_pad_type_0"), val = string("valid")]; + tensor x_389_strides_0 = const()[name = string("x_389_strides_0"), val = tensor([1])]; + tensor x_389_pad_0 = const()[name = string("x_389_pad_0"), val = tensor([0, 0])]; + tensor x_389_dilations_0 = const()[name = string("x_389_dilations_0"), val = tensor([1])]; + int32 x_389_groups_0 = const()[name = string("x_389_groups_0"), val = int32(1)]; + tensor encoder_layers_14_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(297986624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(299035264))))[name = string("encoder_layers_14_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_389_cast_fp16 = conv(dilations = x_389_dilations_0, groups = x_389_groups_0, pad = x_389_pad_0, pad_type = x_389_pad_type_0, strides = x_389_strides_0, weight = encoder_layers_14_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_787_cast_fp16)[name = string("x_389_cast_fp16")]; + tensor input_789_perm_0 = const()[name = string("input_789_perm_0"), val = tensor([0, 2, 1])]; + tensor input_789_cast_fp16 = transpose(perm = input_789_perm_0, x = x_389_cast_fp16)[name = string("transpose_228")]; + tensor input_791_cast_fp16 = add(x = input_775_cast_fp16, y = input_789_cast_fp16)[name = string("input_791_cast_fp16")]; + tensor input_793_axes_0 = const()[name = string("input_793_axes_0"), val = tensor([-1])]; + tensor encoder_layers_14_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_14_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(299037376)))]; + tensor encoder_layers_14_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_14_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(299039488)))]; + tensor input_793_cast_fp16 = layer_norm(axes = input_793_axes_0, beta = encoder_layers_14_norm_feed_forward2_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_14_norm_feed_forward2_weight_to_fp16, x = input_791_cast_fp16)[name = string("input_793_cast_fp16")]; + tensor encoder_layers_14_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(299041600))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(302187392))))[name = string("encoder_layers_14_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_14_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_14_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(302187584)))]; + tensor linear_134_cast_fp16 = linear(bias = encoder_layers_14_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_14_feed_forward2_linear1_weight_to_fp16_palettized, x = input_793_cast_fp16)[name = string("linear_134_cast_fp16")]; + tensor input_797_cast_fp16 = silu(x = linear_134_cast_fp16)[name = string("input_797_cast_fp16")]; + tensor encoder_layers_14_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(302195840))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(305341632))))[name = string("encoder_layers_14_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_14_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_14_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(305341824)))]; + tensor linear_135_cast_fp16 = linear(bias = encoder_layers_14_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_14_feed_forward2_linear2_weight_to_fp16_palettized, x = input_797_cast_fp16)[name = string("linear_135_cast_fp16")]; + fp16 var_3614_to_fp16 = const()[name = string("op_3614_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3615_cast_fp16 = mul(x = linear_135_cast_fp16, y = var_3614_to_fp16)[name = string("op_3615_cast_fp16")]; + tensor input_803_cast_fp16 = add(x = input_791_cast_fp16, y = var_3615_cast_fp16)[name = string("input_803_cast_fp16")]; + tensor input_805_axes_0 = const()[name = string("input_805_axes_0"), val = tensor([-1])]; + tensor encoder_layers_14_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_14_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(305343936)))]; + tensor encoder_layers_14_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_14_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(305346048)))]; + tensor input_805_cast_fp16 = layer_norm(axes = input_805_axes_0, beta = encoder_layers_14_norm_out_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_14_norm_out_weight_to_fp16, x = input_803_cast_fp16)[name = string("input_805_cast_fp16")]; + tensor cache_61_begin_0 = const()[name = string("cache_61_begin_0"), val = tensor([15, 0, 0, 0])]; + tensor cache_61_end_0 = const()[name = string("cache_61_end_0"), val = tensor([16, 1, 42, 1024])]; + tensor cache_61_end_mask_0 = const()[name = string("cache_61_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_61_squeeze_mask_0 = const()[name = string("cache_61_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_61_cast_fp16 = slice_by_index(begin = cache_61_begin_0, end = cache_61_end_0, end_mask = cache_61_end_mask_0, squeeze_mask = cache_61_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_61_cast_fp16")]; + tensor cache_63_begin_0 = const()[name = string("cache_63_begin_0"), val = tensor([15, 0, 0, 0])]; + tensor cache_63_end_0 = const()[name = string("cache_63_end_0"), val = tensor([16, 1, 1024, 8])]; + tensor cache_63_end_mask_0 = const()[name = string("cache_63_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_63_squeeze_mask_0 = const()[name = string("cache_63_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_63_cast_fp16 = slice_by_index(begin = cache_63_begin_0, end = cache_63_end_0, end_mask = cache_63_end_mask_0, squeeze_mask = cache_63_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_63_cast_fp16")]; + tensor input_807_axes_0 = const()[name = string("input_807_axes_0"), val = tensor([-1])]; + tensor encoder_layers_15_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_15_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(305348160)))]; + tensor encoder_layers_15_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_15_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(305350272)))]; + tensor input_807_cast_fp16 = layer_norm(axes = input_807_axes_0, beta = encoder_layers_15_norm_feed_forward1_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_15_norm_feed_forward1_weight_to_fp16, x = input_805_cast_fp16)[name = string("input_807_cast_fp16")]; + tensor encoder_layers_15_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(305352384))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308498176))))[name = string("encoder_layers_15_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_15_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_15_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308498368)))]; + tensor linear_136_cast_fp16 = linear(bias = encoder_layers_15_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_15_feed_forward1_linear1_weight_to_fp16_palettized, x = input_807_cast_fp16)[name = string("linear_136_cast_fp16")]; + tensor input_811_cast_fp16 = silu(x = linear_136_cast_fp16)[name = string("input_811_cast_fp16")]; + tensor encoder_layers_15_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308506624))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(311652416))))[name = string("encoder_layers_15_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_15_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_15_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(311652608)))]; + tensor linear_137_cast_fp16 = linear(bias = encoder_layers_15_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_15_feed_forward1_linear2_weight_to_fp16_palettized, x = input_811_cast_fp16)[name = string("linear_137_cast_fp16")]; + fp16 var_3651_to_fp16 = const()[name = string("op_3651_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3652_cast_fp16 = mul(x = linear_137_cast_fp16, y = var_3651_to_fp16)[name = string("op_3652_cast_fp16")]; + tensor input_817_cast_fp16 = add(x = input_805_cast_fp16, y = var_3652_cast_fp16)[name = string("input_817_cast_fp16")]; + tensor key_31_axes_0 = const()[name = string("key_31_axes_0"), val = tensor([-1])]; + tensor encoder_layers_15_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_15_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(311654720)))]; + tensor encoder_layers_15_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_15_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(311656832)))]; + tensor key_31_cast_fp16 = layer_norm(axes = key_31_axes_0, beta = encoder_layers_15_norm_self_att_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_15_norm_self_att_weight_to_fp16, x = input_817_cast_fp16)[name = string("key_31_cast_fp16")]; + bool input_819_interleave_0 = const()[name = string("input_819_interleave_0"), val = bool(false)]; + tensor input_819_cast_fp16 = concat(axis = var_69, interleave = input_819_interleave_0, values = (cache_61_cast_fp16, key_31_cast_fp16))[name = string("input_819_cast_fp16")]; + bool var_3680_interleave_0 = const()[name = string("op_3680_interleave_0"), val = bool(false)]; + tensor var_3680_cast_fp16 = concat(axis = var_69, interleave = var_3680_interleave_0, values = key_31_cast_fp16)[name = string("op_3680_cast_fp16")]; + tensor encoder_layers_15_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(311658944))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312445440))))[name = string("encoder_layers_15_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_15_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_15_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312445632)))]; + tensor linear_138_cast_fp16 = linear(bias = encoder_layers_15_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_15_self_attn_linear_q_weight_to_fp16_palettized, x = key_31_cast_fp16)[name = string("linear_138_cast_fp16")]; + tensor var_3685 = const()[name = string("op_3685"), val = tensor([1, -1, 8, 128])]; + tensor q_91_cast_fp16 = reshape(shape = var_3685, x = linear_138_cast_fp16)[name = string("q_91_cast_fp16")]; + tensor encoder_layers_15_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312447744))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(313234240))))[name = string("encoder_layers_15_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_15_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_15_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(313234432)))]; + tensor linear_139_cast_fp16 = linear(bias = encoder_layers_15_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_15_self_attn_linear_k_weight_to_fp16_palettized, x = input_819_cast_fp16)[name = string("linear_139_cast_fp16")]; + tensor var_3690 = const()[name = string("op_3690"), val = tensor([1, -1, 8, 128])]; + tensor k_61_cast_fp16 = reshape(shape = var_3690, x = linear_139_cast_fp16)[name = string("k_61_cast_fp16")]; + tensor encoder_layers_15_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(313236544))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(314023040))))[name = string("encoder_layers_15_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_15_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_15_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(314023232)))]; + tensor linear_140_cast_fp16 = linear(bias = encoder_layers_15_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_15_self_attn_linear_v_weight_to_fp16_palettized, x = input_819_cast_fp16)[name = string("linear_140_cast_fp16")]; + tensor var_3695 = const()[name = string("op_3695"), val = tensor([1, -1, 8, 128])]; + tensor v_31_cast_fp16 = reshape(shape = var_3695, x = linear_140_cast_fp16)[name = string("v_31_cast_fp16")]; + tensor value_39_perm_0 = const()[name = string("value_39_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_15_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_15_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(314025344)))]; + tensor var_3708_cast_fp16 = add(x = q_91_cast_fp16, y = encoder_layers_15_self_attn_pos_bias_u_to_fp16)[name = string("op_3708_cast_fp16")]; + tensor encoder_layers_15_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_15_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(314027456)))]; + tensor var_3710_cast_fp16 = add(x = q_91_cast_fp16, y = encoder_layers_15_self_attn_pos_bias_v_to_fp16)[name = string("op_3710_cast_fp16")]; + tensor q_with_bias_v_31_perm_0 = const()[name = string("q_with_bias_v_31_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_397_transpose_x_0 = const()[name = string("x_397_transpose_x_0"), val = bool(false)]; + bool x_397_transpose_y_0 = const()[name = string("x_397_transpose_y_0"), val = bool(false)]; + tensor op_3712_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(314029568))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(314229312))))[name = string("op_3712_to_fp16_quantized")]; + tensor q_with_bias_v_31_cast_fp16 = transpose(perm = q_with_bias_v_31_perm_0, x = var_3710_cast_fp16)[name = string("transpose_227")]; + tensor x_397_cast_fp16 = matmul(transpose_x = x_397_transpose_x_0, transpose_y = x_397_transpose_y_0, x = q_with_bias_v_31_cast_fp16, y = op_3712_to_fp16_quantized)[name = string("x_397_cast_fp16")]; + tensor x_399_pad_0 = const()[name = string("x_399_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_399_mode_0 = const()[name = string("x_399_mode_0"), val = string("constant")]; + fp16 const_274_to_fp16 = const()[name = string("const_274_to_fp16"), val = fp16(0x0p+0)]; + tensor x_399_cast_fp16 = pad(constant_val = const_274_to_fp16, mode = x_399_mode_0, pad = x_399_pad_0, x = x_397_cast_fp16)[name = string("x_399_cast_fp16")]; + tensor var_3720 = const()[name = string("op_3720"), val = tensor([1, 8, -1, 56])]; + tensor x_401_cast_fp16 = reshape(shape = var_3720, x = x_399_cast_fp16)[name = string("x_401_cast_fp16")]; + tensor var_3724_begin_0 = const()[name = string("op_3724_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_3724_end_0 = const()[name = string("op_3724_end_0"), val = tensor([1, 8, 196, 56])]; + tensor var_3724_end_mask_0 = const()[name = string("op_3724_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_3724_cast_fp16 = slice_by_index(begin = var_3724_begin_0, end = var_3724_end_0, end_mask = var_3724_end_mask_0, x = x_401_cast_fp16)[name = string("op_3724_cast_fp16")]; + tensor var_3725 = const()[name = string("op_3725"), val = tensor([1, 8, 56, 195])]; + tensor matrix_bd_61_cast_fp16 = reshape(shape = var_3725, x = var_3724_cast_fp16)[name = string("matrix_bd_61_cast_fp16")]; + bool matrix_ac_31_transpose_x_0 = const()[name = string("matrix_ac_31_transpose_x_0"), val = bool(false)]; + bool matrix_ac_31_transpose_y_0 = const()[name = string("matrix_ac_31_transpose_y_0"), val = bool(false)]; + tensor transpose_126_perm_0 = const()[name = string("transpose_126_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_127_perm_0 = const()[name = string("transpose_127_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_127 = transpose(perm = transpose_127_perm_0, x = k_61_cast_fp16)[name = string("transpose_225")]; + tensor transpose_126 = transpose(perm = transpose_126_perm_0, x = var_3708_cast_fp16)[name = string("transpose_226")]; + tensor matrix_ac_31_cast_fp16 = matmul(transpose_x = matrix_ac_31_transpose_x_0, transpose_y = matrix_ac_31_transpose_y_0, x = transpose_126, y = transpose_127)[name = string("matrix_ac_31_cast_fp16")]; + tensor matrix_bd_63_begin_0 = const()[name = string("matrix_bd_63_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_63_end_0 = const()[name = string("matrix_bd_63_end_0"), val = tensor([1, 8, 56, 98])]; + tensor matrix_bd_63_end_mask_0 = const()[name = string("matrix_bd_63_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_63_cast_fp16 = slice_by_index(begin = matrix_bd_63_begin_0, end = matrix_bd_63_end_0, end_mask = matrix_bd_63_end_mask_0, x = matrix_bd_61_cast_fp16)[name = string("matrix_bd_63_cast_fp16")]; + tensor var_3734_cast_fp16 = add(x = matrix_ac_31_cast_fp16, y = matrix_bd_63_cast_fp16)[name = string("op_3734_cast_fp16")]; + fp16 _inversed_scores_61_y_0_to_fp16 = const()[name = string("_inversed_scores_61_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_61_cast_fp16 = mul(x = var_3734_cast_fp16, y = _inversed_scores_61_y_0_to_fp16)[name = string("_inversed_scores_61_cast_fp16")]; + tensor scores_63_cast_fp16 = select(a = var_46_to_fp16, b = _inversed_scores_61_cast_fp16, cond = mask_11)[name = string("scores_63_cast_fp16")]; + tensor var_3740_cast_fp16 = softmax(axis = var_60, x = scores_63_cast_fp16)[name = string("op_3740_cast_fp16")]; + tensor input_821_cast_fp16 = select(a = var_45_to_fp16, b = var_3740_cast_fp16, cond = mask_11)[name = string("input_821_cast_fp16")]; + bool x_403_transpose_x_0 = const()[name = string("x_403_transpose_x_0"), val = bool(false)]; + bool x_403_transpose_y_0 = const()[name = string("x_403_transpose_y_0"), val = bool(false)]; + tensor value_39_cast_fp16 = transpose(perm = value_39_perm_0, x = v_31_cast_fp16)[name = string("transpose_224")]; + tensor x_403_cast_fp16 = matmul(transpose_x = x_403_transpose_x_0, transpose_y = x_403_transpose_y_0, x = input_821_cast_fp16, y = value_39_cast_fp16)[name = string("x_403_cast_fp16")]; + tensor var_3744_perm_0 = const()[name = string("op_3744_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3745 = const()[name = string("op_3745"), val = tensor([1, -1, 1024])]; + tensor var_3744_cast_fp16 = transpose(perm = var_3744_perm_0, x = x_403_cast_fp16)[name = string("transpose_223")]; + tensor input_823_cast_fp16 = reshape(shape = var_3745, x = var_3744_cast_fp16)[name = string("input_823_cast_fp16")]; + tensor encoder_layers_15_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(314229824))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(315016320))))[name = string("encoder_layers_15_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_15_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_15_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(315016512)))]; + tensor linear_142_cast_fp16 = linear(bias = encoder_layers_15_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_15_self_attn_linear_out_weight_to_fp16_palettized, x = input_823_cast_fp16)[name = string("linear_142_cast_fp16")]; + tensor input_827_cast_fp16 = add(x = input_817_cast_fp16, y = linear_142_cast_fp16)[name = string("input_827_cast_fp16")]; + tensor x_407_axes_0 = const()[name = string("x_407_axes_0"), val = tensor([-1])]; + tensor encoder_layers_15_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_15_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(315018624)))]; + tensor encoder_layers_15_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_15_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(315020736)))]; + tensor x_407_cast_fp16 = layer_norm(axes = x_407_axes_0, beta = encoder_layers_15_norm_conv_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_15_norm_conv_weight_to_fp16, x = input_827_cast_fp16)[name = string("x_407_cast_fp16")]; + tensor input_829_perm_0 = const()[name = string("input_829_perm_0"), val = tensor([0, 2, 1])]; + string input_831_pad_type_0 = const()[name = string("input_831_pad_type_0"), val = string("valid")]; + tensor input_831_strides_0 = const()[name = string("input_831_strides_0"), val = tensor([1])]; + tensor input_831_pad_0 = const()[name = string("input_831_pad_0"), val = tensor([0, 0])]; + tensor input_831_dilations_0 = const()[name = string("input_831_dilations_0"), val = tensor([1])]; + int32 input_831_groups_0 = const()[name = string("input_831_groups_0"), val = int32(1)]; + tensor encoder_layers_15_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(315022848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(317120064))))[name = string("encoder_layers_15_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_829_cast_fp16 = transpose(perm = input_829_perm_0, x = x_407_cast_fp16)[name = string("transpose_222")]; + tensor input_831_cast_fp16 = conv(dilations = input_831_dilations_0, groups = input_831_groups_0, pad = input_831_pad_0, pad_type = input_831_pad_type_0, strides = input_831_strides_0, weight = encoder_layers_15_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_829_cast_fp16)[name = string("input_831_cast_fp16")]; + int32 x_409_split_num_splits_0 = const()[name = string("x_409_split_num_splits_0"), val = int32(2)]; + int32 x_409_split_axis_0 = const()[name = string("x_409_split_axis_0"), val = int32(1)]; + tensor x_409_split_cast_fp16_0, tensor x_409_split_cast_fp16_1 = split(axis = x_409_split_axis_0, num_splits = x_409_split_num_splits_0, x = input_831_cast_fp16)[name = string("x_409_split_cast_fp16")]; + tensor x_409_split_1_sigmoid_cast_fp16 = sigmoid(x = x_409_split_cast_fp16_1)[name = string("x_409_split_1_sigmoid_cast_fp16")]; + tensor x_409_cast_fp16 = mul(x = x_409_split_cast_fp16_0, y = x_409_split_1_sigmoid_cast_fp16)[name = string("x_409_cast_fp16")]; + tensor input_833_cast_fp16 = select(a = var_45_to_fp16, b = x_409_cast_fp16, cond = var_576)[name = string("input_833_cast_fp16")]; + bool new_x_63_interleave_0 = const()[name = string("new_x_63_interleave_0"), val = bool(false)]; + tensor new_x_63_cast_fp16 = concat(axis = var_60, interleave = new_x_63_interleave_0, values = (cache_63_cast_fp16, input_833_cast_fp16))[name = string("new_x_63_cast_fp16")]; + tensor var_3784_begin_0 = const()[name = string("op_3784_begin_0"), val = tensor([0, 0, 56])]; + tensor var_3784_end_0 = const()[name = string("op_3784_end_0"), val = tensor([1, 1024, 64])]; + tensor var_3784_end_mask_0 = const()[name = string("op_3784_end_mask_0"), val = tensor([true, true, true])]; + tensor var_3784_cast_fp16 = slice_by_index(begin = var_3784_begin_0, end = var_3784_end_0, end_mask = var_3784_end_mask_0, x = new_x_63_cast_fp16)[name = string("op_3784_cast_fp16")]; + string x_411_pad_type_0 = const()[name = string("x_411_pad_type_0"), val = string("valid")]; + int32 x_411_groups_0 = const()[name = string("x_411_groups_0"), val = int32(1024)]; + tensor x_411_strides_0 = const()[name = string("x_411_strides_0"), val = tensor([1])]; + tensor x_411_pad_0 = const()[name = string("x_411_pad_0"), val = tensor([0, 0])]; + tensor x_411_dilations_0 = const()[name = string("x_411_dilations_0"), val = tensor([1])]; + tensor encoder_layers_15_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(317124224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(317133504))))[name = string("encoder_layers_15_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_411_cast_fp16 = conv(dilations = x_411_dilations_0, groups = x_411_groups_0, pad = x_411_pad_0, pad_type = x_411_pad_type_0, strides = x_411_strides_0, weight = encoder_layers_15_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_63_cast_fp16)[name = string("x_411_cast_fp16")]; + tensor input_835_perm_0 = const()[name = string("input_835_perm_0"), val = tensor([0, 2, 1])]; + tensor x_413_axes_0 = const()[name = string("x_413_axes_0"), val = tensor([-1])]; + tensor encoder_layers_15_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_15_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(317135616)))]; + tensor encoder_layers_15_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_15_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(317137728)))]; + tensor input_835_cast_fp16 = transpose(perm = input_835_perm_0, x = x_411_cast_fp16)[name = string("transpose_221")]; + tensor x_413_cast_fp16 = layer_norm(axes = x_413_axes_0, beta = encoder_layers_15_conv_batch_norm_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_15_conv_batch_norm_weight_to_fp16, x = input_835_cast_fp16)[name = string("x_413_cast_fp16")]; + tensor input_837_perm_0 = const()[name = string("input_837_perm_0"), val = tensor([0, 2, 1])]; + tensor input_837_cast_fp16 = transpose(perm = input_837_perm_0, x = x_413_cast_fp16)[name = string("transpose_220")]; + tensor input_839_cast_fp16 = silu(x = input_837_cast_fp16)[name = string("input_839_cast_fp16")]; + string x_415_pad_type_0 = const()[name = string("x_415_pad_type_0"), val = string("valid")]; + tensor x_415_strides_0 = const()[name = string("x_415_strides_0"), val = tensor([1])]; + tensor x_415_pad_0 = const()[name = string("x_415_pad_0"), val = tensor([0, 0])]; + tensor x_415_dilations_0 = const()[name = string("x_415_dilations_0"), val = tensor([1])]; + int32 x_415_groups_0 = const()[name = string("x_415_groups_0"), val = int32(1)]; + tensor encoder_layers_15_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(317139840))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(318188480))))[name = string("encoder_layers_15_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_415_cast_fp16 = conv(dilations = x_415_dilations_0, groups = x_415_groups_0, pad = x_415_pad_0, pad_type = x_415_pad_type_0, strides = x_415_strides_0, weight = encoder_layers_15_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_839_cast_fp16)[name = string("x_415_cast_fp16")]; + tensor input_841_perm_0 = const()[name = string("input_841_perm_0"), val = tensor([0, 2, 1])]; + tensor input_841_cast_fp16 = transpose(perm = input_841_perm_0, x = x_415_cast_fp16)[name = string("transpose_219")]; + tensor input_843_cast_fp16 = add(x = input_827_cast_fp16, y = input_841_cast_fp16)[name = string("input_843_cast_fp16")]; + tensor input_845_axes_0 = const()[name = string("input_845_axes_0"), val = tensor([-1])]; + tensor encoder_layers_15_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_15_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(318190592)))]; + tensor encoder_layers_15_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_15_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(318192704)))]; + tensor input_845_cast_fp16 = layer_norm(axes = input_845_axes_0, beta = encoder_layers_15_norm_feed_forward2_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_15_norm_feed_forward2_weight_to_fp16, x = input_843_cast_fp16)[name = string("input_845_cast_fp16")]; + tensor encoder_layers_15_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(318194816))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(321340608))))[name = string("encoder_layers_15_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_15_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_15_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(321340800)))]; + tensor linear_143_cast_fp16 = linear(bias = encoder_layers_15_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_15_feed_forward2_linear1_weight_to_fp16_palettized, x = input_845_cast_fp16)[name = string("linear_143_cast_fp16")]; + tensor input_849_cast_fp16 = silu(x = linear_143_cast_fp16)[name = string("input_849_cast_fp16")]; + tensor encoder_layers_15_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(321349056))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324494848))))[name = string("encoder_layers_15_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_15_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_15_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324495040)))]; + tensor linear_144_cast_fp16 = linear(bias = encoder_layers_15_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_15_feed_forward2_linear2_weight_to_fp16_palettized, x = input_849_cast_fp16)[name = string("linear_144_cast_fp16")]; + fp16 var_3827_to_fp16 = const()[name = string("op_3827_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3828_cast_fp16 = mul(x = linear_144_cast_fp16, y = var_3827_to_fp16)[name = string("op_3828_cast_fp16")]; + tensor input_855_cast_fp16 = add(x = input_843_cast_fp16, y = var_3828_cast_fp16)[name = string("input_855_cast_fp16")]; + tensor input_857_axes_0 = const()[name = string("input_857_axes_0"), val = tensor([-1])]; + tensor encoder_layers_15_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_15_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324497152)))]; + tensor encoder_layers_15_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_15_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324499264)))]; + tensor input_857_cast_fp16 = layer_norm(axes = input_857_axes_0, beta = encoder_layers_15_norm_out_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_15_norm_out_weight_to_fp16, x = input_855_cast_fp16)[name = string("input_857_cast_fp16")]; + tensor cache_65_begin_0 = const()[name = string("cache_65_begin_0"), val = tensor([16, 0, 0, 0])]; + tensor cache_65_end_0 = const()[name = string("cache_65_end_0"), val = tensor([17, 1, 42, 1024])]; + tensor cache_65_end_mask_0 = const()[name = string("cache_65_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_65_squeeze_mask_0 = const()[name = string("cache_65_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_65_cast_fp16 = slice_by_index(begin = cache_65_begin_0, end = cache_65_end_0, end_mask = cache_65_end_mask_0, squeeze_mask = cache_65_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_65_cast_fp16")]; + tensor cache_67_begin_0 = const()[name = string("cache_67_begin_0"), val = tensor([16, 0, 0, 0])]; + tensor cache_67_end_0 = const()[name = string("cache_67_end_0"), val = tensor([17, 1, 1024, 8])]; + tensor cache_67_end_mask_0 = const()[name = string("cache_67_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_67_squeeze_mask_0 = const()[name = string("cache_67_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_67_cast_fp16 = slice_by_index(begin = cache_67_begin_0, end = cache_67_end_0, end_mask = cache_67_end_mask_0, squeeze_mask = cache_67_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_67_cast_fp16")]; + tensor input_859_axes_0 = const()[name = string("input_859_axes_0"), val = tensor([-1])]; + tensor encoder_layers_16_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_16_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324501376)))]; + tensor encoder_layers_16_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_16_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324503488)))]; + tensor input_859_cast_fp16 = layer_norm(axes = input_859_axes_0, beta = encoder_layers_16_norm_feed_forward1_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_16_norm_feed_forward1_weight_to_fp16, x = input_857_cast_fp16)[name = string("input_859_cast_fp16")]; + tensor encoder_layers_16_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324505600))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(327651392))))[name = string("encoder_layers_16_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_16_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_16_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(327651584)))]; + tensor linear_145_cast_fp16 = linear(bias = encoder_layers_16_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_16_feed_forward1_linear1_weight_to_fp16_palettized, x = input_859_cast_fp16)[name = string("linear_145_cast_fp16")]; + tensor input_863_cast_fp16 = silu(x = linear_145_cast_fp16)[name = string("input_863_cast_fp16")]; + tensor encoder_layers_16_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(327659840))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(330805632))))[name = string("encoder_layers_16_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_16_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_16_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(330805824)))]; + tensor linear_146_cast_fp16 = linear(bias = encoder_layers_16_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_16_feed_forward1_linear2_weight_to_fp16_palettized, x = input_863_cast_fp16)[name = string("linear_146_cast_fp16")]; + fp16 var_3864_to_fp16 = const()[name = string("op_3864_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3865_cast_fp16 = mul(x = linear_146_cast_fp16, y = var_3864_to_fp16)[name = string("op_3865_cast_fp16")]; + tensor input_869_cast_fp16 = add(x = input_857_cast_fp16, y = var_3865_cast_fp16)[name = string("input_869_cast_fp16")]; + tensor key_33_axes_0 = const()[name = string("key_33_axes_0"), val = tensor([-1])]; + tensor encoder_layers_16_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_16_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(330807936)))]; + tensor encoder_layers_16_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_16_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(330810048)))]; + tensor key_33_cast_fp16 = layer_norm(axes = key_33_axes_0, beta = encoder_layers_16_norm_self_att_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_16_norm_self_att_weight_to_fp16, x = input_869_cast_fp16)[name = string("key_33_cast_fp16")]; + bool input_871_interleave_0 = const()[name = string("input_871_interleave_0"), val = bool(false)]; + tensor input_871_cast_fp16 = concat(axis = var_69, interleave = input_871_interleave_0, values = (cache_65_cast_fp16, key_33_cast_fp16))[name = string("input_871_cast_fp16")]; + bool var_3893_interleave_0 = const()[name = string("op_3893_interleave_0"), val = bool(false)]; + tensor var_3893_cast_fp16 = concat(axis = var_69, interleave = var_3893_interleave_0, values = key_33_cast_fp16)[name = string("op_3893_cast_fp16")]; + tensor encoder_layers_16_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(330812160))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(331598656))))[name = string("encoder_layers_16_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_16_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_16_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(331598848)))]; + tensor linear_147_cast_fp16 = linear(bias = encoder_layers_16_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_16_self_attn_linear_q_weight_to_fp16_palettized, x = key_33_cast_fp16)[name = string("linear_147_cast_fp16")]; + tensor var_3898 = const()[name = string("op_3898"), val = tensor([1, -1, 8, 128])]; + tensor q_97_cast_fp16 = reshape(shape = var_3898, x = linear_147_cast_fp16)[name = string("q_97_cast_fp16")]; + tensor encoder_layers_16_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(331600960))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(332387456))))[name = string("encoder_layers_16_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_16_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_16_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(332387648)))]; + tensor linear_148_cast_fp16 = linear(bias = encoder_layers_16_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_16_self_attn_linear_k_weight_to_fp16_palettized, x = input_871_cast_fp16)[name = string("linear_148_cast_fp16")]; + tensor var_3903 = const()[name = string("op_3903"), val = tensor([1, -1, 8, 128])]; + tensor k_65_cast_fp16 = reshape(shape = var_3903, x = linear_148_cast_fp16)[name = string("k_65_cast_fp16")]; + tensor encoder_layers_16_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(332389760))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(333176256))))[name = string("encoder_layers_16_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_16_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_16_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(333176448)))]; + tensor linear_149_cast_fp16 = linear(bias = encoder_layers_16_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_16_self_attn_linear_v_weight_to_fp16_palettized, x = input_871_cast_fp16)[name = string("linear_149_cast_fp16")]; + tensor var_3908 = const()[name = string("op_3908"), val = tensor([1, -1, 8, 128])]; + tensor v_33_cast_fp16 = reshape(shape = var_3908, x = linear_149_cast_fp16)[name = string("v_33_cast_fp16")]; + tensor value_41_perm_0 = const()[name = string("value_41_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_16_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_16_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(333178560)))]; + tensor var_3921_cast_fp16 = add(x = q_97_cast_fp16, y = encoder_layers_16_self_attn_pos_bias_u_to_fp16)[name = string("op_3921_cast_fp16")]; + tensor encoder_layers_16_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_16_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(333180672)))]; + tensor var_3923_cast_fp16 = add(x = q_97_cast_fp16, y = encoder_layers_16_self_attn_pos_bias_v_to_fp16)[name = string("op_3923_cast_fp16")]; + tensor q_with_bias_v_33_perm_0 = const()[name = string("q_with_bias_v_33_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_423_transpose_x_0 = const()[name = string("x_423_transpose_x_0"), val = bool(false)]; + bool x_423_transpose_y_0 = const()[name = string("x_423_transpose_y_0"), val = bool(false)]; + tensor op_3925_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(333182784))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(333382528))))[name = string("op_3925_to_fp16_quantized")]; + tensor q_with_bias_v_33_cast_fp16 = transpose(perm = q_with_bias_v_33_perm_0, x = var_3923_cast_fp16)[name = string("transpose_218")]; + tensor x_423_cast_fp16 = matmul(transpose_x = x_423_transpose_x_0, transpose_y = x_423_transpose_y_0, x = q_with_bias_v_33_cast_fp16, y = op_3925_to_fp16_quantized)[name = string("x_423_cast_fp16")]; + tensor x_425_pad_0 = const()[name = string("x_425_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_425_mode_0 = const()[name = string("x_425_mode_0"), val = string("constant")]; + fp16 const_287_to_fp16 = const()[name = string("const_287_to_fp16"), val = fp16(0x0p+0)]; + tensor x_425_cast_fp16 = pad(constant_val = const_287_to_fp16, mode = x_425_mode_0, pad = x_425_pad_0, x = x_423_cast_fp16)[name = string("x_425_cast_fp16")]; + tensor var_3933 = const()[name = string("op_3933"), val = tensor([1, 8, -1, 56])]; + tensor x_427_cast_fp16 = reshape(shape = var_3933, x = x_425_cast_fp16)[name = string("x_427_cast_fp16")]; + tensor var_3937_begin_0 = const()[name = string("op_3937_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_3937_end_0 = const()[name = string("op_3937_end_0"), val = tensor([1, 8, 196, 56])]; + tensor var_3937_end_mask_0 = const()[name = string("op_3937_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_3937_cast_fp16 = slice_by_index(begin = var_3937_begin_0, end = var_3937_end_0, end_mask = var_3937_end_mask_0, x = x_427_cast_fp16)[name = string("op_3937_cast_fp16")]; + tensor var_3938 = const()[name = string("op_3938"), val = tensor([1, 8, 56, 195])]; + tensor matrix_bd_65_cast_fp16 = reshape(shape = var_3938, x = var_3937_cast_fp16)[name = string("matrix_bd_65_cast_fp16")]; + bool matrix_ac_33_transpose_x_0 = const()[name = string("matrix_ac_33_transpose_x_0"), val = bool(false)]; + bool matrix_ac_33_transpose_y_0 = const()[name = string("matrix_ac_33_transpose_y_0"), val = bool(false)]; + tensor transpose_128_perm_0 = const()[name = string("transpose_128_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_129_perm_0 = const()[name = string("transpose_129_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_129 = transpose(perm = transpose_129_perm_0, x = k_65_cast_fp16)[name = string("transpose_216")]; + tensor transpose_128 = transpose(perm = transpose_128_perm_0, x = var_3921_cast_fp16)[name = string("transpose_217")]; + tensor matrix_ac_33_cast_fp16 = matmul(transpose_x = matrix_ac_33_transpose_x_0, transpose_y = matrix_ac_33_transpose_y_0, x = transpose_128, y = transpose_129)[name = string("matrix_ac_33_cast_fp16")]; + tensor matrix_bd_67_begin_0 = const()[name = string("matrix_bd_67_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_67_end_0 = const()[name = string("matrix_bd_67_end_0"), val = tensor([1, 8, 56, 98])]; + tensor matrix_bd_67_end_mask_0 = const()[name = string("matrix_bd_67_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_67_cast_fp16 = slice_by_index(begin = matrix_bd_67_begin_0, end = matrix_bd_67_end_0, end_mask = matrix_bd_67_end_mask_0, x = matrix_bd_65_cast_fp16)[name = string("matrix_bd_67_cast_fp16")]; + tensor var_3947_cast_fp16 = add(x = matrix_ac_33_cast_fp16, y = matrix_bd_67_cast_fp16)[name = string("op_3947_cast_fp16")]; + fp16 _inversed_scores_65_y_0_to_fp16 = const()[name = string("_inversed_scores_65_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_65_cast_fp16 = mul(x = var_3947_cast_fp16, y = _inversed_scores_65_y_0_to_fp16)[name = string("_inversed_scores_65_cast_fp16")]; + tensor scores_67_cast_fp16 = select(a = var_46_to_fp16, b = _inversed_scores_65_cast_fp16, cond = mask_11)[name = string("scores_67_cast_fp16")]; + tensor var_3953_cast_fp16 = softmax(axis = var_60, x = scores_67_cast_fp16)[name = string("op_3953_cast_fp16")]; + tensor input_873_cast_fp16 = select(a = var_45_to_fp16, b = var_3953_cast_fp16, cond = mask_11)[name = string("input_873_cast_fp16")]; + bool x_429_transpose_x_0 = const()[name = string("x_429_transpose_x_0"), val = bool(false)]; + bool x_429_transpose_y_0 = const()[name = string("x_429_transpose_y_0"), val = bool(false)]; + tensor value_41_cast_fp16 = transpose(perm = value_41_perm_0, x = v_33_cast_fp16)[name = string("transpose_215")]; + tensor x_429_cast_fp16 = matmul(transpose_x = x_429_transpose_x_0, transpose_y = x_429_transpose_y_0, x = input_873_cast_fp16, y = value_41_cast_fp16)[name = string("x_429_cast_fp16")]; + tensor var_3957_perm_0 = const()[name = string("op_3957_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3958 = const()[name = string("op_3958"), val = tensor([1, -1, 1024])]; + tensor var_3957_cast_fp16 = transpose(perm = var_3957_perm_0, x = x_429_cast_fp16)[name = string("transpose_214")]; + tensor input_875_cast_fp16 = reshape(shape = var_3958, x = var_3957_cast_fp16)[name = string("input_875_cast_fp16")]; + tensor encoder_layers_16_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(333383040))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(334169536))))[name = string("encoder_layers_16_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_16_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_16_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(334169728)))]; + tensor linear_151_cast_fp16 = linear(bias = encoder_layers_16_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_16_self_attn_linear_out_weight_to_fp16_palettized, x = input_875_cast_fp16)[name = string("linear_151_cast_fp16")]; + tensor input_879_cast_fp16 = add(x = input_869_cast_fp16, y = linear_151_cast_fp16)[name = string("input_879_cast_fp16")]; + tensor x_433_axes_0 = const()[name = string("x_433_axes_0"), val = tensor([-1])]; + tensor encoder_layers_16_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_16_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(334171840)))]; + tensor encoder_layers_16_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_16_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(334173952)))]; + tensor x_433_cast_fp16 = layer_norm(axes = x_433_axes_0, beta = encoder_layers_16_norm_conv_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_16_norm_conv_weight_to_fp16, x = input_879_cast_fp16)[name = string("x_433_cast_fp16")]; + tensor input_881_perm_0 = const()[name = string("input_881_perm_0"), val = tensor([0, 2, 1])]; + string input_883_pad_type_0 = const()[name = string("input_883_pad_type_0"), val = string("valid")]; + tensor input_883_strides_0 = const()[name = string("input_883_strides_0"), val = tensor([1])]; + tensor input_883_pad_0 = const()[name = string("input_883_pad_0"), val = tensor([0, 0])]; + tensor input_883_dilations_0 = const()[name = string("input_883_dilations_0"), val = tensor([1])]; + int32 input_883_groups_0 = const()[name = string("input_883_groups_0"), val = int32(1)]; + tensor encoder_layers_16_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(334176064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(336273280))))[name = string("encoder_layers_16_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_881_cast_fp16 = transpose(perm = input_881_perm_0, x = x_433_cast_fp16)[name = string("transpose_213")]; + tensor input_883_cast_fp16 = conv(dilations = input_883_dilations_0, groups = input_883_groups_0, pad = input_883_pad_0, pad_type = input_883_pad_type_0, strides = input_883_strides_0, weight = encoder_layers_16_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_881_cast_fp16)[name = string("input_883_cast_fp16")]; + int32 x_435_split_num_splits_0 = const()[name = string("x_435_split_num_splits_0"), val = int32(2)]; + int32 x_435_split_axis_0 = const()[name = string("x_435_split_axis_0"), val = int32(1)]; + tensor x_435_split_cast_fp16_0, tensor x_435_split_cast_fp16_1 = split(axis = x_435_split_axis_0, num_splits = x_435_split_num_splits_0, x = input_883_cast_fp16)[name = string("x_435_split_cast_fp16")]; + tensor x_435_split_1_sigmoid_cast_fp16 = sigmoid(x = x_435_split_cast_fp16_1)[name = string("x_435_split_1_sigmoid_cast_fp16")]; + tensor x_435_cast_fp16 = mul(x = x_435_split_cast_fp16_0, y = x_435_split_1_sigmoid_cast_fp16)[name = string("x_435_cast_fp16")]; + tensor input_885_cast_fp16 = select(a = var_45_to_fp16, b = x_435_cast_fp16, cond = var_576)[name = string("input_885_cast_fp16")]; + bool new_x_67_interleave_0 = const()[name = string("new_x_67_interleave_0"), val = bool(false)]; + tensor new_x_67_cast_fp16 = concat(axis = var_60, interleave = new_x_67_interleave_0, values = (cache_67_cast_fp16, input_885_cast_fp16))[name = string("new_x_67_cast_fp16")]; + tensor var_3997_begin_0 = const()[name = string("op_3997_begin_0"), val = tensor([0, 0, 56])]; + tensor var_3997_end_0 = const()[name = string("op_3997_end_0"), val = tensor([1, 1024, 64])]; + tensor var_3997_end_mask_0 = const()[name = string("op_3997_end_mask_0"), val = tensor([true, true, true])]; + tensor var_3997_cast_fp16 = slice_by_index(begin = var_3997_begin_0, end = var_3997_end_0, end_mask = var_3997_end_mask_0, x = new_x_67_cast_fp16)[name = string("op_3997_cast_fp16")]; + string x_437_pad_type_0 = const()[name = string("x_437_pad_type_0"), val = string("valid")]; + int32 x_437_groups_0 = const()[name = string("x_437_groups_0"), val = int32(1024)]; + tensor x_437_strides_0 = const()[name = string("x_437_strides_0"), val = tensor([1])]; + tensor x_437_pad_0 = const()[name = string("x_437_pad_0"), val = tensor([0, 0])]; + tensor x_437_dilations_0 = const()[name = string("x_437_dilations_0"), val = tensor([1])]; + tensor encoder_layers_16_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(336277440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(336286720))))[name = string("encoder_layers_16_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_437_cast_fp16 = conv(dilations = x_437_dilations_0, groups = x_437_groups_0, pad = x_437_pad_0, pad_type = x_437_pad_type_0, strides = x_437_strides_0, weight = encoder_layers_16_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_67_cast_fp16)[name = string("x_437_cast_fp16")]; + tensor input_887_perm_0 = const()[name = string("input_887_perm_0"), val = tensor([0, 2, 1])]; + tensor x_439_axes_0 = const()[name = string("x_439_axes_0"), val = tensor([-1])]; + tensor encoder_layers_16_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_16_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(336288832)))]; + tensor encoder_layers_16_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_16_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(336290944)))]; + tensor input_887_cast_fp16 = transpose(perm = input_887_perm_0, x = x_437_cast_fp16)[name = string("transpose_212")]; + tensor x_439_cast_fp16 = layer_norm(axes = x_439_axes_0, beta = encoder_layers_16_conv_batch_norm_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_16_conv_batch_norm_weight_to_fp16, x = input_887_cast_fp16)[name = string("x_439_cast_fp16")]; + tensor input_889_perm_0 = const()[name = string("input_889_perm_0"), val = tensor([0, 2, 1])]; + tensor input_889_cast_fp16 = transpose(perm = input_889_perm_0, x = x_439_cast_fp16)[name = string("transpose_211")]; + tensor input_891_cast_fp16 = silu(x = input_889_cast_fp16)[name = string("input_891_cast_fp16")]; + string x_441_pad_type_0 = const()[name = string("x_441_pad_type_0"), val = string("valid")]; + tensor x_441_strides_0 = const()[name = string("x_441_strides_0"), val = tensor([1])]; + tensor x_441_pad_0 = const()[name = string("x_441_pad_0"), val = tensor([0, 0])]; + tensor x_441_dilations_0 = const()[name = string("x_441_dilations_0"), val = tensor([1])]; + int32 x_441_groups_0 = const()[name = string("x_441_groups_0"), val = int32(1)]; + tensor encoder_layers_16_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(336293056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(337341696))))[name = string("encoder_layers_16_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_441_cast_fp16 = conv(dilations = x_441_dilations_0, groups = x_441_groups_0, pad = x_441_pad_0, pad_type = x_441_pad_type_0, strides = x_441_strides_0, weight = encoder_layers_16_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_891_cast_fp16)[name = string("x_441_cast_fp16")]; + tensor input_893_perm_0 = const()[name = string("input_893_perm_0"), val = tensor([0, 2, 1])]; + tensor input_893_cast_fp16 = transpose(perm = input_893_perm_0, x = x_441_cast_fp16)[name = string("transpose_210")]; + tensor input_895_cast_fp16 = add(x = input_879_cast_fp16, y = input_893_cast_fp16)[name = string("input_895_cast_fp16")]; + tensor input_897_axes_0 = const()[name = string("input_897_axes_0"), val = tensor([-1])]; + tensor encoder_layers_16_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_16_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(337343808)))]; + tensor encoder_layers_16_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_16_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(337345920)))]; + tensor input_897_cast_fp16 = layer_norm(axes = input_897_axes_0, beta = encoder_layers_16_norm_feed_forward2_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_16_norm_feed_forward2_weight_to_fp16, x = input_895_cast_fp16)[name = string("input_897_cast_fp16")]; + tensor encoder_layers_16_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(337348032))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(340493824))))[name = string("encoder_layers_16_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_16_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_16_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(340494016)))]; + tensor linear_152_cast_fp16 = linear(bias = encoder_layers_16_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_16_feed_forward2_linear1_weight_to_fp16_palettized, x = input_897_cast_fp16)[name = string("linear_152_cast_fp16")]; + tensor input_901_cast_fp16 = silu(x = linear_152_cast_fp16)[name = string("input_901_cast_fp16")]; + tensor encoder_layers_16_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(340502272))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(343648064))))[name = string("encoder_layers_16_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_16_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_16_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(343648256)))]; + tensor linear_153_cast_fp16 = linear(bias = encoder_layers_16_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_16_feed_forward2_linear2_weight_to_fp16_palettized, x = input_901_cast_fp16)[name = string("linear_153_cast_fp16")]; + fp16 var_4040_to_fp16 = const()[name = string("op_4040_to_fp16"), val = fp16(0x1p-1)]; + tensor var_4041_cast_fp16 = mul(x = linear_153_cast_fp16, y = var_4040_to_fp16)[name = string("op_4041_cast_fp16")]; + tensor input_907_cast_fp16 = add(x = input_895_cast_fp16, y = var_4041_cast_fp16)[name = string("input_907_cast_fp16")]; + tensor input_909_axes_0 = const()[name = string("input_909_axes_0"), val = tensor([-1])]; + tensor encoder_layers_16_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_16_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(343650368)))]; + tensor encoder_layers_16_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_16_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(343652480)))]; + tensor input_909_cast_fp16 = layer_norm(axes = input_909_axes_0, beta = encoder_layers_16_norm_out_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_16_norm_out_weight_to_fp16, x = input_907_cast_fp16)[name = string("input_909_cast_fp16")]; + tensor cache_69_begin_0 = const()[name = string("cache_69_begin_0"), val = tensor([17, 0, 0, 0])]; + tensor cache_69_end_0 = const()[name = string("cache_69_end_0"), val = tensor([18, 1, 42, 1024])]; + tensor cache_69_end_mask_0 = const()[name = string("cache_69_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_69_squeeze_mask_0 = const()[name = string("cache_69_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_69_cast_fp16 = slice_by_index(begin = cache_69_begin_0, end = cache_69_end_0, end_mask = cache_69_end_mask_0, squeeze_mask = cache_69_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_69_cast_fp16")]; + tensor cache_71_begin_0 = const()[name = string("cache_71_begin_0"), val = tensor([17, 0, 0, 0])]; + tensor cache_71_end_0 = const()[name = string("cache_71_end_0"), val = tensor([18, 1, 1024, 8])]; + tensor cache_71_end_mask_0 = const()[name = string("cache_71_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_71_squeeze_mask_0 = const()[name = string("cache_71_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_71_cast_fp16 = slice_by_index(begin = cache_71_begin_0, end = cache_71_end_0, end_mask = cache_71_end_mask_0, squeeze_mask = cache_71_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_71_cast_fp16")]; + tensor input_911_axes_0 = const()[name = string("input_911_axes_0"), val = tensor([-1])]; + tensor encoder_layers_17_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_17_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(343654592)))]; + tensor encoder_layers_17_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_17_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(343656704)))]; + tensor input_911_cast_fp16 = layer_norm(axes = input_911_axes_0, beta = encoder_layers_17_norm_feed_forward1_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_17_norm_feed_forward1_weight_to_fp16, x = input_909_cast_fp16)[name = string("input_911_cast_fp16")]; + tensor encoder_layers_17_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(343658816))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(346804608))))[name = string("encoder_layers_17_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_17_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_17_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(346804800)))]; + tensor linear_154_cast_fp16 = linear(bias = encoder_layers_17_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_17_feed_forward1_linear1_weight_to_fp16_palettized, x = input_911_cast_fp16)[name = string("linear_154_cast_fp16")]; + tensor input_915_cast_fp16 = silu(x = linear_154_cast_fp16)[name = string("input_915_cast_fp16")]; + tensor encoder_layers_17_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(346813056))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(349958848))))[name = string("encoder_layers_17_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_17_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_17_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(349959040)))]; + tensor linear_155_cast_fp16 = linear(bias = encoder_layers_17_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_17_feed_forward1_linear2_weight_to_fp16_palettized, x = input_915_cast_fp16)[name = string("linear_155_cast_fp16")]; + fp16 var_4077_to_fp16 = const()[name = string("op_4077_to_fp16"), val = fp16(0x1p-1)]; + tensor var_4078_cast_fp16 = mul(x = linear_155_cast_fp16, y = var_4077_to_fp16)[name = string("op_4078_cast_fp16")]; + tensor input_921_cast_fp16 = add(x = input_909_cast_fp16, y = var_4078_cast_fp16)[name = string("input_921_cast_fp16")]; + tensor key_35_axes_0 = const()[name = string("key_35_axes_0"), val = tensor([-1])]; + tensor encoder_layers_17_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_17_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(349961152)))]; + tensor encoder_layers_17_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_17_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(349963264)))]; + tensor key_35_cast_fp16 = layer_norm(axes = key_35_axes_0, beta = encoder_layers_17_norm_self_att_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_17_norm_self_att_weight_to_fp16, x = input_921_cast_fp16)[name = string("key_35_cast_fp16")]; + bool input_923_interleave_0 = const()[name = string("input_923_interleave_0"), val = bool(false)]; + tensor input_923_cast_fp16 = concat(axis = var_69, interleave = input_923_interleave_0, values = (cache_69_cast_fp16, key_35_cast_fp16))[name = string("input_923_cast_fp16")]; + bool var_4106_interleave_0 = const()[name = string("op_4106_interleave_0"), val = bool(false)]; + tensor var_4106_cast_fp16 = concat(axis = var_69, interleave = var_4106_interleave_0, values = key_35_cast_fp16)[name = string("op_4106_cast_fp16")]; + tensor encoder_layers_17_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(349965376))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350751872))))[name = string("encoder_layers_17_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_17_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_17_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350752064)))]; + tensor linear_156_cast_fp16 = linear(bias = encoder_layers_17_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_17_self_attn_linear_q_weight_to_fp16_palettized, x = key_35_cast_fp16)[name = string("linear_156_cast_fp16")]; + tensor var_4111 = const()[name = string("op_4111"), val = tensor([1, -1, 8, 128])]; + tensor q_103_cast_fp16 = reshape(shape = var_4111, x = linear_156_cast_fp16)[name = string("q_103_cast_fp16")]; + tensor encoder_layers_17_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350754176))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(351540672))))[name = string("encoder_layers_17_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_17_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_17_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(351540864)))]; + tensor linear_157_cast_fp16 = linear(bias = encoder_layers_17_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_17_self_attn_linear_k_weight_to_fp16_palettized, x = input_923_cast_fp16)[name = string("linear_157_cast_fp16")]; + tensor var_4116 = const()[name = string("op_4116"), val = tensor([1, -1, 8, 128])]; + tensor k_69_cast_fp16 = reshape(shape = var_4116, x = linear_157_cast_fp16)[name = string("k_69_cast_fp16")]; + tensor encoder_layers_17_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(351542976))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(352329472))))[name = string("encoder_layers_17_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_17_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_17_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(352329664)))]; + tensor linear_158_cast_fp16 = linear(bias = encoder_layers_17_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_17_self_attn_linear_v_weight_to_fp16_palettized, x = input_923_cast_fp16)[name = string("linear_158_cast_fp16")]; + tensor var_4121 = const()[name = string("op_4121"), val = tensor([1, -1, 8, 128])]; + tensor v_35_cast_fp16 = reshape(shape = var_4121, x = linear_158_cast_fp16)[name = string("v_35_cast_fp16")]; + tensor value_43_perm_0 = const()[name = string("value_43_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_17_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_17_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(352331776)))]; + tensor var_4134_cast_fp16 = add(x = q_103_cast_fp16, y = encoder_layers_17_self_attn_pos_bias_u_to_fp16)[name = string("op_4134_cast_fp16")]; + tensor encoder_layers_17_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_17_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(352333888)))]; + tensor var_4136_cast_fp16 = add(x = q_103_cast_fp16, y = encoder_layers_17_self_attn_pos_bias_v_to_fp16)[name = string("op_4136_cast_fp16")]; + tensor q_with_bias_v_35_perm_0 = const()[name = string("q_with_bias_v_35_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_449_transpose_x_0 = const()[name = string("x_449_transpose_x_0"), val = bool(false)]; + bool x_449_transpose_y_0 = const()[name = string("x_449_transpose_y_0"), val = bool(false)]; + tensor op_4138_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(352336000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(352535744))))[name = string("op_4138_to_fp16_quantized")]; + tensor q_with_bias_v_35_cast_fp16 = transpose(perm = q_with_bias_v_35_perm_0, x = var_4136_cast_fp16)[name = string("transpose_209")]; + tensor x_449_cast_fp16 = matmul(transpose_x = x_449_transpose_x_0, transpose_y = x_449_transpose_y_0, x = q_with_bias_v_35_cast_fp16, y = op_4138_to_fp16_quantized)[name = string("x_449_cast_fp16")]; + tensor x_451_pad_0 = const()[name = string("x_451_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_451_mode_0 = const()[name = string("x_451_mode_0"), val = string("constant")]; + fp16 const_300_to_fp16 = const()[name = string("const_300_to_fp16"), val = fp16(0x0p+0)]; + tensor x_451_cast_fp16 = pad(constant_val = const_300_to_fp16, mode = x_451_mode_0, pad = x_451_pad_0, x = x_449_cast_fp16)[name = string("x_451_cast_fp16")]; + tensor var_4146 = const()[name = string("op_4146"), val = tensor([1, 8, -1, 56])]; + tensor x_453_cast_fp16 = reshape(shape = var_4146, x = x_451_cast_fp16)[name = string("x_453_cast_fp16")]; + tensor var_4150_begin_0 = const()[name = string("op_4150_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_4150_end_0 = const()[name = string("op_4150_end_0"), val = tensor([1, 8, 196, 56])]; + tensor var_4150_end_mask_0 = const()[name = string("op_4150_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_4150_cast_fp16 = slice_by_index(begin = var_4150_begin_0, end = var_4150_end_0, end_mask = var_4150_end_mask_0, x = x_453_cast_fp16)[name = string("op_4150_cast_fp16")]; + tensor var_4151 = const()[name = string("op_4151"), val = tensor([1, 8, 56, 195])]; + tensor matrix_bd_69_cast_fp16 = reshape(shape = var_4151, x = var_4150_cast_fp16)[name = string("matrix_bd_69_cast_fp16")]; + bool matrix_ac_35_transpose_x_0 = const()[name = string("matrix_ac_35_transpose_x_0"), val = bool(false)]; + bool matrix_ac_35_transpose_y_0 = const()[name = string("matrix_ac_35_transpose_y_0"), val = bool(false)]; + tensor transpose_130_perm_0 = const()[name = string("transpose_130_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_131_perm_0 = const()[name = string("transpose_131_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_131 = transpose(perm = transpose_131_perm_0, x = k_69_cast_fp16)[name = string("transpose_207")]; + tensor transpose_130 = transpose(perm = transpose_130_perm_0, x = var_4134_cast_fp16)[name = string("transpose_208")]; + tensor matrix_ac_35_cast_fp16 = matmul(transpose_x = matrix_ac_35_transpose_x_0, transpose_y = matrix_ac_35_transpose_y_0, x = transpose_130, y = transpose_131)[name = string("matrix_ac_35_cast_fp16")]; + tensor matrix_bd_71_begin_0 = const()[name = string("matrix_bd_71_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_71_end_0 = const()[name = string("matrix_bd_71_end_0"), val = tensor([1, 8, 56, 98])]; + tensor matrix_bd_71_end_mask_0 = const()[name = string("matrix_bd_71_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_71_cast_fp16 = slice_by_index(begin = matrix_bd_71_begin_0, end = matrix_bd_71_end_0, end_mask = matrix_bd_71_end_mask_0, x = matrix_bd_69_cast_fp16)[name = string("matrix_bd_71_cast_fp16")]; + tensor var_4160_cast_fp16 = add(x = matrix_ac_35_cast_fp16, y = matrix_bd_71_cast_fp16)[name = string("op_4160_cast_fp16")]; + fp16 _inversed_scores_69_y_0_to_fp16 = const()[name = string("_inversed_scores_69_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_69_cast_fp16 = mul(x = var_4160_cast_fp16, y = _inversed_scores_69_y_0_to_fp16)[name = string("_inversed_scores_69_cast_fp16")]; + tensor scores_71_cast_fp16 = select(a = var_46_to_fp16, b = _inversed_scores_69_cast_fp16, cond = mask_11)[name = string("scores_71_cast_fp16")]; + tensor var_4166_cast_fp16 = softmax(axis = var_60, x = scores_71_cast_fp16)[name = string("op_4166_cast_fp16")]; + tensor input_925_cast_fp16 = select(a = var_45_to_fp16, b = var_4166_cast_fp16, cond = mask_11)[name = string("input_925_cast_fp16")]; + bool x_455_transpose_x_0 = const()[name = string("x_455_transpose_x_0"), val = bool(false)]; + bool x_455_transpose_y_0 = const()[name = string("x_455_transpose_y_0"), val = bool(false)]; + tensor value_43_cast_fp16 = transpose(perm = value_43_perm_0, x = v_35_cast_fp16)[name = string("transpose_206")]; + tensor x_455_cast_fp16 = matmul(transpose_x = x_455_transpose_x_0, transpose_y = x_455_transpose_y_0, x = input_925_cast_fp16, y = value_43_cast_fp16)[name = string("x_455_cast_fp16")]; + tensor var_4170_perm_0 = const()[name = string("op_4170_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_4171 = const()[name = string("op_4171"), val = tensor([1, -1, 1024])]; + tensor var_4170_cast_fp16 = transpose(perm = var_4170_perm_0, x = x_455_cast_fp16)[name = string("transpose_205")]; + tensor input_927_cast_fp16 = reshape(shape = var_4171, x = var_4170_cast_fp16)[name = string("input_927_cast_fp16")]; + tensor encoder_layers_17_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(352536256))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(353322752))))[name = string("encoder_layers_17_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_17_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_17_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(353322944)))]; + tensor linear_160_cast_fp16 = linear(bias = encoder_layers_17_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_17_self_attn_linear_out_weight_to_fp16_palettized, x = input_927_cast_fp16)[name = string("linear_160_cast_fp16")]; + tensor input_931_cast_fp16 = add(x = input_921_cast_fp16, y = linear_160_cast_fp16)[name = string("input_931_cast_fp16")]; + tensor x_459_axes_0 = const()[name = string("x_459_axes_0"), val = tensor([-1])]; + tensor encoder_layers_17_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_17_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(353325056)))]; + tensor encoder_layers_17_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_17_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(353327168)))]; + tensor x_459_cast_fp16 = layer_norm(axes = x_459_axes_0, beta = encoder_layers_17_norm_conv_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_17_norm_conv_weight_to_fp16, x = input_931_cast_fp16)[name = string("x_459_cast_fp16")]; + tensor input_933_perm_0 = const()[name = string("input_933_perm_0"), val = tensor([0, 2, 1])]; + string input_935_pad_type_0 = const()[name = string("input_935_pad_type_0"), val = string("valid")]; + tensor input_935_strides_0 = const()[name = string("input_935_strides_0"), val = tensor([1])]; + tensor input_935_pad_0 = const()[name = string("input_935_pad_0"), val = tensor([0, 0])]; + tensor input_935_dilations_0 = const()[name = string("input_935_dilations_0"), val = tensor([1])]; + int32 input_935_groups_0 = const()[name = string("input_935_groups_0"), val = int32(1)]; + tensor encoder_layers_17_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(353329280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355426496))))[name = string("encoder_layers_17_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_933_cast_fp16 = transpose(perm = input_933_perm_0, x = x_459_cast_fp16)[name = string("transpose_204")]; + tensor input_935_cast_fp16 = conv(dilations = input_935_dilations_0, groups = input_935_groups_0, pad = input_935_pad_0, pad_type = input_935_pad_type_0, strides = input_935_strides_0, weight = encoder_layers_17_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_933_cast_fp16)[name = string("input_935_cast_fp16")]; + int32 x_461_split_num_splits_0 = const()[name = string("x_461_split_num_splits_0"), val = int32(2)]; + int32 x_461_split_axis_0 = const()[name = string("x_461_split_axis_0"), val = int32(1)]; + tensor x_461_split_cast_fp16_0, tensor x_461_split_cast_fp16_1 = split(axis = x_461_split_axis_0, num_splits = x_461_split_num_splits_0, x = input_935_cast_fp16)[name = string("x_461_split_cast_fp16")]; + tensor x_461_split_1_sigmoid_cast_fp16 = sigmoid(x = x_461_split_cast_fp16_1)[name = string("x_461_split_1_sigmoid_cast_fp16")]; + tensor x_461_cast_fp16 = mul(x = x_461_split_cast_fp16_0, y = x_461_split_1_sigmoid_cast_fp16)[name = string("x_461_cast_fp16")]; + tensor input_937_cast_fp16 = select(a = var_45_to_fp16, b = x_461_cast_fp16, cond = var_576)[name = string("input_937_cast_fp16")]; + bool new_x_71_interleave_0 = const()[name = string("new_x_71_interleave_0"), val = bool(false)]; + tensor new_x_71_cast_fp16 = concat(axis = var_60, interleave = new_x_71_interleave_0, values = (cache_71_cast_fp16, input_937_cast_fp16))[name = string("new_x_71_cast_fp16")]; + tensor var_4210_begin_0 = const()[name = string("op_4210_begin_0"), val = tensor([0, 0, 56])]; + tensor var_4210_end_0 = const()[name = string("op_4210_end_0"), val = tensor([1, 1024, 64])]; + tensor var_4210_end_mask_0 = const()[name = string("op_4210_end_mask_0"), val = tensor([true, true, true])]; + tensor var_4210_cast_fp16 = slice_by_index(begin = var_4210_begin_0, end = var_4210_end_0, end_mask = var_4210_end_mask_0, x = new_x_71_cast_fp16)[name = string("op_4210_cast_fp16")]; + string x_463_pad_type_0 = const()[name = string("x_463_pad_type_0"), val = string("valid")]; + int32 x_463_groups_0 = const()[name = string("x_463_groups_0"), val = int32(1024)]; + tensor x_463_strides_0 = const()[name = string("x_463_strides_0"), val = tensor([1])]; + tensor x_463_pad_0 = const()[name = string("x_463_pad_0"), val = tensor([0, 0])]; + tensor x_463_dilations_0 = const()[name = string("x_463_dilations_0"), val = tensor([1])]; + tensor encoder_layers_17_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355430656))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355439936))))[name = string("encoder_layers_17_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_463_cast_fp16 = conv(dilations = x_463_dilations_0, groups = x_463_groups_0, pad = x_463_pad_0, pad_type = x_463_pad_type_0, strides = x_463_strides_0, weight = encoder_layers_17_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_71_cast_fp16)[name = string("x_463_cast_fp16")]; + tensor input_939_perm_0 = const()[name = string("input_939_perm_0"), val = tensor([0, 2, 1])]; + tensor x_465_axes_0 = const()[name = string("x_465_axes_0"), val = tensor([-1])]; + tensor encoder_layers_17_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_17_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355442048)))]; + tensor encoder_layers_17_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_17_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355444160)))]; + tensor input_939_cast_fp16 = transpose(perm = input_939_perm_0, x = x_463_cast_fp16)[name = string("transpose_203")]; + tensor x_465_cast_fp16 = layer_norm(axes = x_465_axes_0, beta = encoder_layers_17_conv_batch_norm_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_17_conv_batch_norm_weight_to_fp16, x = input_939_cast_fp16)[name = string("x_465_cast_fp16")]; + tensor input_941_perm_0 = const()[name = string("input_941_perm_0"), val = tensor([0, 2, 1])]; + tensor input_941_cast_fp16 = transpose(perm = input_941_perm_0, x = x_465_cast_fp16)[name = string("transpose_202")]; + tensor input_943_cast_fp16 = silu(x = input_941_cast_fp16)[name = string("input_943_cast_fp16")]; + string x_467_pad_type_0 = const()[name = string("x_467_pad_type_0"), val = string("valid")]; + tensor x_467_strides_0 = const()[name = string("x_467_strides_0"), val = tensor([1])]; + tensor x_467_pad_0 = const()[name = string("x_467_pad_0"), val = tensor([0, 0])]; + tensor x_467_dilations_0 = const()[name = string("x_467_dilations_0"), val = tensor([1])]; + int32 x_467_groups_0 = const()[name = string("x_467_groups_0"), val = int32(1)]; + tensor encoder_layers_17_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355446272))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(356494912))))[name = string("encoder_layers_17_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_467_cast_fp16 = conv(dilations = x_467_dilations_0, groups = x_467_groups_0, pad = x_467_pad_0, pad_type = x_467_pad_type_0, strides = x_467_strides_0, weight = encoder_layers_17_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_943_cast_fp16)[name = string("x_467_cast_fp16")]; + tensor input_945_perm_0 = const()[name = string("input_945_perm_0"), val = tensor([0, 2, 1])]; + tensor input_945_cast_fp16 = transpose(perm = input_945_perm_0, x = x_467_cast_fp16)[name = string("transpose_201")]; + tensor input_947_cast_fp16 = add(x = input_931_cast_fp16, y = input_945_cast_fp16)[name = string("input_947_cast_fp16")]; + tensor input_949_axes_0 = const()[name = string("input_949_axes_0"), val = tensor([-1])]; + tensor encoder_layers_17_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_17_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(356497024)))]; + tensor encoder_layers_17_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_17_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(356499136)))]; + tensor input_949_cast_fp16 = layer_norm(axes = input_949_axes_0, beta = encoder_layers_17_norm_feed_forward2_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_17_norm_feed_forward2_weight_to_fp16, x = input_947_cast_fp16)[name = string("input_949_cast_fp16")]; + tensor encoder_layers_17_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(356501248))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(359647040))))[name = string("encoder_layers_17_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_17_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_17_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(359647232)))]; + tensor linear_161_cast_fp16 = linear(bias = encoder_layers_17_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_17_feed_forward2_linear1_weight_to_fp16_palettized, x = input_949_cast_fp16)[name = string("linear_161_cast_fp16")]; + tensor input_953_cast_fp16 = silu(x = linear_161_cast_fp16)[name = string("input_953_cast_fp16")]; + tensor encoder_layers_17_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(359655488))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(362801280))))[name = string("encoder_layers_17_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_17_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_17_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(362801472)))]; + tensor linear_162_cast_fp16 = linear(bias = encoder_layers_17_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_17_feed_forward2_linear2_weight_to_fp16_palettized, x = input_953_cast_fp16)[name = string("linear_162_cast_fp16")]; + fp16 var_4253_to_fp16 = const()[name = string("op_4253_to_fp16"), val = fp16(0x1p-1)]; + tensor var_4254_cast_fp16 = mul(x = linear_162_cast_fp16, y = var_4253_to_fp16)[name = string("op_4254_cast_fp16")]; + tensor input_959_cast_fp16 = add(x = input_947_cast_fp16, y = var_4254_cast_fp16)[name = string("input_959_cast_fp16")]; + tensor input_961_axes_0 = const()[name = string("input_961_axes_0"), val = tensor([-1])]; + tensor encoder_layers_17_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_17_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(362803584)))]; + tensor encoder_layers_17_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_17_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(362805696)))]; + tensor input_961_cast_fp16 = layer_norm(axes = input_961_axes_0, beta = encoder_layers_17_norm_out_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_17_norm_out_weight_to_fp16, x = input_959_cast_fp16)[name = string("input_961_cast_fp16")]; + tensor cache_73_begin_0 = const()[name = string("cache_73_begin_0"), val = tensor([18, 0, 0, 0])]; + tensor cache_73_end_0 = const()[name = string("cache_73_end_0"), val = tensor([19, 1, 42, 1024])]; + tensor cache_73_end_mask_0 = const()[name = string("cache_73_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_73_squeeze_mask_0 = const()[name = string("cache_73_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_73_cast_fp16 = slice_by_index(begin = cache_73_begin_0, end = cache_73_end_0, end_mask = cache_73_end_mask_0, squeeze_mask = cache_73_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_73_cast_fp16")]; + tensor cache_75_begin_0 = const()[name = string("cache_75_begin_0"), val = tensor([18, 0, 0, 0])]; + tensor cache_75_end_0 = const()[name = string("cache_75_end_0"), val = tensor([19, 1, 1024, 8])]; + tensor cache_75_end_mask_0 = const()[name = string("cache_75_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_75_squeeze_mask_0 = const()[name = string("cache_75_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_75_cast_fp16 = slice_by_index(begin = cache_75_begin_0, end = cache_75_end_0, end_mask = cache_75_end_mask_0, squeeze_mask = cache_75_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_75_cast_fp16")]; + tensor input_963_axes_0 = const()[name = string("input_963_axes_0"), val = tensor([-1])]; + tensor encoder_layers_18_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_18_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(362807808)))]; + tensor encoder_layers_18_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_18_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(362809920)))]; + tensor input_963_cast_fp16 = layer_norm(axes = input_963_axes_0, beta = encoder_layers_18_norm_feed_forward1_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_18_norm_feed_forward1_weight_to_fp16, x = input_961_cast_fp16)[name = string("input_963_cast_fp16")]; + tensor encoder_layers_18_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(362812032))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(365957824))))[name = string("encoder_layers_18_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_18_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_18_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(365958016)))]; + tensor linear_163_cast_fp16 = linear(bias = encoder_layers_18_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_18_feed_forward1_linear1_weight_to_fp16_palettized, x = input_963_cast_fp16)[name = string("linear_163_cast_fp16")]; + tensor input_967_cast_fp16 = silu(x = linear_163_cast_fp16)[name = string("input_967_cast_fp16")]; + tensor encoder_layers_18_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(365966272))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(369112064))))[name = string("encoder_layers_18_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_18_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_18_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(369112256)))]; + tensor linear_164_cast_fp16 = linear(bias = encoder_layers_18_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_18_feed_forward1_linear2_weight_to_fp16_palettized, x = input_967_cast_fp16)[name = string("linear_164_cast_fp16")]; + fp16 var_4290_to_fp16 = const()[name = string("op_4290_to_fp16"), val = fp16(0x1p-1)]; + tensor var_4291_cast_fp16 = mul(x = linear_164_cast_fp16, y = var_4290_to_fp16)[name = string("op_4291_cast_fp16")]; + tensor input_973_cast_fp16 = add(x = input_961_cast_fp16, y = var_4291_cast_fp16)[name = string("input_973_cast_fp16")]; + tensor key_37_axes_0 = const()[name = string("key_37_axes_0"), val = tensor([-1])]; + tensor encoder_layers_18_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_18_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(369114368)))]; + tensor encoder_layers_18_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_18_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(369116480)))]; + tensor key_37_cast_fp16 = layer_norm(axes = key_37_axes_0, beta = encoder_layers_18_norm_self_att_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_18_norm_self_att_weight_to_fp16, x = input_973_cast_fp16)[name = string("key_37_cast_fp16")]; + bool input_975_interleave_0 = const()[name = string("input_975_interleave_0"), val = bool(false)]; + tensor input_975_cast_fp16 = concat(axis = var_69, interleave = input_975_interleave_0, values = (cache_73_cast_fp16, key_37_cast_fp16))[name = string("input_975_cast_fp16")]; + bool var_4319_interleave_0 = const()[name = string("op_4319_interleave_0"), val = bool(false)]; + tensor var_4319_cast_fp16 = concat(axis = var_69, interleave = var_4319_interleave_0, values = key_37_cast_fp16)[name = string("op_4319_cast_fp16")]; + tensor encoder_layers_18_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(369118592))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(369905088))))[name = string("encoder_layers_18_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_18_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_18_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(369905280)))]; + tensor linear_165_cast_fp16 = linear(bias = encoder_layers_18_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_18_self_attn_linear_q_weight_to_fp16_palettized, x = key_37_cast_fp16)[name = string("linear_165_cast_fp16")]; + tensor var_4324 = const()[name = string("op_4324"), val = tensor([1, -1, 8, 128])]; + tensor q_109_cast_fp16 = reshape(shape = var_4324, x = linear_165_cast_fp16)[name = string("q_109_cast_fp16")]; + tensor encoder_layers_18_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(369907392))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(370693888))))[name = string("encoder_layers_18_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_18_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_18_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(370694080)))]; + tensor linear_166_cast_fp16 = linear(bias = encoder_layers_18_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_18_self_attn_linear_k_weight_to_fp16_palettized, x = input_975_cast_fp16)[name = string("linear_166_cast_fp16")]; + tensor var_4329 = const()[name = string("op_4329"), val = tensor([1, -1, 8, 128])]; + tensor k_73_cast_fp16 = reshape(shape = var_4329, x = linear_166_cast_fp16)[name = string("k_73_cast_fp16")]; + tensor encoder_layers_18_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(370696192))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(371482688))))[name = string("encoder_layers_18_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_18_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_18_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(371482880)))]; + tensor linear_167_cast_fp16 = linear(bias = encoder_layers_18_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_18_self_attn_linear_v_weight_to_fp16_palettized, x = input_975_cast_fp16)[name = string("linear_167_cast_fp16")]; + tensor var_4334 = const()[name = string("op_4334"), val = tensor([1, -1, 8, 128])]; + tensor v_37_cast_fp16 = reshape(shape = var_4334, x = linear_167_cast_fp16)[name = string("v_37_cast_fp16")]; + tensor value_45_perm_0 = const()[name = string("value_45_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_18_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_18_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(371484992)))]; + tensor var_4347_cast_fp16 = add(x = q_109_cast_fp16, y = encoder_layers_18_self_attn_pos_bias_u_to_fp16)[name = string("op_4347_cast_fp16")]; + tensor encoder_layers_18_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_18_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(371487104)))]; + tensor var_4349_cast_fp16 = add(x = q_109_cast_fp16, y = encoder_layers_18_self_attn_pos_bias_v_to_fp16)[name = string("op_4349_cast_fp16")]; + tensor q_with_bias_v_37_perm_0 = const()[name = string("q_with_bias_v_37_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_475_transpose_x_0 = const()[name = string("x_475_transpose_x_0"), val = bool(false)]; + bool x_475_transpose_y_0 = const()[name = string("x_475_transpose_y_0"), val = bool(false)]; + tensor op_4351_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(371489216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(371688960))))[name = string("op_4351_to_fp16_quantized")]; + tensor q_with_bias_v_37_cast_fp16 = transpose(perm = q_with_bias_v_37_perm_0, x = var_4349_cast_fp16)[name = string("transpose_200")]; + tensor x_475_cast_fp16 = matmul(transpose_x = x_475_transpose_x_0, transpose_y = x_475_transpose_y_0, x = q_with_bias_v_37_cast_fp16, y = op_4351_to_fp16_quantized)[name = string("x_475_cast_fp16")]; + tensor x_477_pad_0 = const()[name = string("x_477_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_477_mode_0 = const()[name = string("x_477_mode_0"), val = string("constant")]; + fp16 const_313_to_fp16 = const()[name = string("const_313_to_fp16"), val = fp16(0x0p+0)]; + tensor x_477_cast_fp16 = pad(constant_val = const_313_to_fp16, mode = x_477_mode_0, pad = x_477_pad_0, x = x_475_cast_fp16)[name = string("x_477_cast_fp16")]; + tensor var_4359 = const()[name = string("op_4359"), val = tensor([1, 8, -1, 56])]; + tensor x_479_cast_fp16 = reshape(shape = var_4359, x = x_477_cast_fp16)[name = string("x_479_cast_fp16")]; + tensor var_4363_begin_0 = const()[name = string("op_4363_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_4363_end_0 = const()[name = string("op_4363_end_0"), val = tensor([1, 8, 196, 56])]; + tensor var_4363_end_mask_0 = const()[name = string("op_4363_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_4363_cast_fp16 = slice_by_index(begin = var_4363_begin_0, end = var_4363_end_0, end_mask = var_4363_end_mask_0, x = x_479_cast_fp16)[name = string("op_4363_cast_fp16")]; + tensor var_4364 = const()[name = string("op_4364"), val = tensor([1, 8, 56, 195])]; + tensor matrix_bd_73_cast_fp16 = reshape(shape = var_4364, x = var_4363_cast_fp16)[name = string("matrix_bd_73_cast_fp16")]; + bool matrix_ac_37_transpose_x_0 = const()[name = string("matrix_ac_37_transpose_x_0"), val = bool(false)]; + bool matrix_ac_37_transpose_y_0 = const()[name = string("matrix_ac_37_transpose_y_0"), val = bool(false)]; + tensor transpose_132_perm_0 = const()[name = string("transpose_132_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_133_perm_0 = const()[name = string("transpose_133_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_133 = transpose(perm = transpose_133_perm_0, x = k_73_cast_fp16)[name = string("transpose_198")]; + tensor transpose_132 = transpose(perm = transpose_132_perm_0, x = var_4347_cast_fp16)[name = string("transpose_199")]; + tensor matrix_ac_37_cast_fp16 = matmul(transpose_x = matrix_ac_37_transpose_x_0, transpose_y = matrix_ac_37_transpose_y_0, x = transpose_132, y = transpose_133)[name = string("matrix_ac_37_cast_fp16")]; + tensor matrix_bd_75_begin_0 = const()[name = string("matrix_bd_75_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_75_end_0 = const()[name = string("matrix_bd_75_end_0"), val = tensor([1, 8, 56, 98])]; + tensor matrix_bd_75_end_mask_0 = const()[name = string("matrix_bd_75_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_75_cast_fp16 = slice_by_index(begin = matrix_bd_75_begin_0, end = matrix_bd_75_end_0, end_mask = matrix_bd_75_end_mask_0, x = matrix_bd_73_cast_fp16)[name = string("matrix_bd_75_cast_fp16")]; + tensor var_4373_cast_fp16 = add(x = matrix_ac_37_cast_fp16, y = matrix_bd_75_cast_fp16)[name = string("op_4373_cast_fp16")]; + fp16 _inversed_scores_73_y_0_to_fp16 = const()[name = string("_inversed_scores_73_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_73_cast_fp16 = mul(x = var_4373_cast_fp16, y = _inversed_scores_73_y_0_to_fp16)[name = string("_inversed_scores_73_cast_fp16")]; + tensor scores_75_cast_fp16 = select(a = var_46_to_fp16, b = _inversed_scores_73_cast_fp16, cond = mask_11)[name = string("scores_75_cast_fp16")]; + tensor var_4379_cast_fp16 = softmax(axis = var_60, x = scores_75_cast_fp16)[name = string("op_4379_cast_fp16")]; + tensor input_977_cast_fp16 = select(a = var_45_to_fp16, b = var_4379_cast_fp16, cond = mask_11)[name = string("input_977_cast_fp16")]; + bool x_481_transpose_x_0 = const()[name = string("x_481_transpose_x_0"), val = bool(false)]; + bool x_481_transpose_y_0 = const()[name = string("x_481_transpose_y_0"), val = bool(false)]; + tensor value_45_cast_fp16 = transpose(perm = value_45_perm_0, x = v_37_cast_fp16)[name = string("transpose_197")]; + tensor x_481_cast_fp16 = matmul(transpose_x = x_481_transpose_x_0, transpose_y = x_481_transpose_y_0, x = input_977_cast_fp16, y = value_45_cast_fp16)[name = string("x_481_cast_fp16")]; + tensor var_4383_perm_0 = const()[name = string("op_4383_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_4384 = const()[name = string("op_4384"), val = tensor([1, -1, 1024])]; + tensor var_4383_cast_fp16 = transpose(perm = var_4383_perm_0, x = x_481_cast_fp16)[name = string("transpose_196")]; + tensor input_979_cast_fp16 = reshape(shape = var_4384, x = var_4383_cast_fp16)[name = string("input_979_cast_fp16")]; + tensor encoder_layers_18_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(371689472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372738112))))[name = string("encoder_layers_18_self_attn_linear_out_weight_to_fp16_quantized")]; + tensor encoder_layers_18_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_18_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372740224)))]; + tensor linear_169_cast_fp16 = linear(bias = encoder_layers_18_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_18_self_attn_linear_out_weight_to_fp16_quantized, x = input_979_cast_fp16)[name = string("linear_169_cast_fp16")]; + tensor input_983_cast_fp16 = add(x = input_973_cast_fp16, y = linear_169_cast_fp16)[name = string("input_983_cast_fp16")]; + tensor x_485_axes_0 = const()[name = string("x_485_axes_0"), val = tensor([-1])]; + tensor encoder_layers_18_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_18_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372742336)))]; + tensor encoder_layers_18_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_18_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372744448)))]; + tensor x_485_cast_fp16 = layer_norm(axes = x_485_axes_0, beta = encoder_layers_18_norm_conv_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_18_norm_conv_weight_to_fp16, x = input_983_cast_fp16)[name = string("x_485_cast_fp16")]; + tensor input_985_perm_0 = const()[name = string("input_985_perm_0"), val = tensor([0, 2, 1])]; + string input_987_pad_type_0 = const()[name = string("input_987_pad_type_0"), val = string("valid")]; + tensor input_987_strides_0 = const()[name = string("input_987_strides_0"), val = tensor([1])]; + tensor input_987_pad_0 = const()[name = string("input_987_pad_0"), val = tensor([0, 0])]; + tensor input_987_dilations_0 = const()[name = string("input_987_dilations_0"), val = tensor([1])]; + int32 input_987_groups_0 = const()[name = string("input_987_groups_0"), val = int32(1)]; + tensor encoder_layers_18_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372746560))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(374843776))))[name = string("encoder_layers_18_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_985_cast_fp16 = transpose(perm = input_985_perm_0, x = x_485_cast_fp16)[name = string("transpose_195")]; + tensor input_987_cast_fp16 = conv(dilations = input_987_dilations_0, groups = input_987_groups_0, pad = input_987_pad_0, pad_type = input_987_pad_type_0, strides = input_987_strides_0, weight = encoder_layers_18_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_985_cast_fp16)[name = string("input_987_cast_fp16")]; + int32 x_487_split_num_splits_0 = const()[name = string("x_487_split_num_splits_0"), val = int32(2)]; + int32 x_487_split_axis_0 = const()[name = string("x_487_split_axis_0"), val = int32(1)]; + tensor x_487_split_cast_fp16_0, tensor x_487_split_cast_fp16_1 = split(axis = x_487_split_axis_0, num_splits = x_487_split_num_splits_0, x = input_987_cast_fp16)[name = string("x_487_split_cast_fp16")]; + tensor x_487_split_1_sigmoid_cast_fp16 = sigmoid(x = x_487_split_cast_fp16_1)[name = string("x_487_split_1_sigmoid_cast_fp16")]; + tensor x_487_cast_fp16 = mul(x = x_487_split_cast_fp16_0, y = x_487_split_1_sigmoid_cast_fp16)[name = string("x_487_cast_fp16")]; + tensor input_989_cast_fp16 = select(a = var_45_to_fp16, b = x_487_cast_fp16, cond = var_576)[name = string("input_989_cast_fp16")]; + bool new_x_75_interleave_0 = const()[name = string("new_x_75_interleave_0"), val = bool(false)]; + tensor new_x_75_cast_fp16 = concat(axis = var_60, interleave = new_x_75_interleave_0, values = (cache_75_cast_fp16, input_989_cast_fp16))[name = string("new_x_75_cast_fp16")]; + tensor var_4423_begin_0 = const()[name = string("op_4423_begin_0"), val = tensor([0, 0, 56])]; + tensor var_4423_end_0 = const()[name = string("op_4423_end_0"), val = tensor([1, 1024, 64])]; + tensor var_4423_end_mask_0 = const()[name = string("op_4423_end_mask_0"), val = tensor([true, true, true])]; + tensor var_4423_cast_fp16 = slice_by_index(begin = var_4423_begin_0, end = var_4423_end_0, end_mask = var_4423_end_mask_0, x = new_x_75_cast_fp16)[name = string("op_4423_cast_fp16")]; + string x_489_pad_type_0 = const()[name = string("x_489_pad_type_0"), val = string("valid")]; + int32 x_489_groups_0 = const()[name = string("x_489_groups_0"), val = int32(1024)]; + tensor x_489_strides_0 = const()[name = string("x_489_strides_0"), val = tensor([1])]; + tensor x_489_pad_0 = const()[name = string("x_489_pad_0"), val = tensor([0, 0])]; + tensor x_489_dilations_0 = const()[name = string("x_489_dilations_0"), val = tensor([1])]; + tensor encoder_layers_18_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(374847936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(374857216))))[name = string("encoder_layers_18_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_489_cast_fp16 = conv(dilations = x_489_dilations_0, groups = x_489_groups_0, pad = x_489_pad_0, pad_type = x_489_pad_type_0, strides = x_489_strides_0, weight = encoder_layers_18_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_75_cast_fp16)[name = string("x_489_cast_fp16")]; + tensor input_991_perm_0 = const()[name = string("input_991_perm_0"), val = tensor([0, 2, 1])]; + tensor x_491_axes_0 = const()[name = string("x_491_axes_0"), val = tensor([-1])]; + tensor encoder_layers_18_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_18_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(374859328)))]; + tensor encoder_layers_18_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_18_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(374861440)))]; + tensor input_991_cast_fp16 = transpose(perm = input_991_perm_0, x = x_489_cast_fp16)[name = string("transpose_194")]; + tensor x_491_cast_fp16 = layer_norm(axes = x_491_axes_0, beta = encoder_layers_18_conv_batch_norm_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_18_conv_batch_norm_weight_to_fp16, x = input_991_cast_fp16)[name = string("x_491_cast_fp16")]; + tensor input_993_perm_0 = const()[name = string("input_993_perm_0"), val = tensor([0, 2, 1])]; + tensor input_993_cast_fp16 = transpose(perm = input_993_perm_0, x = x_491_cast_fp16)[name = string("transpose_193")]; + tensor input_995_cast_fp16 = silu(x = input_993_cast_fp16)[name = string("input_995_cast_fp16")]; + string x_493_pad_type_0 = const()[name = string("x_493_pad_type_0"), val = string("valid")]; + tensor x_493_strides_0 = const()[name = string("x_493_strides_0"), val = tensor([1])]; + tensor x_493_pad_0 = const()[name = string("x_493_pad_0"), val = tensor([0, 0])]; + tensor x_493_dilations_0 = const()[name = string("x_493_dilations_0"), val = tensor([1])]; + int32 x_493_groups_0 = const()[name = string("x_493_groups_0"), val = int32(1)]; + tensor encoder_layers_18_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(374863552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(375912192))))[name = string("encoder_layers_18_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_493_cast_fp16 = conv(dilations = x_493_dilations_0, groups = x_493_groups_0, pad = x_493_pad_0, pad_type = x_493_pad_type_0, strides = x_493_strides_0, weight = encoder_layers_18_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_995_cast_fp16)[name = string("x_493_cast_fp16")]; + tensor input_997_perm_0 = const()[name = string("input_997_perm_0"), val = tensor([0, 2, 1])]; + tensor input_997_cast_fp16 = transpose(perm = input_997_perm_0, x = x_493_cast_fp16)[name = string("transpose_192")]; + tensor input_999_cast_fp16 = add(x = input_983_cast_fp16, y = input_997_cast_fp16)[name = string("input_999_cast_fp16")]; + tensor input_1001_axes_0 = const()[name = string("input_1001_axes_0"), val = tensor([-1])]; + tensor encoder_layers_18_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_18_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(375914304)))]; + tensor encoder_layers_18_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_18_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(375916416)))]; + tensor input_1001_cast_fp16 = layer_norm(axes = input_1001_axes_0, beta = encoder_layers_18_norm_feed_forward2_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_18_norm_feed_forward2_weight_to_fp16, x = input_999_cast_fp16)[name = string("input_1001_cast_fp16")]; + tensor encoder_layers_18_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(375918528))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(380112896))))[name = string("encoder_layers_18_feed_forward2_linear1_weight_to_fp16_quantized")]; + tensor encoder_layers_18_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_18_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(380121152)))]; + tensor linear_170_cast_fp16 = linear(bias = encoder_layers_18_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_18_feed_forward2_linear1_weight_to_fp16_quantized, x = input_1001_cast_fp16)[name = string("linear_170_cast_fp16")]; + tensor input_1005_cast_fp16 = silu(x = linear_170_cast_fp16)[name = string("input_1005_cast_fp16")]; + tensor encoder_layers_18_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(380129408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(384323776))))[name = string("encoder_layers_18_feed_forward2_linear2_weight_to_fp16_quantized")]; + tensor encoder_layers_18_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_18_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(384325888)))]; + tensor linear_171_cast_fp16 = linear(bias = encoder_layers_18_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_18_feed_forward2_linear2_weight_to_fp16_quantized, x = input_1005_cast_fp16)[name = string("linear_171_cast_fp16")]; + fp16 var_4466_to_fp16 = const()[name = string("op_4466_to_fp16"), val = fp16(0x1p-1)]; + tensor var_4467_cast_fp16 = mul(x = linear_171_cast_fp16, y = var_4466_to_fp16)[name = string("op_4467_cast_fp16")]; + tensor input_1011_cast_fp16 = add(x = input_999_cast_fp16, y = var_4467_cast_fp16)[name = string("input_1011_cast_fp16")]; + tensor input_1013_axes_0 = const()[name = string("input_1013_axes_0"), val = tensor([-1])]; + tensor encoder_layers_18_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_18_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(384328000)))]; + tensor encoder_layers_18_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_18_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(384330112)))]; + tensor input_1013_cast_fp16 = layer_norm(axes = input_1013_axes_0, beta = encoder_layers_18_norm_out_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_18_norm_out_weight_to_fp16, x = input_1011_cast_fp16)[name = string("input_1013_cast_fp16")]; + tensor cache_77_begin_0 = const()[name = string("cache_77_begin_0"), val = tensor([19, 0, 0, 0])]; + tensor cache_77_end_0 = const()[name = string("cache_77_end_0"), val = tensor([20, 1, 42, 1024])]; + tensor cache_77_end_mask_0 = const()[name = string("cache_77_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_77_squeeze_mask_0 = const()[name = string("cache_77_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_77_cast_fp16 = slice_by_index(begin = cache_77_begin_0, end = cache_77_end_0, end_mask = cache_77_end_mask_0, squeeze_mask = cache_77_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_77_cast_fp16")]; + tensor cache_79_begin_0 = const()[name = string("cache_79_begin_0"), val = tensor([19, 0, 0, 0])]; + tensor cache_79_end_0 = const()[name = string("cache_79_end_0"), val = tensor([20, 1, 1024, 8])]; + tensor cache_79_end_mask_0 = const()[name = string("cache_79_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_79_squeeze_mask_0 = const()[name = string("cache_79_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_79_cast_fp16 = slice_by_index(begin = cache_79_begin_0, end = cache_79_end_0, end_mask = cache_79_end_mask_0, squeeze_mask = cache_79_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_79_cast_fp16")]; + tensor input_1015_axes_0 = const()[name = string("input_1015_axes_0"), val = tensor([-1])]; + tensor encoder_layers_19_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_19_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(384332224)))]; + tensor encoder_layers_19_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_19_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(384334336)))]; + tensor input_1015_cast_fp16 = layer_norm(axes = input_1015_axes_0, beta = encoder_layers_19_norm_feed_forward1_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_19_norm_feed_forward1_weight_to_fp16, x = input_1013_cast_fp16)[name = string("input_1015_cast_fp16")]; + tensor encoder_layers_19_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(384336448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(388530816))))[name = string("encoder_layers_19_feed_forward1_linear1_weight_to_fp16_quantized")]; + tensor encoder_layers_19_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_19_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(388539072)))]; + tensor linear_172_cast_fp16 = linear(bias = encoder_layers_19_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_19_feed_forward1_linear1_weight_to_fp16_quantized, x = input_1015_cast_fp16)[name = string("linear_172_cast_fp16")]; + tensor input_1019_cast_fp16 = silu(x = linear_172_cast_fp16)[name = string("input_1019_cast_fp16")]; + tensor encoder_layers_19_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(388547328))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(392741696))))[name = string("encoder_layers_19_feed_forward1_linear2_weight_to_fp16_quantized")]; + tensor encoder_layers_19_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_19_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(392743808)))]; + tensor linear_173_cast_fp16 = linear(bias = encoder_layers_19_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_19_feed_forward1_linear2_weight_to_fp16_quantized, x = input_1019_cast_fp16)[name = string("linear_173_cast_fp16")]; + fp16 var_4503_to_fp16 = const()[name = string("op_4503_to_fp16"), val = fp16(0x1p-1)]; + tensor var_4504_cast_fp16 = mul(x = linear_173_cast_fp16, y = var_4503_to_fp16)[name = string("op_4504_cast_fp16")]; + tensor input_1025_cast_fp16 = add(x = input_1013_cast_fp16, y = var_4504_cast_fp16)[name = string("input_1025_cast_fp16")]; + tensor key_39_axes_0 = const()[name = string("key_39_axes_0"), val = tensor([-1])]; + tensor encoder_layers_19_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_19_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(392745920)))]; + tensor encoder_layers_19_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_19_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(392748032)))]; + tensor key_39_cast_fp16 = layer_norm(axes = key_39_axes_0, beta = encoder_layers_19_norm_self_att_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_19_norm_self_att_weight_to_fp16, x = input_1025_cast_fp16)[name = string("key_39_cast_fp16")]; + bool input_1027_interleave_0 = const()[name = string("input_1027_interleave_0"), val = bool(false)]; + tensor input_1027_cast_fp16 = concat(axis = var_69, interleave = input_1027_interleave_0, values = (cache_77_cast_fp16, key_39_cast_fp16))[name = string("input_1027_cast_fp16")]; + bool var_4532_interleave_0 = const()[name = string("op_4532_interleave_0"), val = bool(false)]; + tensor var_4532_cast_fp16 = concat(axis = var_69, interleave = var_4532_interleave_0, values = key_39_cast_fp16)[name = string("op_4532_cast_fp16")]; + tensor encoder_layers_19_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(392750144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(393798784))))[name = string("encoder_layers_19_self_attn_linear_q_weight_to_fp16_quantized")]; + tensor encoder_layers_19_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_19_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(393800896)))]; + tensor linear_174_cast_fp16 = linear(bias = encoder_layers_19_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_19_self_attn_linear_q_weight_to_fp16_quantized, x = key_39_cast_fp16)[name = string("linear_174_cast_fp16")]; + tensor var_4537 = const()[name = string("op_4537"), val = tensor([1, -1, 8, 128])]; + tensor q_115_cast_fp16 = reshape(shape = var_4537, x = linear_174_cast_fp16)[name = string("q_115_cast_fp16")]; + tensor encoder_layers_19_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(393803008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(394851648))))[name = string("encoder_layers_19_self_attn_linear_k_weight_to_fp16_quantized")]; + tensor encoder_layers_19_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_19_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(394853760)))]; + tensor linear_175_cast_fp16 = linear(bias = encoder_layers_19_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_19_self_attn_linear_k_weight_to_fp16_quantized, x = input_1027_cast_fp16)[name = string("linear_175_cast_fp16")]; + tensor var_4542 = const()[name = string("op_4542"), val = tensor([1, -1, 8, 128])]; + tensor k_77_cast_fp16 = reshape(shape = var_4542, x = linear_175_cast_fp16)[name = string("k_77_cast_fp16")]; + tensor encoder_layers_19_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(394855872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(395904512))))[name = string("encoder_layers_19_self_attn_linear_v_weight_to_fp16_quantized")]; + tensor encoder_layers_19_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_19_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(395906624)))]; + tensor linear_176_cast_fp16 = linear(bias = encoder_layers_19_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_19_self_attn_linear_v_weight_to_fp16_quantized, x = input_1027_cast_fp16)[name = string("linear_176_cast_fp16")]; + tensor var_4547 = const()[name = string("op_4547"), val = tensor([1, -1, 8, 128])]; + tensor v_39_cast_fp16 = reshape(shape = var_4547, x = linear_176_cast_fp16)[name = string("v_39_cast_fp16")]; + tensor value_47_perm_0 = const()[name = string("value_47_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_19_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_19_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(395908736)))]; + tensor var_4560_cast_fp16 = add(x = q_115_cast_fp16, y = encoder_layers_19_self_attn_pos_bias_u_to_fp16)[name = string("op_4560_cast_fp16")]; + tensor encoder_layers_19_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_19_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(395910848)))]; + tensor var_4562_cast_fp16 = add(x = q_115_cast_fp16, y = encoder_layers_19_self_attn_pos_bias_v_to_fp16)[name = string("op_4562_cast_fp16")]; + tensor q_with_bias_v_39_perm_0 = const()[name = string("q_with_bias_v_39_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_501_transpose_x_0 = const()[name = string("x_501_transpose_x_0"), val = bool(false)]; + bool x_501_transpose_y_0 = const()[name = string("x_501_transpose_y_0"), val = bool(false)]; + tensor op_4564_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(395912960))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(396112704))))[name = string("op_4564_to_fp16_quantized")]; + tensor q_with_bias_v_39_cast_fp16 = transpose(perm = q_with_bias_v_39_perm_0, x = var_4562_cast_fp16)[name = string("transpose_191")]; + tensor x_501_cast_fp16 = matmul(transpose_x = x_501_transpose_x_0, transpose_y = x_501_transpose_y_0, x = q_with_bias_v_39_cast_fp16, y = op_4564_to_fp16_quantized)[name = string("x_501_cast_fp16")]; + tensor x_503_pad_0 = const()[name = string("x_503_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_503_mode_0 = const()[name = string("x_503_mode_0"), val = string("constant")]; + fp16 const_326_to_fp16 = const()[name = string("const_326_to_fp16"), val = fp16(0x0p+0)]; + tensor x_503_cast_fp16 = pad(constant_val = const_326_to_fp16, mode = x_503_mode_0, pad = x_503_pad_0, x = x_501_cast_fp16)[name = string("x_503_cast_fp16")]; + tensor var_4572 = const()[name = string("op_4572"), val = tensor([1, 8, -1, 56])]; + tensor x_505_cast_fp16 = reshape(shape = var_4572, x = x_503_cast_fp16)[name = string("x_505_cast_fp16")]; + tensor var_4576_begin_0 = const()[name = string("op_4576_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_4576_end_0 = const()[name = string("op_4576_end_0"), val = tensor([1, 8, 196, 56])]; + tensor var_4576_end_mask_0 = const()[name = string("op_4576_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_4576_cast_fp16 = slice_by_index(begin = var_4576_begin_0, end = var_4576_end_0, end_mask = var_4576_end_mask_0, x = x_505_cast_fp16)[name = string("op_4576_cast_fp16")]; + tensor var_4577 = const()[name = string("op_4577"), val = tensor([1, 8, 56, 195])]; + tensor matrix_bd_77_cast_fp16 = reshape(shape = var_4577, x = var_4576_cast_fp16)[name = string("matrix_bd_77_cast_fp16")]; + bool matrix_ac_39_transpose_x_0 = const()[name = string("matrix_ac_39_transpose_x_0"), val = bool(false)]; + bool matrix_ac_39_transpose_y_0 = const()[name = string("matrix_ac_39_transpose_y_0"), val = bool(false)]; + tensor transpose_134_perm_0 = const()[name = string("transpose_134_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_135_perm_0 = const()[name = string("transpose_135_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_135 = transpose(perm = transpose_135_perm_0, x = k_77_cast_fp16)[name = string("transpose_189")]; + tensor transpose_134 = transpose(perm = transpose_134_perm_0, x = var_4560_cast_fp16)[name = string("transpose_190")]; + tensor matrix_ac_39_cast_fp16 = matmul(transpose_x = matrix_ac_39_transpose_x_0, transpose_y = matrix_ac_39_transpose_y_0, x = transpose_134, y = transpose_135)[name = string("matrix_ac_39_cast_fp16")]; + tensor matrix_bd_79_begin_0 = const()[name = string("matrix_bd_79_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_79_end_0 = const()[name = string("matrix_bd_79_end_0"), val = tensor([1, 8, 56, 98])]; + tensor matrix_bd_79_end_mask_0 = const()[name = string("matrix_bd_79_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_79_cast_fp16 = slice_by_index(begin = matrix_bd_79_begin_0, end = matrix_bd_79_end_0, end_mask = matrix_bd_79_end_mask_0, x = matrix_bd_77_cast_fp16)[name = string("matrix_bd_79_cast_fp16")]; + tensor var_4586_cast_fp16 = add(x = matrix_ac_39_cast_fp16, y = matrix_bd_79_cast_fp16)[name = string("op_4586_cast_fp16")]; + fp16 _inversed_scores_77_y_0_to_fp16 = const()[name = string("_inversed_scores_77_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_77_cast_fp16 = mul(x = var_4586_cast_fp16, y = _inversed_scores_77_y_0_to_fp16)[name = string("_inversed_scores_77_cast_fp16")]; + tensor scores_79_cast_fp16 = select(a = var_46_to_fp16, b = _inversed_scores_77_cast_fp16, cond = mask_11)[name = string("scores_79_cast_fp16")]; + tensor var_4592_cast_fp16 = softmax(axis = var_60, x = scores_79_cast_fp16)[name = string("op_4592_cast_fp16")]; + tensor input_1029_cast_fp16 = select(a = var_45_to_fp16, b = var_4592_cast_fp16, cond = mask_11)[name = string("input_1029_cast_fp16")]; + bool x_507_transpose_x_0 = const()[name = string("x_507_transpose_x_0"), val = bool(false)]; + bool x_507_transpose_y_0 = const()[name = string("x_507_transpose_y_0"), val = bool(false)]; + tensor value_47_cast_fp16 = transpose(perm = value_47_perm_0, x = v_39_cast_fp16)[name = string("transpose_188")]; + tensor x_507_cast_fp16 = matmul(transpose_x = x_507_transpose_x_0, transpose_y = x_507_transpose_y_0, x = input_1029_cast_fp16, y = value_47_cast_fp16)[name = string("x_507_cast_fp16")]; + tensor var_4596_perm_0 = const()[name = string("op_4596_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_4597 = const()[name = string("op_4597"), val = tensor([1, -1, 1024])]; + tensor var_4596_cast_fp16 = transpose(perm = var_4596_perm_0, x = x_507_cast_fp16)[name = string("transpose_187")]; + tensor input_1031_cast_fp16 = reshape(shape = var_4597, x = var_4596_cast_fp16)[name = string("input_1031_cast_fp16")]; + tensor encoder_layers_19_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(396113216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397161856))))[name = string("encoder_layers_19_self_attn_linear_out_weight_to_fp16_quantized")]; + tensor encoder_layers_19_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_19_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397163968)))]; + tensor linear_178_cast_fp16 = linear(bias = encoder_layers_19_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_19_self_attn_linear_out_weight_to_fp16_quantized, x = input_1031_cast_fp16)[name = string("linear_178_cast_fp16")]; + tensor input_1035_cast_fp16 = add(x = input_1025_cast_fp16, y = linear_178_cast_fp16)[name = string("input_1035_cast_fp16")]; + tensor x_511_axes_0 = const()[name = string("x_511_axes_0"), val = tensor([-1])]; + tensor encoder_layers_19_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_19_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397166080)))]; + tensor encoder_layers_19_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_19_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397168192)))]; + tensor x_511_cast_fp16 = layer_norm(axes = x_511_axes_0, beta = encoder_layers_19_norm_conv_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_19_norm_conv_weight_to_fp16, x = input_1035_cast_fp16)[name = string("x_511_cast_fp16")]; + tensor input_1037_perm_0 = const()[name = string("input_1037_perm_0"), val = tensor([0, 2, 1])]; + string input_1039_pad_type_0 = const()[name = string("input_1039_pad_type_0"), val = string("valid")]; + tensor input_1039_strides_0 = const()[name = string("input_1039_strides_0"), val = tensor([1])]; + tensor input_1039_pad_0 = const()[name = string("input_1039_pad_0"), val = tensor([0, 0])]; + tensor input_1039_dilations_0 = const()[name = string("input_1039_dilations_0"), val = tensor([1])]; + int32 input_1039_groups_0 = const()[name = string("input_1039_groups_0"), val = int32(1)]; + tensor encoder_layers_19_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397170304))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(399267520))))[name = string("encoder_layers_19_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_1037_cast_fp16 = transpose(perm = input_1037_perm_0, x = x_511_cast_fp16)[name = string("transpose_186")]; + tensor input_1039_cast_fp16 = conv(dilations = input_1039_dilations_0, groups = input_1039_groups_0, pad = input_1039_pad_0, pad_type = input_1039_pad_type_0, strides = input_1039_strides_0, weight = encoder_layers_19_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1037_cast_fp16)[name = string("input_1039_cast_fp16")]; + int32 x_513_split_num_splits_0 = const()[name = string("x_513_split_num_splits_0"), val = int32(2)]; + int32 x_513_split_axis_0 = const()[name = string("x_513_split_axis_0"), val = int32(1)]; + tensor x_513_split_cast_fp16_0, tensor x_513_split_cast_fp16_1 = split(axis = x_513_split_axis_0, num_splits = x_513_split_num_splits_0, x = input_1039_cast_fp16)[name = string("x_513_split_cast_fp16")]; + tensor x_513_split_1_sigmoid_cast_fp16 = sigmoid(x = x_513_split_cast_fp16_1)[name = string("x_513_split_1_sigmoid_cast_fp16")]; + tensor x_513_cast_fp16 = mul(x = x_513_split_cast_fp16_0, y = x_513_split_1_sigmoid_cast_fp16)[name = string("x_513_cast_fp16")]; + tensor input_1041_cast_fp16 = select(a = var_45_to_fp16, b = x_513_cast_fp16, cond = var_576)[name = string("input_1041_cast_fp16")]; + bool new_x_79_interleave_0 = const()[name = string("new_x_79_interleave_0"), val = bool(false)]; + tensor new_x_79_cast_fp16 = concat(axis = var_60, interleave = new_x_79_interleave_0, values = (cache_79_cast_fp16, input_1041_cast_fp16))[name = string("new_x_79_cast_fp16")]; + tensor var_4636_begin_0 = const()[name = string("op_4636_begin_0"), val = tensor([0, 0, 56])]; + tensor var_4636_end_0 = const()[name = string("op_4636_end_0"), val = tensor([1, 1024, 64])]; + tensor var_4636_end_mask_0 = const()[name = string("op_4636_end_mask_0"), val = tensor([true, true, true])]; + tensor var_4636_cast_fp16 = slice_by_index(begin = var_4636_begin_0, end = var_4636_end_0, end_mask = var_4636_end_mask_0, x = new_x_79_cast_fp16)[name = string("op_4636_cast_fp16")]; + string x_515_pad_type_0 = const()[name = string("x_515_pad_type_0"), val = string("valid")]; + int32 x_515_groups_0 = const()[name = string("x_515_groups_0"), val = int32(1024)]; + tensor x_515_strides_0 = const()[name = string("x_515_strides_0"), val = tensor([1])]; + tensor x_515_pad_0 = const()[name = string("x_515_pad_0"), val = tensor([0, 0])]; + tensor x_515_dilations_0 = const()[name = string("x_515_dilations_0"), val = tensor([1])]; + tensor encoder_layers_19_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(399271680))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(399280960))))[name = string("encoder_layers_19_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_515_cast_fp16 = conv(dilations = x_515_dilations_0, groups = x_515_groups_0, pad = x_515_pad_0, pad_type = x_515_pad_type_0, strides = x_515_strides_0, weight = encoder_layers_19_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_79_cast_fp16)[name = string("x_515_cast_fp16")]; + tensor input_1043_perm_0 = const()[name = string("input_1043_perm_0"), val = tensor([0, 2, 1])]; + tensor x_517_axes_0 = const()[name = string("x_517_axes_0"), val = tensor([-1])]; + tensor encoder_layers_19_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_19_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(399283072)))]; + tensor encoder_layers_19_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_19_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(399285184)))]; + tensor input_1043_cast_fp16 = transpose(perm = input_1043_perm_0, x = x_515_cast_fp16)[name = string("transpose_185")]; + tensor x_517_cast_fp16 = layer_norm(axes = x_517_axes_0, beta = encoder_layers_19_conv_batch_norm_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_19_conv_batch_norm_weight_to_fp16, x = input_1043_cast_fp16)[name = string("x_517_cast_fp16")]; + tensor input_1045_perm_0 = const()[name = string("input_1045_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1045_cast_fp16 = transpose(perm = input_1045_perm_0, x = x_517_cast_fp16)[name = string("transpose_184")]; + tensor input_1047_cast_fp16 = silu(x = input_1045_cast_fp16)[name = string("input_1047_cast_fp16")]; + string x_519_pad_type_0 = const()[name = string("x_519_pad_type_0"), val = string("valid")]; + tensor x_519_strides_0 = const()[name = string("x_519_strides_0"), val = tensor([1])]; + tensor x_519_pad_0 = const()[name = string("x_519_pad_0"), val = tensor([0, 0])]; + tensor x_519_dilations_0 = const()[name = string("x_519_dilations_0"), val = tensor([1])]; + int32 x_519_groups_0 = const()[name = string("x_519_groups_0"), val = int32(1)]; + tensor encoder_layers_19_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(399287296))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(400335936))))[name = string("encoder_layers_19_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_519_cast_fp16 = conv(dilations = x_519_dilations_0, groups = x_519_groups_0, pad = x_519_pad_0, pad_type = x_519_pad_type_0, strides = x_519_strides_0, weight = encoder_layers_19_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1047_cast_fp16)[name = string("x_519_cast_fp16")]; + tensor input_1049_perm_0 = const()[name = string("input_1049_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1049_cast_fp16 = transpose(perm = input_1049_perm_0, x = x_519_cast_fp16)[name = string("transpose_183")]; + tensor input_1051_cast_fp16 = add(x = input_1035_cast_fp16, y = input_1049_cast_fp16)[name = string("input_1051_cast_fp16")]; + tensor input_1053_axes_0 = const()[name = string("input_1053_axes_0"), val = tensor([-1])]; + tensor encoder_layers_19_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_19_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(400338048)))]; + tensor encoder_layers_19_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_19_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(400340160)))]; + tensor input_1053_cast_fp16 = layer_norm(axes = input_1053_axes_0, beta = encoder_layers_19_norm_feed_forward2_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_19_norm_feed_forward2_weight_to_fp16, x = input_1051_cast_fp16)[name = string("input_1053_cast_fp16")]; + tensor encoder_layers_19_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(400342272))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(404536640))))[name = string("encoder_layers_19_feed_forward2_linear1_weight_to_fp16_quantized")]; + tensor encoder_layers_19_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_19_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(404544896)))]; + tensor linear_179_cast_fp16 = linear(bias = encoder_layers_19_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_19_feed_forward2_linear1_weight_to_fp16_quantized, x = input_1053_cast_fp16)[name = string("linear_179_cast_fp16")]; + tensor input_1057_cast_fp16 = silu(x = linear_179_cast_fp16)[name = string("input_1057_cast_fp16")]; + tensor encoder_layers_19_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(404553152))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(408747520))))[name = string("encoder_layers_19_feed_forward2_linear2_weight_to_fp16_quantized")]; + tensor encoder_layers_19_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_19_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(408749632)))]; + tensor linear_180_cast_fp16 = linear(bias = encoder_layers_19_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_19_feed_forward2_linear2_weight_to_fp16_quantized, x = input_1057_cast_fp16)[name = string("linear_180_cast_fp16")]; + fp16 var_4679_to_fp16 = const()[name = string("op_4679_to_fp16"), val = fp16(0x1p-1)]; + tensor var_4680_cast_fp16 = mul(x = linear_180_cast_fp16, y = var_4679_to_fp16)[name = string("op_4680_cast_fp16")]; + tensor input_1063_cast_fp16 = add(x = input_1051_cast_fp16, y = var_4680_cast_fp16)[name = string("input_1063_cast_fp16")]; + tensor input_1065_axes_0 = const()[name = string("input_1065_axes_0"), val = tensor([-1])]; + tensor encoder_layers_19_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_19_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(408751744)))]; + tensor encoder_layers_19_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_19_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(408753856)))]; + tensor input_1065_cast_fp16 = layer_norm(axes = input_1065_axes_0, beta = encoder_layers_19_norm_out_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_19_norm_out_weight_to_fp16, x = input_1063_cast_fp16)[name = string("input_1065_cast_fp16")]; + tensor cache_81_begin_0 = const()[name = string("cache_81_begin_0"), val = tensor([20, 0, 0, 0])]; + tensor cache_81_end_0 = const()[name = string("cache_81_end_0"), val = tensor([21, 1, 42, 1024])]; + tensor cache_81_end_mask_0 = const()[name = string("cache_81_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_81_squeeze_mask_0 = const()[name = string("cache_81_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_81_cast_fp16 = slice_by_index(begin = cache_81_begin_0, end = cache_81_end_0, end_mask = cache_81_end_mask_0, squeeze_mask = cache_81_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_81_cast_fp16")]; + tensor cache_83_begin_0 = const()[name = string("cache_83_begin_0"), val = tensor([20, 0, 0, 0])]; + tensor cache_83_end_0 = const()[name = string("cache_83_end_0"), val = tensor([21, 1, 1024, 8])]; + tensor cache_83_end_mask_0 = const()[name = string("cache_83_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_83_squeeze_mask_0 = const()[name = string("cache_83_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_83_cast_fp16 = slice_by_index(begin = cache_83_begin_0, end = cache_83_end_0, end_mask = cache_83_end_mask_0, squeeze_mask = cache_83_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_83_cast_fp16")]; + tensor input_1067_axes_0 = const()[name = string("input_1067_axes_0"), val = tensor([-1])]; + tensor encoder_layers_20_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_20_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(408755968)))]; + tensor encoder_layers_20_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_20_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(408758080)))]; + tensor input_1067_cast_fp16 = layer_norm(axes = input_1067_axes_0, beta = encoder_layers_20_norm_feed_forward1_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_20_norm_feed_forward1_weight_to_fp16, x = input_1065_cast_fp16)[name = string("input_1067_cast_fp16")]; + tensor encoder_layers_20_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(408760192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(412954560))))[name = string("encoder_layers_20_feed_forward1_linear1_weight_to_fp16_quantized")]; + tensor encoder_layers_20_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_20_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(412962816)))]; + tensor linear_181_cast_fp16 = linear(bias = encoder_layers_20_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_20_feed_forward1_linear1_weight_to_fp16_quantized, x = input_1067_cast_fp16)[name = string("linear_181_cast_fp16")]; + tensor input_1071_cast_fp16 = silu(x = linear_181_cast_fp16)[name = string("input_1071_cast_fp16")]; + tensor encoder_layers_20_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(412971072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(417165440))))[name = string("encoder_layers_20_feed_forward1_linear2_weight_to_fp16_quantized")]; + tensor encoder_layers_20_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_20_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(417167552)))]; + tensor linear_182_cast_fp16 = linear(bias = encoder_layers_20_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_20_feed_forward1_linear2_weight_to_fp16_quantized, x = input_1071_cast_fp16)[name = string("linear_182_cast_fp16")]; + fp16 var_4716_to_fp16 = const()[name = string("op_4716_to_fp16"), val = fp16(0x1p-1)]; + tensor var_4717_cast_fp16 = mul(x = linear_182_cast_fp16, y = var_4716_to_fp16)[name = string("op_4717_cast_fp16")]; + tensor input_1077_cast_fp16 = add(x = input_1065_cast_fp16, y = var_4717_cast_fp16)[name = string("input_1077_cast_fp16")]; + tensor key_41_axes_0 = const()[name = string("key_41_axes_0"), val = tensor([-1])]; + tensor encoder_layers_20_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_20_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(417169664)))]; + tensor encoder_layers_20_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_20_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(417171776)))]; + tensor key_41_cast_fp16 = layer_norm(axes = key_41_axes_0, beta = encoder_layers_20_norm_self_att_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_20_norm_self_att_weight_to_fp16, x = input_1077_cast_fp16)[name = string("key_41_cast_fp16")]; + bool input_1079_interleave_0 = const()[name = string("input_1079_interleave_0"), val = bool(false)]; + tensor input_1079_cast_fp16 = concat(axis = var_69, interleave = input_1079_interleave_0, values = (cache_81_cast_fp16, key_41_cast_fp16))[name = string("input_1079_cast_fp16")]; + bool var_4745_interleave_0 = const()[name = string("op_4745_interleave_0"), val = bool(false)]; + tensor var_4745_cast_fp16 = concat(axis = var_69, interleave = var_4745_interleave_0, values = key_41_cast_fp16)[name = string("op_4745_cast_fp16")]; + tensor encoder_layers_20_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(417173888))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(418222528))))[name = string("encoder_layers_20_self_attn_linear_q_weight_to_fp16_quantized")]; + tensor encoder_layers_20_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_20_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(418224640)))]; + tensor linear_183_cast_fp16 = linear(bias = encoder_layers_20_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_20_self_attn_linear_q_weight_to_fp16_quantized, x = key_41_cast_fp16)[name = string("linear_183_cast_fp16")]; + tensor var_4750 = const()[name = string("op_4750"), val = tensor([1, -1, 8, 128])]; + tensor q_121_cast_fp16 = reshape(shape = var_4750, x = linear_183_cast_fp16)[name = string("q_121_cast_fp16")]; + tensor encoder_layers_20_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(418226752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(419275392))))[name = string("encoder_layers_20_self_attn_linear_k_weight_to_fp16_quantized")]; + tensor encoder_layers_20_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_20_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(419277504)))]; + tensor linear_184_cast_fp16 = linear(bias = encoder_layers_20_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_20_self_attn_linear_k_weight_to_fp16_quantized, x = input_1079_cast_fp16)[name = string("linear_184_cast_fp16")]; + tensor var_4755 = const()[name = string("op_4755"), val = tensor([1, -1, 8, 128])]; + tensor k_81_cast_fp16 = reshape(shape = var_4755, x = linear_184_cast_fp16)[name = string("k_81_cast_fp16")]; + tensor encoder_layers_20_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(419279616))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(420328256))))[name = string("encoder_layers_20_self_attn_linear_v_weight_to_fp16_quantized")]; + tensor encoder_layers_20_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_20_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(420330368)))]; + tensor linear_185_cast_fp16 = linear(bias = encoder_layers_20_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_20_self_attn_linear_v_weight_to_fp16_quantized, x = input_1079_cast_fp16)[name = string("linear_185_cast_fp16")]; + tensor var_4760 = const()[name = string("op_4760"), val = tensor([1, -1, 8, 128])]; + tensor v_41_cast_fp16 = reshape(shape = var_4760, x = linear_185_cast_fp16)[name = string("v_41_cast_fp16")]; + tensor value_49_perm_0 = const()[name = string("value_49_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_20_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_20_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(420332480)))]; + tensor var_4773_cast_fp16 = add(x = q_121_cast_fp16, y = encoder_layers_20_self_attn_pos_bias_u_to_fp16)[name = string("op_4773_cast_fp16")]; + tensor encoder_layers_20_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_20_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(420334592)))]; + tensor var_4775_cast_fp16 = add(x = q_121_cast_fp16, y = encoder_layers_20_self_attn_pos_bias_v_to_fp16)[name = string("op_4775_cast_fp16")]; + tensor q_with_bias_v_41_perm_0 = const()[name = string("q_with_bias_v_41_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_527_transpose_x_0 = const()[name = string("x_527_transpose_x_0"), val = bool(false)]; + bool x_527_transpose_y_0 = const()[name = string("x_527_transpose_y_0"), val = bool(false)]; + tensor op_4777_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(420336704))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(420536448))))[name = string("op_4777_to_fp16_quantized")]; + tensor q_with_bias_v_41_cast_fp16 = transpose(perm = q_with_bias_v_41_perm_0, x = var_4775_cast_fp16)[name = string("transpose_182")]; + tensor x_527_cast_fp16 = matmul(transpose_x = x_527_transpose_x_0, transpose_y = x_527_transpose_y_0, x = q_with_bias_v_41_cast_fp16, y = op_4777_to_fp16_quantized)[name = string("x_527_cast_fp16")]; + tensor x_529_pad_0 = const()[name = string("x_529_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_529_mode_0 = const()[name = string("x_529_mode_0"), val = string("constant")]; + fp16 const_339_to_fp16 = const()[name = string("const_339_to_fp16"), val = fp16(0x0p+0)]; + tensor x_529_cast_fp16 = pad(constant_val = const_339_to_fp16, mode = x_529_mode_0, pad = x_529_pad_0, x = x_527_cast_fp16)[name = string("x_529_cast_fp16")]; + tensor var_4785 = const()[name = string("op_4785"), val = tensor([1, 8, -1, 56])]; + tensor x_531_cast_fp16 = reshape(shape = var_4785, x = x_529_cast_fp16)[name = string("x_531_cast_fp16")]; + tensor var_4789_begin_0 = const()[name = string("op_4789_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_4789_end_0 = const()[name = string("op_4789_end_0"), val = tensor([1, 8, 196, 56])]; + tensor var_4789_end_mask_0 = const()[name = string("op_4789_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_4789_cast_fp16 = slice_by_index(begin = var_4789_begin_0, end = var_4789_end_0, end_mask = var_4789_end_mask_0, x = x_531_cast_fp16)[name = string("op_4789_cast_fp16")]; + tensor var_4790 = const()[name = string("op_4790"), val = tensor([1, 8, 56, 195])]; + tensor matrix_bd_81_cast_fp16 = reshape(shape = var_4790, x = var_4789_cast_fp16)[name = string("matrix_bd_81_cast_fp16")]; + bool matrix_ac_41_transpose_x_0 = const()[name = string("matrix_ac_41_transpose_x_0"), val = bool(false)]; + bool matrix_ac_41_transpose_y_0 = const()[name = string("matrix_ac_41_transpose_y_0"), val = bool(false)]; + tensor transpose_136_perm_0 = const()[name = string("transpose_136_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_137_perm_0 = const()[name = string("transpose_137_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_137 = transpose(perm = transpose_137_perm_0, x = k_81_cast_fp16)[name = string("transpose_180")]; + tensor transpose_136 = transpose(perm = transpose_136_perm_0, x = var_4773_cast_fp16)[name = string("transpose_181")]; + tensor matrix_ac_41_cast_fp16 = matmul(transpose_x = matrix_ac_41_transpose_x_0, transpose_y = matrix_ac_41_transpose_y_0, x = transpose_136, y = transpose_137)[name = string("matrix_ac_41_cast_fp16")]; + tensor matrix_bd_83_begin_0 = const()[name = string("matrix_bd_83_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_83_end_0 = const()[name = string("matrix_bd_83_end_0"), val = tensor([1, 8, 56, 98])]; + tensor matrix_bd_83_end_mask_0 = const()[name = string("matrix_bd_83_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_83_cast_fp16 = slice_by_index(begin = matrix_bd_83_begin_0, end = matrix_bd_83_end_0, end_mask = matrix_bd_83_end_mask_0, x = matrix_bd_81_cast_fp16)[name = string("matrix_bd_83_cast_fp16")]; + tensor var_4799_cast_fp16 = add(x = matrix_ac_41_cast_fp16, y = matrix_bd_83_cast_fp16)[name = string("op_4799_cast_fp16")]; + fp16 _inversed_scores_81_y_0_to_fp16 = const()[name = string("_inversed_scores_81_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_81_cast_fp16 = mul(x = var_4799_cast_fp16, y = _inversed_scores_81_y_0_to_fp16)[name = string("_inversed_scores_81_cast_fp16")]; + tensor scores_83_cast_fp16 = select(a = var_46_to_fp16, b = _inversed_scores_81_cast_fp16, cond = mask_11)[name = string("scores_83_cast_fp16")]; + tensor var_4805_cast_fp16 = softmax(axis = var_60, x = scores_83_cast_fp16)[name = string("op_4805_cast_fp16")]; + tensor input_1081_cast_fp16 = select(a = var_45_to_fp16, b = var_4805_cast_fp16, cond = mask_11)[name = string("input_1081_cast_fp16")]; + bool x_533_transpose_x_0 = const()[name = string("x_533_transpose_x_0"), val = bool(false)]; + bool x_533_transpose_y_0 = const()[name = string("x_533_transpose_y_0"), val = bool(false)]; + tensor value_49_cast_fp16 = transpose(perm = value_49_perm_0, x = v_41_cast_fp16)[name = string("transpose_179")]; + tensor x_533_cast_fp16 = matmul(transpose_x = x_533_transpose_x_0, transpose_y = x_533_transpose_y_0, x = input_1081_cast_fp16, y = value_49_cast_fp16)[name = string("x_533_cast_fp16")]; + tensor var_4809_perm_0 = const()[name = string("op_4809_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_4810 = const()[name = string("op_4810"), val = tensor([1, -1, 1024])]; + tensor var_4809_cast_fp16 = transpose(perm = var_4809_perm_0, x = x_533_cast_fp16)[name = string("transpose_178")]; + tensor input_1083_cast_fp16 = reshape(shape = var_4810, x = var_4809_cast_fp16)[name = string("input_1083_cast_fp16")]; + tensor encoder_layers_20_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(420536960))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(421585600))))[name = string("encoder_layers_20_self_attn_linear_out_weight_to_fp16_quantized")]; + tensor encoder_layers_20_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_20_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(421587712)))]; + tensor linear_187_cast_fp16 = linear(bias = encoder_layers_20_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_20_self_attn_linear_out_weight_to_fp16_quantized, x = input_1083_cast_fp16)[name = string("linear_187_cast_fp16")]; + tensor input_1087_cast_fp16 = add(x = input_1077_cast_fp16, y = linear_187_cast_fp16)[name = string("input_1087_cast_fp16")]; + tensor x_537_axes_0 = const()[name = string("x_537_axes_0"), val = tensor([-1])]; + tensor encoder_layers_20_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_20_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(421589824)))]; + tensor encoder_layers_20_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_20_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(421591936)))]; + tensor x_537_cast_fp16 = layer_norm(axes = x_537_axes_0, beta = encoder_layers_20_norm_conv_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_20_norm_conv_weight_to_fp16, x = input_1087_cast_fp16)[name = string("x_537_cast_fp16")]; + tensor input_1089_perm_0 = const()[name = string("input_1089_perm_0"), val = tensor([0, 2, 1])]; + string input_1091_pad_type_0 = const()[name = string("input_1091_pad_type_0"), val = string("valid")]; + tensor input_1091_strides_0 = const()[name = string("input_1091_strides_0"), val = tensor([1])]; + tensor input_1091_pad_0 = const()[name = string("input_1091_pad_0"), val = tensor([0, 0])]; + tensor input_1091_dilations_0 = const()[name = string("input_1091_dilations_0"), val = tensor([1])]; + int32 input_1091_groups_0 = const()[name = string("input_1091_groups_0"), val = int32(1)]; + tensor encoder_layers_20_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(421594048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(423691264))))[name = string("encoder_layers_20_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_1089_cast_fp16 = transpose(perm = input_1089_perm_0, x = x_537_cast_fp16)[name = string("transpose_177")]; + tensor input_1091_cast_fp16 = conv(dilations = input_1091_dilations_0, groups = input_1091_groups_0, pad = input_1091_pad_0, pad_type = input_1091_pad_type_0, strides = input_1091_strides_0, weight = encoder_layers_20_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1089_cast_fp16)[name = string("input_1091_cast_fp16")]; + int32 x_539_split_num_splits_0 = const()[name = string("x_539_split_num_splits_0"), val = int32(2)]; + int32 x_539_split_axis_0 = const()[name = string("x_539_split_axis_0"), val = int32(1)]; + tensor x_539_split_cast_fp16_0, tensor x_539_split_cast_fp16_1 = split(axis = x_539_split_axis_0, num_splits = x_539_split_num_splits_0, x = input_1091_cast_fp16)[name = string("x_539_split_cast_fp16")]; + tensor x_539_split_1_sigmoid_cast_fp16 = sigmoid(x = x_539_split_cast_fp16_1)[name = string("x_539_split_1_sigmoid_cast_fp16")]; + tensor x_539_cast_fp16 = mul(x = x_539_split_cast_fp16_0, y = x_539_split_1_sigmoid_cast_fp16)[name = string("x_539_cast_fp16")]; + tensor input_1093_cast_fp16 = select(a = var_45_to_fp16, b = x_539_cast_fp16, cond = var_576)[name = string("input_1093_cast_fp16")]; + bool new_x_83_interleave_0 = const()[name = string("new_x_83_interleave_0"), val = bool(false)]; + tensor new_x_83_cast_fp16 = concat(axis = var_60, interleave = new_x_83_interleave_0, values = (cache_83_cast_fp16, input_1093_cast_fp16))[name = string("new_x_83_cast_fp16")]; + tensor var_4849_begin_0 = const()[name = string("op_4849_begin_0"), val = tensor([0, 0, 56])]; + tensor var_4849_end_0 = const()[name = string("op_4849_end_0"), val = tensor([1, 1024, 64])]; + tensor var_4849_end_mask_0 = const()[name = string("op_4849_end_mask_0"), val = tensor([true, true, true])]; + tensor var_4849_cast_fp16 = slice_by_index(begin = var_4849_begin_0, end = var_4849_end_0, end_mask = var_4849_end_mask_0, x = new_x_83_cast_fp16)[name = string("op_4849_cast_fp16")]; + string x_541_pad_type_0 = const()[name = string("x_541_pad_type_0"), val = string("valid")]; + int32 x_541_groups_0 = const()[name = string("x_541_groups_0"), val = int32(1024)]; + tensor x_541_strides_0 = const()[name = string("x_541_strides_0"), val = tensor([1])]; + tensor x_541_pad_0 = const()[name = string("x_541_pad_0"), val = tensor([0, 0])]; + tensor x_541_dilations_0 = const()[name = string("x_541_dilations_0"), val = tensor([1])]; + tensor encoder_layers_20_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(423695424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(423704704))))[name = string("encoder_layers_20_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_541_cast_fp16 = conv(dilations = x_541_dilations_0, groups = x_541_groups_0, pad = x_541_pad_0, pad_type = x_541_pad_type_0, strides = x_541_strides_0, weight = encoder_layers_20_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_83_cast_fp16)[name = string("x_541_cast_fp16")]; + tensor input_1095_perm_0 = const()[name = string("input_1095_perm_0"), val = tensor([0, 2, 1])]; + tensor x_543_axes_0 = const()[name = string("x_543_axes_0"), val = tensor([-1])]; + tensor encoder_layers_20_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_20_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(423706816)))]; + tensor encoder_layers_20_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_20_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(423708928)))]; + tensor input_1095_cast_fp16 = transpose(perm = input_1095_perm_0, x = x_541_cast_fp16)[name = string("transpose_176")]; + tensor x_543_cast_fp16 = layer_norm(axes = x_543_axes_0, beta = encoder_layers_20_conv_batch_norm_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_20_conv_batch_norm_weight_to_fp16, x = input_1095_cast_fp16)[name = string("x_543_cast_fp16")]; + tensor input_1097_perm_0 = const()[name = string("input_1097_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1097_cast_fp16 = transpose(perm = input_1097_perm_0, x = x_543_cast_fp16)[name = string("transpose_175")]; + tensor input_1099_cast_fp16 = silu(x = input_1097_cast_fp16)[name = string("input_1099_cast_fp16")]; + string x_545_pad_type_0 = const()[name = string("x_545_pad_type_0"), val = string("valid")]; + tensor x_545_strides_0 = const()[name = string("x_545_strides_0"), val = tensor([1])]; + tensor x_545_pad_0 = const()[name = string("x_545_pad_0"), val = tensor([0, 0])]; + tensor x_545_dilations_0 = const()[name = string("x_545_dilations_0"), val = tensor([1])]; + int32 x_545_groups_0 = const()[name = string("x_545_groups_0"), val = int32(1)]; + tensor encoder_layers_20_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(423711040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(424759680))))[name = string("encoder_layers_20_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_545_cast_fp16 = conv(dilations = x_545_dilations_0, groups = x_545_groups_0, pad = x_545_pad_0, pad_type = x_545_pad_type_0, strides = x_545_strides_0, weight = encoder_layers_20_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1099_cast_fp16)[name = string("x_545_cast_fp16")]; + tensor input_1101_perm_0 = const()[name = string("input_1101_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1101_cast_fp16 = transpose(perm = input_1101_perm_0, x = x_545_cast_fp16)[name = string("transpose_174")]; + tensor input_1103_cast_fp16 = add(x = input_1087_cast_fp16, y = input_1101_cast_fp16)[name = string("input_1103_cast_fp16")]; + tensor input_1105_axes_0 = const()[name = string("input_1105_axes_0"), val = tensor([-1])]; + tensor encoder_layers_20_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_20_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(424761792)))]; + tensor encoder_layers_20_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_20_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(424763904)))]; + tensor input_1105_cast_fp16 = layer_norm(axes = input_1105_axes_0, beta = encoder_layers_20_norm_feed_forward2_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_20_norm_feed_forward2_weight_to_fp16, x = input_1103_cast_fp16)[name = string("input_1105_cast_fp16")]; + tensor encoder_layers_20_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(424766016))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(428960384))))[name = string("encoder_layers_20_feed_forward2_linear1_weight_to_fp16_quantized")]; + tensor encoder_layers_20_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_20_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(428968640)))]; + tensor linear_188_cast_fp16 = linear(bias = encoder_layers_20_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_20_feed_forward2_linear1_weight_to_fp16_quantized, x = input_1105_cast_fp16)[name = string("linear_188_cast_fp16")]; + tensor input_1109_cast_fp16 = silu(x = linear_188_cast_fp16)[name = string("input_1109_cast_fp16")]; + tensor encoder_layers_20_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(428976896))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(433171264))))[name = string("encoder_layers_20_feed_forward2_linear2_weight_to_fp16_quantized")]; + tensor encoder_layers_20_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_20_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(433173376)))]; + tensor linear_189_cast_fp16 = linear(bias = encoder_layers_20_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_20_feed_forward2_linear2_weight_to_fp16_quantized, x = input_1109_cast_fp16)[name = string("linear_189_cast_fp16")]; + fp16 var_4892_to_fp16 = const()[name = string("op_4892_to_fp16"), val = fp16(0x1p-1)]; + tensor var_4893_cast_fp16 = mul(x = linear_189_cast_fp16, y = var_4892_to_fp16)[name = string("op_4893_cast_fp16")]; + tensor input_1115_cast_fp16 = add(x = input_1103_cast_fp16, y = var_4893_cast_fp16)[name = string("input_1115_cast_fp16")]; + tensor input_1117_axes_0 = const()[name = string("input_1117_axes_0"), val = tensor([-1])]; + tensor encoder_layers_20_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_20_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(433175488)))]; + tensor encoder_layers_20_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_20_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(433177600)))]; + tensor input_1117_cast_fp16 = layer_norm(axes = input_1117_axes_0, beta = encoder_layers_20_norm_out_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_20_norm_out_weight_to_fp16, x = input_1115_cast_fp16)[name = string("input_1117_cast_fp16")]; + tensor cache_85_begin_0 = const()[name = string("cache_85_begin_0"), val = tensor([21, 0, 0, 0])]; + tensor cache_85_end_0 = const()[name = string("cache_85_end_0"), val = tensor([22, 1, 42, 1024])]; + tensor cache_85_end_mask_0 = const()[name = string("cache_85_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_85_squeeze_mask_0 = const()[name = string("cache_85_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_85_cast_fp16 = slice_by_index(begin = cache_85_begin_0, end = cache_85_end_0, end_mask = cache_85_end_mask_0, squeeze_mask = cache_85_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_85_cast_fp16")]; + tensor cache_87_begin_0 = const()[name = string("cache_87_begin_0"), val = tensor([21, 0, 0, 0])]; + tensor cache_87_end_0 = const()[name = string("cache_87_end_0"), val = tensor([22, 1, 1024, 8])]; + tensor cache_87_end_mask_0 = const()[name = string("cache_87_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_87_squeeze_mask_0 = const()[name = string("cache_87_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_87_cast_fp16 = slice_by_index(begin = cache_87_begin_0, end = cache_87_end_0, end_mask = cache_87_end_mask_0, squeeze_mask = cache_87_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_87_cast_fp16")]; + tensor input_1119_axes_0 = const()[name = string("input_1119_axes_0"), val = tensor([-1])]; + tensor encoder_layers_21_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_21_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(433179712)))]; + tensor encoder_layers_21_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_21_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(433181824)))]; + tensor input_1119_cast_fp16 = layer_norm(axes = input_1119_axes_0, beta = encoder_layers_21_norm_feed_forward1_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_21_norm_feed_forward1_weight_to_fp16, x = input_1117_cast_fp16)[name = string("input_1119_cast_fp16")]; + tensor encoder_layers_21_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(433183936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(437378304))))[name = string("encoder_layers_21_feed_forward1_linear1_weight_to_fp16_quantized")]; + tensor encoder_layers_21_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_21_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(437386560)))]; + tensor linear_190_cast_fp16 = linear(bias = encoder_layers_21_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_21_feed_forward1_linear1_weight_to_fp16_quantized, x = input_1119_cast_fp16)[name = string("linear_190_cast_fp16")]; + tensor input_1123_cast_fp16 = silu(x = linear_190_cast_fp16)[name = string("input_1123_cast_fp16")]; + tensor encoder_layers_21_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(437394816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(441589184))))[name = string("encoder_layers_21_feed_forward1_linear2_weight_to_fp16_quantized")]; + tensor encoder_layers_21_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_21_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(441591296)))]; + tensor linear_191_cast_fp16 = linear(bias = encoder_layers_21_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_21_feed_forward1_linear2_weight_to_fp16_quantized, x = input_1123_cast_fp16)[name = string("linear_191_cast_fp16")]; + fp16 var_4929_to_fp16 = const()[name = string("op_4929_to_fp16"), val = fp16(0x1p-1)]; + tensor var_4930_cast_fp16 = mul(x = linear_191_cast_fp16, y = var_4929_to_fp16)[name = string("op_4930_cast_fp16")]; + tensor input_1129_cast_fp16 = add(x = input_1117_cast_fp16, y = var_4930_cast_fp16)[name = string("input_1129_cast_fp16")]; + tensor key_43_axes_0 = const()[name = string("key_43_axes_0"), val = tensor([-1])]; + tensor encoder_layers_21_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_21_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(441593408)))]; + tensor encoder_layers_21_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_21_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(441595520)))]; + tensor key_43_cast_fp16 = layer_norm(axes = key_43_axes_0, beta = encoder_layers_21_norm_self_att_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_21_norm_self_att_weight_to_fp16, x = input_1129_cast_fp16)[name = string("key_43_cast_fp16")]; + bool input_1131_interleave_0 = const()[name = string("input_1131_interleave_0"), val = bool(false)]; + tensor input_1131_cast_fp16 = concat(axis = var_69, interleave = input_1131_interleave_0, values = (cache_85_cast_fp16, key_43_cast_fp16))[name = string("input_1131_cast_fp16")]; + bool var_4958_interleave_0 = const()[name = string("op_4958_interleave_0"), val = bool(false)]; + tensor var_4958_cast_fp16 = concat(axis = var_69, interleave = var_4958_interleave_0, values = key_43_cast_fp16)[name = string("op_4958_cast_fp16")]; + tensor encoder_layers_21_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(441597632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(442646272))))[name = string("encoder_layers_21_self_attn_linear_q_weight_to_fp16_quantized")]; + tensor encoder_layers_21_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_21_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(442648384)))]; + tensor linear_192_cast_fp16 = linear(bias = encoder_layers_21_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_21_self_attn_linear_q_weight_to_fp16_quantized, x = key_43_cast_fp16)[name = string("linear_192_cast_fp16")]; + tensor var_4963 = const()[name = string("op_4963"), val = tensor([1, -1, 8, 128])]; + tensor q_127_cast_fp16 = reshape(shape = var_4963, x = linear_192_cast_fp16)[name = string("q_127_cast_fp16")]; + tensor encoder_layers_21_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(442650496))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(443699136))))[name = string("encoder_layers_21_self_attn_linear_k_weight_to_fp16_quantized")]; + tensor encoder_layers_21_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_21_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(443701248)))]; + tensor linear_193_cast_fp16 = linear(bias = encoder_layers_21_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_21_self_attn_linear_k_weight_to_fp16_quantized, x = input_1131_cast_fp16)[name = string("linear_193_cast_fp16")]; + tensor var_4968 = const()[name = string("op_4968"), val = tensor([1, -1, 8, 128])]; + tensor k_85_cast_fp16 = reshape(shape = var_4968, x = linear_193_cast_fp16)[name = string("k_85_cast_fp16")]; + tensor encoder_layers_21_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(443703360))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(444752000))))[name = string("encoder_layers_21_self_attn_linear_v_weight_to_fp16_quantized")]; + tensor encoder_layers_21_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_21_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(444754112)))]; + tensor linear_194_cast_fp16 = linear(bias = encoder_layers_21_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_21_self_attn_linear_v_weight_to_fp16_quantized, x = input_1131_cast_fp16)[name = string("linear_194_cast_fp16")]; + tensor var_4973 = const()[name = string("op_4973"), val = tensor([1, -1, 8, 128])]; + tensor v_43_cast_fp16 = reshape(shape = var_4973, x = linear_194_cast_fp16)[name = string("v_43_cast_fp16")]; + tensor value_51_perm_0 = const()[name = string("value_51_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_21_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_21_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(444756224)))]; + tensor var_4986_cast_fp16 = add(x = q_127_cast_fp16, y = encoder_layers_21_self_attn_pos_bias_u_to_fp16)[name = string("op_4986_cast_fp16")]; + tensor encoder_layers_21_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_21_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(444758336)))]; + tensor var_4988_cast_fp16 = add(x = q_127_cast_fp16, y = encoder_layers_21_self_attn_pos_bias_v_to_fp16)[name = string("op_4988_cast_fp16")]; + tensor q_with_bias_v_43_perm_0 = const()[name = string("q_with_bias_v_43_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_553_transpose_x_0 = const()[name = string("x_553_transpose_x_0"), val = bool(false)]; + bool x_553_transpose_y_0 = const()[name = string("x_553_transpose_y_0"), val = bool(false)]; + tensor op_4990_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(444760448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(444960192))))[name = string("op_4990_to_fp16_quantized")]; + tensor q_with_bias_v_43_cast_fp16 = transpose(perm = q_with_bias_v_43_perm_0, x = var_4988_cast_fp16)[name = string("transpose_173")]; + tensor x_553_cast_fp16 = matmul(transpose_x = x_553_transpose_x_0, transpose_y = x_553_transpose_y_0, x = q_with_bias_v_43_cast_fp16, y = op_4990_to_fp16_quantized)[name = string("x_553_cast_fp16")]; + tensor x_555_pad_0 = const()[name = string("x_555_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_555_mode_0 = const()[name = string("x_555_mode_0"), val = string("constant")]; + fp16 const_352_to_fp16 = const()[name = string("const_352_to_fp16"), val = fp16(0x0p+0)]; + tensor x_555_cast_fp16 = pad(constant_val = const_352_to_fp16, mode = x_555_mode_0, pad = x_555_pad_0, x = x_553_cast_fp16)[name = string("x_555_cast_fp16")]; + tensor var_4998 = const()[name = string("op_4998"), val = tensor([1, 8, -1, 56])]; + tensor x_557_cast_fp16 = reshape(shape = var_4998, x = x_555_cast_fp16)[name = string("x_557_cast_fp16")]; + tensor var_5002_begin_0 = const()[name = string("op_5002_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_5002_end_0 = const()[name = string("op_5002_end_0"), val = tensor([1, 8, 196, 56])]; + tensor var_5002_end_mask_0 = const()[name = string("op_5002_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_5002_cast_fp16 = slice_by_index(begin = var_5002_begin_0, end = var_5002_end_0, end_mask = var_5002_end_mask_0, x = x_557_cast_fp16)[name = string("op_5002_cast_fp16")]; + tensor var_5003 = const()[name = string("op_5003"), val = tensor([1, 8, 56, 195])]; + tensor matrix_bd_85_cast_fp16 = reshape(shape = var_5003, x = var_5002_cast_fp16)[name = string("matrix_bd_85_cast_fp16")]; + bool matrix_ac_43_transpose_x_0 = const()[name = string("matrix_ac_43_transpose_x_0"), val = bool(false)]; + bool matrix_ac_43_transpose_y_0 = const()[name = string("matrix_ac_43_transpose_y_0"), val = bool(false)]; + tensor transpose_138_perm_0 = const()[name = string("transpose_138_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_139_perm_0 = const()[name = string("transpose_139_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_139 = transpose(perm = transpose_139_perm_0, x = k_85_cast_fp16)[name = string("transpose_171")]; + tensor transpose_138 = transpose(perm = transpose_138_perm_0, x = var_4986_cast_fp16)[name = string("transpose_172")]; + tensor matrix_ac_43_cast_fp16 = matmul(transpose_x = matrix_ac_43_transpose_x_0, transpose_y = matrix_ac_43_transpose_y_0, x = transpose_138, y = transpose_139)[name = string("matrix_ac_43_cast_fp16")]; + tensor matrix_bd_87_begin_0 = const()[name = string("matrix_bd_87_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_87_end_0 = const()[name = string("matrix_bd_87_end_0"), val = tensor([1, 8, 56, 98])]; + tensor matrix_bd_87_end_mask_0 = const()[name = string("matrix_bd_87_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_87_cast_fp16 = slice_by_index(begin = matrix_bd_87_begin_0, end = matrix_bd_87_end_0, end_mask = matrix_bd_87_end_mask_0, x = matrix_bd_85_cast_fp16)[name = string("matrix_bd_87_cast_fp16")]; + tensor var_5012_cast_fp16 = add(x = matrix_ac_43_cast_fp16, y = matrix_bd_87_cast_fp16)[name = string("op_5012_cast_fp16")]; + fp16 _inversed_scores_85_y_0_to_fp16 = const()[name = string("_inversed_scores_85_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_85_cast_fp16 = mul(x = var_5012_cast_fp16, y = _inversed_scores_85_y_0_to_fp16)[name = string("_inversed_scores_85_cast_fp16")]; + tensor scores_87_cast_fp16 = select(a = var_46_to_fp16, b = _inversed_scores_85_cast_fp16, cond = mask_11)[name = string("scores_87_cast_fp16")]; + tensor var_5018_cast_fp16 = softmax(axis = var_60, x = scores_87_cast_fp16)[name = string("op_5018_cast_fp16")]; + tensor input_1133_cast_fp16 = select(a = var_45_to_fp16, b = var_5018_cast_fp16, cond = mask_11)[name = string("input_1133_cast_fp16")]; + bool x_559_transpose_x_0 = const()[name = string("x_559_transpose_x_0"), val = bool(false)]; + bool x_559_transpose_y_0 = const()[name = string("x_559_transpose_y_0"), val = bool(false)]; + tensor value_51_cast_fp16 = transpose(perm = value_51_perm_0, x = v_43_cast_fp16)[name = string("transpose_170")]; + tensor x_559_cast_fp16 = matmul(transpose_x = x_559_transpose_x_0, transpose_y = x_559_transpose_y_0, x = input_1133_cast_fp16, y = value_51_cast_fp16)[name = string("x_559_cast_fp16")]; + tensor var_5022_perm_0 = const()[name = string("op_5022_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_5023 = const()[name = string("op_5023"), val = tensor([1, -1, 1024])]; + tensor var_5022_cast_fp16 = transpose(perm = var_5022_perm_0, x = x_559_cast_fp16)[name = string("transpose_169")]; + tensor input_1135_cast_fp16 = reshape(shape = var_5023, x = var_5022_cast_fp16)[name = string("input_1135_cast_fp16")]; + tensor encoder_layers_21_self_attn_linear_out_weight_to_fp16 = const()[name = string("encoder_layers_21_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(444960704)))]; + tensor encoder_layers_21_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_21_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(447057920)))]; + tensor linear_196_cast_fp16 = linear(bias = encoder_layers_21_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_21_self_attn_linear_out_weight_to_fp16, x = input_1135_cast_fp16)[name = string("linear_196_cast_fp16")]; + tensor input_1139_cast_fp16 = add(x = input_1129_cast_fp16, y = linear_196_cast_fp16)[name = string("input_1139_cast_fp16")]; + tensor x_563_axes_0 = const()[name = string("x_563_axes_0"), val = tensor([-1])]; + tensor encoder_layers_21_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_21_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(447060032)))]; + tensor encoder_layers_21_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_21_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(447062144)))]; + tensor x_563_cast_fp16 = layer_norm(axes = x_563_axes_0, beta = encoder_layers_21_norm_conv_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_21_norm_conv_weight_to_fp16, x = input_1139_cast_fp16)[name = string("x_563_cast_fp16")]; + tensor input_1141_perm_0 = const()[name = string("input_1141_perm_0"), val = tensor([0, 2, 1])]; + string input_1143_pad_type_0 = const()[name = string("input_1143_pad_type_0"), val = string("valid")]; + tensor input_1143_strides_0 = const()[name = string("input_1143_strides_0"), val = tensor([1])]; + tensor input_1143_pad_0 = const()[name = string("input_1143_pad_0"), val = tensor([0, 0])]; + tensor input_1143_dilations_0 = const()[name = string("input_1143_dilations_0"), val = tensor([1])]; + int32 input_1143_groups_0 = const()[name = string("input_1143_groups_0"), val = int32(1)]; + tensor encoder_layers_21_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(447064256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(449161472))))[name = string("encoder_layers_21_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_1141_cast_fp16 = transpose(perm = input_1141_perm_0, x = x_563_cast_fp16)[name = string("transpose_168")]; + tensor input_1143_cast_fp16 = conv(dilations = input_1143_dilations_0, groups = input_1143_groups_0, pad = input_1143_pad_0, pad_type = input_1143_pad_type_0, strides = input_1143_strides_0, weight = encoder_layers_21_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1141_cast_fp16)[name = string("input_1143_cast_fp16")]; + int32 x_565_split_num_splits_0 = const()[name = string("x_565_split_num_splits_0"), val = int32(2)]; + int32 x_565_split_axis_0 = const()[name = string("x_565_split_axis_0"), val = int32(1)]; + tensor x_565_split_cast_fp16_0, tensor x_565_split_cast_fp16_1 = split(axis = x_565_split_axis_0, num_splits = x_565_split_num_splits_0, x = input_1143_cast_fp16)[name = string("x_565_split_cast_fp16")]; + tensor x_565_split_1_sigmoid_cast_fp16 = sigmoid(x = x_565_split_cast_fp16_1)[name = string("x_565_split_1_sigmoid_cast_fp16")]; + tensor x_565_cast_fp16 = mul(x = x_565_split_cast_fp16_0, y = x_565_split_1_sigmoid_cast_fp16)[name = string("x_565_cast_fp16")]; + tensor input_1145_cast_fp16 = select(a = var_45_to_fp16, b = x_565_cast_fp16, cond = var_576)[name = string("input_1145_cast_fp16")]; + bool new_x_87_interleave_0 = const()[name = string("new_x_87_interleave_0"), val = bool(false)]; + tensor new_x_87_cast_fp16 = concat(axis = var_60, interleave = new_x_87_interleave_0, values = (cache_87_cast_fp16, input_1145_cast_fp16))[name = string("new_x_87_cast_fp16")]; + tensor var_5062_begin_0 = const()[name = string("op_5062_begin_0"), val = tensor([0, 0, 56])]; + tensor var_5062_end_0 = const()[name = string("op_5062_end_0"), val = tensor([1, 1024, 64])]; + tensor var_5062_end_mask_0 = const()[name = string("op_5062_end_mask_0"), val = tensor([true, true, true])]; + tensor var_5062_cast_fp16 = slice_by_index(begin = var_5062_begin_0, end = var_5062_end_0, end_mask = var_5062_end_mask_0, x = new_x_87_cast_fp16)[name = string("op_5062_cast_fp16")]; + string x_567_pad_type_0 = const()[name = string("x_567_pad_type_0"), val = string("valid")]; + int32 x_567_groups_0 = const()[name = string("x_567_groups_0"), val = int32(1024)]; + tensor x_567_strides_0 = const()[name = string("x_567_strides_0"), val = tensor([1])]; + tensor x_567_pad_0 = const()[name = string("x_567_pad_0"), val = tensor([0, 0])]; + tensor x_567_dilations_0 = const()[name = string("x_567_dilations_0"), val = tensor([1])]; + tensor encoder_layers_21_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(449165632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(449174912))))[name = string("encoder_layers_21_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_567_cast_fp16 = conv(dilations = x_567_dilations_0, groups = x_567_groups_0, pad = x_567_pad_0, pad_type = x_567_pad_type_0, strides = x_567_strides_0, weight = encoder_layers_21_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_87_cast_fp16)[name = string("x_567_cast_fp16")]; + tensor input_1147_perm_0 = const()[name = string("input_1147_perm_0"), val = tensor([0, 2, 1])]; + tensor x_569_axes_0 = const()[name = string("x_569_axes_0"), val = tensor([-1])]; + tensor encoder_layers_21_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_21_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(449177024)))]; + tensor encoder_layers_21_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_21_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(449179136)))]; + tensor input_1147_cast_fp16 = transpose(perm = input_1147_perm_0, x = x_567_cast_fp16)[name = string("transpose_167")]; + tensor x_569_cast_fp16 = layer_norm(axes = x_569_axes_0, beta = encoder_layers_21_conv_batch_norm_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_21_conv_batch_norm_weight_to_fp16, x = input_1147_cast_fp16)[name = string("x_569_cast_fp16")]; + tensor input_1149_perm_0 = const()[name = string("input_1149_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1149_cast_fp16 = transpose(perm = input_1149_perm_0, x = x_569_cast_fp16)[name = string("transpose_166")]; + tensor input_1151_cast_fp16 = silu(x = input_1149_cast_fp16)[name = string("input_1151_cast_fp16")]; + string x_571_pad_type_0 = const()[name = string("x_571_pad_type_0"), val = string("valid")]; + tensor x_571_strides_0 = const()[name = string("x_571_strides_0"), val = tensor([1])]; + tensor x_571_pad_0 = const()[name = string("x_571_pad_0"), val = tensor([0, 0])]; + tensor x_571_dilations_0 = const()[name = string("x_571_dilations_0"), val = tensor([1])]; + int32 x_571_groups_0 = const()[name = string("x_571_groups_0"), val = int32(1)]; + tensor encoder_layers_21_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(449181248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(450229888))))[name = string("encoder_layers_21_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_571_cast_fp16 = conv(dilations = x_571_dilations_0, groups = x_571_groups_0, pad = x_571_pad_0, pad_type = x_571_pad_type_0, strides = x_571_strides_0, weight = encoder_layers_21_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1151_cast_fp16)[name = string("x_571_cast_fp16")]; + tensor input_1153_perm_0 = const()[name = string("input_1153_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1153_cast_fp16 = transpose(perm = input_1153_perm_0, x = x_571_cast_fp16)[name = string("transpose_165")]; + tensor input_1155_cast_fp16 = add(x = input_1139_cast_fp16, y = input_1153_cast_fp16)[name = string("input_1155_cast_fp16")]; + tensor input_1157_axes_0 = const()[name = string("input_1157_axes_0"), val = tensor([-1])]; + tensor encoder_layers_21_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_21_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(450232000)))]; + tensor encoder_layers_21_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_21_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(450234112)))]; + tensor input_1157_cast_fp16 = layer_norm(axes = input_1157_axes_0, beta = encoder_layers_21_norm_feed_forward2_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_21_norm_feed_forward2_weight_to_fp16, x = input_1155_cast_fp16)[name = string("input_1157_cast_fp16")]; + tensor encoder_layers_21_feed_forward2_linear1_weight_to_fp16 = const()[name = string("encoder_layers_21_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(450236224)))]; + tensor encoder_layers_21_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_21_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(458624896)))]; + tensor linear_197_cast_fp16 = linear(bias = encoder_layers_21_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_21_feed_forward2_linear1_weight_to_fp16, x = input_1157_cast_fp16)[name = string("linear_197_cast_fp16")]; + tensor input_1161_cast_fp16 = silu(x = linear_197_cast_fp16)[name = string("input_1161_cast_fp16")]; + tensor encoder_layers_21_feed_forward2_linear2_weight_to_fp16 = const()[name = string("encoder_layers_21_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(458633152)))]; + tensor encoder_layers_21_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_21_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(467021824)))]; + tensor linear_198_cast_fp16 = linear(bias = encoder_layers_21_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_21_feed_forward2_linear2_weight_to_fp16, x = input_1161_cast_fp16)[name = string("linear_198_cast_fp16")]; + fp16 var_5105_to_fp16 = const()[name = string("op_5105_to_fp16"), val = fp16(0x1p-1)]; + tensor var_5106_cast_fp16 = mul(x = linear_198_cast_fp16, y = var_5105_to_fp16)[name = string("op_5106_cast_fp16")]; + tensor input_1167_cast_fp16 = add(x = input_1155_cast_fp16, y = var_5106_cast_fp16)[name = string("input_1167_cast_fp16")]; + tensor input_1169_axes_0 = const()[name = string("input_1169_axes_0"), val = tensor([-1])]; + tensor encoder_layers_21_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_21_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(467023936)))]; + tensor encoder_layers_21_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_21_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(467026048)))]; + tensor input_1169_cast_fp16 = layer_norm(axes = input_1169_axes_0, beta = encoder_layers_21_norm_out_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_21_norm_out_weight_to_fp16, x = input_1167_cast_fp16)[name = string("input_1169_cast_fp16")]; + tensor cache_89_begin_0 = const()[name = string("cache_89_begin_0"), val = tensor([22, 0, 0, 0])]; + tensor cache_89_end_0 = const()[name = string("cache_89_end_0"), val = tensor([23, 1, 42, 1024])]; + tensor cache_89_end_mask_0 = const()[name = string("cache_89_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_89_squeeze_mask_0 = const()[name = string("cache_89_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_89_cast_fp16 = slice_by_index(begin = cache_89_begin_0, end = cache_89_end_0, end_mask = cache_89_end_mask_0, squeeze_mask = cache_89_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_89_cast_fp16")]; + tensor cache_91_begin_0 = const()[name = string("cache_91_begin_0"), val = tensor([22, 0, 0, 0])]; + tensor cache_91_end_0 = const()[name = string("cache_91_end_0"), val = tensor([23, 1, 1024, 8])]; + tensor cache_91_end_mask_0 = const()[name = string("cache_91_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_91_squeeze_mask_0 = const()[name = string("cache_91_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_91_cast_fp16 = slice_by_index(begin = cache_91_begin_0, end = cache_91_end_0, end_mask = cache_91_end_mask_0, squeeze_mask = cache_91_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_91_cast_fp16")]; + tensor input_1171_axes_0 = const()[name = string("input_1171_axes_0"), val = tensor([-1])]; + tensor encoder_layers_22_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_22_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(467028160)))]; + tensor encoder_layers_22_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_22_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(467030272)))]; + tensor input_1171_cast_fp16 = layer_norm(axes = input_1171_axes_0, beta = encoder_layers_22_norm_feed_forward1_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_22_norm_feed_forward1_weight_to_fp16, x = input_1169_cast_fp16)[name = string("input_1171_cast_fp16")]; + tensor encoder_layers_22_feed_forward1_linear1_weight_to_fp16 = const()[name = string("encoder_layers_22_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(467032384)))]; + tensor encoder_layers_22_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_22_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(475421056)))]; + tensor linear_199_cast_fp16 = linear(bias = encoder_layers_22_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_22_feed_forward1_linear1_weight_to_fp16, x = input_1171_cast_fp16)[name = string("linear_199_cast_fp16")]; + tensor input_1175_cast_fp16 = silu(x = linear_199_cast_fp16)[name = string("input_1175_cast_fp16")]; + tensor encoder_layers_22_feed_forward1_linear2_weight_to_fp16 = const()[name = string("encoder_layers_22_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(475429312)))]; + tensor encoder_layers_22_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_22_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483817984)))]; + tensor linear_200_cast_fp16 = linear(bias = encoder_layers_22_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_22_feed_forward1_linear2_weight_to_fp16, x = input_1175_cast_fp16)[name = string("linear_200_cast_fp16")]; + fp16 var_5142_to_fp16 = const()[name = string("op_5142_to_fp16"), val = fp16(0x1p-1)]; + tensor var_5143_cast_fp16 = mul(x = linear_200_cast_fp16, y = var_5142_to_fp16)[name = string("op_5143_cast_fp16")]; + tensor input_1181_cast_fp16 = add(x = input_1169_cast_fp16, y = var_5143_cast_fp16)[name = string("input_1181_cast_fp16")]; + tensor key_45_axes_0 = const()[name = string("key_45_axes_0"), val = tensor([-1])]; + tensor encoder_layers_22_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_22_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483820096)))]; + tensor encoder_layers_22_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_22_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483822208)))]; + tensor key_45_cast_fp16 = layer_norm(axes = key_45_axes_0, beta = encoder_layers_22_norm_self_att_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_22_norm_self_att_weight_to_fp16, x = input_1181_cast_fp16)[name = string("key_45_cast_fp16")]; + bool input_1183_interleave_0 = const()[name = string("input_1183_interleave_0"), val = bool(false)]; + tensor input_1183_cast_fp16 = concat(axis = var_69, interleave = input_1183_interleave_0, values = (cache_89_cast_fp16, key_45_cast_fp16))[name = string("input_1183_cast_fp16")]; + bool var_5171_interleave_0 = const()[name = string("op_5171_interleave_0"), val = bool(false)]; + tensor var_5171_cast_fp16 = concat(axis = var_69, interleave = var_5171_interleave_0, values = key_45_cast_fp16)[name = string("op_5171_cast_fp16")]; + tensor encoder_layers_22_self_attn_linear_q_weight_to_fp16 = const()[name = string("encoder_layers_22_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483824320)))]; + tensor encoder_layers_22_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_22_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(485921536)))]; + tensor linear_201_cast_fp16 = linear(bias = encoder_layers_22_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_22_self_attn_linear_q_weight_to_fp16, x = key_45_cast_fp16)[name = string("linear_201_cast_fp16")]; + tensor var_5176 = const()[name = string("op_5176"), val = tensor([1, -1, 8, 128])]; + tensor q_133_cast_fp16 = reshape(shape = var_5176, x = linear_201_cast_fp16)[name = string("q_133_cast_fp16")]; + tensor encoder_layers_22_self_attn_linear_k_weight_to_fp16 = const()[name = string("encoder_layers_22_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(485923648)))]; + tensor encoder_layers_22_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_22_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488020864)))]; + tensor linear_202_cast_fp16 = linear(bias = encoder_layers_22_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_22_self_attn_linear_k_weight_to_fp16, x = input_1183_cast_fp16)[name = string("linear_202_cast_fp16")]; + tensor var_5181 = const()[name = string("op_5181"), val = tensor([1, -1, 8, 128])]; + tensor k_89_cast_fp16 = reshape(shape = var_5181, x = linear_202_cast_fp16)[name = string("k_89_cast_fp16")]; + tensor encoder_layers_22_self_attn_linear_v_weight_to_fp16 = const()[name = string("encoder_layers_22_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488022976)))]; + tensor encoder_layers_22_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_22_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(490120192)))]; + tensor linear_203_cast_fp16 = linear(bias = encoder_layers_22_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_22_self_attn_linear_v_weight_to_fp16, x = input_1183_cast_fp16)[name = string("linear_203_cast_fp16")]; + tensor var_5186 = const()[name = string("op_5186"), val = tensor([1, -1, 8, 128])]; + tensor v_45_cast_fp16 = reshape(shape = var_5186, x = linear_203_cast_fp16)[name = string("v_45_cast_fp16")]; + tensor value_53_perm_0 = const()[name = string("value_53_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_22_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_22_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(490122304)))]; + tensor var_5199_cast_fp16 = add(x = q_133_cast_fp16, y = encoder_layers_22_self_attn_pos_bias_u_to_fp16)[name = string("op_5199_cast_fp16")]; + tensor encoder_layers_22_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_22_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(490124416)))]; + tensor var_5201_cast_fp16 = add(x = q_133_cast_fp16, y = encoder_layers_22_self_attn_pos_bias_v_to_fp16)[name = string("op_5201_cast_fp16")]; + tensor q_with_bias_v_45_perm_0 = const()[name = string("q_with_bias_v_45_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_579_transpose_x_0 = const()[name = string("x_579_transpose_x_0"), val = bool(false)]; + bool x_579_transpose_y_0 = const()[name = string("x_579_transpose_y_0"), val = bool(false)]; + tensor op_5203_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(490126528))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(490326272))))[name = string("op_5203_to_fp16_quantized")]; + tensor q_with_bias_v_45_cast_fp16 = transpose(perm = q_with_bias_v_45_perm_0, x = var_5201_cast_fp16)[name = string("transpose_164")]; + tensor x_579_cast_fp16 = matmul(transpose_x = x_579_transpose_x_0, transpose_y = x_579_transpose_y_0, x = q_with_bias_v_45_cast_fp16, y = op_5203_to_fp16_quantized)[name = string("x_579_cast_fp16")]; + tensor x_581_pad_0 = const()[name = string("x_581_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_581_mode_0 = const()[name = string("x_581_mode_0"), val = string("constant")]; + fp16 const_365_to_fp16 = const()[name = string("const_365_to_fp16"), val = fp16(0x0p+0)]; + tensor x_581_cast_fp16 = pad(constant_val = const_365_to_fp16, mode = x_581_mode_0, pad = x_581_pad_0, x = x_579_cast_fp16)[name = string("x_581_cast_fp16")]; + tensor var_5211 = const()[name = string("op_5211"), val = tensor([1, 8, -1, 56])]; + tensor x_583_cast_fp16 = reshape(shape = var_5211, x = x_581_cast_fp16)[name = string("x_583_cast_fp16")]; + tensor var_5215_begin_0 = const()[name = string("op_5215_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_5215_end_0 = const()[name = string("op_5215_end_0"), val = tensor([1, 8, 196, 56])]; + tensor var_5215_end_mask_0 = const()[name = string("op_5215_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_5215_cast_fp16 = slice_by_index(begin = var_5215_begin_0, end = var_5215_end_0, end_mask = var_5215_end_mask_0, x = x_583_cast_fp16)[name = string("op_5215_cast_fp16")]; + tensor var_5216 = const()[name = string("op_5216"), val = tensor([1, 8, 56, 195])]; + tensor matrix_bd_89_cast_fp16 = reshape(shape = var_5216, x = var_5215_cast_fp16)[name = string("matrix_bd_89_cast_fp16")]; + bool matrix_ac_45_transpose_x_0 = const()[name = string("matrix_ac_45_transpose_x_0"), val = bool(false)]; + bool matrix_ac_45_transpose_y_0 = const()[name = string("matrix_ac_45_transpose_y_0"), val = bool(false)]; + tensor transpose_140_perm_0 = const()[name = string("transpose_140_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_141_perm_0 = const()[name = string("transpose_141_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_141 = transpose(perm = transpose_141_perm_0, x = k_89_cast_fp16)[name = string("transpose_162")]; + tensor transpose_140 = transpose(perm = transpose_140_perm_0, x = var_5199_cast_fp16)[name = string("transpose_163")]; + tensor matrix_ac_45_cast_fp16 = matmul(transpose_x = matrix_ac_45_transpose_x_0, transpose_y = matrix_ac_45_transpose_y_0, x = transpose_140, y = transpose_141)[name = string("matrix_ac_45_cast_fp16")]; + tensor matrix_bd_91_begin_0 = const()[name = string("matrix_bd_91_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_91_end_0 = const()[name = string("matrix_bd_91_end_0"), val = tensor([1, 8, 56, 98])]; + tensor matrix_bd_91_end_mask_0 = const()[name = string("matrix_bd_91_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_91_cast_fp16 = slice_by_index(begin = matrix_bd_91_begin_0, end = matrix_bd_91_end_0, end_mask = matrix_bd_91_end_mask_0, x = matrix_bd_89_cast_fp16)[name = string("matrix_bd_91_cast_fp16")]; + tensor var_5225_cast_fp16 = add(x = matrix_ac_45_cast_fp16, y = matrix_bd_91_cast_fp16)[name = string("op_5225_cast_fp16")]; + fp16 _inversed_scores_89_y_0_to_fp16 = const()[name = string("_inversed_scores_89_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_89_cast_fp16 = mul(x = var_5225_cast_fp16, y = _inversed_scores_89_y_0_to_fp16)[name = string("_inversed_scores_89_cast_fp16")]; + tensor scores_91_cast_fp16 = select(a = var_46_to_fp16, b = _inversed_scores_89_cast_fp16, cond = mask_11)[name = string("scores_91_cast_fp16")]; + tensor var_5231_cast_fp16 = softmax(axis = var_60, x = scores_91_cast_fp16)[name = string("op_5231_cast_fp16")]; + tensor input_1185_cast_fp16 = select(a = var_45_to_fp16, b = var_5231_cast_fp16, cond = mask_11)[name = string("input_1185_cast_fp16")]; + bool x_585_transpose_x_0 = const()[name = string("x_585_transpose_x_0"), val = bool(false)]; + bool x_585_transpose_y_0 = const()[name = string("x_585_transpose_y_0"), val = bool(false)]; + tensor value_53_cast_fp16 = transpose(perm = value_53_perm_0, x = v_45_cast_fp16)[name = string("transpose_161")]; + tensor x_585_cast_fp16 = matmul(transpose_x = x_585_transpose_x_0, transpose_y = x_585_transpose_y_0, x = input_1185_cast_fp16, y = value_53_cast_fp16)[name = string("x_585_cast_fp16")]; + tensor var_5235_perm_0 = const()[name = string("op_5235_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_5236 = const()[name = string("op_5236"), val = tensor([1, -1, 1024])]; + tensor var_5235_cast_fp16 = transpose(perm = var_5235_perm_0, x = x_585_cast_fp16)[name = string("transpose_160")]; + tensor input_1187_cast_fp16 = reshape(shape = var_5236, x = var_5235_cast_fp16)[name = string("input_1187_cast_fp16")]; + tensor encoder_layers_22_self_attn_linear_out_weight_to_fp16 = const()[name = string("encoder_layers_22_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(490326784)))]; + tensor encoder_layers_22_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_22_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(492424000)))]; + tensor linear_205_cast_fp16 = linear(bias = encoder_layers_22_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_22_self_attn_linear_out_weight_to_fp16, x = input_1187_cast_fp16)[name = string("linear_205_cast_fp16")]; + tensor input_1191_cast_fp16 = add(x = input_1181_cast_fp16, y = linear_205_cast_fp16)[name = string("input_1191_cast_fp16")]; + tensor x_589_axes_0 = const()[name = string("x_589_axes_0"), val = tensor([-1])]; + tensor encoder_layers_22_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_22_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(492426112)))]; + tensor encoder_layers_22_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_22_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(492428224)))]; + tensor x_589_cast_fp16 = layer_norm(axes = x_589_axes_0, beta = encoder_layers_22_norm_conv_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_22_norm_conv_weight_to_fp16, x = input_1191_cast_fp16)[name = string("x_589_cast_fp16")]; + tensor input_1193_perm_0 = const()[name = string("input_1193_perm_0"), val = tensor([0, 2, 1])]; + string input_1195_pad_type_0 = const()[name = string("input_1195_pad_type_0"), val = string("valid")]; + tensor input_1195_strides_0 = const()[name = string("input_1195_strides_0"), val = tensor([1])]; + tensor input_1195_pad_0 = const()[name = string("input_1195_pad_0"), val = tensor([0, 0])]; + tensor input_1195_dilations_0 = const()[name = string("input_1195_dilations_0"), val = tensor([1])]; + int32 input_1195_groups_0 = const()[name = string("input_1195_groups_0"), val = int32(1)]; + tensor encoder_layers_22_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(492430336))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(494527552))))[name = string("encoder_layers_22_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_1193_cast_fp16 = transpose(perm = input_1193_perm_0, x = x_589_cast_fp16)[name = string("transpose_159")]; + tensor input_1195_cast_fp16 = conv(dilations = input_1195_dilations_0, groups = input_1195_groups_0, pad = input_1195_pad_0, pad_type = input_1195_pad_type_0, strides = input_1195_strides_0, weight = encoder_layers_22_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1193_cast_fp16)[name = string("input_1195_cast_fp16")]; + int32 x_591_split_num_splits_0 = const()[name = string("x_591_split_num_splits_0"), val = int32(2)]; + int32 x_591_split_axis_0 = const()[name = string("x_591_split_axis_0"), val = int32(1)]; + tensor x_591_split_cast_fp16_0, tensor x_591_split_cast_fp16_1 = split(axis = x_591_split_axis_0, num_splits = x_591_split_num_splits_0, x = input_1195_cast_fp16)[name = string("x_591_split_cast_fp16")]; + tensor x_591_split_1_sigmoid_cast_fp16 = sigmoid(x = x_591_split_cast_fp16_1)[name = string("x_591_split_1_sigmoid_cast_fp16")]; + tensor x_591_cast_fp16 = mul(x = x_591_split_cast_fp16_0, y = x_591_split_1_sigmoid_cast_fp16)[name = string("x_591_cast_fp16")]; + tensor input_1197_cast_fp16 = select(a = var_45_to_fp16, b = x_591_cast_fp16, cond = var_576)[name = string("input_1197_cast_fp16")]; + bool new_x_91_interleave_0 = const()[name = string("new_x_91_interleave_0"), val = bool(false)]; + tensor new_x_91_cast_fp16 = concat(axis = var_60, interleave = new_x_91_interleave_0, values = (cache_91_cast_fp16, input_1197_cast_fp16))[name = string("new_x_91_cast_fp16")]; + tensor var_5275_begin_0 = const()[name = string("op_5275_begin_0"), val = tensor([0, 0, 56])]; + tensor var_5275_end_0 = const()[name = string("op_5275_end_0"), val = tensor([1, 1024, 64])]; + tensor var_5275_end_mask_0 = const()[name = string("op_5275_end_mask_0"), val = tensor([true, true, true])]; + tensor var_5275_cast_fp16 = slice_by_index(begin = var_5275_begin_0, end = var_5275_end_0, end_mask = var_5275_end_mask_0, x = new_x_91_cast_fp16)[name = string("op_5275_cast_fp16")]; + string x_593_pad_type_0 = const()[name = string("x_593_pad_type_0"), val = string("valid")]; + int32 x_593_groups_0 = const()[name = string("x_593_groups_0"), val = int32(1024)]; + tensor x_593_strides_0 = const()[name = string("x_593_strides_0"), val = tensor([1])]; + tensor x_593_pad_0 = const()[name = string("x_593_pad_0"), val = tensor([0, 0])]; + tensor x_593_dilations_0 = const()[name = string("x_593_dilations_0"), val = tensor([1])]; + tensor encoder_layers_22_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(494531712))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(494540992))))[name = string("encoder_layers_22_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_593_cast_fp16 = conv(dilations = x_593_dilations_0, groups = x_593_groups_0, pad = x_593_pad_0, pad_type = x_593_pad_type_0, strides = x_593_strides_0, weight = encoder_layers_22_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_91_cast_fp16)[name = string("x_593_cast_fp16")]; + tensor input_1199_perm_0 = const()[name = string("input_1199_perm_0"), val = tensor([0, 2, 1])]; + tensor x_595_axes_0 = const()[name = string("x_595_axes_0"), val = tensor([-1])]; + tensor encoder_layers_22_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_22_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(494543104)))]; + tensor encoder_layers_22_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_22_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(494545216)))]; + tensor input_1199_cast_fp16 = transpose(perm = input_1199_perm_0, x = x_593_cast_fp16)[name = string("transpose_158")]; + tensor x_595_cast_fp16 = layer_norm(axes = x_595_axes_0, beta = encoder_layers_22_conv_batch_norm_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_22_conv_batch_norm_weight_to_fp16, x = input_1199_cast_fp16)[name = string("x_595_cast_fp16")]; + tensor input_1201_perm_0 = const()[name = string("input_1201_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1201_cast_fp16 = transpose(perm = input_1201_perm_0, x = x_595_cast_fp16)[name = string("transpose_157")]; + tensor input_1203_cast_fp16 = silu(x = input_1201_cast_fp16)[name = string("input_1203_cast_fp16")]; + string x_597_pad_type_0 = const()[name = string("x_597_pad_type_0"), val = string("valid")]; + tensor x_597_strides_0 = const()[name = string("x_597_strides_0"), val = tensor([1])]; + tensor x_597_pad_0 = const()[name = string("x_597_pad_0"), val = tensor([0, 0])]; + tensor x_597_dilations_0 = const()[name = string("x_597_dilations_0"), val = tensor([1])]; + int32 x_597_groups_0 = const()[name = string("x_597_groups_0"), val = int32(1)]; + tensor encoder_layers_22_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(494547328))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(495595968))))[name = string("encoder_layers_22_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_597_cast_fp16 = conv(dilations = x_597_dilations_0, groups = x_597_groups_0, pad = x_597_pad_0, pad_type = x_597_pad_type_0, strides = x_597_strides_0, weight = encoder_layers_22_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1203_cast_fp16)[name = string("x_597_cast_fp16")]; + tensor input_1205_perm_0 = const()[name = string("input_1205_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1205_cast_fp16 = transpose(perm = input_1205_perm_0, x = x_597_cast_fp16)[name = string("transpose_156")]; + tensor input_1207_cast_fp16 = add(x = input_1191_cast_fp16, y = input_1205_cast_fp16)[name = string("input_1207_cast_fp16")]; + tensor input_1209_axes_0 = const()[name = string("input_1209_axes_0"), val = tensor([-1])]; + tensor encoder_layers_22_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_22_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(495598080)))]; + tensor encoder_layers_22_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_22_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(495600192)))]; + tensor input_1209_cast_fp16 = layer_norm(axes = input_1209_axes_0, beta = encoder_layers_22_norm_feed_forward2_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_22_norm_feed_forward2_weight_to_fp16, x = input_1207_cast_fp16)[name = string("input_1209_cast_fp16")]; + tensor encoder_layers_22_feed_forward2_linear1_weight_to_fp16 = const()[name = string("encoder_layers_22_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(495602304)))]; + tensor encoder_layers_22_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_22_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(503990976)))]; + tensor linear_206_cast_fp16 = linear(bias = encoder_layers_22_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_22_feed_forward2_linear1_weight_to_fp16, x = input_1209_cast_fp16)[name = string("linear_206_cast_fp16")]; + tensor input_1213_cast_fp16 = silu(x = linear_206_cast_fp16)[name = string("input_1213_cast_fp16")]; + tensor encoder_layers_22_feed_forward2_linear2_weight_to_fp16 = const()[name = string("encoder_layers_22_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(503999232)))]; + tensor encoder_layers_22_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_22_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(512387904)))]; + tensor linear_207_cast_fp16 = linear(bias = encoder_layers_22_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_22_feed_forward2_linear2_weight_to_fp16, x = input_1213_cast_fp16)[name = string("linear_207_cast_fp16")]; + fp16 var_5318_to_fp16 = const()[name = string("op_5318_to_fp16"), val = fp16(0x1p-1)]; + tensor var_5319_cast_fp16 = mul(x = linear_207_cast_fp16, y = var_5318_to_fp16)[name = string("op_5319_cast_fp16")]; + tensor input_1219_cast_fp16 = add(x = input_1207_cast_fp16, y = var_5319_cast_fp16)[name = string("input_1219_cast_fp16")]; + tensor input_1221_axes_0 = const()[name = string("input_1221_axes_0"), val = tensor([-1])]; + tensor encoder_layers_22_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_22_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(512390016)))]; + tensor encoder_layers_22_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_22_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(512392128)))]; + tensor input_1221_cast_fp16 = layer_norm(axes = input_1221_axes_0, beta = encoder_layers_22_norm_out_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_22_norm_out_weight_to_fp16, x = input_1219_cast_fp16)[name = string("input_1221_cast_fp16")]; + tensor cache_93_begin_0 = const()[name = string("cache_93_begin_0"), val = tensor([23, 0, 0, 0])]; + tensor cache_93_end_0 = const()[name = string("cache_93_end_0"), val = tensor([24, 1, 42, 1024])]; + tensor cache_93_end_mask_0 = const()[name = string("cache_93_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_93_squeeze_mask_0 = const()[name = string("cache_93_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_93_cast_fp16 = slice_by_index(begin = cache_93_begin_0, end = cache_93_end_0, end_mask = cache_93_end_mask_0, squeeze_mask = cache_93_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_93_cast_fp16")]; + tensor cache_begin_0 = const()[name = string("cache_begin_0"), val = tensor([23, 0, 0, 0])]; + tensor cache_end_0 = const()[name = string("cache_end_0"), val = tensor([24, 1, 1024, 8])]; + tensor cache_end_mask_0 = const()[name = string("cache_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_squeeze_mask_0 = const()[name = string("cache_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_cast_fp16 = slice_by_index(begin = cache_begin_0, end = cache_end_0, end_mask = cache_end_mask_0, squeeze_mask = cache_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_cast_fp16")]; + tensor input_1223_axes_0 = const()[name = string("input_1223_axes_0"), val = tensor([-1])]; + tensor encoder_layers_23_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_23_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(512394240)))]; + tensor encoder_layers_23_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_23_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(512396352)))]; + tensor input_1223_cast_fp16 = layer_norm(axes = input_1223_axes_0, beta = encoder_layers_23_norm_feed_forward1_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_23_norm_feed_forward1_weight_to_fp16, x = input_1221_cast_fp16)[name = string("input_1223_cast_fp16")]; + tensor encoder_layers_23_feed_forward1_linear1_weight_to_fp16 = const()[name = string("encoder_layers_23_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(512398464)))]; + tensor encoder_layers_23_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_23_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(520787136)))]; + tensor linear_208_cast_fp16 = linear(bias = encoder_layers_23_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_23_feed_forward1_linear1_weight_to_fp16, x = input_1223_cast_fp16)[name = string("linear_208_cast_fp16")]; + tensor input_1227_cast_fp16 = silu(x = linear_208_cast_fp16)[name = string("input_1227_cast_fp16")]; + tensor encoder_layers_23_feed_forward1_linear2_weight_to_fp16 = const()[name = string("encoder_layers_23_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(520795392)))]; + tensor encoder_layers_23_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_23_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(529184064)))]; + tensor linear_209_cast_fp16 = linear(bias = encoder_layers_23_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_23_feed_forward1_linear2_weight_to_fp16, x = input_1227_cast_fp16)[name = string("linear_209_cast_fp16")]; + fp16 var_5355_to_fp16 = const()[name = string("op_5355_to_fp16"), val = fp16(0x1p-1)]; + tensor var_5356_cast_fp16 = mul(x = linear_209_cast_fp16, y = var_5355_to_fp16)[name = string("op_5356_cast_fp16")]; + tensor input_1233_cast_fp16 = add(x = input_1221_cast_fp16, y = var_5356_cast_fp16)[name = string("input_1233_cast_fp16")]; + tensor key_axes_0 = const()[name = string("key_axes_0"), val = tensor([-1])]; + tensor encoder_layers_23_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_23_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(529186176)))]; + tensor encoder_layers_23_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_23_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(529188288)))]; + tensor key_cast_fp16 = layer_norm(axes = key_axes_0, beta = encoder_layers_23_norm_self_att_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_23_norm_self_att_weight_to_fp16, x = input_1233_cast_fp16)[name = string("key_cast_fp16")]; + bool input_1235_interleave_0 = const()[name = string("input_1235_interleave_0"), val = bool(false)]; + tensor input_1235_cast_fp16 = concat(axis = var_69, interleave = input_1235_interleave_0, values = (cache_93_cast_fp16, key_cast_fp16))[name = string("input_1235_cast_fp16")]; + bool cache_last_channel_cur_interleave_0 = const()[name = string("cache_last_channel_cur_interleave_0"), val = bool(false)]; + tensor cache_last_channel_cur_cast_fp16 = concat(axis = var_69, interleave = cache_last_channel_cur_interleave_0, values = key_cast_fp16)[name = string("cache_last_channel_cur_cast_fp16")]; + tensor encoder_layers_23_self_attn_linear_q_weight_to_fp16 = const()[name = string("encoder_layers_23_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(529190400)))]; + tensor encoder_layers_23_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_23_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531287616)))]; + tensor linear_210_cast_fp16 = linear(bias = encoder_layers_23_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_23_self_attn_linear_q_weight_to_fp16, x = key_cast_fp16)[name = string("linear_210_cast_fp16")]; + tensor var_5389 = const()[name = string("op_5389"), val = tensor([1, -1, 8, 128])]; + tensor q_139_cast_fp16 = reshape(shape = var_5389, x = linear_210_cast_fp16)[name = string("q_139_cast_fp16")]; + tensor encoder_layers_23_self_attn_linear_k_weight_to_fp16 = const()[name = string("encoder_layers_23_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531289728)))]; + tensor encoder_layers_23_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_23_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(533386944)))]; + tensor linear_211_cast_fp16 = linear(bias = encoder_layers_23_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_23_self_attn_linear_k_weight_to_fp16, x = input_1235_cast_fp16)[name = string("linear_211_cast_fp16")]; + tensor var_5394 = const()[name = string("op_5394"), val = tensor([1, -1, 8, 128])]; + tensor k_93_cast_fp16 = reshape(shape = var_5394, x = linear_211_cast_fp16)[name = string("k_93_cast_fp16")]; + tensor encoder_layers_23_self_attn_linear_v_weight_to_fp16 = const()[name = string("encoder_layers_23_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(533389056)))]; + tensor encoder_layers_23_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_23_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(535486272)))]; + tensor linear_212_cast_fp16 = linear(bias = encoder_layers_23_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_23_self_attn_linear_v_weight_to_fp16, x = input_1235_cast_fp16)[name = string("linear_212_cast_fp16")]; + tensor var_5399 = const()[name = string("op_5399"), val = tensor([1, -1, 8, 128])]; + tensor v_cast_fp16 = reshape(shape = var_5399, x = linear_212_cast_fp16)[name = string("v_cast_fp16")]; + tensor value_perm_0 = const()[name = string("value_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_23_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_23_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(535488384)))]; + tensor var_5412_cast_fp16 = add(x = q_139_cast_fp16, y = encoder_layers_23_self_attn_pos_bias_u_to_fp16)[name = string("op_5412_cast_fp16")]; + tensor encoder_layers_23_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_23_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(535490496)))]; + tensor var_5414_cast_fp16 = add(x = q_139_cast_fp16, y = encoder_layers_23_self_attn_pos_bias_v_to_fp16)[name = string("op_5414_cast_fp16")]; + tensor q_with_bias_v_perm_0 = const()[name = string("q_with_bias_v_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_605_transpose_x_0 = const()[name = string("x_605_transpose_x_0"), val = bool(false)]; + bool x_605_transpose_y_0 = const()[name = string("x_605_transpose_y_0"), val = bool(false)]; + tensor op_5416_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(535492608))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(535692352))))[name = string("op_5416_to_fp16_quantized")]; + tensor q_with_bias_v_cast_fp16 = transpose(perm = q_with_bias_v_perm_0, x = var_5414_cast_fp16)[name = string("transpose_155")]; + tensor x_605_cast_fp16 = matmul(transpose_x = x_605_transpose_x_0, transpose_y = x_605_transpose_y_0, x = q_with_bias_v_cast_fp16, y = op_5416_to_fp16_quantized)[name = string("x_605_cast_fp16")]; + tensor x_607_pad_0 = const()[name = string("x_607_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_607_mode_0 = const()[name = string("x_607_mode_0"), val = string("constant")]; + fp16 const_378_to_fp16 = const()[name = string("const_378_to_fp16"), val = fp16(0x0p+0)]; + tensor x_607_cast_fp16 = pad(constant_val = const_378_to_fp16, mode = x_607_mode_0, pad = x_607_pad_0, x = x_605_cast_fp16)[name = string("x_607_cast_fp16")]; + tensor var_5424 = const()[name = string("op_5424"), val = tensor([1, 8, -1, 56])]; + tensor x_609_cast_fp16 = reshape(shape = var_5424, x = x_607_cast_fp16)[name = string("x_609_cast_fp16")]; + tensor var_5428_begin_0 = const()[name = string("op_5428_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_5428_end_0 = const()[name = string("op_5428_end_0"), val = tensor([1, 8, 196, 56])]; + tensor var_5428_end_mask_0 = const()[name = string("op_5428_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_5428_cast_fp16 = slice_by_index(begin = var_5428_begin_0, end = var_5428_end_0, end_mask = var_5428_end_mask_0, x = x_609_cast_fp16)[name = string("op_5428_cast_fp16")]; + tensor var_5429 = const()[name = string("op_5429"), val = tensor([1, 8, 56, 195])]; + tensor matrix_bd_93_cast_fp16 = reshape(shape = var_5429, x = var_5428_cast_fp16)[name = string("matrix_bd_93_cast_fp16")]; + bool matrix_ac_transpose_x_0 = const()[name = string("matrix_ac_transpose_x_0"), val = bool(false)]; + bool matrix_ac_transpose_y_0 = const()[name = string("matrix_ac_transpose_y_0"), val = bool(false)]; + tensor transpose_142_perm_0 = const()[name = string("transpose_142_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_143_perm_0 = const()[name = string("transpose_143_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_143 = transpose(perm = transpose_143_perm_0, x = k_93_cast_fp16)[name = string("transpose_153")]; + tensor transpose_142 = transpose(perm = transpose_142_perm_0, x = var_5412_cast_fp16)[name = string("transpose_154")]; + tensor matrix_ac_cast_fp16 = matmul(transpose_x = matrix_ac_transpose_x_0, transpose_y = matrix_ac_transpose_y_0, x = transpose_142, y = transpose_143)[name = string("matrix_ac_cast_fp16")]; + tensor matrix_bd_begin_0 = const()[name = string("matrix_bd_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_end_0 = const()[name = string("matrix_bd_end_0"), val = tensor([1, 8, 56, 98])]; + tensor matrix_bd_end_mask_0 = const()[name = string("matrix_bd_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_cast_fp16 = slice_by_index(begin = matrix_bd_begin_0, end = matrix_bd_end_0, end_mask = matrix_bd_end_mask_0, x = matrix_bd_93_cast_fp16)[name = string("matrix_bd_cast_fp16")]; + tensor var_5438_cast_fp16 = add(x = matrix_ac_cast_fp16, y = matrix_bd_cast_fp16)[name = string("op_5438_cast_fp16")]; + fp16 _inversed_scores_93_y_0_to_fp16 = const()[name = string("_inversed_scores_93_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_93_cast_fp16 = mul(x = var_5438_cast_fp16, y = _inversed_scores_93_y_0_to_fp16)[name = string("_inversed_scores_93_cast_fp16")]; + tensor scores_cast_fp16 = select(a = var_46_to_fp16, b = _inversed_scores_93_cast_fp16, cond = mask_11)[name = string("scores_cast_fp16")]; + tensor var_5444_cast_fp16 = softmax(axis = var_60, x = scores_cast_fp16)[name = string("op_5444_cast_fp16")]; + tensor input_1237_cast_fp16 = select(a = var_45_to_fp16, b = var_5444_cast_fp16, cond = mask_11)[name = string("input_1237_cast_fp16")]; + bool x_611_transpose_x_0 = const()[name = string("x_611_transpose_x_0"), val = bool(false)]; + bool x_611_transpose_y_0 = const()[name = string("x_611_transpose_y_0"), val = bool(false)]; + tensor value_cast_fp16 = transpose(perm = value_perm_0, x = v_cast_fp16)[name = string("transpose_152")]; + tensor x_611_cast_fp16 = matmul(transpose_x = x_611_transpose_x_0, transpose_y = x_611_transpose_y_0, x = input_1237_cast_fp16, y = value_cast_fp16)[name = string("x_611_cast_fp16")]; + tensor var_5448_perm_0 = const()[name = string("op_5448_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_5449 = const()[name = string("op_5449"), val = tensor([1, -1, 1024])]; + tensor var_5448_cast_fp16 = transpose(perm = var_5448_perm_0, x = x_611_cast_fp16)[name = string("transpose_151")]; + tensor input_1239_cast_fp16 = reshape(shape = var_5449, x = var_5448_cast_fp16)[name = string("input_1239_cast_fp16")]; + tensor encoder_layers_23_self_attn_linear_out_weight_to_fp16 = const()[name = string("encoder_layers_23_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(535692864)))]; + tensor encoder_layers_23_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_23_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(537790080)))]; + tensor linear_214_cast_fp16 = linear(bias = encoder_layers_23_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_23_self_attn_linear_out_weight_to_fp16, x = input_1239_cast_fp16)[name = string("linear_214_cast_fp16")]; + tensor input_1243_cast_fp16 = add(x = input_1233_cast_fp16, y = linear_214_cast_fp16)[name = string("input_1243_cast_fp16")]; + tensor x_615_axes_0 = const()[name = string("x_615_axes_0"), val = tensor([-1])]; + tensor encoder_layers_23_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_23_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(537792192)))]; + tensor encoder_layers_23_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_23_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(537794304)))]; + tensor x_615_cast_fp16 = layer_norm(axes = x_615_axes_0, beta = encoder_layers_23_norm_conv_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_23_norm_conv_weight_to_fp16, x = input_1243_cast_fp16)[name = string("x_615_cast_fp16")]; + tensor input_1245_perm_0 = const()[name = string("input_1245_perm_0"), val = tensor([0, 2, 1])]; + string input_1247_pad_type_0 = const()[name = string("input_1247_pad_type_0"), val = string("valid")]; + tensor input_1247_strides_0 = const()[name = string("input_1247_strides_0"), val = tensor([1])]; + tensor input_1247_pad_0 = const()[name = string("input_1247_pad_0"), val = tensor([0, 0])]; + tensor input_1247_dilations_0 = const()[name = string("input_1247_dilations_0"), val = tensor([1])]; + int32 input_1247_groups_0 = const()[name = string("input_1247_groups_0"), val = int32(1)]; + tensor encoder_layers_23_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(537796416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(539893632))))[name = string("encoder_layers_23_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_1245_cast_fp16 = transpose(perm = input_1245_perm_0, x = x_615_cast_fp16)[name = string("transpose_150")]; + tensor input_1247_cast_fp16 = conv(dilations = input_1247_dilations_0, groups = input_1247_groups_0, pad = input_1247_pad_0, pad_type = input_1247_pad_type_0, strides = input_1247_strides_0, weight = encoder_layers_23_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1245_cast_fp16)[name = string("input_1247_cast_fp16")]; + int32 x_617_split_num_splits_0 = const()[name = string("x_617_split_num_splits_0"), val = int32(2)]; + int32 x_617_split_axis_0 = const()[name = string("x_617_split_axis_0"), val = int32(1)]; + tensor x_617_split_cast_fp16_0, tensor x_617_split_cast_fp16_1 = split(axis = x_617_split_axis_0, num_splits = x_617_split_num_splits_0, x = input_1247_cast_fp16)[name = string("x_617_split_cast_fp16")]; + tensor x_617_split_1_sigmoid_cast_fp16 = sigmoid(x = x_617_split_cast_fp16_1)[name = string("x_617_split_1_sigmoid_cast_fp16")]; + tensor x_617_cast_fp16 = mul(x = x_617_split_cast_fp16_0, y = x_617_split_1_sigmoid_cast_fp16)[name = string("x_617_cast_fp16")]; + tensor input_1249_cast_fp16 = select(a = var_45_to_fp16, b = x_617_cast_fp16, cond = var_576)[name = string("input_1249_cast_fp16")]; + bool new_x_interleave_0 = const()[name = string("new_x_interleave_0"), val = bool(false)]; + tensor new_x_cast_fp16 = concat(axis = var_60, interleave = new_x_interleave_0, values = (cache_cast_fp16, input_1249_cast_fp16))[name = string("new_x_cast_fp16")]; + tensor cache_last_time_cur_begin_0 = const()[name = string("cache_last_time_cur_begin_0"), val = tensor([0, 0, 56])]; + tensor cache_last_time_cur_end_0 = const()[name = string("cache_last_time_cur_end_0"), val = tensor([1, 1024, 64])]; + tensor cache_last_time_cur_end_mask_0 = const()[name = string("cache_last_time_cur_end_mask_0"), val = tensor([true, true, true])]; + tensor cache_last_time_cur_cast_fp16 = slice_by_index(begin = cache_last_time_cur_begin_0, end = cache_last_time_cur_end_0, end_mask = cache_last_time_cur_end_mask_0, x = new_x_cast_fp16)[name = string("cache_last_time_cur_cast_fp16")]; + string x_619_pad_type_0 = const()[name = string("x_619_pad_type_0"), val = string("valid")]; + int32 x_619_groups_0 = const()[name = string("x_619_groups_0"), val = int32(1024)]; + tensor x_619_strides_0 = const()[name = string("x_619_strides_0"), val = tensor([1])]; + tensor x_619_pad_0 = const()[name = string("x_619_pad_0"), val = tensor([0, 0])]; + tensor x_619_dilations_0 = const()[name = string("x_619_dilations_0"), val = tensor([1])]; + tensor encoder_layers_23_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(539897792))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(539907072))))[name = string("encoder_layers_23_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_619_cast_fp16 = conv(dilations = x_619_dilations_0, groups = x_619_groups_0, pad = x_619_pad_0, pad_type = x_619_pad_type_0, strides = x_619_strides_0, weight = encoder_layers_23_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_cast_fp16)[name = string("x_619_cast_fp16")]; + tensor input_1251_perm_0 = const()[name = string("input_1251_perm_0"), val = tensor([0, 2, 1])]; + tensor x_621_axes_0 = const()[name = string("x_621_axes_0"), val = tensor([-1])]; + tensor encoder_layers_23_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_23_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(539909184)))]; + tensor encoder_layers_23_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_23_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(539911296)))]; + tensor input_1251_cast_fp16 = transpose(perm = input_1251_perm_0, x = x_619_cast_fp16)[name = string("transpose_149")]; + tensor x_621_cast_fp16 = layer_norm(axes = x_621_axes_0, beta = encoder_layers_23_conv_batch_norm_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_23_conv_batch_norm_weight_to_fp16, x = input_1251_cast_fp16)[name = string("x_621_cast_fp16")]; + tensor input_1253_perm_0 = const()[name = string("input_1253_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1253_cast_fp16 = transpose(perm = input_1253_perm_0, x = x_621_cast_fp16)[name = string("transpose_148")]; + tensor input_1255_cast_fp16 = silu(x = input_1253_cast_fp16)[name = string("input_1255_cast_fp16")]; + string x_623_pad_type_0 = const()[name = string("x_623_pad_type_0"), val = string("valid")]; + tensor x_623_strides_0 = const()[name = string("x_623_strides_0"), val = tensor([1])]; + tensor x_623_pad_0 = const()[name = string("x_623_pad_0"), val = tensor([0, 0])]; + tensor x_623_dilations_0 = const()[name = string("x_623_dilations_0"), val = tensor([1])]; + int32 x_623_groups_0 = const()[name = string("x_623_groups_0"), val = int32(1)]; + tensor encoder_layers_23_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(539913408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(540962048))))[name = string("encoder_layers_23_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_623_cast_fp16 = conv(dilations = x_623_dilations_0, groups = x_623_groups_0, pad = x_623_pad_0, pad_type = x_623_pad_type_0, strides = x_623_strides_0, weight = encoder_layers_23_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1255_cast_fp16)[name = string("x_623_cast_fp16")]; + tensor input_1257_perm_0 = const()[name = string("input_1257_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1257_cast_fp16 = transpose(perm = input_1257_perm_0, x = x_623_cast_fp16)[name = string("transpose_147")]; + tensor input_1259_cast_fp16 = add(x = input_1243_cast_fp16, y = input_1257_cast_fp16)[name = string("input_1259_cast_fp16")]; + tensor input_1261_axes_0 = const()[name = string("input_1261_axes_0"), val = tensor([-1])]; + tensor encoder_layers_23_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_23_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(540964160)))]; + tensor encoder_layers_23_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_23_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(540966272)))]; + tensor input_1261_cast_fp16 = layer_norm(axes = input_1261_axes_0, beta = encoder_layers_23_norm_feed_forward2_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_23_norm_feed_forward2_weight_to_fp16, x = input_1259_cast_fp16)[name = string("input_1261_cast_fp16")]; + tensor encoder_layers_23_feed_forward2_linear1_weight_to_fp16 = const()[name = string("encoder_layers_23_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(540968384)))]; + tensor encoder_layers_23_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_23_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(549357056)))]; + tensor linear_215_cast_fp16 = linear(bias = encoder_layers_23_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_23_feed_forward2_linear1_weight_to_fp16, x = input_1261_cast_fp16)[name = string("linear_215_cast_fp16")]; + tensor input_1265_cast_fp16 = silu(x = linear_215_cast_fp16)[name = string("input_1265_cast_fp16")]; + tensor encoder_layers_23_feed_forward2_linear2_weight_to_fp16 = const()[name = string("encoder_layers_23_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(549365312)))]; + tensor encoder_layers_23_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_23_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(557753984)))]; + tensor linear_216_cast_fp16 = linear(bias = encoder_layers_23_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_23_feed_forward2_linear2_weight_to_fp16, x = input_1265_cast_fp16)[name = string("linear_216_cast_fp16")]; + fp16 var_5531_to_fp16 = const()[name = string("op_5531_to_fp16"), val = fp16(0x1p-1)]; + tensor var_5532_cast_fp16 = mul(x = linear_216_cast_fp16, y = var_5531_to_fp16)[name = string("op_5532_cast_fp16")]; + tensor input_1271_cast_fp16 = add(x = input_1259_cast_fp16, y = var_5532_cast_fp16)[name = string("input_1271_cast_fp16")]; + tensor audio_signal_axes_0 = const()[name = string("audio_signal_axes_0"), val = tensor([-1])]; + tensor encoder_layers_23_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_23_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(557756096)))]; + tensor encoder_layers_23_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_23_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(557758208)))]; + tensor audio_signal_cast_fp16 = layer_norm(axes = audio_signal_axes_0, beta = encoder_layers_23_norm_out_bias_to_fp16, epsilon = var_43_to_fp16, gamma = encoder_layers_23_norm_out_weight_to_fp16, x = input_1271_cast_fp16)[name = string("audio_signal_cast_fp16")]; + int32 obj_5_axis_0 = const()[name = string("obj_5_axis_0"), val = int32(0)]; + tensor obj_5_cast_fp16 = stack(axis = obj_5_axis_0, values = (var_485_cast_fp16, var_698_cast_fp16, var_911_cast_fp16, var_1124_cast_fp16, var_1337_cast_fp16, var_1550_cast_fp16, var_1763_cast_fp16, var_1976_cast_fp16, var_2189_cast_fp16, var_2402_cast_fp16, var_2615_cast_fp16, var_2828_cast_fp16, var_3041_cast_fp16, var_3254_cast_fp16, var_3467_cast_fp16, var_3680_cast_fp16, var_3893_cast_fp16, var_4106_cast_fp16, var_4319_cast_fp16, var_4532_cast_fp16, var_4745_cast_fp16, var_4958_cast_fp16, var_5171_cast_fp16, cache_last_channel_cur_cast_fp16))[name = string("obj_5_cast_fp16")]; + int32 obj_7_axis_0 = const()[name = string("obj_7_axis_0"), val = int32(0)]; + tensor obj_7_cast_fp16 = stack(axis = obj_7_axis_0, values = (var_589_cast_fp16, var_802_cast_fp16, var_1015_cast_fp16, var_1228_cast_fp16, var_1441_cast_fp16, var_1654_cast_fp16, var_1867_cast_fp16, var_2080_cast_fp16, var_2293_cast_fp16, var_2506_cast_fp16, var_2719_cast_fp16, var_2932_cast_fp16, var_3145_cast_fp16, var_3358_cast_fp16, var_3571_cast_fp16, var_3784_cast_fp16, var_3997_cast_fp16, var_4210_cast_fp16, var_4423_cast_fp16, var_4636_cast_fp16, var_4849_cast_fp16, var_5062_cast_fp16, var_5275_cast_fp16, cache_last_time_cur_cast_fp16))[name = string("obj_7_cast_fp16")]; + tensor var_5548 = add(x = cache_len, y = max_audio_length_1)[name = string("op_5548")]; + string var_5548_promoted_to_fp16_dtype_0 = const()[name = string("op_5548_promoted_to_fp16_dtype_0"), val = string("fp16")]; + fp16 const_384_to_fp16 = const()[name = string("const_384_to_fp16"), val = fp16(-inf)]; + fp16 var_50_promoted_to_fp16 = const()[name = string("op_50_promoted_to_fp16"), val = fp16(0x1.5p+5)]; + tensor var_5548_to_fp16 = cast(dtype = var_5548_promoted_to_fp16_dtype_0, x = var_5548)[name = string("cast_10")]; + tensor clip_1_cast_fp16 = clip(alpha = const_384_to_fp16, beta = var_50_promoted_to_fp16, x = var_5548_to_fp16)[name = string("clip_1_cast_fp16")]; + tensor var_5570_begin_0 = const()[name = string("op_5570_begin_0"), val = tensor([0, 0, 14, 0])]; + tensor var_5570_end_0 = const()[name = string("op_5570_end_0"), val = tensor([24, 1, 56, 1024])]; + tensor var_5570_end_mask_0 = const()[name = string("op_5570_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_5570_cast_fp16 = slice_by_index(begin = var_5570_begin_0, end = var_5570_end_0, end_mask = var_5570_end_mask_0, x = obj_5_cast_fp16)[name = string("op_5570_cast_fp16")]; + int32 one_hot_1_batch_dims_0 = const()[name = string("one_hot_1_batch_dims_0"), val = int32(0)]; + bool one_hot_1_validate_indices_0 = const()[name = string("one_hot_1_validate_indices_0"), val = bool(false)]; + tensor to_onehot_identity_table_to_fp16 = const()[name = string("to_onehot_identity_table_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(557760320)))]; + string prompt_id_to_int16_dtype_0 = const()[name = string("prompt_id_to_int16_dtype_0"), val = string("int16")]; + string cast_230_dtype_0 = const()[name = string("cast_230_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor prompt_id_to_int16 = cast(dtype = prompt_id_to_int16_dtype_0, x = prompt_id)[name = string("cast_9")]; + tensor cast_230 = cast(dtype = cast_230_dtype_0, x = prompt_id_to_int16)[name = string("cast_8")]; + tensor greater_equal_0 = greater_equal(x = cast_230, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_2 = const()[name = string("slice_by_index_2"), val = int32(128)]; + tensor add_0 = add(x = cast_230, y = slice_by_index_2)[name = string("add_0")]; + tensor select_0 = select(a = cast_230, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_7")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_6")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(128)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 greater_equal_0_y_0_2 = const()[name = string("greater_equal_0_y_0_2"), val = int32(0)]; + tensor greater_equal_0_2 = greater_equal(x = select_0_1, y = greater_equal_0_y_0_2)[name = string("greater_equal_0_2")]; + int32 slice_by_index_0_1 = const()[name = string("slice_by_index_0_1"), val = int32(128)]; + tensor add_0_2 = add(x = select_0_1, y = slice_by_index_0_1)[name = string("add_0_2")]; + tensor select_0_2 = select(a = select_0_1, b = add_0_2, cond = greater_equal_0_2)[name = string("select_0_2")]; + int32 one_hot_1_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("one_hot_1_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor one_hot_1_cast_fp16_cast_uint16_cast_uint16 = gather(axis = one_hot_1_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = one_hot_1_batch_dims_0, indices = select_0_2, validate_indices = one_hot_1_validate_indices_0, x = to_onehot_identity_table_to_fp16)[name = string("one_hot_1_cast_fp16_cast_uint16_cast_uint16")]; + tensor var_5594_axes_0 = const()[name = string("op_5594_axes_0"), val = tensor([1])]; + tensor var_5594_cast_fp16 = expand_dims(axes = var_5594_axes_0, x = one_hot_1_cast_fp16_cast_uint16_cast_uint16)[name = string("op_5594_cast_fp16")]; + tensor one_hot_reps_0 = const()[name = string("one_hot_reps_0"), val = tensor([1, 56, 1])]; + tensor one_hot_cast_fp16 = tile(reps = one_hot_reps_0, x = var_5594_cast_fp16)[name = string("one_hot_cast_fp16")]; + int32 var_5603 = const()[name = string("op_5603"), val = int32(-1)]; + bool input_1273_interleave_0 = const()[name = string("input_1273_interleave_0"), val = bool(false)]; + tensor input_1273_cast_fp16 = concat(axis = var_5603, interleave = input_1273_interleave_0, values = (audio_signal_cast_fp16, one_hot_cast_fp16))[name = string("input_1273_cast_fp16")]; + tensor prompt_kernel_0_weight_to_fp16 = const()[name = string("prompt_kernel_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(557793152)))]; + tensor prompt_kernel_0_bias_to_fp16 = const()[name = string("prompt_kernel_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(562511808)))]; + tensor linear_217_cast_fp16 = linear(bias = prompt_kernel_0_bias_to_fp16, weight = prompt_kernel_0_weight_to_fp16, x = input_1273_cast_fp16)[name = string("linear_217_cast_fp16")]; + tensor input_1277_cast_fp16 = relu(x = linear_217_cast_fp16)[name = string("input_1277_cast_fp16")]; + tensor prompt_kernel_2_weight_to_fp16 = const()[name = string("prompt_kernel_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(562515968)))]; + tensor prompt_kernel_2_bias_to_fp16 = const()[name = string("prompt_kernel_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(566710336)))]; + tensor linear_218_cast_fp16 = linear(bias = prompt_kernel_2_bias_to_fp16, weight = prompt_kernel_2_weight_to_fp16, x = input_1277_cast_fp16)[name = string("linear_218_cast_fp16")]; + string conditioned_cast_fp16_to_fp32_dtype_0 = const()[name = string("conditioned_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor transpose_72_perm_0_1 = const()[name = string("transpose_72_perm_0_1"), val = tensor([0, 2, 1])]; + string var_5621_dtype_0 = const()[name = string("op_5621_dtype_0"), val = string("int32")]; + tensor var_5624_perm_0 = const()[name = string("op_5624_perm_0"), val = tensor([1, 0, 2, 3])]; + string var_5624_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_5624_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor var_5627_perm_0 = const()[name = string("op_5627_perm_0"), val = tensor([1, 0, 2, 3])]; + string var_5627_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_5627_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + string var_5632_dtype_0 = const()[name = string("op_5632_dtype_0"), val = string("int32")]; + tensor joint_enc_weight_to_fp16 = const()[name = string("joint_enc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(566712448)))]; + tensor joint_enc_bias_to_fp16 = const()[name = string("joint_enc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(568023232)))]; + tensor linear_219_cast_fp16 = linear(bias = joint_enc_bias_to_fp16, weight = joint_enc_weight_to_fp16, x = linear_218_cast_fp16)[name = string("linear_219_cast_fp16")]; + string linear_219_cast_fp16_to_fp32_dtype_0 = const()[name = string("linear_219_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor encoder_proj = cast(dtype = linear_219_cast_fp16_to_fp32_dtype_0, x = linear_219_cast_fp16)[name = string("cast_0")]; + tensor cache_len_out = cast(dtype = var_5632_dtype_0, x = clip_1_cast_fp16)[name = string("cast_1")]; + tensor var_5627_cast_fp16 = transpose(perm = var_5627_perm_0, x = obj_7_cast_fp16)[name = string("transpose_144")]; + tensor cache_time_out = cast(dtype = var_5627_cast_fp16_to_fp32_dtype_0, x = var_5627_cast_fp16)[name = string("cast_2")]; + tensor var_5624_cast_fp16 = transpose(perm = var_5624_perm_0, x = var_5570_cast_fp16)[name = string("transpose_145")]; + tensor cache_channel_out = cast(dtype = var_5624_cast_fp16_to_fp32_dtype_0, x = var_5624_cast_fp16)[name = string("cast_3")]; + tensor encoded_length = cast(dtype = var_5621_dtype_0, x = clip_0_cast_fp16)[name = string("cast_4")]; + tensor transpose_72_1 = transpose(perm = transpose_72_perm_0_1, x = linear_218_cast_fp16)[name = string("transpose_146")]; + tensor encoded = cast(dtype = conditioned_cast_fp16_to_fp32_dtype_0, x = transpose_72_1)[name = string("cast_5")]; + } -> (encoded, encoded_length, cache_channel_out, cache_time_out, cache_len_out, 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b/multilingual/4480ms/joint.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..62017de668d385dd347533f28d2c3744dab6e8e7 --- /dev/null +++ b/multilingual/4480ms/joint.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f785878c200d19b9a02beba2acb5dc2f2004fe1b0ad578e41360e4c0fb95a5db +size 341 diff --git a/multilingual/4480ms/joint.mlmodelc/model.mil b/multilingual/4480ms/joint.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..ed1622830370095ef3dc9ffc07f8ed95de1105d7 --- /dev/null +++ b/multilingual/4480ms/joint.mlmodelc/model.mil @@ -0,0 +1,31 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.5.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] +{ + func main(tensor decoder, tensor encoder) { + tensor input_1_perm_0 = const()[name = string("input_1_perm_0"), val = tensor([0, 2, 1])]; + string encoder_to_fp16_dtype_0 = const()[name = string("encoder_to_fp16_dtype_0"), val = string("fp16")]; + tensor input_3_perm_0 = const()[name = string("input_3_perm_0"), val = tensor([0, 2, 1])]; + string decoder_to_fp16_dtype_0 = const()[name = string("decoder_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_enc_weight_to_fp16 = const()[name = string("module_enc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor module_enc_bias_to_fp16 = const()[name = string("module_enc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1310848)))]; + tensor encoder_to_fp16 = cast(dtype = encoder_to_fp16_dtype_0, x = encoder)[name = string("cast_2")]; + tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = encoder_to_fp16)[name = string("transpose_1")]; + tensor linear_0_cast_fp16 = linear(bias = module_enc_bias_to_fp16, weight = module_enc_weight_to_fp16, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor module_pred_weight_to_fp16 = const()[name = string("module_pred_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1312192)))]; + tensor module_pred_bias_to_fp16 = const()[name = string("module_pred_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2131456)))]; + tensor decoder_to_fp16 = cast(dtype = decoder_to_fp16_dtype_0, x = decoder)[name = string("cast_1")]; + tensor input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = decoder_to_fp16)[name = string("transpose_0")]; + tensor linear_1_cast_fp16 = linear(bias = module_pred_bias_to_fp16, weight = module_pred_weight_to_fp16, x = input_3_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_23_axes_0 = const()[name = string("op_23_axes_0"), val = tensor([2])]; + tensor var_23_cast_fp16 = expand_dims(axes = var_23_axes_0, x = linear_0_cast_fp16)[name = string("op_23_cast_fp16")]; + tensor var_25_axes_0 = const()[name = string("op_25_axes_0"), val = tensor([1])]; + tensor var_25_cast_fp16 = expand_dims(axes = var_25_axes_0, x = linear_1_cast_fp16)[name = string("op_25_cast_fp16")]; + tensor input_5_cast_fp16 = add(x = var_23_cast_fp16, y = var_25_cast_fp16)[name = string("input_5_cast_fp16")]; + tensor input_7_cast_fp16 = relu(x = input_5_cast_fp16)[name = string("input_7_cast_fp16")]; + tensor module_joint_net_2_weight_to_fp16 = const()[name = string("module_joint_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2132800)))]; + tensor module_joint_net_2_bias_to_fp16 = const()[name = string("module_joint_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18885504)))]; + tensor linear_2_cast_fp16 = linear(bias = module_joint_net_2_bias_to_fp16, weight = module_joint_net_2_weight_to_fp16, x = input_7_cast_fp16)[name = string("linear_2_cast_fp16")]; + string linear_2_cast_fp16_to_fp32_dtype_0 = const()[name = string("linear_2_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor logits = cast(dtype = linear_2_cast_fp16_to_fp32_dtype_0, x = linear_2_cast_fp16)[name = string("cast_0")]; + } -> (logits); +} \ No newline at end of file diff --git a/multilingual/4480ms/joint.mlmodelc/weights/weight.bin b/multilingual/4480ms/joint.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..0d899ae2b3a3c9be8967b683e91cd8ca7252c8ec --- /dev/null +++ b/multilingual/4480ms/joint.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f4c8ae93e304a187ebfa0b88c812b70e79b625a549727922e7f63d61c1c7b6dd +size 18911744 diff --git a/multilingual/4480ms/joint.mlpackage/Data/com.apple.CoreML/model.mlmodel 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0000000000000000000000000000000000000000..89b37c4bfd82ec0f8905ac19299fbe7a5f1d7e73 --- /dev/null +++ b/multilingual/4480ms/joint_noencproj_batched.mlmodelc/model.mil @@ -0,0 +1,26 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.10.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] +{ + func main(tensor decoder, tensor encoder_proj) { + tensor input_1_perm_0 = const()[name = string("input_1_perm_0"), val = tensor([0, 2, 1])]; + string decoder_to_fp16_dtype_0 = const()[name = string("decoder_to_fp16_dtype_0"), val = string("fp16")]; + tensor joint_module_pred_weight_to_fp16 = const()[name = string("joint_module_pred_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor joint_module_pred_bias_to_fp16 = const()[name = string("joint_module_pred_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(819328)))]; + tensor decoder_to_fp16 = cast(dtype = decoder_to_fp16_dtype_0, x = decoder)[name = string("cast_2")]; + tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = decoder_to_fp16)[name = string("transpose_0")]; + tensor linear_0_cast_fp16 = linear(bias = joint_module_pred_bias_to_fp16, weight = joint_module_pred_weight_to_fp16, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_15_axes_0 = const()[name = string("op_15_axes_0"), val = tensor([2])]; + string encoder_proj_to_fp16_dtype_0 = const()[name = string("encoder_proj_to_fp16_dtype_0"), val = string("fp16")]; + tensor encoder_proj_to_fp16 = cast(dtype = encoder_proj_to_fp16_dtype_0, x = encoder_proj)[name = string("cast_1")]; + tensor var_15_cast_fp16 = expand_dims(axes = var_15_axes_0, x = encoder_proj_to_fp16)[name = string("op_15_cast_fp16")]; + tensor var_17_axes_0 = const()[name = string("op_17_axes_0"), val = tensor([1])]; + tensor var_17_cast_fp16 = expand_dims(axes = var_17_axes_0, x = linear_0_cast_fp16)[name = string("op_17_cast_fp16")]; + tensor input_3_cast_fp16 = add(x = var_15_cast_fp16, y = var_17_cast_fp16)[name = string("input_3_cast_fp16")]; + tensor input_5_cast_fp16 = relu(x = input_3_cast_fp16)[name = string("input_5_cast_fp16")]; + tensor joint_module_joint_net_2_weight_to_fp16 = const()[name = string("joint_module_joint_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(820672)))]; + tensor joint_module_joint_net_2_bias_to_fp16 = const()[name = string("joint_module_joint_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17573376)))]; + tensor linear_1_cast_fp16 = linear(bias = joint_module_joint_net_2_bias_to_fp16, weight = joint_module_joint_net_2_weight_to_fp16, x = input_5_cast_fp16)[name = string("linear_1_cast_fp16")]; + string linear_1_cast_fp16_to_fp32_dtype_0 = const()[name = string("linear_1_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor logits = cast(dtype = linear_1_cast_fp16_to_fp32_dtype_0, x = linear_1_cast_fp16)[name = string("cast_0")]; + } -> (logits); +} \ No newline at end of file diff --git a/multilingual/4480ms/joint_noencproj_batched.mlmodelc/weights/weight.bin b/multilingual/4480ms/joint_noencproj_batched.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..defecb9c76ab612924f900c8d498e0e5ff52cc43 --- /dev/null +++ b/multilingual/4480ms/joint_noencproj_batched.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8d6b104e9d6990c07d6cd41bafe27cae8d39cfe037ec701584c47af1094daeeb +size 17599616 diff --git a/multilingual/4480ms/joint_noencproj_batched.mlpackage/Data/com.apple.CoreML/model.mlmodel b/multilingual/4480ms/joint_noencproj_batched.mlpackage/Data/com.apple.CoreML/model.mlmodel new file mode 100644 index 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b/multilingual/4480ms/preprocessor.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..ad2f75126e610a1d2b9c57b0159359bd06a40490 --- /dev/null +++ b/multilingual/4480ms/preprocessor.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:63c74dcaace5d0cef0b6bcd65225e8985e605517fa97f95b13218d02735b6a42 +size 243 diff --git a/multilingual/4480ms/preprocessor.mlmodelc/coremldata.bin b/multilingual/4480ms/preprocessor.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..22424e803aac20ad868e8f2d398295f9b0f919d3 --- /dev/null +++ b/multilingual/4480ms/preprocessor.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:66115dc0006e9aac8b4b2340806691cb4083df62b5757a8e5861b3e30b6065c6 +size 371 diff --git a/multilingual/4480ms/preprocessor.mlmodelc/model.mil b/multilingual/4480ms/preprocessor.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..0b8261362f9cbf465b530a0d2d0ee9a2b2f462cd --- /dev/null +++ b/multilingual/4480ms/preprocessor.mlmodelc/model.mil @@ -0,0 +1,122 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.5.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] +{ + func main(tensor audio, tensor audio_length) [FlexibleShapeInformation = tuple>>, tuple, ?>>>>((("DefaultShapes", {{"audio", [1, 1]}}), ("RangeDims", {{"audio", [[1, 1], [1, 480000]]}})))] { + int32 var_9 = const()[name = string("op_9"), val = int32(1)]; + int32 var_10 = const()[name = string("op_10"), val = int32(160)]; + int32 var_12 = const()[name = string("op_12"), val = int32(0)]; + int32 var_33 = const()[name = string("op_33"), val = int32(512)]; + tensor var_34 = add(x = audio_length, y = var_33)[name = string("op_34")]; + int32 var_35 = const()[name = string("op_35"), val = int32(512)]; + tensor var_36 = sub(x = var_34, y = var_35)[name = string("op_36")]; + tensor floor_div_0 = floor_div(x = var_36, y = var_10)[name = string("floor_div_0")]; + tensor var_39 = equal(x = audio_length, y = var_12)[name = string("op_39")]; + tensor var_40 = const()[name = string("op_40"), val = tensor([0])]; + tensor mel_length = select(a = var_40, b = floor_div_0, cond = var_39)[name = string("seq_len")]; + string audio_to_fp16_dtype_0 = const()[name = string("audio_to_fp16_dtype_0"), val = string("fp16")]; + tensor audio_to_fp16 = cast(dtype = audio_to_fp16_dtype_0, x = audio)[name = string("cast_14")]; + tensor var_42_shape_cast_fp16 = shape(x = audio_to_fp16)[name = string("op_42_shape_cast_fp16")]; + int32 gather_0_axis_0 = const()[name = string("gather_0_axis_0"), val = int32(0)]; + int32 gather_0_batch_dims_0 = const()[name = string("gather_0_batch_dims_0"), val = int32(0)]; + bool gather_0_validate_indices_0 = const()[name = string("gather_0_validate_indices_0"), val = bool(false)]; + string var_42_shape_cast_fp16_to_int16_dtype_0 = const()[name = string("op_42_shape_cast_fp16_to_int16_dtype_0"), val = string("int16")]; + uint16 select_0_to_uint16 = const()[name = string("select_0_to_uint16"), val = uint16(1)]; + tensor var_42_shape_cast_fp16_to_int16 = cast(dtype = var_42_shape_cast_fp16_to_int16_dtype_0, x = var_42_shape_cast_fp16)[name = string("cast_13")]; + int16 gather_0_cast_uint16 = gather(axis = gather_0_axis_0, batch_dims = gather_0_batch_dims_0, indices = select_0_to_uint16, validate_indices = gather_0_validate_indices_0, x = var_42_shape_cast_fp16_to_int16)[name = string("gather_0_cast_uint16")]; + string gather_0_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_0_cast_uint16_to_int32_dtype_0"), val = string("int32")]; + int32 const_0 = const()[name = string("const_0"), val = int32(0)]; + int32 const_1 = const()[name = string("const_1"), val = int32(1)]; + int32 gather_0_cast_uint16_to_int32 = cast(dtype = gather_0_cast_uint16_to_int32_dtype_0, x = gather_0_cast_uint16)[name = string("cast_12")]; + tensor var_43 = range_1d(end = gather_0_cast_uint16_to_int32, start = const_0, step = const_1)[name = string("op_43")]; + tensor var_44_axes_0 = const()[name = string("op_44_axes_0"), val = tensor([0])]; + tensor var_44 = expand_dims(axes = var_44_axes_0, x = var_43)[name = string("op_44")]; + tensor var_45_axes_0 = const()[name = string("op_45_axes_0"), val = tensor([1])]; + tensor var_45 = expand_dims(axes = var_45_axes_0, x = audio_length)[name = string("op_45")]; + tensor timemask = less(x = var_44, y = var_45)[name = string("timemask")]; + tensor var_48_begin_0 = const()[name = string("op_48_begin_0"), val = tensor([0, 0])]; + tensor var_48_end_0 = const()[name = string("op_48_end_0"), val = tensor([1, 1])]; + tensor var_48_end_mask_0 = const()[name = string("op_48_end_mask_0"), val = tensor([true, false])]; + tensor var_48_squeeze_mask_0 = const()[name = string("op_48_squeeze_mask_0"), val = tensor([false, true])]; + tensor var_48_cast_fp16 = slice_by_index(begin = var_48_begin_0, end = var_48_end_0, end_mask = var_48_end_mask_0, squeeze_mask = var_48_squeeze_mask_0, x = audio_to_fp16)[name = string("op_48_cast_fp16")]; + tensor var_49_axes_0 = const()[name = string("op_49_axes_0"), val = tensor([1])]; + tensor var_49_cast_fp16 = expand_dims(axes = var_49_axes_0, x = var_48_cast_fp16)[name = string("op_49_cast_fp16")]; + tensor var_51_begin_0 = const()[name = string("op_51_begin_0"), val = tensor([0, 1])]; + tensor var_51_end_0 = const()[name = string("op_51_end_0"), val = tensor([1, 0])]; + tensor var_51_end_mask_0 = const()[name = string("op_51_end_mask_0"), val = tensor([true, true])]; + tensor var_51_cast_fp16 = slice_by_index(begin = var_51_begin_0, end = var_51_end_0, end_mask = var_51_end_mask_0, x = audio_to_fp16)[name = string("op_51_cast_fp16")]; + tensor var_53_begin_0 = const()[name = string("op_53_begin_0"), val = tensor([0, 0])]; + tensor var_53_end_0 = const()[name = string("op_53_end_0"), val = tensor([1, -1])]; + tensor var_53_end_mask_0 = const()[name = string("op_53_end_mask_0"), val = tensor([true, false])]; + tensor var_53_cast_fp16 = slice_by_index(begin = var_53_begin_0, end = var_53_end_0, end_mask = var_53_end_mask_0, x = audio_to_fp16)[name = string("op_53_cast_fp16")]; + fp16 var_54_to_fp16 = const()[name = string("op_54_to_fp16"), val = fp16(0x1.f0cp-1)]; + tensor var_55_cast_fp16 = mul(x = var_53_cast_fp16, y = var_54_to_fp16)[name = string("op_55_cast_fp16")]; + tensor var_56_cast_fp16 = sub(x = var_51_cast_fp16, y = var_55_cast_fp16)[name = string("op_56_cast_fp16")]; + bool x_3_interleave_0 = const()[name = string("x_3_interleave_0"), val = bool(false)]; + tensor x_3_cast_fp16 = concat(axis = var_9, interleave = x_3_interleave_0, values = (var_49_cast_fp16, var_56_cast_fp16))[name = string("x_3_cast_fp16")]; + tensor var_59 = logical_not(x = timemask)[name = string("op_59")]; + fp16 var_16_to_fp16 = const()[name = string("op_16_to_fp16"), val = fp16(0x0p+0)]; + tensor input_1_cast_fp16 = select(a = var_16_to_fp16, b = x_3_cast_fp16, cond = var_59)[name = string("input_1_cast_fp16")]; + tensor concat_1x = const()[name = string("concat_1x"), val = tensor([1, 1, -1])]; + tensor input_3_cast_fp16 = reshape(shape = concat_1x, x = input_1_cast_fp16)[name = string("input_3_cast_fp16")]; + tensor input_5_pad_0 = const()[name = string("input_5_pad_0"), val = tensor([0, 0, 0, 0, 256, 256])]; + string input_5_mode_0 = const()[name = string("input_5_mode_0"), val = string("constant")]; + fp16 const_3_to_fp16 = const()[name = string("const_3_to_fp16"), val = fp16(0x0p+0)]; + tensor input_5_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = input_5_mode_0, pad = input_5_pad_0, x = input_3_cast_fp16)[name = string("input_5_cast_fp16")]; + tensor concat_2x = const()[name = string("concat_2x"), val = tensor([1, -1])]; + tensor input_cast_fp16 = reshape(shape = concat_2x, x = input_5_cast_fp16)[name = string("input_cast_fp16")]; + tensor expand_dims_3 = const()[name = string("expand_dims_3"), val = tensor([160])]; + tensor expand_dims_4_axes_0 = const()[name = string("expand_dims_4_axes_0"), val = tensor([1])]; + tensor expand_dims_4_cast_fp16 = expand_dims(axes = expand_dims_4_axes_0, x = input_cast_fp16)[name = string("expand_dims_4_cast_fp16")]; + string conv_0_pad_type_0 = const()[name = string("conv_0_pad_type_0"), val = string("valid")]; + tensor conv_0_pad_0 = const()[name = string("conv_0_pad_0"), val = tensor([0, 0])]; + tensor conv_0_dilations_0 = const()[name = string("conv_0_dilations_0"), val = tensor([1])]; + int32 conv_0_groups_0 = const()[name = string("conv_0_groups_0"), val = int32(1)]; + tensor expand_dims_1_to_fp16 = const()[name = string("expand_dims_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor conv_0_cast_fp16 = conv(dilations = conv_0_dilations_0, groups = conv_0_groups_0, pad = conv_0_pad_0, pad_type = conv_0_pad_type_0, strides = expand_dims_3, weight = expand_dims_1_to_fp16, x = expand_dims_4_cast_fp16)[name = string("conv_0_cast_fp16")]; + string conv_1_pad_type_0 = const()[name = string("conv_1_pad_type_0"), val = string("valid")]; + tensor conv_1_pad_0 = const()[name = string("conv_1_pad_0"), val = tensor([0, 0])]; + tensor conv_1_dilations_0 = const()[name = string("conv_1_dilations_0"), val = tensor([1])]; + int32 conv_1_groups_0 = const()[name = string("conv_1_groups_0"), val = int32(1)]; + tensor expand_dims_2_to_fp16 = const()[name = string("expand_dims_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263296)))]; + tensor conv_1_cast_fp16 = conv(dilations = conv_1_dilations_0, groups = conv_1_groups_0, pad = conv_1_pad_0, pad_type = conv_1_pad_type_0, strides = expand_dims_3, weight = expand_dims_2_to_fp16, x = expand_dims_4_cast_fp16)[name = string("conv_1_cast_fp16")]; + int32 stack_0_axis_0 = const()[name = string("stack_0_axis_0"), val = int32(-1)]; + tensor stack_0_cast_fp16 = stack(axis = stack_0_axis_0, values = (conv_0_cast_fp16, conv_1_cast_fp16))[name = string("stack_0_cast_fp16")]; + fp16 var_19_promoted_to_fp16 = const()[name = string("op_19_promoted_to_fp16"), val = fp16(0x1p+1)]; + tensor var_74_cast_fp16 = pow(x = stack_0_cast_fp16, y = var_19_promoted_to_fp16)[name = string("op_74_cast_fp16")]; + tensor var_76_axes_0 = const()[name = string("op_76_axes_0"), val = tensor([-1])]; + bool var_76_keep_dims_0 = const()[name = string("op_76_keep_dims_0"), val = bool(false)]; + tensor var_76_cast_fp16 = reduce_sum(axes = var_76_axes_0, keep_dims = var_76_keep_dims_0, x = var_74_cast_fp16)[name = string("op_76_cast_fp16")]; + tensor x_11_cast_fp16 = identity(x = var_76_cast_fp16)[name = string("x_11_cast_fp16")]; + bool x_13_transpose_x_0 = const()[name = string("x_13_transpose_x_0"), val = bool(false)]; + bool x_13_transpose_y_0 = const()[name = string("x_13_transpose_y_0"), val = bool(false)]; + tensor const_4_to_fp16 = const()[name = string("const_4_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(526528)))]; + tensor x_13_cast_fp16 = matmul(transpose_x = x_13_transpose_x_0, transpose_y = x_13_transpose_y_0, x = const_4_to_fp16, y = x_11_cast_fp16)[name = string("x_13_cast_fp16")]; + fp16 var_83_to_fp16 = const()[name = string("op_83_to_fp16"), val = fp16(0x1p-24)]; + tensor var_84_cast_fp16 = add(x = x_13_cast_fp16, y = var_83_to_fp16)[name = string("op_84_cast_fp16")]; + fp32 x_epsilon_0 = const()[name = string("x_epsilon_0"), val = fp32(0x1p-149)]; + tensor x_cast_fp16 = log(epsilon = x_epsilon_0, x = var_84_cast_fp16)[name = string("x_cast_fp16")]; + tensor var_86_shape_cast_fp16 = shape(x = x_cast_fp16)[name = string("op_86_shape_cast_fp16")]; + int32 gather_5_axis_0 = const()[name = string("gather_5_axis_0"), val = int32(0)]; + int32 gather_5_batch_dims_0 = const()[name = string("gather_5_batch_dims_0"), val = int32(0)]; + bool gather_5_validate_indices_0 = const()[name = string("gather_5_validate_indices_0"), val = bool(false)]; + string var_86_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_86_shape_cast_fp16_to_uint16_dtype_0"), val = string("uint16")]; + uint16 select_5_to_uint16 = const()[name = string("select_5_to_uint16"), val = uint16(2)]; + tensor var_86_shape_cast_fp16_to_uint16 = cast(dtype = var_86_shape_cast_fp16_to_uint16_dtype_0, x = var_86_shape_cast_fp16)[name = string("cast_11")]; + uint16 gather_5_cast_uint16 = gather(axis = gather_5_axis_0, batch_dims = gather_5_batch_dims_0, indices = select_5_to_uint16, validate_indices = gather_5_validate_indices_0, x = var_86_shape_cast_fp16_to_uint16)[name = string("gather_5_cast_uint16")]; + string gather_5_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_5_cast_uint16_to_int32_dtype_0"), val = string("int32")]; + int32 const_5 = const()[name = string("const_5"), val = int32(0)]; + int32 const_6 = const()[name = string("const_6"), val = int32(1)]; + int32 gather_5_cast_uint16_to_int32 = cast(dtype = gather_5_cast_uint16_to_int32_dtype_0, x = gather_5_cast_uint16)[name = string("cast_10")]; + tensor mask_1 = range_1d(end = gather_5_cast_uint16_to_int32, start = const_5, step = const_6)[name = string("mask_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([0])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = mask_1)[name = string("expand_dims_0")]; + tensor var_91_axes_0 = const()[name = string("op_91_axes_0"), val = tensor([1])]; + tensor var_91 = expand_dims(axes = var_91_axes_0, x = mel_length)[name = string("op_91")]; + tensor mask = greater_equal(x = expand_dims_0, y = var_91)[name = string("mask")]; + tensor var_93_axes_0 = const()[name = string("op_93_axes_0"), val = tensor([1])]; + tensor var_93 = expand_dims(axes = var_93_axes_0, x = mask)[name = string("op_93")]; + tensor processed_signal_cast_fp16 = select(a = var_16_to_fp16, b = x_cast_fp16, cond = var_93)[name = string("processed_signal_cast_fp16")]; + string processed_signal_cast_fp16_to_fp32_dtype_0 = const()[name = string("processed_signal_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor mel = cast(dtype = processed_signal_cast_fp16_to_fp32_dtype_0, x = processed_signal_cast_fp16)[name = string("cast_9")]; + } -> (mel, mel_length); +} \ No newline at end of file diff --git a/multilingual/4480ms/preprocessor.mlmodelc/weights/weight.bin b/multilingual/4480ms/preprocessor.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..86dd375f6649d262d58c9cf8b89006ceab216411 --- /dev/null +++ b/multilingual/4480ms/preprocessor.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:297514e2b211d14b0e53cb97193d679bb89ead98d28e578f3f1d049ddbcc36b3 +size 592384 diff --git a/multilingual/4480ms/preprocessor.mlpackage/Data/com.apple.CoreML/model.mlmodel b/multilingual/4480ms/preprocessor.mlpackage/Data/com.apple.CoreML/model.mlmodel new file mode 100644 index 0000000000000000000000000000000000000000..b6e14692a665c169fa38c86657ad10abf52ef336 --- /dev/null +++ b/multilingual/4480ms/preprocessor.mlpackage/Data/com.apple.CoreML/model.mlmodel @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b636481110d0a364e9d3379470371c0eda62de5eeac1e58ed3e6371c26c14cb3 +size 15878 diff --git a/multilingual/4480ms/preprocessor.mlpackage/Data/com.apple.CoreML/weights/weight.bin b/multilingual/4480ms/preprocessor.mlpackage/Data/com.apple.CoreML/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..86dd375f6649d262d58c9cf8b89006ceab216411 --- /dev/null +++ b/multilingual/4480ms/preprocessor.mlpackage/Data/com.apple.CoreML/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:297514e2b211d14b0e53cb97193d679bb89ead98d28e578f3f1d049ddbcc36b3 +size 592384 diff --git a/multilingual/4480ms/preprocessor.mlpackage/Manifest.json b/multilingual/4480ms/preprocessor.mlpackage/Manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..f37d623ccae92b31ab7e0394862e22e42ab33ed9 --- /dev/null +++ b/multilingual/4480ms/preprocessor.mlpackage/Manifest.json @@ -0,0 +1,18 @@ +{ + "fileFormatVersion": "1.0.0", + "itemInfoEntries": { + "3E6D2C42-B6EA-47F8-9EF3-C237CF3E03ED": { + "author": "com.apple.CoreML", + "description": "CoreML Model Weights", + "name": "weights", + "path": "com.apple.CoreML/weights" + }, + "B931B309-6180-4936-9202-560DF3279ED9": { + "author": "com.apple.CoreML", + "description": "CoreML Model Specification", + "name": "model.mlmodel", + "path": "com.apple.CoreML/model.mlmodel" + } + }, + "rootModelIdentifier": "B931B309-6180-4936-9202-560DF3279ED9" +} diff --git a/multilingual/4480ms/tokenizer.json b/multilingual/4480ms/tokenizer.json new file mode 100644 index 0000000000000000000000000000000000000000..5c9c31a266fd62950553b9d5fef65598813f55e0 --- /dev/null +++ b/multilingual/4480ms/tokenizer.json @@ -0,0 +1,13089 @@ +{ + "0": "", + "1": "", + "2": "\u2581", + "3": "\u0438", + "4": ".", + "5": "\u0435", + "6": ",", + "7": "\u0430", + "8": "\u0441", + "9": "\u043e", + "10": "\u043d", + "11": "\u0442", + "12": "\u0442\u0430", + "13": "\u044f", + "14": "\u043a", + "15": "\u2581\u043d\u0430", + "16": "\u043b", + "17": "\u0443", + "18": "\u0437", + "19": "\u0440", + "20": "\u0442\u043e", + "21": "\u043d\u0430", + "22": "\u2581\u0434\u0430", + "23": "\u0432\u0430", + "24": "\u0440\u0430", + "25": "\u0434", + "26": "e", + "27": "\u043a\u0430", + "28": "\u2581\u0437\u0430", + "29": "\u043d\u043e", + "30": "\u043c", + "31": "\u043d\u0438", + "32": "\u044a", + "33": "\u043c\u0435", + "34": "t", + "35": "\u0441\u0442", + "36": "\u043f", + "37": "\u2581\u043f\u043e", + "38": "a", + "39": 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"\u0423", + "202": "D", + "203": "B", + "204": "\u0427", + "205": "U", + "206": "W", + "207": "\u0428", + "208": "\u0426", + "209": "N", + "210": "O", + "211": "G", + "212": "\u00e0", + "213": "F", + "214": "L", + "215": "\u00e8", + "216": "V", + "217": "R", + "218": "\u0627", + "219": "\u042e", + "220": "\u00f3", + "221": "\u042f", + "222": "H", + "223": "\u03b1", + "224": "\u00fc", + "225": "\u00e4", + "226": "\u0644", + "227": "\u0416", + "228": "J", + "229": "\u00ed", + "230": "\u03c4", + "231": "\u03b9", + "232": "\u00e1", + "233": "\u03bf", + "234": "K", + "235": "\u03b5", + "236": "\u064a", + "237": "\u00ea", + "238": "Y", + "239": "\u03bd", + "240": "\u0646", + "241": "\u00f6", + "242": "\u0645", + "243": "\u00e7", + "244": "\u03c1", + "245": "\u0419", + "246": "\u0648", + "247": "\u03c3", + "248": "\u03c0", + "249": "\u0119", + "250": "\u03c5", + "251": "\u062a", + "252": "\u03b7", + "253": "\u0631", + "254": "\u03bc", + "255": "\u03ba", + "256": "", + "257": "st", + "258": "ch", + "259": "n\u00ed", + "260": "\u2581s", + "261": "le", + "262": "li", + "263": "\u2581po", + "264": "\u2581v", + "265": "\u017e", + "266": "\u010d", + "267": "\u2581to", + "268": "no", + "269": "to", + "270": "\u2581z", + "271": "me", + "272": "\u2581se", + "273": "\u2581a", + "274": "te", + "275": "\u2581je", + "276": "ho", + "277": "\u2581pro", + "278": "\u016f", + "279": "n\u011b", + "280": "ro", + "281": "\u2581na", + "282": "ce", + "283": "\u2581o", + "284": "la", + "285": "\u0161", + "286": "\u2581ne", + "287": "ni", + "288": "ra", + "289": "ti", + "290": "lo", + "291": "ko", + "292": "\u2581\u017ee", + "293": "n\u00e1", + "294": "po", + "295": "je", + "296": "\u011b", + "297": "de", + "298": "na", + "299": "mi", + "300": "\u2581do", + "301": "ci", + "302": "\u2581k", + "303": "ku", + "304": "\u0159e", + "305": "\u2581by", + "306": "ve", + "307": "\u2581za", + "308": "m\u011b", + "309": "\u2581A", + "310": "\u00fd", + "311": "re", + "312": "v\u00e1", + "313": "ou", + "314": "vo", + "315": "n\u00e9", + "316": "va", + "317": "\u017ee", + "318": "mo", + "319": "v\u011b", + "320": "j\u00ed", + "321": "t\u011b", + "322": "v\u00fd", + "323": "\u2581tak", + "324": "ze", + "325": "\u0159\u00ed", + "326": "ne", + "327": "\u0161e", + "328": "\u2581vy", + "329": "ka", + "330": "ji", + "331": "ky", + "332": "r\u00e1", + "333": "ovat", + "334": "\u2581ob", + "335": "c\u00ed", + "336": "\u2581jak", + "337": "\u2581p\u0159e", + "338": "ny", + "339": "v\u00ed", + "340": "n\u00fd", + "341": "vi", + "342": "\u2581in", + "343": "pr\u00e1v", + "344": "\u00fa", + "345": "\u2581co", + "346": "\u2581tak\u00e9", + "347": "ent", + "348": "\u2581pan", + "349": "\u2581D\u011bkuji", + "350": "\u2581kter\u00e9", + "351": "\u0159i", + "352": "\u2581aby", + "353": "\u2581p\u0159\u00ed", + "354": "\u2581p\u0159i", + "355": "prav", + "356": "\u0159", + "357": "vrop", + "358": "\u2581bude", + "359": "\u2581roz", + "360": "\u2581jsou", + "361": "ov\u00e9", + "362": "\u2581jsme", + "363": 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"412": "\u00e5", + "413": "\u00f8", + "414": "and", + "415": "\u2581har", + "416": "at", + "417": "\u2581f", + "418": "\u2581i", + "419": "\u2581s\u00e5", + "420": "\u2581af", + "421": "ge", + "422": "ar", + "423": "is", + "424": "ing", + "425": "\u2581med", + "426": "\u2581p\u00e5", + "427": "\u2581be", + "428": "un", + "429": "lig", + "430": "\u2581ikke", + "431": "\u2581man", + "432": "ig", + "433": "\u2581som", + "434": "\u00f8r", + "435": "\u2581Og", + "436": "el", + "437": "ag", + "438": "\u2581skal", + "439": "erne", + "440": "\u2581Det", + "441": "\u2581den", + "442": "ste", + "443": "ning", + "444": "\u2581jeg", + "445": "id", + "446": "\u2581kan", + "447": "\u2581ogs\u00e5", + "448": "\u2581vil", + "449": "ske", + "450": "iv", + "451": "\u2581ud", + "452": "\u2581her", + "453": "ion", + "454": "am", + "455": "ur", + "456": "for", + "457": "\u2581pr", + "458": "else", + "459": "\u2581sig", + "460": "\u2581men", + "461": "\u2581ind", + "462": "\u2581jo", + "463": "ende", + "464": "\u2581v\u00e6re", + "465": "\u2581Vi", + "466": "ation", + "467": "\u2581m\u00e5", + "468": "mme", + "469": "ighed", + "470": "tage", + "471": "\u2581op", + "472": "\u2581Jeg", + "473": "\u2581hvor", + "474": "\u2581ved", + "475": "\u2581f\u00e5", + "476": "\u2581fra", + "477": "\u2581over", + "478": "\u2581have", + "479": "kke", + "480": "\u2581meget", + "481": "\u2581S\u00e5", + "482": "\u2581Tak", + "483": "\u2581noget", + "484": "\u2581alle", + "485": "brug", + "486": "\u2581komme", + "487": "\u2581Men", + "488": "\u2581var", + "489": "hold", + "490": "arbejde", + "491": "\u2581eller", + "492": "\u2581vores", + "493": "\u2581frem", + "494": "\u2581alts\u00e5", + "495": "\u2581vigtig", + "496": "v\u00e6r", + "497": "\u2581EU", + "498": "\u2581g\u00f8re", + "499": "\u2581nogle", + "500": "skab", + "501": "\u2581sp\u00f8rgsm\u00e5l", + "502": "\u2581kunne", + "503": "\u2581kommissionen", + "504": "\u2581hvis", + "505": "\u00d8", + "506": "\u00c6", + "507": "\u03c2", + "508": "\u03bb", + "509": "\u03af", + "510": "\u03cc", + "511": "\u0131", + "512": "\u03ad", + "513": "\u03ac", + "514": "\u03c9", + "515": "\u03b3", + "516": "\u03b4", + "517": "\u03ae", + "518": "", + "519": "\u2581die", + "520": "\u2581und", + "521": "\u2581das", + "522": "sch", + "523": "\u2581ist", + "524": "\u2581ich", + "525": "\u2581ein", + "526": "\u2581ge", + "527": "ung", + "528": "it", + "529": "\u2581wir", + "530": "\u2581zu", + "531": "\u2581so", + "532": "\u2581da", + "533": "\u2581S", + "534": "\u2581auch", + "535": "gen", + "536": "\u2581nicht", + "537": "\u2581W", + "538": "\u2581B", + "539": "\u2581E", + "540": "\u2581F", + "541": "ll", + "542": "\u2581es", + "543": "\u2581K", + "544": "ie", + "545": "au", + "546": "\u2581P", + "547": "ich", + "548": "\u2581eine", + "549": "lich", + "550": "ck", + "551": "ten", + "552": "mal", + "553": "ein", + "554": "\u2581T", + "555": "\u2581dann", + "556": "\u2581Und", + "557": "\u2581mit", + "558": "\u2581auf", + "559": "hr", + "560": "ter", + "561": "tz", + "562": "\u2581dass", + "563": "\u2581G", + "564": "ben", + "565": "um", + "566": "us", + "567": "cht", + "568": "il", + "569": "\u2581Das", + "570": "\u2581diese", + "571": "\u2581noch", + "572": "\u2581jetzt", + "573": "ut", + "574": "\u2581ver", + "575": "kt", + "576": "\u2581Ich", + "577": "\u2581hier", + "578": "\u2581hat", + "579": "\u2581haben", + "580": "\u2581von", + "581": "ri", + "582": "ach", + "583": "ol", + "584": "\u2581Da", + "585": "\u2581als", + "586": "sp", + "587": "\u2581f\u00fcr", + "588": "ell", + "589": "\u2581sich", + "590": "\u2581was", + "591": "\u2581ja", + "592": "uch", + "593": "\u2581kann", + "594": "\u2581sind", + "595": "wi", + "596": "\u2581aus", + "597": "rei", + "598": "\u2581wie", + "599": "\u2581Ge", + "600": "und", + "601": "\u2581St", + "602": "isch", + "603": "\u2581sie", + "604": "\u2581Ja", + "605": "\u2581du", + "606": "\u2581war", + "607": "\u2581im", + "608": "\u2581dem", + "609": "\u2581aber", + "610": "\u2581oder", + "611": "\u00df", + "612": "\u2581Sch", + "613": "\u2581uns", + "614": "\u2581habe", + "615": "\u2581wenn", + "616": "\u2581wo", + "617": "\u2581bei", + "618": "\u2581ihr", + "619": "\u2581Ma", + "620": "zu", + "621": "\u2581schon", + "622": "\u2581De", + "623": "\u2581Sie", + "624": "\u2581\u00fcber", + "625": "\u2581vor", + "626": "\u2581Die", + "627": "\u2581ganz", + "628": "iert", + "629": "\u2581Le", + "630": "\u2581viel", + "631": "\u2581In", + "632": "\u2581Also", + "633": "\u2581Ver", + "634": "\u2581sehr", + "635": "\u2581Re", + "636": "halt", + "637": "\u2581einfach", + "638": "\u2581werden", + "639": "\u2581sein", + "640": "\u2581Wir", + "641": "\u2581nur", + "642": "\u2581immer", + "643": "ieren", + "644": "\u2581muss", + "645": "\u2581wieder", + "646": "\u2581mir", + "647": "\u2581gut", + "648": "\u2581mehr", + "649": "\u2581Mi", + "650": "\u2581nach", + "651": "\u2581Ha", + "652": "\u2581weil", + "653": "\u2581Aber", + "654": "kommen", + "655": "\u2581gibt", + "656": "\u2581meine", + "657": "\u2581andere", + "658": "\u2581k\u00f6nnen", + "659": "\u2581machen", + "660": "\u2581nat\u00fcrlich", + "661": "\u2581bisschen", + "662": "\u2581durch", + "663": "sehen", + "664": "\u2581weiter", + "665": "\u2581keine", + "666": "\u2581sagen", + "667": "\u2581wirklich", + "668": "\u2581eigentlich", + "669": "\u2581jede", + "670": "schaft", + "671": "\u2581glaube", + "672": "\u00dc", + "673": "", + "674": "\u03c7", + "675": "\u03c4\u03b1", + "676": "\u2581\u03bd\u03b1", + "677": "\u03b5\u03b9", + "678": "\u2581\u03ba\u03b1\u03b9", + "679": "\u03bc\u03b1", + "680": "\u03b2", + "681": "\u03c3\u03b7", + "682": "\u03c4\u03b5", + "683": "\u03ce", + "684": "\u03b8", + "685": "\u03c6", + "686": "\u03c0\u03bf", + "687": "\u03cd", + "688": "\u2581\u03c4\u03bf", + "689": "\u03af\u03b1", + "690": "\u03c4\u03b9", + "691": "\u03b1\u03bd", + "692": "\u03bf\u03c5", + "693": "\u03c1\u03b1", + "694": "\u2581\u03b3\u03b9\u03b1", + "695": "\u03b5\u03af", + "696": "\u03c4\u03b7", + "697": "\u03be", + "698": "\u03ba\u03b1", + "699": "\u2581\u03c4\u03b7\u03bd", + "700": "\u2581\u03c4\u03b7", + "701": "\u03bc\u03b5", + "702": "\u03c4\u03bf", + "703": "\u03bf\u03cd", + "704": "\u2581\u03c4\u03bf\u03c5", + "705": "\u2581\u03c0\u03c1\u03bf", + "706": "\u2581\u03bc\u03b5", + "707": "\u03b6", + "708": "\u2581\u03b8\u03b1", + "709": "\u2581\u03b5\u03af\u03bd\u03b1\u03b9", + "710": "\u03c1\u03bf", + "711": "\u03c9\u03bd", + "712": "\u03bc\u03ad", + "713": "\u2581\u03c0\u03bf\u03c5", + "714": "\u03b9\u03b1", + "715": "\u03bd\u03bf", + "716": "\u03b9\u03ba\u03ae", + "717": "\u03ce\u03bd", + "718": "\u03c1\u03b9", + "719": "\u03b8\u03b5", + "720": "\u0395", + "721": "\u03c1\u03af", + "722": "\u2581\u03cc\u03c4\u03b9", + "723": "\u03bf\u03c5\u03bc\u03b5", + "724": "\u2581\u03b1\u03c0\u03cc", + "725": "\u03bb\u03bf", + "726": "\u03c1\u03ac", + "727": "\u03b9\u03bf", + "728": "\u2581\u03c4\u03c9\u03bd", + "729": "\u03b5\u03c5", + "730": "\u03bb\u03b7", + "731": "\u03bf\u03c5\u03bd", + "732": "\u0391", + "733": "\u2581\u03c3\u03b5", + "734": "\u03a0", + "735": "\u2581\u03c3\u03c5\u03bd", + "736": "\u03c6\u03bf\u03c1", + "737": "\u2581\u03b4\u03b5\u03bd", + "738": "\u03a3", + "739": "\u2581\u03c3\u03c4\u03bf", + "740": "\u2581\u03b4\u03b9", + "741": "\u03c4\u03ac", + "742": "\u2581\u03b1\u03c5\u03c4\u03cc", + "743": "\u2581\u03b4\u03b9\u03b1", + "744": "\u03b9\u03c3\u03c4", + "745": "\u2581\u03c0\u03bf\u03bb\u03cd", + "746": "\u2581\u03c0\u03c1\u03ad\u03c0\u03b5\u03b9", + "747": "\u2581\u03c3\u03c4\u03b7\u03bd", + "748": "\u03c3\u03bf\u03c5\u03bc\u03b5", + "749": "\u03b9\u03ba\u03ac", + "750": "\u03a4", + "751": "\u2581\u03b5\u03c0", + "752": "\u039a", + "753": "\u03c8", + "754": "\u2581\u03b1\u03c0\u03bf", + "755": "\u2581\u03bf\u03b9", + "756": "\u03b5\u03c4\u03b1\u03b9", + "757": "\u2581\u03b5\u03c0\u03b9", + "758": "\u2581\u03a5\u03c0\u03cc\u03c4\u03b9\u03c4\u03bb\u03bf\u03b9", + "759": "\u2581AUTHORWAVE", + "760": "\u03bf\u03cd\u03bc\u03b5", + "761": "\u03b9\u03ba\u03cc", + "762": "\u2581\u039a\u03b1\u03b9", + "763": "\u03c0\u03c1\u03cc", + "764": "\u2581\u0395\u03c5\u03c7\u03b1\u03c1\u03b9\u03c3\u03c4\u03ce", + "765": "\u2581\u03bc\u03b9\u03b1", + "766": "\u2581\u03ad\u03bd\u03b1", + "767": "\u2581\u03c3\u03c5\u03bc", + "768": "\u039c", + "769": "\u2581\u03c0\u03b5\u03c1\u03b9", + "770": "\u2581\u03b1\u03c5\u03c4\u03ae", + "771": "\u03ae\u03c3\u03b5\u03b9", + "772": "\u039f", + "773": "\u03b9\u03ba\u03ad", + "774": "\u2581\u03ba\u03b1\u03c4\u03ac", + "775": "\u0393", + "776": "\u0398", + "777": "\u2581\u0395\u03c5\u03c1\u03c9\u03c0\u03b1\u03ca\u03ba\u03ae", + "778": "\u2581\u03ad\u03c7\u03bf\u03c5\u03bc\u03b5", + "779": "\u2581\u03b1\u03bb\u03bb\u03ac", + "780": "\u03b5\u03c1\u03b3", + "781": "\u0397", + "782": "\u2581\u03b8\u03ad\u03bc\u03b1", + "783": "\u03bf\u03bb\u03bf\u03b3", + "784": "\u03cc\u03c4\u03b7\u03c4\u03b1", + "785": "\u2581\u03ad\u03c7\u03b5\u03b9", + "786": "\u03c0\u03bf\u03bb\u03b9\u03c4", + "787": "\u0394", + "788": "\u2581\u03bb\u03bf\u03b9\u03c0\u03cc\u03bd", + "789": "\u03bf\u03bd\u03c4\u03b1\u03b9", + "790": "\u039d", + "791": "\u03c6\u03ad\u03c1", + "792": "\u2581\u0395\u03c0\u03b9\u03c4\u03c1\u03bf\u03c0\u03ae", + "793": "\u2581\u03b1\u03c5\u03c4\u03ac", + "794": "\u2581\u0388\u03bd\u03c9\u03c3\u03b7", + "795": "\u03a5", + "796": "\u03ca", + "797": "\u2581\u0394\u03b5\u03bd", + "798": "\u2581\u03ad\u03c7\u03bf\u03c5\u03bd", + "799": "\u2581\u03c5\u03c0\u03ac\u03c1\u03c7\u03b5\u03b9", + "800": "\u0392", + "801": "\u0399", + "802": "\u039b", + "803": "\u03a6", + "804": "\u03a1", + "805": "\u03a7", + "806": "\u039e", + "807": "\u03a9", + "808": "\u0396", + "809": "\u03a8", + "810": "\u0389", + "811": "\u0386", + "812": "\u038c", + "813": "\u0388", + "814": "", + "815": "ma", + "816": "ta", + "817": "se", + "818": "da", + "819": "si", + "820": "\u2581on", + "821": "\u00f5", + "822": "ks", + "823": "ga", + "824": "\u2581et", + "825": "\u2581ka", + "826": "he", + "827": "mu", + "828": "tu", + "829": "ha", + "830": "ja", + "831": "gi", + "832": "\u2581selle", + "833": "\u2581ole", + "834": "nd", + "835": "oo", + "836": "gu", + "837": "ju", + "838": "est", + "839": "\u2581ei", + "840": "\u2581pa", + "841": "nud", + "842": "\u2581v\u00e4ga", + "843": "\u2581see", + "844": "tud", + "845": "\u2581pea", + "846": "nda", + "847": "\u00e4r", + "848": "\u2581Euroopa", + "849": "\u2581kui", + "850": "vad", + "851": "ke", + "852": "sta", + "853": "sed", + "854": "\u2581v\u00f5i", + "855": "di", + "856": "\u2581saa", + "857": "mise", + "858": "\u2581siis", + "859": "\u2581su", + "860": "ide", + "861": "pool", + "862": "val", + "863": "tus", + "864": "\u2581seda", + "865": "\u2581Me", + "866": "\u2581vastu", + "867": "\u2581j\u00e4", + "868": "\u2581tule", + "869": "selt", + "870": "ment", + "871": "\u2581kes", + "872": "ndus", + "873": "\u2581t\u00f6\u00f6", + "874": "\u2581k\u00f5ik", + "875": "dus", + "876": "\u2581m\u00f5", + "877": "eeri", + "878": "\u2581meie", + "879": "\u2581meil", + "880": "\u2581ning", + "881": "v\u00f5t", + "882": "\u2581mida", + "883": "\u2581arv", + "884": "\u2581See", + "885": "takse", + "886": "\u2581vaja", + "887": "\u2581osa", + "888": "\u00f5igus", + "889": "\u2581nende", + "890": "\u2581n\u00fc\u00fcd", + "891": "\u2581aasta", + "892": "tsiooni", + "893": "\u2581inim", + "894": "\u2581need", + "895": "tsus", + "896": "riigi", + "897": "\u2581t\u00e4h", + "898": "\u2581Liidu", + "899": "\u2581v\u00e4lja", + "900": "\u00c4", + "901": "\u00d5", + "902": "\u00e3", + "903": "Q", + "904": "\u0107", + "905": "\u0639", + "906": "\u00f1", + "907": "", + "908": "t\u00e4", + "909": "ssa", + "910": "lla", + "911": "\u2581ett\u00e4", + "912": "ksi", + "913": "ty", + "914": "ki", + "915": "v\u00e4", + "916": "pa", + "917": "lle", + "918": "lu", + "919": "tta", + "920": "st\u00e4", + "921": "isi", + "922": "ise", + "923": "ll\u00e4", + "924": "kin", + "925": "n\u00e4", + "926": "\u00e4\u00e4n", + "927": "kse", + "928": "tte", + "929": "j\u00e4", + "930": "tt\u00e4", + "931": "ss\u00e4", + "932": "ista", + "933": "inen", + "934": "k\u00e4", + "935": "llis", + "936": "t\u00f6", + "937": "\u2581my\u00f6s", + "938": "vu", + "939": "taan", + "940": "\u2581t\u00e4m\u00e4", + "941": "\u2581voi", + "942": "utta", + "943": "iden", + "944": "nyt", + "945": "\u2581niin", + "946": "\u2581Kiitos", + "947": "\u2581ovat", + "948": "h\u00e4n", + "949": "suu", + "950": "\u2581toimi", + "951": "aika", + "952": "\u2581T\u00e4m\u00e4", + "953": "\u2581p\u00e4\u00e4", + "954": "\u2581mutta", + "955": "\u2581k\u00e4y", + "956": "\u2581t\u00e4ss\u00e4", + "957": "\u2581asia", + "958": "\u2581T\u00e4", + "959": "\u2581jotka", + "960": "\u2581ty\u00f6", + "961": "neet", + "962": "\u2581t\u00e4ytyy", + "963": "\u2581sitten", + "964": "\u2581Euroopan", + "965": "\u2581puolesta", + "966": "\u2581halua", + "967": "\u2581siit\u00e4", + "968": "\u2581komissio", + "969": "\u2581hyv\u00e4", + "970": "\u2581hyvin", + "971": "\u2581puhu", + "972": "\u2581meid\u00e4n", + "973": "\u2581vastaan", + "974": "\u2581t\u00e4rke\u00e4", + "975": "\u2581kaikki", + "976": "\u2581Kiitoksia", + "977": "\u2581viel\u00e4", + "978": "\u2581muut", + "979": "\u2581paljon", + "980": "mahdollis", + "981": "parlament", + "982": "\u2581pit\u00e4isi", + "983": "\u2581hyv\u00e4ksy", + "984": "\u2581puheenjohtaja", + "985": "\u2581liitty", + "986": "\u0101", + "987": "\u10d0", + "988": "\u10d8", + "989": "\u012b", + "990": "\u0113", + "991": "\u00eb", + "992": "\u10d4", + "993": "", + "994": "\u2581est", + "995": "\u2581c", + "996": "\u2581d", + "997": "\u2581la", + "998": "\u2581p", + "999": "\u2581que", + "1000": "\u2581en", + "1001": "\u2581le", + "1002": "\u2581\u00e0", + "1003": "es", + "1004": "\u2581l", + "1005": "\u2581un", + "1006": "\u2581pas", + "1007": "\u2581les", + "1008": "\u2581qui", + "1009": "\u2581il", + "1010": "\u2581vous", + "1011": "\u2581des", + "1012": "\u2581ce", + "1013": "\u2581qu", + "1014": "\u2581pour", + "1015": "\u2581n", + "1016": "\u2581par", + "1017": "\u2581\u00e7a", + "1018": "\u2581une", + "1019": "\u2581b", + "1020": "ant", + "1021": "\u2581j", + "1022": "ais", + "1023": "ez", + "1024": "\u2581dans", + "1025": "\u2581va", + "1026": "\u2581C", + "1027": "tre", + "1028": "ir", + "1029": "elle", + "1030": "eur", + "1031": "\u2581sur", + "1032": "\u2581re", + "1033": "\u2581con", + "1034": "\u2581ma", + "1035": "\u2581Et", + "1036": "\u2581au", + "1037": "ement", + "1038": "tion", + "1039": "t\u00e9", + "1040": "\u2581tout", + "1041": "mp", + "1042": "ique", + "1043": "\u2581plus", + "1044": "eux", + "1045": "\u2581d\u00e9", + "1046": "\u2581fait", + "1047": "qu", + "1048": "\u2581ai", + "1049": "\u2581comme", + "1050": "ens", + "1051": "ac", + "1052": "\u2581l\u00e0", + "1053": "\u2581si", + "1054": "ait", + "1055": "che", + "1056": "\u2581mais", + "1057": "que", + "1058": "ul", + "1059": "\u2581avec", + "1060": "\u2581bien", + "1061": "\u2581tu", + "1062": "age", + "1063": "\u2581mon", + "1064": "\u2581Donc", + "1065": "end", + "1066": "\u2581faire", + "1067": "\u2581\u00eatre", + "1068": "ver", + "1069": "\u2581peu", + "1070": "\u2581m\u00eame", + "1071": "tra", + "1072": "cha", + "1073": "\u2581nous", + "1074": "\u2581donc", + "1075": "\u2581sont", + "1076": "\u2581moi", + "1077": "ille", + "1078": "ff", + "1079": "\u2581ex", + "1080": "ien", + "1081": "\u2581Il", + "1082": "\u2581tr\u00e8s", + "1083": "\u2581cette", + "1084": "im", + "1085": "it\u00e9", + "1086": "\u2581dire", + "1087": "\u2581peut", + "1088": "ance", + "1089": "aire", + "1090": "m\u00e9", + "1091": "\u2581app", + "1092": "\u2581aussi", + "1093": "\u2581petit", + "1094": "aux", + "1095": "\u2581parce", + "1096": "onne", + "1097": "mb", + "1098": "man", + "1099": "\u2581On", + "1100": "\u2581quand", + "1101": "\u2581autre", + "1102": "\u00f4", + "1103": "\u2581chose", + "1104": "\u2581puis", + "1105": "\u2581\u00e9tait", + "1106": "ndre", + "1107": "port", + "1108": "\u2581vraiment", + "1109": "ence", + "1110": "\u2581Mais", + "1111": "\u00ee", + "1112": "\u2581avoir", + "1113": "form", + "1114": "\u2581faut", + "1115": "\u2581Alors", + "1116": "ign", + "1117": "\u2581o\u00f9", + "1118": "pr\u00e8s", + "1119": "\u2581beaucoup", + "1120": "ture", + "1121": "\u00fb", + "1122": "\u00c7", + "1123": "\u00e2", + "1124": "\u00f9", + "1125": "", + "1126": "sz", + "1127": "\u2581az", + "1128": "\u2581hogy", + "1129": "\u0151", + "1130": "\u00e1s", + "1131": "ok", + "1132": "gy", + "1133": "ek", + "1134": "\u00e1l", + "1135": "\u00e9s", + "1136": "em", + "1137": "\u00e1r", + "1138": "\u2581meg", + "1139": "\u2581\u00e9s", + "1140": "\u2581is", + "1141": "\u2581ez", + "1142": "\u2581egy", + "1143": "os", + "1144": "ak", + "1145": "ban", + "1146": "nak", + "1147": "\u00edt", + "1148": "ik", + "1149": "unk", + "1150": "\u2581nem", + "1151": "oz", + "1152": "\u00fcl", + "1153": "\u00e1n", + "1154": "\u00e1t", + "1155": "cs", + "1156": "\u00e9l", + "1157": "\u00e9r", + "1158": "nek", + "1159": "\u2581mi", + "1160": "szer", + "1161": "bb", + "1162": "\u2581K\u00f6sz\u00f6n\u00f6m", + "1163": "s\u00e9g", + "1164": "\u2581kell", + "1165": "\u00e9n", + "1166": "hat", + "1167": "\u2581ha", + "1168": "s\u00e1g", + "1169": "\u2581sz\u00e9pen", + "1170": "\u00e9rt", + "1171": "\u00e9k", + "1172": "ott", + "1173": "\u00f6n", + "1174": "\u00e9p", + "1175": "el\u0151", + "1176": "\u00fcnk", + "1177": "\u2581van", + "1178": "\u2581ki", + "1179": "\u2581fel", + "1180": "\u00e9ny", + "1181": "v\u00e9", + "1182": "leg", + "1183": "eket", + "1184": "\u2581Az", + "1185": "juk", + "1186": "\u2581k\u00f6z", + "1187": "\u0171", + "1188": "\u2581nagyon", + "1189": "\u2581tud", + "1190": "\u2581jelen", + "1191": "\u2581amely", + "1192": "\u2581lehet", + "1193": "\u2581ami", + "1194": "\u2581k\u00e9rd\u00e9s", + "1195": "\u2581ellen", + "1196": "tart", + "1197": "r\u0151l", + "1198": "\u00c9", + "1199": "orsz\u00e1g", + "1200": "rend", + "1201": "r\u00f3l", + "1202": "\u2581vagy", + "1203": "\u2581fontos", + "1204": "\u2581Eur\u00f3pai", + "1205": "\u2581akkor", + "1206": "\u2581jog", + "1207": "\u2581fog", + "1208": "fogad", + "1209": "kapcsol", + "1210": "\u2581r\u00e9sz", + "1211": "\u00e1ci\u00f3", + "1212": "\u2581volt", + "1213": "\u2581eln\u00f6k", + "1214": "\u2581bizotts\u00e1g", + "1215": "\u2581gondol", + "1216": "\u2581olyan", + "1217": "\u2581illetve", + "1218": "\u2581tag\u00e1llam", + "1219": "\u2581pedig", + "1220": "\u2581Teh\u00e1t", + "1221": "\u2581eur\u00f3pai", + "1222": "\u2581sz\u00fcks\u00e9g", + "1223": "szavaz", + "1224": "\u2581teh\u00e1t", + "1225": "k\u00f6vetkez", + "1226": "\u2581\u00f6ssze", + "1227": "\u2581biztos", + "1228": "\u00d6", + "1229": "\u00c1", + "1230": "\u00cd", + "1231": "\u0150", + "1232": "", + "1233": "\u2581u", + "1234": "\u2581bi", + "1235": "\u2581sa", + "1236": "\u0107e", + "1237": "\u2581od", + "1238": "ru", + "1239": "\u2581iz", + "1240": "go", + "1241": "nje", + "1242": "sti", + "1243": "\u0111", + "1244": "\u2581pri", + "1245": "ima", + "1246": "nu", + "1247": "\u2581pre", + "1248": "\u2581Hvala", + "1249": "lje", + "1250": "\u2581\u0161to", + "1251": "\u010di", + "1252": "nja", + "1253": "zi", + "1254": "vr", + "1255": "\u0107i", + "1256": "\u010de", + "1257": "ca", + "1258": "\u2581koji", + "1259": "ba", + "1260": "\u2581raz", + "1261": "\u05d9", + "1262": "\u05d5", + "1263": "\u05d4", + "1264": "\u062f", + "1265": "\u05dc", + "1266": "\u0629", + "1267": "\u0628", + "1268": "\u0647", + "1269": "\u0623", + "1270": "\u05d0", + "1271": "\u0633", + "1272": "\u0643", + "1273": "\u05ea", + "1274": "\u05e8", + "1275": "\u021b", + "1276": "\u05de", + "1277": "\u0642", + "1278": "\u05e9", + "1279": "", + "1280": "\u2581di", + "1281": "\u2581e", + "1282": "\u2581che", + "1283": "\u2581\u00e8", + "1284": "co", + "1285": "\u2581per", + "1286": "\u2581al", + "1287": "\u2581non", + "1288": "do", + "1289": "gli", + "1290": "so", + "1291": "amo", + "1292": "sa", + "1293": "ndo", + "1294": "\u2581una", + "1295": "fi", + "1296": "pi", + "1297": "nti", + "1298": "tto", + "1299": "tro", + "1300": "\u2581fa", + "1301": "chi", + "1302": "\u2581qua", + "1303": "zione", + "1304": "bi", + "1305": "\u2581del", + "1306": "mente", + "1307": "pe", + "1308": "ssi", + "1309": "\u2581ri", + "1310": "\u2581sono", + "1311": "\u2581me", + "1312": "\u2581questo", + "1313": "nte", + "1314": "tti", + "1315": "t\u00e0", + "1316": "\u2581nel", + "1317": "\u2581anche", + "1318": "sso", + "1319": "\u2581perch\u00e9", + "1320": "\u2581pi\u00f9", + "1321": "nta", + "1322": "\u2581come", + "1323": "cu", + "1324": "\u2581quindi", + "1325": "ggi", + "1326": "nza", + "1327": "sto", + "1328": "\u2581ho", + "1329": "\u00f2", + "1330": "\u2581della", + "1331": "gra", + "1332": "\u2581fare", + "1333": "spe", + "1334": "cco", + "1335": "nde", + "1336": "mento", + "1337": "fe", + "1338": "gio", + "1339": "pu", + "1340": "\u2581questa", + "1341": "\u2581tra", + "1342": "zza", + "1343": "sci", + "1344": "\u2581ba", + "1345": "\u2581dei", + "1346": "\u2581poi", + "1347": "sco", + "1348": "stra", + "1349": "\u2581quel", + "1350": "qui", + "1351": "\u2581delle", + "1352": "\u2581cosa", + "1353": "\u2581molto", + "1354": "sse", + "1355": "zioni", + "1356": "\u2581vol", + "1357": "\u2581inter", + "1358": "sce", + "1359": "\u2581fatto", + "1360": "\u2581com", + "1361": "\u2581quello", + "1362": "\u2581essere", + "1363": "\u2581due", + "1364": "\u2581abbiamo", + "1365": "\u2581comp", + "1366": "\u2581tutti", + "1367": "\u00ec", + "1368": "\u2581prima", + "1369": "\u2581parte", + "1370": "\u2581cos\u00ec", + "1371": "\u2581sempre", + "1372": "\u2581tutto", + "1373": "\u2581video", + "1374": "\u2581maglia", + "1375": "\u2581imp", + "1376": "\u2581cui", + "1377": "\u2581dove", + "1378": "\u2581col", + "1379": "\u2581Quindi", + "1380": "sione", + "1381": "rebbe", + "1382": "scri", + "1383": "", + "1384": "\u0117", + "1385": "ai", + "1386": "\u0173", + "1387": "\u2581ir", + "1388": "as", + "1389": "\u012f", + "1390": "\u2581kad", + "1391": "\u0117s", + "1392": "\u2581tai", + "1393": "\u016b", + "1394": "t\u0173", + "1395": "\u2581yra", + "1396": "i\u0173", + "1397": "uo", + "1398": "\u2581ko", + "1399": "\u2581i\u0161", + "1400": "tin", + "1401": "\u2581vis", + "1402": "\u010dia", + "1403": "\u2581kuri", + "1404": "d\u0117", + "1405": "ly", + "1406": "gal", + "1407": "\u2581\u0161i", + "1408": "iau", + "1409": "jo", + "1410": "tar", + "1411": "yb", + "1412": "\u2581Ir", + "1413": "\u2581tik", + "1414": "ijos", + "1415": "sak", + "1416": "\u2581turi", + "1417": "oje", + "1418": "\u2581Tai", + "1419": "j\u0173", + "1420": "\u2581apie", + "1421": "\u2581nu", + "1422": "\u2581mes", + "1423": "\u2581u\u017e", + "1424": "i\u0161k", + "1425": "\u2581gali", + "1426": "\u2581d\u0117l", + "1427": "\u2581labai", + "1428": "imas", + "1429": "klaus", + "1430": "laik", + "1431": "\u2581Europos", + "1432": "\u2581a\u0161", + "1433": "veik", + "1434": "\u2581b\u016bt\u0173", + "1435": "darb", + "1436": "\u2581kaip", + "1437": "\u2581teis", + "1438": "\u2581daug", + "1439": "\u2581tikrai", + "1440": "\u2581pra", + "1441": "reik", + "1442": "\u2581buvo", + "1443": "tur\u0117", + "1444": "\u2581valstyb", + "1445": "\u2581reikia", + "1446": "\u2581b\u016bti", + "1447": "\u2581A\u0161", + "1448": "\u2581m\u016bs\u0173", + "1449": "\u2581j\u016bs", + "1450": "vyk", + "1451": "\u2581A\u010di\u016b", + "1452": "cija", + "1453": "\u012e", + "1454": "\u0146", + "1455": "", + "1456": "\u2581no", + "1457": "j\u0101", + "1458": "iem", + "1459": "t\u0101", + "1460": "\u0101k", + "1461": "\u2581ar", + "1462": "\u0101m", + "1463": "\u2581pie", + "1464": "ies", + "1465": "ot", + "1466": "k\u0101", + "1467": "\u013c", + "1468": "tr", + "1469": "\u2581t\u0101", + "1470": "\u012bt", + "1471": "n\u0101", + "1472": "\u2581uz", + "1473": "\u2581tas", + "1474": "\u0113t", + "1475": "dz", + "1476": "\u2581ar\u012b", + "1477": "\u2581vien", + "1478": "\u2581jau", + "1479": "\u2581k\u0101", + "1480": "\u2581ie", + "1481": "gad", + "1482": "\u2581kur", + "1483": "\u2581kas", + "1484": "\u2581Un", + "1485": "\u2581m\u0113s", + "1486": "iet", + "1487": "d\u0101", + "1488": "\u012bg", + "1489": "\u2581Ta", + "1490": "\u2581k\u0101d", + "1491": "kaut", + "1492": "\u0113m", + "1493": "\u2581lie", + "1494": "umu", + "1495": "ties", + "1496": "dar", + "1497": "l\u0113", + "1498": "\u2581vai", + "1499": "\u2581bija", + "1500": "\u2581mums", + "1501": "\u2581tad", + "1502": "\u2581bet", + "1503": "\u012bba", + "1504": "\u2581ga", + "1505": "\u2581Latvijas", + "1506": "ija", + "1507": "kr", + "1508": "v\u0113", + "1509": "sim", + "1510": "\u2581\u0161o", + "1511": "dien", + "1512": "gan", + "1513": "\u012bgi", + "1514": "\u2581ap", + "1515": "\u0123", + "1516": "\u2581b\u016bt", + "1517": "dom\u0101", + "1518": "\u2581tev", + "1519": "m\u0113r", + "1520": "\u2581daudz", + "1521": "\u2581aiz", + "1522": "\u2581T\u0101", + "1523": "\u2581t\u0101d", + "1524": "\u2581tur", + "1525": "\u2581mon\u0113t", + "1526": "\u2581v\u0113l", + "1527": "\u2581laik", + "1528": "\u2581cilv\u0113", + "1529": "\u2581nav", + "1530": "\u2581lab", + "1531": "\u2581\u013coti", + "1532": "aug", + "1533": "\u2581l\u012bdz", + "1534": "\u2581lai", + "1535": "\u0161ana", + "1536": "\u2581Nu", + "1537": "\u2581vi\u0146a", + "1538": "\u2581savu", + "1539": "\u2581cit", + "1540": "teik", + "1541": "\u2581darb", + "1542": "\u2581Ne", + "1543": "zin", + "1544": "\u2581pirm", + "1545": "\u2581Latvi", + "1546": "\u2581tie\u0161", + "1547": "\u2581vi\u0146i", + "1548": "\u0113ja", + "1549": "dz\u012bvo", + "1550": "\u2581vi\u0146\u0161", + "1551": "\u2581pils\u0113", + "1552": "in\u0101t", + "1553": "\u2581vi\u0146u", + "1554": "\u2581tagad", + "1555": "k\u0101rt", + "1556": "\u2581pats", + "1557": "\u2581vair\u0101k", + "1558": "reiz", + "1559": "\u2581tikai", + "1560": "sakta", + "1561": "\u2581bij", + "1562": "\u2581Vi\u0146", + "1563": "\u2581sev", + "1564": "\u2581m\u0101j", + "1565": "v\u0113rt", + "1566": "\u258120", + "1567": "\u2581ce\u013c", + "1568": "tiek", + "1569": "iski", + "1570": "\u2581dz\u012bv", + "1571": "\u2581k\u0101p\u0113c", + "1572": "\u2581Bet", + "1573": "\u2581p\u0113c", + "1574": "\u2581noz\u012bm\u0113", + "1575": "niek", + "1576": "\u012bb\u0101", + "1577": "\u2581pal\u012bdz", + "1578": "\u2581protams", + "1579": "\u2581stils", + "1580": "\u2581vajadz", + "1581": "\u2581att\u012bst\u012b", + "1582": "\u2581svar\u012bg", + "1583": "\u2581sievie", + "1584": "\u2581grib", + "1585": "\u2581da\u017e\u0101d", + "1586": "\u2581valst", + "1587": "\u2581banka", + "1588": "\u2581iesp\u0113ja", + "1589": "\u2581bez", + "1590": "pr\u0101t", + "1591": "v\u0113rt\u012bb", + "1592": "\u2581person", + "1593": "pasaules", + "1594": "\u2581varb\u016bt", + "1595": "\u2581vienk\u0101r\u0161i", + "1596": "\u2581nauda", + "1597": "mekl\u0113", + "1598": "brauc", + "1599": "\u2581nevar", + "1600": "\u0101cijas", + "1601": "sp\u0113j", + "1602": "\u0137", + "1603": "", + "1604": "\u2581een", + "1605": "\u2581het", + "1606": "\u2581dat", + "1607": "\u2581we", + "1608": "\u2581ik", + "1609": "ij", + "1610": "\u2581En", + "1611": "\u2581te", + "1612": "\u2581ook", + "1613": "\u2581niet", + "1614": "\u2581dan", + "1615": "\u2581zo", + "1616": "\u2581voor", + "1617": "\u2581met", + "1618": "\u2581aan", + "1619": "\u2581zijn", + "1620": "\u2581Ik", + "1621": "\u2581wel", + "1622": "\u2581wat", + "1623": "aar", + "1624": "\u2581ze", + "1625": "ken", + "1626": "\u2581heb", + "1627": "der", + "1628": "ui", + "1629": "den", + "1630": "\u2581daar", + "1631": "\u2581maar", + "1632": "op", + "1633": "\u2581heel", + "1634": "\u2581nog", + "1635": "\u2581Dus", + "1636": "oor", + "1637": "\u2581hebben", + "1638": "\u2581uit", + "1639": "\u2581of", + "1640": "ven", + "1641": "\u2581Maar", + "1642": "\u2581Dat", + "1643": "\u2581gaan", + "1644": "elijk", + "1645": "\u2581naar", + "1646": "\u2581moet", + "1647": "acht", + "1648": "\u2581waar", + "1649": "\u2581dus", + "1650": "\u2581ben", + "1651": "\u2581goed", + "1652": "\u2581Het", + "1653": "\u2581even", + "1654": "ond", + "1655": "eld", + "1656": "\u2581dit", + "1657": "\u2581wil", + "1658": "rij", + "1659": "\u2581echt", + "1660": "\u2581doen", + "1661": "\u2581gewoon", + "1662": "lijk", + "1663": "tijd", + "1664": "\u2581meer", + "1665": "\u2581mijn", + "1666": "\u2581We", + "1667": "\u2581gaat", + "1668": "werk", + "1669": "\u2581hoe", + "1670": "uw", + "1671": "\u2581eigenlijk", + "1672": "\u2581deze", + "1673": "zelf", + "1674": "vol", + "1675": "\u2581veel", + "1676": "atie", + "1677": "\u2581kunnen", + "1678": "\u2581door", + "1679": "llen", + "1680": "\u2581mee", + "1681": "\u2581onder", + "1682": "\u2581toe", + "1683": "\u2581zit", + "1684": "\u2581mensen", + "1685": "\u2581hij", + "1686": "\u2581denk", + "1687": "\u2581zie", + "1688": "\u2581heeft", + "1689": "\u2581kl", + "1690": "nnen", + "1691": "\u2581zien", + "1692": "komen", + "1693": "\u2581natuurlijk", + "1694": "heid", + "1695": "\u2581Dan", + "1696": "\u2581vind", + "1697": "\u2581wordt", + "1698": "\u2581iets", + "1699": "\u2581maken", + "1700": "\u2581doe", + "1701": "\u2581Wat", + "1702": "\u2581wij", + "1703": "\u2581beetje", + "1704": "\u2581worden", + "1705": "\u2581Want", + "1706": "\u2581twee", + "1707": "\u2581hem", + "1708": "\u2581had", + "1709": "\u2581jullie", + "1710": "\u2581Als", + "1711": "\u2581kijken", + "1712": "\u2581toch", + "1713": "\u2581tot", + "1714": "nieuw", + "1715": "lang", + "1716": "\u2581Nou", + "1717": "\u2581krijg", + "1718": "houd", + "1719": "\u2581hele", + "1720": "\u2581allemaal", + "1721": "\u2581want", + "1722": "\u2581zeggen", + "1723": "\u2581leuk", + "1724": "", + "1725": "nie", + "1726": "\u2581w", + "1727": "cz", + "1728": "wa", + "1729": "\u2581si\u0119", + "1730": "\u2581jest", + "1731": "my", + "1732": "\u0142a", + "1733": "cie", + "1734": "czy", + "1735": "\u2581nie", + "1736": "wie", + "1737": "\u2581wy", + "1738": "nia", + "1739": "wo", + "1740": "rze", + "1741": "\u0142o", + "1742": "\u2581\u017ce", + "1743": "dzi", + "1744": "ej", + "1745": "\u00f3w", + "1746": "dzie", + "1747": "\u2581prze", + "1748": "\u015bci", + "1749": "by", + "1750": "za", + "1751": "dy", + "1752": "ry", + "1753": "\u0144", + "1754": "j\u0105", + "1755": "we", + "1756": "cze", + "1757": "owa", + "1758": "ego", + "1759": "\u017ce", + "1760": "cy", + "1761": "rzy", + "1762": "mie", + "1763": "\u2581przy", + "1764": "\u0142y", + "1765": "rz", + "1766": "szy", + "1767": "sze", + "1768": "\u015b\u0107", + "1769": "wia", + "1770": "zy", + "1771": "\u017cy", + "1772": "\u2581tutaj", + "1773": "j\u0119", + "1774": "pie", + "1775": "nych", + "1776": "\u2581tym", + "1777": "\u2581mo\u017ce", + "1778": "cji", + "1779": "\u2581pod", + "1780": "\u2581ale", + "1781": "\u2581tego", + "1782": "owy", + "1783": "uje", + "1784": "\u2581bo", + "1785": "\u2581by\u0142", + "1786": "n\u0105", + "1787": "bie", + "1788": "sy", + "1789": "\u2581te\u017c", + "1790": "\u2581bardzo", + "1791": "\u2581s\u0105", + "1792": "\u2581b\u0119dzie", + "1793": "\u2581Po", + "1794": "ski", + "1795": "\u2581kt\u00f3re", + "1796": "\u017a", + "1797": "\u2581ju\u017c", + "1798": "\u2581dla", + "1799": "\u0142em", + "1800": "nego", + "1801": "\u2581Nie", + "1802": "\u2581No", + "1803": "\u2581praw", + "1804": "cja", + "1805": "\u2581ten", + "1806": "\u2581takie", + "1807": "owa\u0107", + "1808": "\u2581kt\u00f3ry", + "1809": "\u2581w\u0142a\u015bnie", + "1810": "\u2581jeszcze", + "1811": "\u2581tam", + "1812": "\u2581\u017ceby", + "1813": "\u2581by\u0107", + "1814": "\u2581wi\u0119c", + "1815": "\u2581czyli", + "1816": "\u2581sobie", + "1817": "\u2581sam", + "1818": "\u2581tylko", + "1819": "\u2581tej", + "1820": "\u2581spraw", + "1821": "\u2581Na", + "1822": "\u2581m\u00f3wi", + "1823": "\u2581osob", + "1824": "\u2581czas", + "1825": "\u2581prac", + "1826": "\u2581Czy", + "1827": "\u2581prostu", + "1828": "\u2581teraz", + "1829": "st\u0119p", + "1830": "\u2581Was", + "1831": "\u2581my\u015bl", + "1832": "\u2581powiedz", + "1833": "\u2581zrobi", + "1834": "li\u015bmy", + "1835": "\u2581jakie\u015b", + "1836": "aj\u0105c", + "1837": "\u2581widz", + "1838": "\u2581kart", + "1839": "\u2581musi", + "1840": "\u2581pyta", + "1841": "", + "1842": "pt", + "1843": "PT", + "1844": "<", + "1845": ">", + "1846": "-", + "1847": "\u2581\u00e9", + "1848": "\u2581n\u00e3o", + "1849": "\u2581eu", + "1850": "\u2581um", + "1851": "\u2581voc\u00ea", + "1852": "\u2581para", + "1853": "\u00e3o", + "1854": "\u2581aqui", + "1855": "\u2581uma", + "1856": "\u00e7\u00e3o", + "1857": "\u2581ca", + "1858": "\u2581pe", + "1859": "\u2581tem", + "1860": "\u2581em", + "1861": "\u2581gente", + "1862": "\u2581O", + "1863": "\u2581ele", + "1864": "pre", + "1865": "ria", + "1866": "\u2581fo", + "1867": "mos", + "1868": "nho", + "1869": "\u2581Ent\u00e3o", + "1870": "bo", + "1871": "io", + "1872": "nha", + "1873": "\u2581isso", + "1874": "\u2581por", + "1875": "\u2581muito", + "1876": "nto", + "1877": "\u2581Eu", + "1878": "\u2581est\u00e1", + "1879": "idade", + "1880": "\u2581a\u00ed", + "1881": "be", + "1882": "\u2581esse", + "1883": "\u2581pode", + "1884": "\u2581como", + "1885": "ente", + "1886": "\u2581tamb\u00e9m", + "1887": "\u2581essa", + "1888": "lha", + "1889": "\u2581j\u00e1", + "1890": "\u2581mas", + "1891": "\u2581pessoa", + "1892": "qua", + "1893": "\u2581n\u00e9", + "1894": "\u2581fazer", + "1895": "\u2581t\u00e1", + "1896": "lho", + "1897": "\u2581l\u00e1", + "1898": "fica", + "1899": "\u2581vou", + "1900": "\u2581porque", + "1901": "\u2581Se", + "1902": "\u2581fala", + "1903": "\u2581coisa", + "1904": "\u2581N\u00e3o", + "1905": "...", + "1906": "\u2581s\u00f3", + "1907": "\u2581n\u00f3s", + "1908": "\u00e7o", + "1909": "\u2581Por", + "1910": "\u2581assim", + "1911": "\u2581Co", + "1912": "iza", + "1913": "\u2581bem", + "1914": "\u2581todo", + "1915": "eira", + "1916": "\u2581sua", + "1917": "\u00eancia", + "1918": "\u00e7\u00f5es", + "1919": "\u2581Voc\u00ea", + "1920": "\u2581tudo", + "1921": "\u2581agora", + "1922": "eiro", + "1923": "\u00e1rio", + "1924": "\u2581at\u00e9", + "1925": "\u2581mesmo", + "1926": "\u2581vamos", + "1927": "\u2581quando", + "1928": "ciona", + "1929": "", + "1930": "\u2581\u00een", + "1931": "\u021bi", + "1932": "\u2581s\u0103", + "1933": "\u2581\u0219i", + "1934": "\u2581cu", + "1935": "\u2581c\u0103", + "1936": "\u2581care", + "1937": "\u2581mai", + "1938": "r\u0103", + "1939": "sc", + "1940": "c\u0103", + "1941": "\u2581am", + "1942": "are", + "1943": "\u2581din", + "1944": "\u2581fi", + "1945": "\u2581este", + "1946": "t\u0103", + "1947": "\u2581pentru", + "1948": "rea", + "1949": "\u0219ti", + "1950": "\u0219", + "1951": "ele", + "1952": "du", + "1953": "\u2581M", + "1954": "\u2581fac", + "1955": "\u00e2n", + "1956": "\u2581sunt", + "1957": "\u2581I", + "1958": "\u2581acest", + "1959": "ului", + "1960": "lor", + "1961": "\u2581mult", + "1962": "\u0219i", + "1963": "\u2581mo", + "1964": "\u2581fost", + "1965": "per", + "1966": "\u2581foarte", + "1967": "\u2581\u0218i", + "1968": "\u2581m\u0103", + "1969": "s\u0103", + "1970": "cur", + "1971": "tor", + "1972": "\u2581cum", + "1973": "inte", + "1974": "at\u0103", + "1975": "\u0219te", + "1976": "\u2581dac\u0103", + "1977": "\u00e2nd", + "1978": "\u2581subliniere", + "1979": "\u2581dar", + "1980": "\u2581sau", + "1981": "tat", + "1982": "ori", + "1983": "\u2581v\u0103", + "1984": "\u2581asta", + "1985": "n\u0103", + "1986": "\u2581prim", + "1987": "\u2581a\u0219a", + "1988": "eaz\u0103", + "1989": "\u2581\u00eentr", + "1990": "\u2581spun", + "1991": "\u2581lui", + "1992": "\u2581sub", + "1993": "itate", + "1994": "\u2581aici", + "1995": "\u2581bine", + "1996": "\u2581c\u00e2nd", + "1997": "\u2581prin", + "1998": "\u2581alt", + "1999": "\u2581nici", + "2000": "stru", + "2001": "\u2581c\u00e2t", + "2002": "\u2581vede", + "2003": "fer", + "2004": "\u2581dup\u0103", + "2005": "\u2581ju", + "2006": "\u2581despre", + "2007": "\u2581timp", + "2008": "\u2581acum", + "2009": "\u2581poate", + "2010": "\u2581spus", + "2011": "\u2581lucru", + "2012": "\u2581f\u0103cut", + "2013": "p\u0103r", + "2014": "\u2581urm\u0103", + "2015": "\u2581atunci", + "2016": "\u2581fr", + "2017": "\u2581chiar", + "2018": "\u2581\u00eencep", + "2019": "\u0218", + "2020": "\u00ce", + "2021": "", + "2022": "\u2581\u043d\u0435", + "2023": "\u044b", + "2024": "\u0442\u044c", + "2025": "\u2581\u044d\u0442\u043e", + "2026": "\u0436\u0435", + "2027": "\u2581\u0447\u0442\u043e", + "2028": "\u2581\u0442\u043e", + "2029": "\u043b\u044c", + "2030": "\u2581\u043e", + "2031": "\u2581\u0443", + "2032": "\u0430\u0442\u044c", + "2033": "\u2581\u0442\u0430\u043a", + "2034": "\u2581\u043a\u0430\u043a", + "2035": "\u043a\u0438", + "2036": "\u0441\u044f", + "2037": "\u0435\u043c", + "2038": "\u2581\u0432\u044b", + "2039": "\u2581\u0431\u044b", + "2040": "\u2581\u0432\u0441\u0435", + "2041": "\u0440\u0443", + "2042": "\u0431\u043e", + "2043": "\u2581\u0418", + "2044": "\u2581\u0432\u043e\u0442", + "2045": "\u043a\u0443", + "2046": "\u2581\u0412", + "2047": "\u0447\u0438", + "2048": "\u043e\u0439", + "2049": "\u043c\u0443", + "2050": "\u2581\u0441\u043e", + "2051": "\u0442\u044b", + "2052": "\u043d\u0443", + "2053": "\u0441\u044c", + "2054": "\u2581\u0435\u0441\u0442\u044c", + "2055": "\u0442\u0443", + "2056": "\u043d\u044b", + "2057": "\u0448\u0435", + "2058": "\u2581\u043c\u044b", + "2059": "\u0434\u0443", + "2060": "\u0438\u0442\u044c", + "2061": "\u044d", + "2062": "\u0434\u0435\u043b", + "2063": "\u043b\u044f", + "2064": "\u043c\u0435\u043d", + "2065": "\u0436\u0438", + "2066": "\u0441\u0442\u043e", + "2067": "\u0445\u043e", + "2068": "\u0441\u0442\u0432", + "2069": "\u0432\u044b", + "2070": "\u0432\u0435\u0440", + "2071": "\u0437\u043d\u0430", + "2072": "\u0441\u0442\u0438", + "2073": "\u0448\u0438", + "2074": "\u0435\u0442\u0441\u044f", + "2075": "\u0443\u044e", + "2076": "\u0440\u044b", + "2077": "\u0445\u043e\u0434", + "2078": "\u0430\u0435\u0442", + "2079": "\u043d\u044b\u0439", + "2080": "\u043f\u0435\u0440", + "2081": "\u2581\u041f\u043e", + "2082": "\u043b\u0443\u0447", + "2083": "\u043d\u044b\u0435", + "2084": "\u0442\u043e\u0440", + "2085": "\u2581\u0442\u0430\u043c", + "2086": "\u2581\u0431\u0443\u0434\u0435\u0442", + "2087": "\u2581\u0441\u0430\u043c", + "2088": "\u2581\u0434\u043b\u044f", + "2089": "\u2581\u043e\u0447\u0435\u043d\u044c", + "2090": "\u0435\u043d\u0438\u044f", + "2091": "\u0430\u044e\u0442", + "2092": "\u2581\u041d\u0443", + "2093": "\u2581\u042d\u0442\u043e", + "2094": "\u2581\u0414\u0430", + "2095": "\u2581\u043c\u0435\u043d\u044f", + "2096": "\u2581\u0435\u0441\u043b\u0438", + "2097": "\u2581\u0422\u043e", + "2098": "\u0435\u043d\u044c", + "2099": "\u043d\u044b\u0445", + "2100": "\u2581\u0435\u0449\u0435", + "2101": "\u2581\u0432\u0430\u043c", + "2102": "\u2581\u043f\u0435\u0440\u0435", + "2103": "\u2581\u0437\u0434\u0435\u0441\u044c", + "2104": "\u2581\u043f\u0440\u043e\u0441\u0442\u043e", + "2105": "\u2581\u0412\u043e\u0442", + "2106": "\u2581\u041d\u043e", + "2107": "\u2581\u0447\u0442\u043e\u0431\u044b", + "2108": "\u0441\u043c\u043e\u0442\u0440", + "2109": "\u2581\u0441\u0435\u0439\u0447\u0430\u0441", + "2110": "\u2581\u043c\u043e\u0436\u0435\u0442", + "2111": "\u2581\u044d\u0442\u0438", + "2112": "\u0430\u043b\u044c\u043d\u043e", + "2113": "\u0434\u043e\u043b", + "2114": "\u2581\u041d\u0430", + "2115": "\u2581\u0422\u0430\u043a", + "2116": "\u2581\u043a\u043e\u0433\u0434\u0430", + "2117": "\u0451", + "2118": "\u0430\u0439\u0442\u0435", + "2119": "\u043f\u0438\u0441", + "2120": "\u0442\u0435\u043b\u044c\u043d\u043e", + "2121": "\u0435\u0448\u044c", + "2122": "\u2581\u0434\u0440\u0443\u0433", + "2123": "\u042d", + "2124": "", + "2125": "ov", + "2126": "\u013e", + "2127": "sk", + "2128": "\u2581aj", + "2129": "ob", + "2130": "t\u00e1", + "2131": "a\u0165", + "2132": "\u2581bol", + "2133": "\u2581s\u00fa", + "2134": "\u2581ako", + "2135": "\u017ei", + "2136": "\u2581sme", + "2137": "\u2581V", + "2138": "ali", + "2139": "\u2581alebo", + "2140": "\u2581\u010do", + "2141": "i\u0165", + "2142": "\u2581m\u00e1", + "2143": "\u00fdch", + "2144": "\u2581z\u00e1", + "2145": "\u2581tie", + "2146": "\u2581nejak", + "2147": "\u2581v\u00fd", + "2148": "\u010das", + "2149": "nov", + "2150": "rov", + "2151": "\u2581ktor\u00e9", + "2152": "aj\u00fa", + "2153": "ova\u0165", + "2154": "\u2581ke\u010f", + "2155": "\u2581str", + "2156": "\u2581\u0161kol", + "2157": "n\u00fa", + "2158": "\u2581ktor", + "2159": "\u2581vlastne", + "2160": "\u2581pr\u00ed", + "2161": "nej", + "2162": "\u2581ve\u013emi", + "2163": "\u0161ie", + "2164": "rob", + "2165": "\u2581tr", + "2166": "n\u00fdch", + "2167": "enie", + "2168": "\u2581spo", + "2169": "\u2581rok", + "2170": "osti", + "2171": "\u2581t\u00fdm", + "2172": "\u2581m\u00f4\u017ee", + "2173": "\u2581ktor\u00fd", + "2174": "os\u0165", + "2175": "\u2581projekt", + "2176": "\u2581kon", + "2177": "\u2581vzdel\u00e1va", + "2178": "\u2581Tak\u017ee", + "2179": "\u2581e\u0161te", + "2180": "\u2581t\u00fdch", + "2181": "\u2581mal", + "2182": "\u2581cel", + "2183": "\u2581potom", + "2184": "\u2581svoj", + "2185": "enia", + "2186": "\u00e1lne", + "2187": "ie\u0165", + "2188": "\u2581teda", + "2189": "jedn", + "2190": "sled", + "2191": "\u2581mo\u017eno", + "2192": "\u2581v\u00e1m", + "2193": "chod", + "2194": "uj\u00fa", + "2195": "tvor", + "2196": "\u2581druh", + "2197": "\u2581Slovensk", + "2198": "h\u013ead", + "2199": "stup", + "2200": "\u2581\u013eud\u00ed", + "2201": "\u2581napr\u00edklad", + "2202": "\u2581ve\u013ek", + "2203": "\u2581nie\u010do", + "2204": "\u010e", + "2205": "", + "2206": "sl", + "2207": "lj", + "2208": "kot", + "2209": "ih", + "2210": "\u2581svet", + "2211": "\u2581ta", + "2212": "\u2581tako", + "2213": "\u2581kar", + "2214": "\u2581nek", + "2215": "jih", + "2216": "udi", + "2217": "\u2581vse", + "2218": "\u2581drug", + "2219": "\u2581ima", + "2220": "kaj", + "2221": "\u2581smo", + "2222": "del", + "2223": "\u2581sem", + "2224": "\u2581lahko", + "2225": "\u2581samo", + "2226": "\u2581ve\u010d", + "2227": "nih", + "2228": "\u2581dr\u017eav", + "2229": "\u2581zelo", + "2230": "\u2581zdaj", + "2231": "\u2581razum", + "2232": "\u2581\u0161e", + "2233": "\u2581tega", + "2234": "\u2581ljudi", + "2235": "\u2581pred", + "2236": "\u2581sta", + "2237": "nost", + "2238": "\u2581ampak", + "2239": "\u2581novinar", + "2240": "\u2581naprej", + "2241": "\u2581mora", + "2242": "\u2581Vs", + "2243": "krat", + "2244": "\u2581Ampak", + "2245": "\u2581vedno", + "2246": "\u2581velik", + "2247": "\u2581kako", + "2248": "\u2581najbolj", + "2249": "ziroma", + "2250": "\u2581vsi", + "2251": "\u2581nekaj", + "2252": "\u2581kater", + "2253": "\u2581res", + "2254": "\u2581tukaj", + "2255": "\u2581dogaja", + "2256": "\u2581svoje", + "2257": "\u2581let", + "2258": "daj", + "2259": "\u2581pripri\u010da", + "2260": "\u2581\u010dlovek", + "2261": "\u2581ho\u010de", + "2262": "\u2581vojn", + "2263": "\u2581Pre", + "2264": "\u2581dobr", + "2265": "ljan", + "2266": "\u2581moj", + "2267": "\u2581dejansko", + "2268": "\u2581ljudje", + "2269": "\u2581mediji", + "2270": "\u2581prot", + "2271": "\u2581narav", + "2272": "bilo", + "2273": "\u2581Afrik", + "2274": "\u2581vzhod", + "2275": "\u2581\u010dlove\u0161tva", + "2276": "\u2581kriz", + "2277": "\u2581pogled", + "2278": "\u2581medije", + "2279": "poved", + "2280": "\u2581za\u010del", + "2281": "\u2581ve\u010din", + "2282": "imajo", + "2283": "\u2581Ljudje", + "2284": "\u2581dru\u017eb", + "2285": "\u2581govorim", + "2286": "\u2581informacij", + "2287": "\u2581kultur", + "2288": "\u2581bli\u017enj", + "2289": "\u2581podobno", + "2290": "\u2581njihov", + "2291": "\u2581konc", + "2292": "\u2581pisa", + "2293": "\u2581zaveda", + "2294": "\u2581vsak", + "2295": "\u017eivel", + "2296": "\u2581funkcionira", + "2297": "\u2581internet", + "2298": "\u2581islamsk", + "2299": "\u2581film", + "2300": "\u2581otroci", + "2301": "\u2581prihaja", + "2302": "\u2581politi\u010dn", + "2303": "\u2581popoln", + "2304": "\u2581Velik", + "2305": "\u2581druga\u010den", + "2306": "\u2581recimo", + "2307": "\u2581resnic", + "2308": "solutno", + "2309": "\u2581Bli\u017en", + "2310": "\u2581Evropsk", + "2311": "\u2581muslimani", + "2312": "\u2581nadzoruje", + "2313": "\u2581socialne", + "2314": "\u2581zgodovin", + "2315": "\u2581\u010dlove\u0161k", + "2316": "\u2581\u017eivljenj", + "2317": "\u2581prijatelj", + "2318": "\u2581vendar", + "2319": "\u2581ljudem", + "2320": "\u2581\u0161tevil", + "2321": "\u2581Sirij", + "2322": "", + "2323": "\u2581att", + "2324": "\u2581och", + "2325": "\u2581\u00e4r", + "2326": "\u2581f\u00f6r", + "2327": "\u2581h\u00e4r", + "2328": "\u2581jag", + "2329": "\u00e4n", + "2330": "\u2581till", + "2331": "\u2581h", + "2332": "\u2581inte", + "2333": "\u2581Och", + "2334": "\u2581av", + "2335": "\u2581om", + "2336": "\u2581ska", + "2337": "\u2581ut", + "2338": "\u2581ett", + "2339": "all", + "2340": "\u2581ocks\u00e5", + "2341": "\u2581Jag", + "2342": "era", + "2343": "pp", + "2344": "\u2581upp", + "2345": "\u2581d\u00e5", + "2346": "\u2581d\u00e4r", + "2347": "\u2581lite", + "2348": "\u00e5r", + "2349": "sam", + "2350": "isk", + "2351": "het", + "2352": "f\u00f6r", + "2353": "\u2581kommer", + "2354": "\u2581vill", + "2355": "\u00f6r", + "2356": "erna", + "2357": "ande", + "2358": "s\u00e4tt", + "2359": "\u2581finns", + "2360": "\u2581n\u00e4r", + "2361": "\u2581vara", + "2362": "ade", + "2363": "s\u00f6k", + "2364": "\u2581hur", + "2365": "\u2581vad", + "2366": "bil", + "2367": "\u2581g\u00f6ra", + "2368": "\u2581f\u00e5r", + "2369": "verk", + "2370": "\u2581mycket", + "2371": "\u2581v\u00e4l", + "2372": "kom", + "2373": "\u2581g\u00f6r", + "2374": "\u2581ni", + "2375": "\u2581bara", + "2376": "\u2581fr\u00e5n", + "2377": "st\u00e4ll", + 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"2417": "l\u00e4gg", + "2418": "\u2581m\u00e5ste", + "2419": "\u2581efter", + "2420": "text", + "2421": "\u2581prata", + "2422": "\u2581klicka", + "2423": "\u2581hitta", + "2424": "\u2581tror", + "2425": "\u2581n\u00e5gonting", + "2426": "fr\u00e5ga", + "2427": "\u2581titta", + "2428": "\u2581tycker", + "2429": "\u2581ganska", + "2430": "\u2581j\u00e4tte", + "2431": "\u2581Vad", + "2432": "\u2581genom", + "2433": "\u2581\u00e4ven", + "2434": "\u2581t\u00e4nker", + "2435": "arbete", + "2436": "\u2581faktiskt", + "2437": "person", + "2438": "\u2581komma", + "2439": "bygg", + "2440": "", + "2441": "\u0456", + "2442": "\u043d\u0456", + "2443": "\u0454", + "2444": "\u0457", + "2445": "\u2581\u0437", + "2446": "\u2581\u0449\u043e", + "2447": "\u0432\u0456", + "2448": "\u0440\u0456", + "2449": "\u0446\u0456", + "2450": "\u2581\u0456", + "2451": "\u043b\u0456", + "2452": "\u043c\u0456", + "2453": "\u0431\u0443", + "2454": "\u0434\u0456", + "2455": "\u043e\u0433\u043e", + "2456": 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"\u2581\u0627\u0644\u0652\u0639\u064e", + "2741": "\u064e\u0647\u064f", + "2742": "\u0634\u0639\u0631", + "2743": "\u2581\u0644\u0643\u0646", + "2744": "\u0639\u0644\u0645", + "2745": "\u064e\u0629\u064f", + "2746": "\u064b", + "2747": "\u2581\u0646\u0641\u0633", + "2748": "\u0650\u064a\u064e\u0651\u0629\u0650", + "2749": "\u064e\u062a\u0652", + "2750": "\u2581\u0648\u064e\u0623\u064e", + "2751": "\u064e\u0629\u064d", + "2752": "\u0645\u062b\u0644", + "2753": "\u2581\u063a\u064a\u0631", + "2754": "\u0627\u0626\u064a", + "2755": "\u2581\u0625\u0650\u0644\u064e\u0649", + "2756": "\u2581\u0648\u0627\u062d\u062f", + "2757": "\u2581\u0623\u064e\u0646\u064e\u0651", + "2758": "\u2581\u0647\u064e\u0630\u064e\u0627", + "2759": "\u2581\u0630\u0644\u0643", + "2760": "\u064e\u0629\u064e", + "2761": "\u2581\u062d\u062a\u0649", + "2762": "\u2581\u0647\u064e\u0644\u0652", + "2763": "\u061f", + "2764": "\u060c", + "2765": "vy", + "2766": "\u2581byl", + "2767": "\u0147", + "2768": "\u0164", + "2769": "\u00d3", + "2770": "\u00e6r", + "2771": "\u2581blev", + "2772": "ft", + "2773": "lige", + "2774": "ved", + "2775": "'", + "2776": "\u00c5", + "2777": "\u2581H", + "2778": "\u2581D", + "2779": "aus", + "2780": "\u2581N", + "2781": "\u2581Be", + "2782": "mm", + "2783": "ab", + "2784": "\u2581Er", + "2785": "ssen", + "2786": "hl", + "2787": "hn", + "2788": "ischen", + "2789": "\u2581wurde", + "2790": "rie", + "2791": "lei", + "2792": "\u2581An", + "2793": "\u2581Ein", + "2794": "etz", + "2795": "rau", + "2796": "ische", + "2797": "\u00e4h", + "2798": "\u2581mein", + "2799": "\u2581So", + "2800": "\u2581hatte", + "2801": "\u2581unter", + "2802": "\u2581Zu", + "2803": "\u2581ihn", + "2804": "\u2581Jahr", + "2805": "\u2581zwei", + "2806": "keit", + "2807": "\u2581ihm", + "2808": "\u2581Aus", + "2809": "", + "2810": "\u2581you", + "2811": "\u2581that", + "2812": "\u2581and", + "2813": "\u2581can", + "2814": "\u2581it", + "2815": "\u2581your", + "2816": "ed", + "2817": "\u2581Okay", + "2818": "\u2581just", + "2819": "ay", + "2820": "\u2581Yeah", + "2821": "\u2581with", + "2822": "th", + "2823": "\u2581Thank", + "2824": "\u2581thank", + "2825": "\u2581help", + "2826": "\u2581please", + "2827": "\u2581one", + "2828": "\u2581there", + "2829": "ic", + "2830": "\u2581much", + "2831": "\u2581what", + "2832": "\u2581my", + "2833": "hi", + "2834": "\u2581will", + "2835": "\u2581would", + "2836": "\u2581if", + "2837": "\u2581two", + "2838": "\u2581this", + "2839": "\u2581he", + "2840": "\u2581go", + "2841": "\u2581all", + "2842": "\u2581Oh", + "2843": "\u2581like", + "2844": "\u2581very", + "2845": "\u2581The", + "2846": "\u2581today", + "2847": "\u2581not", + "2848": "\u2581yeah", + "2849": "\u2581take", + "2850": "ight", + "2851": "ex", + "2852": "\u2581Ok", + "2853": "\u2581seven", + "2854": "\u2581number", + "2855": "\u2581know", + "2856": "\u2581about", + "2857": "\u2581four", + "2858": "\u2581okay", + "2859": "\u2581name", + "2860": "\u2581And", + "2861": "\u2581five", + "2862": "\u2581How", + "2863": "\u2581account", + "2864": "\u2581any", + "2865": "\u2581three", + "2866": "\u2581could", + "2867": "\u2581up", + "2868": "\u2581get", + "2869": "\u2581phone", + "2870": "\u2581great", + "2871": "\u2581six", + "2872": "\u2581eight", + "2873": "\u2581now", + "2874": "\u2581nine", + "2875": "\u2581That", + "2876": "\u2581address", + "2877": "\u2581look", + "2878": "\u2581call", + "2879": "ill", + "2880": "\u2581You", + "2881": "\u2581but", + "2882": "\u2581got", + "2883": "\u2581don", + "2884": "\u2581email", + "2885": "\u2581calling", + "2886": "\u2581problem", + "2887": "\u2581right", + "2888": "\u2581good", + "2889": "\u2581well", + "2890": "\u2581out", + "2891": "\u2581What", + "2892": "\u2581how", + "2893": "\u2581really", + "2894": "\u2581anything", + "2895": "\u2581actually", + "2896": "\u2581from", + "2897": "\u2581think", + "2898": "\u2581time", + "2899": "\u2581some", + "2900": "\u2581ask", + "2901": "\u2581else", + "2902": "other", + "2903": "\u2581fine", + "2904": "able", + "2905": "\u2581Good", + "2906": "\u2581when", + "2907": "\u2581full", + "2908": "\u2581confirm", + "2909": "\u2581give", + "2910": "\u2581more", + "2911": "ever", + "2912": "\u2581month", + "2913": "\u2581information", + "2914": "\u2581sure", + "2915": "\u2581survey", + "2916": "\u2581sorry", + "2917": "\u2581send", + "2918": "\u2581through", + "2919": "\u2581check", + "2920": "\u2581long", + "2921": "\u2581birth", + "2922": "\u2581should", + "2923": "\u2581twenty", + "2924": "\u2581make", + "2925": "\u2581zero", + "2926": "ful", + "2927": "\u2581store", + "2928": "\u2581policy", + "2929": "\u2581back", + "2930": "\u2581again", + "2931": "\u2581first", + "2932": "\u2581Could", + "2933": "\u2581work", + "2934": "\u2581afternoon", + "2935": "\u2581after", + "2936": "\u2581insurance", + "2937": "\u2581customer", + "2938": "\u2581payment", + "2939": "\u2581question", + "2940": "\u2581receive", + "2941": "\u2581possible", + "2942": "\u2581moment", + "2943": "\u2581system", + "2944": "\u2581change", + "2945": "\u2581hundred", + "2946": "\u2581nineteen", + "2947": "", + "2948": "\u2581.", + "2949": "\u2581,", + "2950": "\u2581st", + "2951": "\u2581are", + "2952": "ow", + "2953": "ive", + "2954": "ate", + "2955": "ad", + "2956": "ect", + "2957": "\u2581they", + "2958": "\u2581as", + "2959": "ng", + "2960": "ity", + "2961": "ther", + "2962": "act", + "2963": "ist", + "2964": "\u2581our", + "2965": "\u2581sp", + "2966": "ally", + "2967": "\u2581his", + "2968": "\u2581But", + "2969": "\u2581has", + "2970": "\u2581also", + "2971": "\u2581which", + "2972": "\u2581He", + "2973": "\u2581uh", + "2974": "day", + "2975": "\u2581people", + "2976": "\u2581who", + "2977": "\u2581thing", + "2978": "\u2581because", + "2979": "\u2581other", + "2980": "ough", + "2981": "\u2581part", + "2982": "\u2581say", + "2983": "\u2581year", + "2984": "side", + "2985": "\"", + "2986": "", + "2987": "\u2581y", + "2988": "\u2581el", + "2989": "ci\u00f3n", + "2990": "\u2581Es", + "2991": "res", + "2992": "\u2581los", + "2993": "\u2581La", + "2994": "dos", + "2995": "\u00eda", + "2996": "\u2581El", + "2997": "\u2581las", + "2998": "\u2581m\u00e1s", + "2999": "men", + "3000": "\u00f1o", + "3001": "\u2581esta", + "3002": "idad", + "3003": "par", + "3004": "\u00bf", + "3005": "r\u00eda", + "3006": "\u2581fue", + "3007": "rio", + "3008": "enta", + "3009": "\u00f3n", + "3010": "cho", + "3011": "ciones", + "3012": "ble", + "3013": "\u2581Ca", + "3014": "\u2581muy", + "3015": "\u2581tambi\u00e9n", + "3016": "\u2581tiene", + "3017": "\u00f1a", + "3018": "\u2581Su", + "3019": "\u2581pero", + "3020": "\u2581son", + "3021": "encia", + "3022": "si\u00f3n", + "3023": "\u2581hay", + "3024": "\u2581puede", + "3025": "ncia", + "3026": "\u2581mucho", + "3027": "\u2581Si", + "3028": "\u2581pues", + "3029": "miento", + "3030": "\u2581Con", + "3031": "ones", + "3032": "ecto", + "3033": "iendo", + "3034": "\u2581d\u00eda", + "3035": "\u2581sobre", + "3036": "\u2581primer", + "3037": "\u2581qu\u00e9", + "3038": "\u2581gusta", + "3039": "\u2581San", + "3040": "\u2581hacer", + "3041": "cional", + "3042": "\u2581verdad", + "3043": "\u2581persona", + "3044": "\u2581pasa", + "3045": "\u2581mejor", + "3046": "qu\u00ed", + "3047": "\u2581Fue", + "3048": "\u2581Com", + "3049": "\u2581ciudad", + "3050": "\u00d1", + "3051": "", + "3052": "cia", + "3053": "\u2581lo", + "3054": "\u2581Y", + "3055": "ron", + "3056": "les", + "3057": "\u2581mu", + "3058": "cio", + "3059": "\u2581yo", + "3060": "bu", + "3061": "\u2581s\u00ed", + "3062": "\u2581Pero", + "3063": "\u2581as\u00ed", + "3064": "", + "3065": "r\u00e9", + "3066": "\u00e9e", + "3067": "\u2581Les", + "3068": "nt", + "3069": "our", + "3070": "\u2581Ce", + "3071": "com", + "3072": "\u2581Elle", + "3073": "\u2581Cet", + "3074": "ux", + "3075": "ale", + "3076": "ier", + "3077": "ction", + "3078": "\u2581cha", + "3079": "\u2581pr\u00e9", + "3080": "\u2581deux", + "3081": "if", + "3082": "l\u00e9", + "3083": "\u00e8re", + "3084": "i\u00e8re", + "3085": "iste", + "3086": "\u2581parti", + "3087": "\u2581\u00e9t\u00e9", + "3088": "cette", + "3089": "avec", + "3090": "\u2581tou", + "3091": "jour", + "3092": "app", + "3093": "cul", + "3094": "\u2581\u00e9gale", + "3095": "aine", + "3096": "gue", + "3097": "\u2581tr\u00e8", + "3098": "\u2581nombre", + "3099": "\u2581\u00e9tai", + "3100": "tout", + "3101": "\u2581grand", + "3102": "\u2581commun", + "3103": "Une", + "3104": "\u0153", + "3105": "\u00ef", + "3106": "\u00c0", + "3107": "\u0152", + "3108": "\u00c8", + "3109": "\u014d", + "3110": "\u00ff", + "3111": "\u014c", + "3112": "\u00d4", + "3113": "\u00ca", + "3114": "\u00c2", + "3115": "\u2581m", + "3116": "av", + "3117": "ouv", + "3118": "\u00eat", + "3119": "ois", + "3120": "pri", + "3121": "voir", + "3122": "sion", + "3123": "ix", + "3124": "ang", + "3125": "\u00e9tait", + "3126": "ard", + "3127": "aient", + "3128": "\u0106", + "3129": "\u0130", + "3130": "\u00d9", + "3131": "\u00db", + "3132": "\u00cb", + "3133": "\u00cf", + "3134": "", + "3135": "\u05d1", + "3136": "\u05e2", + "3137": "\u05e7", + "3138": "\u05d7", + "3139": "\u05db", + "3140": "\u05d3", + "3141": "\u05e0", + "3142": "\u2581\u05d1", + "3143": "\u05d9\u05dd", + "3144": "\u05d2", + "3145": "\u2581\u05de", + "3146": "\u05e1", + "3147": "\u05dd", + "3148": "\u05d5\u05ea", + "3149": "\u05e6", + "3150": "\u05e4", + "3151": "\u05d8", + "3152": "\u05d5\u05e8", + "3153": "\u05d6", + "3154": "\u2581\u05dc", + "3155": "\u05e0\u05d9", + "3156": "\u2581\u05e9\u05dc", + "3157": "\u2581\u05d4\u05de", + "3158": "\u05df", + "3159": "\u05da", + "3160": "\u05de\u05d5", + "3161": "\u05d1\u05d9", + "3162": "\u05e0\u05d5", + "3163": "\u05d5\u05dc", + "3164": "\u05dc\u05d9", + "3165": "\u05d1\u05e8", + "3166": "\u05d3\u05d9", + "3167": "\u2581\u05d0\u05ea", + "3168": "\u2581\u05e2\u05dc", + "3169": "\u05e9\u05d9", + "3170": "\u05de\u05e9", + "3171": "\u05d5\u05df", + "3172": "\u05d9\u05e8", + "3173": "\u05e0\u05d4", + "3174": "\u05d9\u05ea", + "3175": "\u05e4\u05e8", + "3176": "\u05e3", + "3177": "\u05db\u05dc", + "3178": "\u2581\u05d4\u05d5\u05d0", + "3179": "\u05de\u05d9", + "3180": "\u05d5\u05d1", + "3181": "\u05e8\u05d5", + "3182": "\u2581\u05d1\u05de", + "3183": "\u05e4\u05d9", + "3184": "\u05d0\u05d9", + "3185": "\u2581\u05d4\u05e9", + "3186": "\u05d7\u05d9", + "3187": "\u05d7\u05d5", + "3188": "\u05dc\u05d5", + "3189": "\u05d1\u05e2", + "3190": "\u2581\u05d4\u05d0", + "3191": "\u05e7\u05e8", + "3192": "\u2581\u05dc\u05d0", + "3193": "\u05e0\u05d9\u05dd", + "3194": "\u05e1\u05d9", + "3195": "\u05e8\u05d9", + "3196": "\u2581\u05dc\u05d4", + "3197": "\u05e9\u05e8", + "3198": "\u05d5\u05d3", + "3199": "\u05d9\u05df", + "3200": "\u05d5\u05e4", + "3201": "\u05d0\u05dc", + "3202": "\u2581\u05d4\u05d7", + "3203": "\u05d3\u05e8", + "3204": "\u05e0\u05d5\u05ea", + "3205": "\u2581\u05d4\u05e2", + "3206": "\u05e8\u05d9\u05dd", + "3207": "\u05e4\u05d5", + "3208": "\u05e6\u05d9", + "3209": "\u2581\u05dc\u05de", + "3210": "\u05d0\u05e8", + "3211": "\u05d0\u05d5\u05ea", + "3212": "\u05d8\u05d9", + "3213": "\u2581\u05d4\u05e1", + "3214": "\u05d9\u05d5\u05ea", + "3215": "\u05db\u05d9", + "3216": "\u05e5", + "3217": "\u2581\u05d0\u05d5", + "3218": "\u2581\u05d5\u05d4", + "3219": "\u2581\u05d6\u05d4", + "3220": "\u2581\u05d4\u05d9\u05d0", + "3221": "\u2581\u05d4\u05e6", + "3222": "\u05de\u05e8", + "3223": "\u05e4\u05e2", + "3224": "\u2581\u05d4\u05e4", + "3225": "\u05db\u05df", + "3226": "\u2581\u05d4\u05d9\u05d4", + "3227": "\u05d8\u05e8", + "3228": "\u05d6\u05e8", + "3229": "\u2581\u05e9\u05e0", + "3230": "\u05d0\u05d7\u05e8", + "3231": "\u2581\u05e8\u05d1", + "3232": "\u2581\u05d6\u05d5", + "3233": "\u2581\u05d4\u05e8", + "3234": "\u05de\u05d9\u05dd", + "3235": "\u2581\u05d5\u05de", + "3236": "\u05e8\u05d0\u05e9", + "3237": "\u2581\u05dc\u05d0\u05d7\u05e8", + "3238": "\u05d7\u05dc\u05e7", + "3239": "\u05de\u05df", + "3240": "\u2581\u05d4\u05d9\u05d5", + "3241": "\u05de\u05e1\u05e4\u05e8", + "3242": "\u2581\u05d9\u05d5\u05ea\u05e8", + "3243": "\u05d0\u05d7\u05d3", + "3244": "\u2581\u05d4\u05d9\u05d9\u05ea", + "3245": "\u05e2\u05e6\u05de", + "3246": "\u05de\u05e7\u05d5\u05dd", + "3247": "", + "3248": "\u093e", + "3249": "\u0930", + "3250": "\u0928", + "3251": "\u0915", + "3252": "\u0938", + "3253": "\u0964", + "3254": "\u093f", + "3255": "\u092e", + "3256": "\u0940", + "3257": "\u0932", + "3258": "\u0947", + "3259": "\u092a", + "3260": "\u2581\u0939\u0948", + "3261": "\u094d", + "3262": "\u0939", + "3263": "\u091c", + "3264": "\u0935", + "3265": "\u0924", + "3266": "\u0902", + "3267": "\u091f", + "3268": "\u094b", + "3269": "\u0941", + "3270": "\u0917", + "3271": "\u2581\u0915\u0947", + "3272": "\u2581\u092c", + "3273": "\u2581\u092e\u0947\u0902", + "3274": "\u0936", + "3275": "\u0928\u0947", + "3276": "\u0942", + "3277": "\u092f", + "3278": "\u0928\u093e", + "3279": "\u0924\u093e", + "3280": "\u0926", + "3281": "\u091a", + "3282": "\u2581\u0906", + "3283": "\u092c", + "3284": "\u2581\u0915\u0930", + "3285": "\u2581\u0915\u0940", + "3286": "\u2581\u0905", + "3287": "\u0930\u094d", + "3288": "\u2581\u0939\u094b", + "3289": "\u2581\u0914\u0930", + "3290": "\u090f", + "3291": "\u0916", + "3292": "\u2581\u0924\u094b", + "3293": "\u2581\u0939\u0948\u0902", + "3294": "\u2581\u0938\u0947", + "3295": "\u2581\u0915\u093e", + "3296": "\u094b\u0902", + "3297": "\u2581\u0915\u094b", + "3298": "\u2581\u0915\u093f", + "3299": "\u0924\u0947", + "3300": "\u092b", + "3301": "\u0927", + "3302": "\u0930\u093e", + "3303": "\u0935\u093e", + "3304": "\u2581\u091c\u093e", + "3305": "\u0921", + "3306": "\u0948", + "3307": "\u2581\u0928\u0939\u0940\u0902", + "3308": "\u0909", + "3309": "\u094d\u092f", + "3310": "\u0908", + "3311": "\u2581\u092d\u0940", + "3312": "\u0915\u093e", + "3313": "\u2581\u0926", + "3314": "\u0921\u093c", + "3315": "\u0915\u0947", + "3316": "\u0930\u0940", + "3317": "\u0924\u0940", + "3318": "\u0907", + "3319": "\u2581\u090f\u0915", + "3320": "\u094d\u0930", + "3321": "\u2581\u0907\u0938", + "3322": "\u2581\u092a\u094d\u0930", + "3323": "\u2581\u0909\u0938", + "3324": "\u092f\u093e", + "3325": "\u2581\u092a\u0930", + "3326": "\u092e\u093e", + "3327": "\u092d", + "3328": "\u0947\u0902", + "3329": "\u0932\u0947", + "3330": "\u2581\u0935\u094b", + "3331": "\u0932\u093e", + "3332": "\u094c", + "3333": "\u0938\u0947", + "3334": "\u2581\u0939\u092e", + "3335": "\u2581\u091c\u094b", + "3336": "\u0915\u094d", + "3337": "\u0917\u093e", + "3338": "\u0923", + "3339": "\u2581\u0935\u093f", + "3340": "\u0939\u093e", + "3341": "\u0928\u0940", + "3342": "\u2581\u0906\u092a", + "3343": "\u093f\u092f\u093e", + "3344": "\u2581\u092e\u0948\u0902", + "3345": "\u0902\u0917", + "3346": "\u0938\u094d", + "3347": "\u2581\u0939\u0940", + "3348": "\u0925", + "3349": "\u0930\u0947", + "3350": "\u2581\u092a\u093e", + "3351": "\u093f\u0924", + "3352": "\u0949", + "3353": "\u092d\u093e", + "3354": "\u0938\u0940", + "3355": "\u0901", + "3356": "\u2581\u092f\u0947", + "3357": "\u0915\u094d\u0937", + "3358": "\u091b", + "3359": "\u2581\u0925\u093e", + "3360": "\u0924\u093f", + "3361": "\u2581\u0932\u093f\u090f", + "3362": "\u2581\u0926\u0947", + "3363": "\u0932\u0940", + "3364": "\u2581\u0915\u094d\u092f\u093e", + "3365": "\u2581\u0938\u0902", + "3366": "\u0937", + "3367": "\u2581\u092f\u0939", + "3368": "\u2581\u0939\u093e\u0901", + "3369": "\u0920", + "3370": "\u0924\u094d\u0930", + "3371": "\u0902\u0926", + "3372": "\u0918", + "3373": "\u2581\u092c\u0939\u0941\u0924", + "3374": "\u2581\u0938\u092e", + "3375": "\u094d\u092f\u093e", + "3376": "\u2581\u0932\u0917", + "3377": "\u2581\u0926\u094b", + "3378": "\u093c", + "3379": "\u2581\u0926\u0947\u0916", + "3380": "\u0913", + "3381": "\u0926\u093e", + "3382": "\u2581\u0928\u093f", + "3383": "\u0902\u0921", + "3384": "\u0926\u0940", + "3385": "\u2581\u0930\u0939\u0947", + "3386": "\u2581\u0932\u094b\u0917", + "3387": "\u2581\u092c\u093e\u0924", + "3388": "\u2581\u0915\u0941\u091b", + "3389": "\u093e\u0907", + "3390": "\u2581\u0905\u091a\u094d\u091b\u093e", + "3391": "\u2581\u0938\u0941", + "3392": "\u2581\u0938\u093e\u0925", + "3393": "\u2581\u0915\u0939\u093e", + "3394": "\u2581\u0915\u093f\u092f\u093e", + "3395": "\u0938\u094d\u091f", + "3396": "\u2581\u0938\u092c", + "3397": "\u0922\u093c", + "3398": "\u2581\u0930\u0939\u093e", + "3399": "\u2581\u0917\u092f\u093e", + "3400": "\u2581\u092b\u093f\u0930", + "3401": "\u2581\u092a\u0947", + "3402": "\u2581\u0905\u092c", + "3403": "\u0938\u094d\u0925", + "3404": "\u2581\u091c\u0940", + "3405": "\u2581\u091a\u0932", + "3406": "\u2581\u092c\u093e\u0930", + "3407": "\u2581\u0925\u0947", + "3408": "\u0938\u094d\u0924", + "3409": "\u2581\u0925\u0940", + "3410": "\u2581\u092e\u093f\u0932", + "3411": "\u2581\u0915\u094b\u0908", + "3412": "\u0943", + "3413": "\u2581\u092e\u0924\u0932\u092c", + "3414": "\u093f\u092f\u094b\u0902", + "3415": "\u2581\u0939\u0942\u0901", + "3416": "\u2581\u0905\u092d\u0940", + "3417": "\u0947\u0902\u0917\u0947", + "3418": "\u2581\u092c\u094b\u0932", + "3419": "\u091d", + "3420": "\u2581\u0930\u0939\u0940", + "3421": "\u091a\u093e\u0930", + "3422": "\u2581\u0905\u092a\u0928\u0947", + "3423": "\u2581\u092c\u093e\u0926", + "3424": "\u2581\u0932\u0947\u0915\u093f\u0928", + "3425": "\u0924\u094d\u0924", + "3426": "\u0910", + "3427": "\u2581\u092e\u0941\u091d\u0947", + "3428": "\u2581\u092e\u0947\u0930\u0947", + "3429": "\u0911", + "3430": "!", + "3431": "\u0906", + "3432": "\u090a", + "3433": "\u0922", + "3434": "\u091e", + "3435": "\u0905", + "3436": "\u0903", + "3437": "\u0914", + "3438": "\u090b", + "3439": "\u0945", + "3440": "\u0919", + "3441": "\u090d", + "3442": "\u0950", + "3443": "\u0960", + "3444": "\u0931", + "3445": "\u00cc", + "3446": "", + "3447": "\u2581\u3044", + "3448": "\u2581\u3002", + "3449": "\u2581\u3001", + "3450": "\u2581\u306e", + "3451": "\u2581\u3046", + "3452": "\u2581\u3093", + "3453": "\u2581\u306a", + "3454": "\u2581\u304b", + "3455": "\u2581\u3067", + "3456": "\u2581\u3063", + "3457": "\u2581\u3066", + "3458": "\u2581\u3042", + "3459": "\u2581\u305f", + "3460": "\u2581\u3068", + "3461": "\u2581\u3059", + "3462": "\u2581\u308b", + "3463": "\u2581\u306f", + "3464": "\u2581\u306b", + "3465": "\u2581\u3057", + "3466": "\u2581\u305d", + "3467": "\u2581\u3082", + "3468": "\u2581\u30fc", + "3469": "\u2581\u307e", + "3470": "\u2581\u304c", + "3471": "\u2581\u306d", + "3472": "\u2581\u3089", + "3473": "\u2581\u308c", + "3474": "\u2581\u3060", + "3475": "\u2581\u30f3", + "3476": "\u2581\u3053", + "3477": "\u2581\u3088", + "3478": "\u2581\u308a", + "3479": "\u2581\u3092", + "3480": "\u4ee5", + "3481": "\u4ed5", + "3482": "\u2581\u53cb", + "3483": "\u2581\u6771", + "3484": "\u2581\u9055", + "3485": "\u2581\u6587", + "3486": "\u2581\u30a1", + "3487": "\u2581\u30ce", + "3488": "\u2581\u6210", + "3489": "\u2581\u660e", + "3490": "\u2581\u4e16", + "3491": "\u2581\u5f37", + "3492": "\u2581\u66f2", + "3493": "\u2581\u8868", + "3494": "\u2581\u6708", + "3495": "\u2581\u60c5", + "3496": "\u2581\u6d3b", + "3497": "\u2581\u753a", + "3498": "\u2581\u4ed8", + "3499": "\u2581\u3075", + "3500": "\u2581\u3072", + "3501": "\u2581\u8cb7", + "3502": "\u2581\u9023", + "3503": "\u2581\u3080", + "3504": "\u2581\u533a", + "3505": "\u2581\u30da", + "3506": "\u2581\u78ba", + "3507": "\u2581\u6d41", + "3508": "\u2581\u671f", + "3509": "\u2581\u6d77", + "3510": "\u2581\u8a2d", + "3511": "\u2581\u8a9e", + "3512": "\u2581\u66f8", + "3513": "\u2581\u6599", + "3514": "\u2581\u8981", + "3515": "\u2581\u79d1", + "3516": "\u2581\u80b2", + "3517": "\u2581\u30b4", + "3518": "\u2581\u5b89", + "3519": "\u2581\u516d", + "3520": "\u2581\u6709", + "3521": "\u2581\u30b2", + "3522": "\u2581\u539f", + "3523": "\u2581\u80fd", + "3524": "\u2581\u58f2", + "3525": "\u2581\u611b", + "3526": "\u2581\u4eac", + "3527": "\u2581\u5236", + "3528": "\u2581\u30b6", + "3529": "\u2581\u826f", + "3530": "\u2581\u30ae", + "3531": "\u2581\u30e4", + "3532": "\u2581\u4e03", + "3533": "\u2581\u7121", + "3534": "\u2581\u8003", + "3535": "\u2581\u7279", + "3536": "\u2581\u767e", + "3537": "\u2581\u5c11", + "3538": "\u2581\u53c2", + "3539": "\u2581\u7537", + "3540": "\u2581\u4fdd", + "3541": "\u2581\u5712", + "3542": "\u666e", + "3543": "\u2581\u4ed6", + "3544": "\u2581\u30a9", + "3545": "\u2581\u6b21", + "3546": "\u2581\u512a", + "3547": "\u2581\u304e", + "3548": "\u2581\u8abf", + "3549": "\u2581\u6f14", + "3550": "\u2581\u53e3", + "3551": "\u2581\u98a8", + "3552": "\u2581\u9001", + "3553": "\u2581\u904b", + "3554": "\u2581\u99c5", + "3555": "\u2581\u5c40", + "3556": "\u53d7", + "3557": "\u2581\u7f6e", + "3558": "\u2581\u90fd", + "3559": "\u2581\u4fe1", + "3560": "\u2581\u7f8e", + "3561": "\u2581\u89aa", + "3562": "\u2581\u3005", + "3563": "\u2581\u96f6", + "3564": "\u2581\u5143", + "3565": "\u2581\u59cb", + "3566": "\u2581\u9078", + "3567": "\u2581\u5de5", + "3568": "\u2581\u754c", + "3569": "\u2581\u8eab", + "3570": "\u2581\u5e83", + "3571": "\u2581\u5411", + "3572": "\u2581\u7d44", + "3573": "\u2581\u5728", + "3574": "\u2581\u5354", + "3575": "\u2581\u6c34", + "3576": "\u2581\u5dde", + "3577": "\u2581\u4f11", + "3578": "\u2581\u548c", + "3579": "\u2581\u653e", + "3580": "\u2581\u69d8", + "3581": "\u2581\u7d42", + "3582": "\u2581\u969b", + "3583": "\u2581\u52a0", + "3584": "\u2581\u5357", + "3585": "\u2581\u5207", + "3586": "\u2581\u4e0d", + "3587": "\u2581\u50d5", + "3588": "\u2581\u4f8b", + "3589": "\u2581\u65e9", + "3590": "\u2581\u65cf", + "3591": "\u2581\u3047", + "3592": "\u2581\u7d4c", + "3593": "\u2581\u4f9b", + "3594": "\u2581\u5f62", + "3595": "\u2581\u767d", + "3596": "\u2581\u6728", + "3597": "\u2581\u7b49", + "3598": "\u2581\u5929", + "3599": "\u2581\u5229", + "3600": "\u2581\u73fe", + "3601": "\u2581\u5fdc", + "3602": "\u2581\u9928", + "3603": "\u2581\u5404", + "3604": "\u2581\u70b9", + "3605": "\u2581\u52d9", + "3606": "\u2581\u30f4", + "3607": "\u2581\u771f", + "3608": "\u2581\u6307", + "3609": "\u2581\u671d", + "3610": "\u2581\u97f3", + "3611": "\u2581\u4e88", + "3612": "\u2581\u5e73", + "3613": "\u2581\u984c", + "3614": "\u2581\u4f4f", + "3615": "\u2581\u5186", + "3616": "\u2581\u4f1d", + "3617": "\u2581\u56e3", + "3618": "\u2581\u6751", + "3619": "\u2581\u76f8", + "3620": "\u2581\u8853", + "3621": "\u2581\u5e30", + "3622": "\u2581\u53e4", + "3623": "\u2581\u8ab0", + "3624": "\u2581\u53ef", + "3625": "\u2581\u592b", + "3626": "\u2581\u5f7c", + "3627": "\u2581\u533b", + "3628": "\u2581\u7a7a", + "3629": "\u2581\u8cde", + "3630": "\u2581\u653f", + "3631": "\u2581\u4ea4", + "3632": "\u2581\u6c11", + "3633": "\u2581\u6c7a", + "3634": "\u5c02", + "3635": "\u2581\u7523", + "3636": "\u2581\u9650", + "3637": "\u2581\u795e", + "3638": "\u2581\u57fa", + "3639": "\u2581\u60aa", + "3640": "\u2581\u9662", + "3641": "\u2581\u7cfb", + "3642": "\u2581\u5f15", + "3643": "\u2581\u65c5", + "3644": "\u2581\u6280", + "3645": "\u2581\u53f0", + "3646": "\u2581\u52dd", + "3647": "\u2581\u3086", + "3648": "\u2581\u57df", + "3649": "\u2581\u518d", + "3650": "\u2581\u554f", + "3651": "\u2581\u8272", + "3652": "\u2581\u30d2", + "3653": "\u4f01", + "3654": "\u767b", + "3655": "\u7dcf", + "3656": "\u6539", + "3657": "\u63a2", + "3658": "\u7570", + "3659": "\u5b8c", + "3660": "\u4f0a", + "3661": "\u9811", + "3662": "\u6df1", + "3663": "\u7af6", + "3664": "\u63a8", + "3665": "\u8fb2", + "3666": "\u6628", + "3667": "\u639b", + "3668": "\u5bc4", + "3669": "\u4ed9", + "3670": "\u5371", + "3671": "\u8f9e", + "3672": "\u6f2b", + "3673": "\u57fc", + "3674": "\u8a73", + "3675": "\u5e7c", + "3676": "\u6271", + "3677": "\u4f59", + "3678": "\u63cf", + "3679": "\u63a1", + "3680": "\u88ab", + "3681": "\u30f6", + "3682": "\u4ff3", + "3683": "\u6803", + "3684": "\u56fa", + "3685": "\u526f", + "3686": "\u6df7", + "3687": "\u6551", + "3688": "\u7518", + "3689": "\u4e92", + "3690": "\u9589", + "3691": "\u75b2", + "3692": "\u4e9c", + "3693": "\u501f", + "3694": "\u690d", + "3695": "\u8cac", + "3696": "\u4eee", + "3697": "\u8da3", + "3698": "\u8f9b", + "3699": "\u8131", + "3700": "\u6050", + "3701": "\u6ce3", + "3702": "\u5951", + "3703": "\u60b2", + "3704": "\u96a0", + "3705": "\u662d", + "3706": "\u9ebb", + "3707": "\u54f2", + "3708": "\u5ba3", + "3709": "\u6c96", + "3710": "\u60a9", + "3711": "\u6d6e", + "3712": "\u8af8", + "3713": "\u5de8", + "3714": "\u5348", + "3715": "\u5360", + "3716": "\u8ddd", + "3717": "\u7e4b", + "3718": "\u6e0b", + "3719": "\u5fd9", + "3720": "\u6c5a", + "3721": "\u5ef6", + "3722": "\u5192", + "3723": "\u8a2a", + "3724": "\u6cbf", + "3725": "\u552f", + "3726": "\u6279", + "3727": "\u90f5", + "3728": "\u4f9d", + "3729": "\u63da", + "3730": "\u52c7", + "3731": "\u8a95", + "3732": "\u67d4", + "3733": "\u50be", + "3734": "\u5bc2", + "3735": "\u8a89", + "3736": "\u61f8", + "3737": "\u9ec4", + "3738": "\u90a6", + "3739": "\u81e8", + "3740": "\u5b09", + "3741": "\u7dba", + "3742": "\u5d29", + "3743": "\u8cfc", + "3744": "\u6d45", + "3745": "\u7e70", + "3746": "\u7dad", + "3747": "\u55ab", + "3748": "\u7a3c", + "3749": "\u71c3", + "3750": "\u65e2", + "3751": "\u8e0f", + "3752": "\u55a7", + "3753": "\u61a7", + "3754": "\u795d", + "3755": "\u6f01", + "3756": "\u8352", + "3757": "\u7dca", + "3758": "\u7372", + "3759": "\u98fe", + "3760": "\u70ad", + "3761": "\u642d", + "3762": "\u52aa", + "3763": "\u72d9", + "3764": "\u8a34", + "3765": "\u5bc5", + "3766": "\u9867", + "3767": "\u6311", + "3768": "\u61d0", + "3769": "\u72ed", + "3770": "\u96f0", + "3771": "\u62db", + "3772": "\u5857", + "3773": "\u6392", + "3774": "\u963f", + "3775": "\u596a", + "3776": "\u96c7", + "3777": "\u57cb", + "3778": "\u5c65", + "3779": "\u4fb5", + "3780": "\u61b2", + "3781": "\u8a72", + "3782": "\u786c", + "3783": "\u8caf", + "3784": "\u80f8", + "3785": "\u983b", + "3786": "\u52e7", + "3787": "\u9b45", + "3788": "\u5fe0", + "3789": "\u8328", + "3790": "\u6291", + "3791": "\u9a5a", + "3792": "\u75e9", + "3793": "\u5996", + "3794": "\u63c3", + "3795": "\u885d", + "3796": "\u54c0", + "3797": "\u829d", + "3798": "\u504f", + "3799": "\u5f27", + "3800": "\u4ef0", + "3801": "\u6f70", + "3802": "\u6dbc", + "3803": "\u8ae6", + "3804": "\u98fd", + "3805": "\u598a", + "3806": "\u633f", + "3807": "\u8010", + "3808": "\u8ce2", + "3809": "\u902e", + "3810": "\u62ab", + "3811": "\u6d69", + "3812": "\u900f", + "3813": "\u6328", + "3814": "\u4fc3", + "3815": "\u667a", + "3816": "\u507d", + "3817": "\u62d3", + "3818": "\u63a7", + "3819": "\u64a4", + "3820": "\u6f5c", + "3821": "\u6817", + "3822": "\u5553", + "3823": "\u7fa8", + "3824": "\u8d08", + "3825": "\u52b1", + "3826": "\u4f3a", + "3827": "\u5410", + "3828": "\u5faa", + "3829": "\u9700", + "3830": "\u6442", + "3831": "\u6dfb", + "3832": "\u7ffb", + "3833": "\u7761", + "3834": "\u5b64", + "3835": "\u7b20", + "3836": "\u6606", + "3837": "\u583a", + "3838": "\u6c88", + "3839": "\u4fd7", + "3840": "\u51fd", + "3841": "\u5302", + "3842": "\u906d", + "3843": "\u6eb6", + "3844": "\u52f2", + "3845": "\u7f70", + "3846": "\u8a87", + "3847": "\u659c", + "3848": "\u935b", + "3849": "\u8cb0", + "3850": "\u7de9", + "3851": "\u62bd", + "3852": "\u7652", + "3853": "\u53e9", + "3854": "\u4f46", + "3855": "\u683d", + "3856": "\u8cbf", + "3857": "\u8107", + "3858": "\u5036", + "3859": "\u9022", + "3860": "\u5949", + "3861": "\u662f", + "3862": "\u8912", + "3863": "\u9271", + "3864": "\u8cbc", + "3865": "\u4f73", + "3866": "\u75be", + "3867": "\u5e61", + "3868": "\u67b6", + "3869": "\u546a", + "3870": "\u4e32", + "3871": "\u5e7e", + "3872": "\u6c99", + "3873": "\u62d2", + "3874": "\u8105", + "3875": "\u8b21", + "3876": "\u631f", + "3877": "\u62cd", + "3878": "\u938c", + "3879": "\u80c3", + "3880": "\u99b4", + "3881": "\u9077", + "3882": "\u5197", + "3883": "\u7b51", + "3884": "\u6f2c", + "3885": "\u6068", + "3886": "\u6bb4", + "3887": "\u66c7", + "3888": "\u7fcc", + "3889": "\u8ecc", + "3890": "\u5378", + "3891": "\u6b53", + "3892": "\u6de1", + "3893": "\u6f0f", + "3894": "\u8986", + "3895": "\u72e9", + "3896": "\u755c", + "3897": "\u84b8", + "3898": "\u854e", + "3899": "\u6cf0", + "3900": "\u7d1b", + "3901": "\u7d5e", + "3902": "\u8a50", + "3903": "\u6905", + "3904": "\u6052", + "3905": "\u5132", + "3906": "\u64ec", + "3907": "\u53d4", + "3908": "\u53ec", + "3909": "\u5e7d", + "3910": "\u80ba", + "3911": "\u7b87", + "3912": "\u80a5", + "3913": "\u758e", + "3914": "\u9676", + "3915": "\u65e8", + "3916": "\u90b8", + "3917": "\u5449", + "3918": "\u51c6", + "3919": "\u8df3", + "3920": "\u757f", + "3921": "\u5ef7", + "3922": "\u920d", + "3923": "\u6e9c", + "3924": "\u6170", + "3925": "\u72a0", + "3926": "\u7e4a", + "3927": "\u82b3", + "3928": "\u7272", + "3929": "\u773a", + "3930": "\u90ca", + "3931": "\u618e", + "3932": "\u514b", + "3933": "\u731b", + "3934": "\u63aa", + "3935": "\u9a19", + "3936": "\u6d78", + "3937": "\u6148", + "3938": "\u52a3", + "3939": "\u93ae", + "3940": "\u8650", + "3941": "\u8e74", + "3942": "\u82d7", + "3943": "\u9665", + "3944": "\u5f90", + "3945": "\u62ed", + "3946": "\u58cc", + "3947": "\u614c", + "3948": "\u6349", + "3949": "\u819c", + "3950": "\u508d", + "3951": "\u565b", + "3952": "\u819a", + "3953": "\u6f20", + "3954": "\u606d", + "3955": "\u81a8", + "3956": "\u6a3d", + "3957": "\u820c", + "3958": "\u611a", + "3959": "\u7881", + "3960": "\u82a6", + "3961": "\u5eca", + "3962": "\u5674", + "3963": "\u7f8a", + "3964": "\u85ab", + "3965": "\u7be0", + "3966": "\u59a5", + "3967": "\u78ef", + "3968": "\u6851", + "3969": "\u7092", + "3970": "\u62d8", + "3971": "\u690e", + "3972": "\u7c98", + "3973": "\u5208", + "3974": "\u8061", + "3975": "\u537f", + "3976": "\u80e1", + "3977": "\u5d07", + "3978": "\u84b2", + "3979": "\u5270", + "3980": "\u745e", + "3981": "\u6e13", + "3982": "\u8ced", + "3983": "\u6e67", + "3984": "\u70f9", + "3985": "\u51dd", + "3986": "\u7d3a", + "3987": "\u9038", + "3988": "\u7261", + "3989": "\u58a8", + "3990": "\u840c", + "3991": "\u622f", + "3992": "\u8429", + "3993": "\u79e9", + "3994": "\u6367", + "3995": "\u69fb", + "3996": "\u8154", + "3997": "\u8776", + "3998": "\u8d05", + "3999": "\u7a4f", + "4000": "\u6562", + "4001": "\u64c1", + "4002": "\u8d74", + "4003": "\u78d0", + "4004": "\u58ee", + "4005": "\u8a93", + "4006": "\u62b9", + "4007": "\u6ea2", + "4008": "\u53f1", + "4009": "\u53f6", + "4010": "\u59a8", + "4011": "\u6cb8", + "4012": "\u7d33", + "4013": "\u963b", + "4014": "\u5984", + "4015": "\u6590", + "4016": "\u5983", + "4017": "\u5de7", + "4018": "\u540a", + "4019": "\u60da", + "4020": "\u8236", + "4021": "\u52ff", + "4022": "\u61c7", + "4023": "\u7525", + "4024": "\u60dc", + "4025": "\u7b39", + "4026": "\u6b86", + "4027": "\u6fe1", + "4028": "\u60e3", + "4029": "\u6020", + "4030": "\u6dc0", + "4031": "\u5265", + "4032": "\u66d6", + "4033": "\u6b64", + "4034": "\u85dd", + "4035": "\u8fb0", + "4036": "\u632b", + "4037": "\u66ab", + "4038": "\u6155", + "4039": "\u78a7", + "4040": "\u5634", + "4041": "\u3062", + "4042": "\u6d2a", + "4043": "\u865c", + "4044": "\u9065", + "4045": "\u92ed", + "4046": "\u5a2f", + "4047": "\u814e", + "4048": "\u871c", + "4049": "\u8a02", + "4050": "\u74e6", + "4051": "\u5944", + "4052": "\u64ad", + "4053": "\u75d5", + "4054": "\u7db4", + "4055": "\u7a40", + "4056": "\u9699", + "4057": "\u5384", + "4058": "\u5448", + "4059": "\u66f0", + "4060": "\u5d16", + "4061": "\u64e6", + "4062": "\u70cf", + "4063": "\u62c9", + "4064": "\u8861", + "4065": "\u6731", + "4066": "\u5606", + "4067": "\u8339", + "4068": "\u5cef", + "4069": "\u6ff1", + "4070": "\u84bc", + "4071": "\u30f1", + "4072": "\u6d12", + "4073": "\u85a9", + "4074": "\u8acf", + "4075": "\u55c5", + "4076": "\u689d", + "4077": "\u8096", + "4078": "\u785d", + "4079": "\u8a63", + "4080": "\u8cd1", + "4081": "\u67a2", + "4082": "\u6e9d", + "4083": "\u7a00", + "4084": "\u6a58", + "4085": "\u7766", + "4086": "\u9673", + "4087": "\u91e7", + "4088": "\u91b8", + "4089": "\u55aa", + "4090": "\u67af", + "4091": "\u6881", + "4092": "\u86cd", + "4093": "\u7ce7", + "4094": "\u90ed", + "4095": "\u7058", + "4096": "\u723d", + "4097": "\u7c97", + "4098": "\u8702", + "4099": "\u636e", + "4100": "\u5112", + "4101": "\u80a1", + "4102": "\u978d", + "4103": "\u61f2", + "4104": "\u5b54", + "4105": "\u6f06", + "4106": "\u8499", + "4107": "\u693f", + "4108": "\u7345", + "4109": "\u73c8", + "4110": "\u7554", + "4111": "\u9a28", + "4112": "\u675c", + "4113": "\u7984", + "4114": "\u52c3", + "4115": "\u9ac4", + "4116": "\u5f0a", + "4117": "\u77ef", + "4118": "\u9df2", + "4119": "\u58ec", + "4120": "\u6666", + "4121": "\u6e15", + "4122": "\u85cd", + "4123": "\u533f", + "4124": "\u582a", + "4125": "\u7aaa", + "4126": "\u5289", + "4127": "\u6182", + "4128": "\u5091", + "4129": "\u63b4", + "4130": "\u540e", + "4131": "\u916a", + "4132": "\u5176", + "4133": "\u82eb", + "4134": "\u30c5", + "4135": "\u63c9", + "4136": "\u73a9", + "4137": "\u80f4", + "4138": "\u8910", + "4139": "\u8afe", + "4140": "\u5598", + "4141": "\u559a", + "4142": "\u8594", + "4143": "\u8cc4", + "4144": "\u7fe0", + "4145": "\u5023", + "4146": "\u576a", + "4147": "\u6109", + "4148": "\u6276", + "4149": "\u670b", + "4150": "\u5351", + "4151": "\u66fe", + "4152": "\u786b", + "4153": "\u51a8", + "4154": "\u5b78", + "4155": "\u6c7d", + "4156": "\u837b", + "4157": "\u8461", + "4158": "\u6eba", + "4159": "\u8fbf", + "4160": "\u4e91", + "4161": "\u5fcc", + "4162": "\u7815", + "4163": "\u6734", + "4164": "\u6a8e", + "4165": "\u9320", + "4166": "\u5e63", + "4167": "\u80af", + "4168": "\u81b5", + "4169": "\u52c5", + "4170": "\u65bc", + "4171": "\u7947", + "4172": "\u8304", + "4173": "\u6591", + "4174": "\u50c5", + "4175": "\u8a60", + "4176": "\u96bc", + "4177": "\u98e2", + "4178": "\u7a3d", + "4179": "\u5dba", + "4180": "\u6df5", + "4181": "\u8b83", + "4182": "\u7aae", + "4183": "\u7be4", + "4184": "\u97fb", + "4185": "\u6897", + "4186": "\u72f8", + "4187": "\u69cd", + "4188": "\u8b17", + "4189": "\u8ab9", + "4190": "\u9010", + "4191": "\u53d9", + "4192": "\u5420", + "4193": "\u725f", + "4194": "\u9838", + "4195": "\u52fe", + "4196": "\u717d", + "4197": "\u7460", + "4198": "\u4fb6", + "4199": "\u68b6", + "4200": "\u8997", + "4201": "\u95a4", + "4202": "\u51a5", + "4203": "\u5dfe", + "4204": "\u5f04", + "4205": "\u83e9", + "4206": "\u8526", + "4207": "\u99a8", + "4208": "\u6fc1", + "4209": "\u714e", + "4210": "\u8218", + "4211": "\u6876", + "4212": "\u79e6", + "4213": "\u9061", + "4214": "\u5806", + "4215": "\u6afb", + "4216": "\u6e07", + "4217": "\u77ad", + "4218": "\u81c6", + "4219": "\u4fe3", + "4220": "\u7169", + "4221": "\u54b3", + "4222": "\u5506", + "4223": "\u60f9", + "4224": "\u6775", + "4225": "\u7c9f", + "4226": "\u9091", + "4227": "\u553e", + "4228": "\u6756", + "4229": "\u6960", + "4230": "\u6b6a", + "4231": "\u711a", + "4232": "\u8fb1", + "4233": "\u559d", + "4234": "\u6e58", + "4235": "\u76f2", + "4236": "\u8b39", + "4237": "\u8e2a", + "4238": "\u965b", + "4239": "\u589f", + "4240": "\u64b0", + "4241": "\u6ccc", + "4242": "\u6f15", + "4243": "\u8a6b", + "4244": "\u771e", + "4245": "\u90c1", + "4246": "\u6e1a", + "4247": "\u8210", + "4248": "\u8235", + "4249": "\u8e8a", + "4250": "\u58f9", + "4251": "\u5c6f", + "4252": "\u7435", + "4253": "\u7436", + "4254": "\u7a92", + "4255": "\u82af", + "4256": "\u8e87", + "4257": "\u4e1e", + "4258": "\u7262", + "4259": "\u8305", + "4260": "\u5f57", + "4261": "\u699b", + "4262": "\u7b95", + "4263": "\u82ad", + "4264": "\u918d", + "4265": "\u9190", + "4266": "\u9945", + "4267": "\u5815", + "4268": "\u5deb", + "4269": "\u6a9c", + "4270": "\u914c", + "4271": "\u96eb", + "4272": "\u6b3e", + "4273": "\u9d3b", + "4274": "\u4f10", + "4275": "\u7901", + "4276": "\u7a83", + "4277": "\u8389", + "4278": "\u929a", + "4279": "\u6191", + "4280": "\u639f", + "4281": "\u6492", + "4282": "\u6a0b", + "4283": "\u7336", + "4284": "\u868a", + "4285": "\u88fe", + "4286": "\u96cc", + "4287": "\u6216", + "4288": "\u643e", + "4289": "\u6cc4", + "4290": "\u7109", + "4291": "\u7940", + "4292": "\u7b8b", + "4293": "\u919c", + "4294": "\u9d5c", + "4295": "\u51f1", + "4296": "\u5c16", + "4297": "\u6c23", + "4298": "\u75d2", + "4299": "\u830e", + "4300": "\u745b", + "4301": "\u602f", + "4302": "\u698e", + "4303": "\u6feb", + "4304": "\u7099", + "4305": "\u97ad", + "4306": "\u9b4f", + "4307": "\u4f5b", + "4308": "\u51b6", + "4309": "\u55dc", + "4310": "\u5750", + "4311": "\u6144", + "4312": "\u61c9", + "4313": "\u6c50", + "4314": "\u73c2", + "4315": "\u8fc5", + "4316": "\u62f7", + "4317": "\u9019", + "4318": "\u5ae1", + "4319": "\u60bc", + "4320": "\u637b", + "4321": "\u6a3a", + "4322": "\u85c1", + "4323": "\u932c", + "4324": "\u50ad", + "4325": "\u5243", + "4326": "\u5d4c", + "4327": "\u727d", + "4328": "\u937c", + "4329": "\u4fae", + "4330": "\u5f59", + "4331": "\u6bec", + "4332": "\u4ea8", + "4333": "\u4f86", + "4334": "\u5b8d", + "4335": "\u8a1b", + "4336": "\u9ab8", + "4337": "\u4ec7", + "4338": "\u5df4", + "4339": "\u6c3e", + "4340": "\u71e6", + "4341": "\u783a", + "4342": "\u79df", + "4343": "\u8549", + "4344": "\u5614", + "4345": "\u6703", + "4346": "\u67da", + "4347": "\u69cc", + "4348": "\u83ab", + "4349": "\u88d4", + "4350": "\u91d8", + "4351": "\u51a4", + "4352": "\u51b4", + "4353": "\u64ab", + "4354": "\u8d0b", + "4355": "\u30f5", + "4356": "\u4e9b", + "4357": "\u4f43", + "4358": "\u72d0", + "4359": "\u56a2", + "4360": "\u92f3", + "4361": "\u5dbd", + "4362": "\u9f4b", + "4363": "\u51f9", + "4364": "\u54fa", + "4365": "\u57f4", + "4366": "\u65fa", + "4367": "\u86cb", + "4368": "\u8cdc", + "4369": "\u4f0d", + "4370": "\u545f", + "4371": "\u5937", + "4372": "\u5dbc", + "4373": "\u6c4e", + "4374": "\u9739", + "4375": "\u5875", + "4376": "\u6101", + "4377": "\u8106", + "4378": "\u97ee", + "4379": "\u540f", + "4380": "\u5957", + "4381": "\u5993", + "4382": "\u68b1", + "4383": "\u6d1b", + "4384": "\u6f31", + "4385": "\u725d", + "4386": "\u798d", + "4387": "\u7d21", + "4388": "\u810a", + "4389": "\u8cd3", + "4390": "\u586b", + "4391": "\u673d", + "4392": "\u6a2b", + "4393": "\u8299", + "4394": "\u84c9", + "4395": "\u9310", + "4396": "\u5835", + "4397": "\u5f14", + "4398": "\u633d", + "4399": "\u6955", + "4400": "\u6c72", + "4401": "\u5294", + "4402": "\u5eb8", + "4403": "\u694a", + "4404": "\u7826", + "4405": "\u9c57", + "4406": "\u61a4", + "4407": "\u634f", + "4408": "\u6d29", + "4409": "\u723e", + "4410": "\u750d", + "4411": "\u817f", + "4412": "\u9c52", + "4413": "\u685f", + "4414": "\u6a7f", + "4415": "\u82db", + "4416": "\u982c", + "4417": "\u55da", + "4418": "\u5751", + "4419": "\u5b75", + "4420": "\u5e87", + "4421": "\u68a2", + "4422": "\u6b05", + "4423": "\u7560", + "4424": "\u7a7f", + "4425": "\u8513", + "4426": "\u8d99", + "4427": "\u927e", + "4428": "\u4f51", + "4429": "\u5dcc", + "4430": "\u5f77", + "4431": "\u65a7", + "4432": "\u68d8", + "4433": "\u6dd8", + "4434": "\u7b94", + "4435": "\u7d2f", + "4436": "\u8729", + "4437": "\u908a", + "4438": "\u9ebf", + "4439": "\u5713", + "4440": "\u66a2", + "4441": "\u69ae", + "4442": "\u6b89", + "4443": "\u6d8c", + "4444": "\u7aea", + "4445": "\u8aee", + "4446": "\u96db", + "4447": "\u9bf5", + "4448": "\u4e14", + "4449": "\u5347", + "4450": "\u5954", + "4451": "\u5ce8", + "4452": "\u7149", + "4453": "\u7791", + "4454": "\u8276", + "4455": "\u840e", + "4456": "\u8568", + "4457": "\u85aa", + "4458": "\u8f0c", + "4459": "\u5dfd", + "4460": "\u66f3", + "4461": "\u6cab", + "4462": "\u82a5", + "4463": "\u8511", + "4464": "\u93a7", + "4465": "\u9f20", + "4466": "\u51ea", + "4467": "\u5c51", + "4468": "\u5d14", + "4469": "\u5d6f", + "4470": "\u6a59", + "4471": "\u6e38", + "4472": "\u7a1c", + "4473": "\u8072", + "4474": "\u511a", + "4475": "\u695a", + "4476": "\u8006", + "4477": "\u82b9", + "4478": "\u83d6", + "4479": "\u88f3", + "4480": "\u9017", + "4481": "\u905c", + "4482": "\u9640", + "4483": "\u4ff8", + "4484": "\u5a29", + "4485": "\u5cd9", + "4486": "\u6190", + "4487": "\u6241", + "4488": "\u626e", + "4489": "\u6faa", + "4490": "\u7729", + "4491": "\u7f75", + "4492": "\u8036", + "4493": "\u8058", + "4494": "\u9c3b", + "4495": "\u309d", + "4496": "\u56c3", + "4497": "\u5f7f", + "4498": "\u6167", + "4499": "\u66dd", + "4500": "\u6fe0", + "4501": "\u8309", + "4502": "\u976d", + "4503": "\u9daf", + "4504": "\u9e92", + "4505": "\u30f0", + "4506": "\u4e5e", + "4507": "\u50b2", + "4508": "\u54e8", + "4509": "\u5f6c", + "4510": "\u73c0", + "4511": "\u79e4", + "4512": "\u84ec", + "4513": "\u8ebe", + "4514": "\u9075", + "4515": "\u51f8", + "4516": "\u53a9", + "4517": "\u6168", + "4518": "\u698a", + "4519": "\u6c8c", + "4520": "\u75b1", + "4521": "\u8fe6", + "4522": "\u53e1", + "4523": "\u543b", + "4524": "\u5b2c", + "4525": "\u5d69", + "4526": "\u660f", + "4527": "\u8171", + "4528": "\u8888", + "4529": "\u9c39", + "4530": "\u57e0", + "4531": "\u5be1", + "4532": "\u5cfb", + "4533": "\u5df7", + "4534": "\u62d7", + "4535": "\u62d9", + "4536": "\u63c4", + "4537": "\u63f6", + "4538": "\u65a1", + "4539": "\u6962", + "4540": "\u6dcb", + "4541": "\u722c", + "4542": "\u7425", + "4543": "\u805a", + "4544": "\u80da", + "4545": "\u81a0", + "4546": "\u8292", + "4547": "\u8703", + "4548": "\u87ba", + "4549": "\u9910", + "4550": "\u9dfa", + "4551": "\u51e0", + "4552": "\u52ab", + "4553": "\u5321", + "4554": "\u63d6", + "4555": "\u6b3d", + "4556": "\u7422", + "4557": "\u7825", + "4558": "\u877f", + "4559": "\u8adc", + "4560": "\u8ae7", + "4561": "\u8dbe", + "4562": "\u50d1", + "4563": "\u5a9a", + "4564": "\u5b5f", + "4565": "\u5b95", + "4566": "\u5bd3", + "4567": "\u5f8a", + "4568": "\u5f98", + "4569": "\u6357", + "4570": "\u66d9", + "4571": "\u7e82", + "4572": "\u7fc1", + "4573": "\u81bf", + "4574": "\u85ea", + "4575": "\u8a0a", + "4576": "\u8fc2", + "4577": "\u932b", + "4578": "\u4fef", + "4579": "\u5a3c", + "4580": "\u689f", + "4581": "\u6e3e", + "4582": "\u6ffe", + "4583": "\u79bf", + "4584": "\u7ce0", + "4585": "\u8180", + "4586": "\u82c5", + "4587": "\u8877", + "4588": "\u8c79", + "4589": "\u9798", + "4590": "\u9eb9", + "4591": "\u9ece", + "4592": "\u6abb", + "4593": "\u6e25", + "4594": "\u9149", + "4595": "\u97a0", + "4596": "\u567a", + "4597": "\u60f0", + "4598": "\u646f", + "4599": "\u65db", + "4600": "\u6bc0", + "4601": "\u6d38", + "4602": "\u6dd1", + "4603": "\u71fb", + "4604": "\u77b0", + "4605": "\u7ac8", + "4606": "\u7cfe", + "4607": "\u86d9", + "4608": "\u8e44", + "4609": "\u502d", + "4610": "\u536f", + "4611": "\u56c1", + "4612": "\u5830", + "4613": "\u6652", + "4614": "\u6a13", + "4615": "\u72db", + "4616": "\u84fc", + "4617": "\u86db", + "4618": "\u8718", + "4619": "\u8b33", + "4620": "\u52be", + "4621": "\u5403", + "4622": "\u5484", + "4623": "\u5631", + "4624": "\u6070", + "4625": "\u60b6", + "4626": "\u69c7", + "4627": "\u7325", + "4628": "\u7396", + "4629": "\u792b", + "4630": "\u7977", + "4631": "\u7ad9", + "4632": "\u7ae3", + "4633": "\u7d68", + "4634": "\u7e1e", + "4635": "\u966a", + "4636": "\u4e58", + "4637": "\u53e2", + "4638": "\u5c39", + "4639": "\u61be", + "4640": "\u62ee", + "4641": "\u633a", + "4642": "\u6582", + "4643": "\u6714", + "4644": "\u701e", + "4645": "\u7587", + "4646": "\u77a5", + "4647": "\u7a63", + "4648": "\u7f79", + "4649": "\u8aeb", + "4650": "\u9013", + "4651": "\u96f9", + "4652": "\u981a", + "4653": "\u4f3d", + "4654": "\u5eff", + "4655": "\u60df", + "4656": "\u63bb", + "4657": "\u6523", + "4658": "\u6bb2", + "4659": "\u6c5d", + "4660": "\u6d59", + "4661": "\u806f", + "4662": "\u8a54", + "4663": "\u96bb", + "4664": "\u9801", + "4665": "\u9913", + "4666": "\u50b3", + "4667": "\u51b2", + "4668": "\u65a5", + "4669": "\u7e3d", + "4670": "\u8151", + "4671": "\u92f8", + "4672": "\u9695", + "4673": "\u9812", + "4674": "\u9837", + "4675": "\u4ec0", + "4676": "\u54ed", + "4677": "\u5718", + "4678": "\u5851", + "4679": "\u59e6", + "4680": "\u5bf5", + "4681": "\u615f", + "4682": "\u6b12", + "4683": "\u7953", + "4684": "\u79bd", + "4685": "\u7c50", + "4686": "\u8695", + "4687": "\u8ce6", + "4688": "\u8f62", + "4689": "\u912d", + "4690": "\u92d2", + "4691": "\u985b", + "4692": "\u9c48", + "4693": "\u4e11", + "4694": "\u5b30", + "4695": "\u5ba6", + "4696": "\u5be6", + "4697": "\u5c4d", + "4698": "\u67e9", + "4699": "\u6d9b", + "4700": "\u7473", + "4701": "\u75bc", + "4702": "\u7aa9", + "4703": "\u7dfb", + "4704": "\u811b", + "4705": "\u936c", + "4706": "\u4eab", + "4707": "\u53ad", + "4708": "\u54bd", + "4709": "\u5632", + "4710": "\u6a05", + "4711": "\u71ed", + "4712": "\u75d9", + "4713": "\u7624", + "4714": "\u7e23", + "4715": "\u808b", + "4716": "\u809b", + "4717": "\u8654", + "4718": "\u895f", + "4719": "\u9583", + "4720": "\u9b6f", + "4721": "\u55a9", + "4722": "\u55fd", + "4723": "\u56a5", + "4724": "\u58d5", + "4725": "\u601c", + "4726": "\u634c", + "4727": "\u7b4f", + "4728": "\u7baa", + "4729": "\u7e6d", + "4730": "\u85cf", + "4731": "\u86fe", + "4732": "\u8a03", + "4733": "\u8caa", + "4734": "\u98af", + "4735": "\u531d", + "4736": "\u5480", + "4737": "\u548e", + "4738": "\u56bc", + "4739": "\u5c53", + "4740": "\u5e9a", + "4741": "\u6115", + "4742": "\u6ef8", + "4743": "\u707c", + "4744": "\u7b25", + "4745": "\u8700", + "4746": "\u8a36", + "4747": "\u8a85", + "4748": "\u8d14", + "4749": "\u91ac", + "4750": "\u9c10", + "4751": "\u4fc4", + "4752": "\u5026", + "4753": "\u5039", + "4754": "\u5239", + "4755": "\u5699", + "4756": "\u5859", + "4757": "\u685d", + "4758": "\u6adb", + "4759": "\u7119", + "4760": "\u76e7", + "4761": "\u7ac4", + "4762": "\u7d18", + "4763": "\u7d62", + "4764": "\u83f0", + "4765": "\u8466", + "4766": "\u849c", + "4767": "\u8541", + "4768": "\u8599", + "4769": "\u8606", + "4770": "\u8b01", + "4771": "\u8fa3", + "4772": "\u9761", + "4773": "\u99d5", + "4774": "\u9d0e", + "4775": "\u4ec4", + "4776": "\u4f98", + "4777": "\u5016", + "4778": "\u5080", + "4779": "\u50fb", + "4780": "\u5121", + "4781": "\u524b", + "4782": "\u5f45", + "4783": "\u6802", + "4784": "\u6854", + "4785": "\u68b5", + "4786": "\u6ef2", + "4787": "\u6fb3", + "4788": "\u6fe4", + "4789": "\u7368", + "4790": "\u7577", + "4791": "\u75d4", + "4792": "\u7626", + "4793": "\u7960", + "4794": "\u79b0", + "4795": "\u81a3", + "4796": "\u834f", + "4797": "\u8944", + "4798": "\u8a25", + "4799": "\u8de8", + "4800": "\u8e93", + "4801": "\u90b1", + "4802": "\u9264", + "4803": "\u93d1", + "4804": "\u95ca", + "4805": "\u96c9", + "4806": "\u9d6c", + "4807": "\u53db", + "4808": "\u543c", + "4809": "\u59d0", + "4810": "\u5f4c", + "4811": "\u66fc", + "4812": "\u6c83", + "4813": "\u6f23", + "4814": "\u6f38", + "4815": "\u700b", + "4816": "\u721b", + "4817": "\u7690", + "4818": "\u7c3e", + "4819": "\u7fe1", + "4820": "\u82d3", + "4821": "\u839e", + "4822": "\u84d1", + "4823": "\u857e", + "4824": "\u874b", + "4825": "\u8766", + "4826": "\u892a", + "4827": "\u9119", + "4828": "\u914b", + "4829": "\u92e4", + "4830": "\u937e", + "4831": "\u9435", + "4832": "\u5191", + "4833": "\u557c", + "4834": "\u5617", + "4835": "\u5c4f", + "4836": "\u65af", + "4837": "\u6900", + "4838": "\u6e20", + "4839": "\u71be", + "4840": "\u7280", + "4841": "\u76ba", + "4842": "\u7768", + "4843": "\u78cb", + "4844": "\u7b67", + "4845": "\u7cca", + "4846": "\u837c", + "4847": "\u83b1", + "4848": "\u8fa8", + "4849": "\u901e", + "4850": "\u9081", + "4851": "\u936e", + "4852": "\u968b", + "4853": "\u9786", + "4854": "\u978b", + "4855": "\u4e56", + "4856": "\u55df", + "4857": "\u5700", + "4858": "\u5fd6", + "4859": "\u60e0", + "4860": "\u61ba", + "4861": "\u6518", + "4862": "\u6727", + "4863": "\u675e", + "4864": "\u69d9", + "4865": "\u6b98", + "4866": "\u6deb", + "4867": "\u7015", + "4868": "\u70b8", + "4869": "\u71d0", + "4870": "\u7b50", + "4871": "\u7ff3", + "4872": "\u813e", + "4873": "\u81c0", + "4874": "\u8b49", + "4875": "\u9318", + "4876": "\u9d2c", + "4877": "\u308e", + "4878": "\u4e8e", + "4879": "\u5055", + "4880": "\u54ac", + "4881": "\u5516", + "4882": "\u555c", + "4883": "\u5703", + "4884": "\u58fd", + "4885": "\u59da", + "4886": "\u59e5", + "4887": "\u5a49", + "4888": "\u5b0c", + "4889": "\u5b55", + "4890": "\u5c60", + "4891": "\u5cb1", + "4892": "\u5ed3", + "4893": "\u61ab", + "4894": "\u621f", + "4895": "\u6309", + "4896": "\u637a", + "4897": "\u6853", + "4898": "\u6939", + "4899": "\u6977", + "4900": "\u6ac2", + "4901": "\u704c", + "4902": "\u71d7", + "4903": "\u7526", + "4904": "\u788d", + "4905": "\u795f", + "4906": "\u79ae", + "4907": "\u7a79", + "4908": "\u7b4d", + "4909": "\u7c17", + "4910": "\u814b", + "4911": "\u832b", + "4912": "\u8494", + "4913": "\u8afa", + "4914": "\u8cb6", + "4915": "\u9059", + "4916": "\u9211", + "4917": "\u9328", + "4918": "\u9771", + "4919": "\u98c4", + "4920": "\u9af7", + "4921": "\u9d60", + "4922": "\u9f0e", + "4923": "\u4ea6", + "4924": "\u4f47", + "4925": "\u5072", + "4926": "\u526a", + "4927": "\u5271", + "4928": "\u57d2", + "4929": "\u59f6", + "4930": "\u5c0d", + "4931": "\u5e47", + "4932": "\u5fbd", + "4933": "\u606b", + "4934": "\u652b", + "4935": "\u6b78", + "4936": "\u72e1", + "4937": "\u77bc", + "4938": "\u786f", + "4939": "\u7afa", + "4940": "\u7b0f", + "4941": "\u7bdd", + "4942": "\u7c00", + "4943": "\u7c7e", + "4944": "\u7f6b", + "4945": "\u807e", + "4946": "\u8139", + "4947": "\u8521", + "4948": "\u8557", + "4949": "\u876e", + "4950": "\u8cfd", + "4951": "\u8d16", + "4952": "\u8fad", + "4953": "\u92ea", + "4954": "\u9b93", + "4955": "\u9c2f", + "4956": "\u9c3a", + "4957": "\u4e24", + "4958": "\u4e4e", + "4959": "\u5118", + "4960": "\u530d", + "4961": "\u5310", + "4962": "\u5686", + "4963": "\u5f1b", + "4964": "\u5fa8", + "4965": "\u60e1", + "4966": "\u619a", + "4967": "\u6698", + "4968": "\u68c9", + "4969": "\u6a02", + "4970": "\u6bb7", + "4971": "\u6beb", + "4972": "\u6c40", + "4973": "\u70d9", + "4974": "\u72c4", + "4975": "\u73ea", + "4976": "\u7433", + "4977": "\u74e3", + "4978": "\u7b8f", + "4979": "\u7e5a", + "4980": "\u8207", + "4981": "\u822b", + "4982": "\u8237", + "4983": "\u8317", + "4984": "\u849f", + "4985": "\u84bb", + "4986": "\u86ed", + "4987": "\u88a2", + "4988": "\u8956", + "4989": "\u8966", + "4990": "\u8cf4", + "4991": "\u8d04", + "4992": "\u8e59", + "4993": "\u8f4d", + "4994": "\u8f9f", + "4995": "\u8faf", + "4996": "\u9182", + "4997": "\u9187", + "4998": "\u947d", + "4999": "\u9846", + "5000": "\u9870", + "5001": "\u9c2d", + "5002": "\u51f0", + "5003": "\u5475", + "5004": "\u566a", + "5005": "\u5bf6", + "5006": "\u61fa", + "5007": "\u6372", + "5008": "\u63a0", + "5009": "\u69b4", + "5010": "\u71df", + "5011": "\u7370", + "5012": "\u754f", + "5013": "\u755d", + "5014": "\u7566", + "5015": "\u76c8", + "5016": "\u7827", + "5017": "\u7a62", + "5018": "\u7d06", + "5019": "\u7fc6", + "5020": "\u803d", + "5021": "\u8205", + "5022": "\u8569", + "5023": "\u86f8", + "5024": "\u8882", + "5025": "\u893b", + "5026": "\u8eaf", + "5027": "\u8fed", + "5028": "\u9005", + "5029": "\u9082", + "5030": "\u9089", + "5031": "\u920e", + "5032": "\u929b", + "5033": "\u95dc", + "5034": "\u9e1e", + "5035": "\u9f67", + "5036": "\u4ea5", + "5037": "\u52f8", + "5038": "\u543d", + "5039": "\u54a5", + "5040": "\u5967", + "5041": "\u598d", + "5042": "\u5a62", + "5043": "\u5c24", + "5044": "\u5c41", + "5045": "\u6134", + "5046": "\u65b7", + "5047": "\u65f1", + "5048": "\u6688", + "5049": "\u67b7", + "5050": "\u67d8", + "5051": "\u6ac3", + "5052": "\u6adf", + "5053": "\u6bd8", + "5054": "\u6c6a", + "5055": "\u6f74", + "5056": "\u6fb1", + "5057": "\u7164", + "5058": "\u7194", + "5059": "\u7576", + "5060": "\u777e", + "5061": "\u7893", + "5062": "\u7a84", + "5063": "\u7bc1", + "5064": "\u7c2a", + "5065": "\u7e79", + "5066": "\u7ff9", + "5067": "\u8000", + "5068": "\u8387", + "5069": "\u83f4", + "5070": "\u8602", + "5071": "\u8737", + "5072": "\u8904", + "5073": "\u890c", + "5074": "\u8b2c", + "5075": "\u8ce3", + "5076": "\u8eb0", + "5077": "\u8ecb", + "5078": "\u903c", + "5079": "\u93ac", + "5080": "\u975c", + "5081": "\u9b43", + "5082": "\u9b9f", + "5083": "\u9cf6", + "5084": "\u9f5f", + "5085": "\u9f6c", + "5086": "\u301c", + "5087": "\u30ee", + "5088": "\u4e9f", + "5089": "\u4ec6", + "5090": "\u51cb", + "5091": "\u54a4", + "5092": "\u5544", + "5093": "\u57dc", + "5094": "\u5a11", + "5095": "\u5a36", + "5096": "\u6089", + "5097": "\u620a", + "5098": "\u620e", + "5099": "\u64bc", + "5100": "\u64f2", + "5101": "\u6578", + "5102": "\u6726", + "5103": "\u687f", + "5104": "\u6a1f", + "5105": "\u6aae", + "5106": "\u6c81", + "5107": "\u6d63", + "5108": "\u6d9c", + "5109": "\u6ed3", + "5110": "\u703e", + "5111": "\u71e7", + "5112": "\u7232", + "5113": "\u733e", + "5114": "\u7464", + "5115": "\u7469", + "5116": "\u766c", + "5117": "\u776b", + "5118": "\u77ee", + "5119": "\u788c", + "5120": "\u7a1f", + "5121": "\u7a4e", + "5122": "\u7be5", + "5123": "\u7bf3", + "5124": "\u7cb9", + "5125": "\u7dec", + "5126": "\u7f77", + "5127": "\u7f9e", + "5128": "\u8216", + "5129": "\u847a", + "5130": "\u8acd", + "5131": "\u8af7", + "5132": "\u8b04", + "5133": "\u8da8", + "5134": "\u8e4a", + "5135": "\u8e81", + "5136": "\u8f3b", + "5137": "\u900d", + "5138": "\u970d", + "5139": "\u9b06", + "5140": "\u9baa", + "5141": "\u9ef4", + "5142": "\u4f7b", + "5143": "\u5167", + "5144": "\u51c9", + "5145": "\u525d", + "5146": "\u52d2", + "5147": "\u5396", + "5148": "\u53b6", + "5149": "\u5538", + "5150": "\u5556", + "5151": "\u5885", + "5152": "\u592d", + "5153": "\u5ba5", + "5154": "\u5be2", + "5155": "\u5df2", + "5156": "\u608d", + "5157": "\u62c7", + "5158": "\u6350", + "5159": "\u6426", + "5160": "\u649a", + "5161": "\u64a5", + "5162": "\u64d4", + "5163": "\u652a", + "5164": "\u665d", + "5165": "\u6753", + "5166": "\u6763", + "5167": "\u6787", + "5168": "\u6867", + "5169": "\u6930", + "5170": "\u6a47", + "5171": "\u6b23", + "5172": "\u6cd7", + "5173": "\u6db8", + "5174": "\u6df9", + "5175": "\u6e2d", + "5176": "\u6eff", + "5177": "\u6f58", + "5178": "\u6fd4", + "5179": "\u6fd8", + "5180": "\u6fdf", + "5181": "\u70ac", + "5182": "\u7147", + "5183": "\u71a8", + "5184": "\u71f5", + "5185": "\u72fd", + "5186": "\u73bb", + "5187": "\u763b", + "5188": "\u7647", + "5189": "\u779e", + "5190": "\u7895", + "5191": "\u79a7", + "5192": "\u79be", + "5193": "\u79c9", + "5194": "\u7d72", + "5195": "\u7d89", + "5196": "\u7e0b", + "5197": "\u7e37", + "5198": "\u7e6b", + "5199": "\u81fa", + "5200": "\u8271", + "5201": "\u856a", + "5202": "\u867b", + "5203": "\u8778", + "5204": "\u89ba", + "5205": "\u8a1d", + "5206": "\u8abc", + "5207": "\u8b6f", + "5208": "\u8f15", + "5209": "\u9438", + "5210": "\u958f", + "5211": "\u9a5b", + "5212": "\u9ad9", + "5213": "\u9b18", + "5214": "\u9b4d", + "5215": "\u9b4e", + "5216": "\u9bf0", + "5217": "\u9bf1", + "5218": "\u9d61", + "5219": "\u9e1a", + "5220": "\u9edb", + "5221": "\u9f3e", + "5222": "\u4e9e", + "5223": "\u4f83", + "5224": "\u4fad", + "5225": "\u4fce", + "5226": "\u5011", + "5227": "\u52de", + "5228": "\u5319", + "5229": "\u541e", + "5230": "\u54b8", + "5231": "\u54c8", + "5232": "\u564e", + "5233": "\u5664", + "5234": "\u56d3", + "5235": "\u58de", + "5236": "\u5abd", + "5237": "\u5ff8", + "5238": "\u5ffd", + "5239": "\u6029", + "5240": "\u604d", + "5241": "\u6063", + "5242": "\u60c7", + "5243": "\u61ae", + "5244": "\u622a", + "5245": "\u6258", + "5246": "\u64bb", + "5247": "\u6572", + "5248": "\u658c", + "5249": "\u660a", + "5250": "\u6919", + "5251": "\u69ce", + "5252": "\u6d8e", + "5253": "\u6dee", + "5254": "\u6dfa", + "5255": "\u6e5b", + "5256": "\u6eaf", + "5257": "\u6f09", + "5258": "\u6f6f", + "5259": "\u6fb9", + "5260": "\u7114", + "5261": "\u711c", + "5262": "\u7156", + "5263": "\u71d4", + "5264": "\u7337", + "5265": "\u736a", + "5266": "\u73ca", + "5267": "\u743f", + "5268": "\u745a", + "5269": "\u751c", + "5270": "\u752b", + "5271": "\u7564", + "5272": "\u7586", + "5273": "\u766a", + "5274": "\u76ea", + "5275": "\u77a0", + "5276": "\u783f", + "5277": "\u7957", + "5278": "\u798a", + "5279": "\u7aba", + "5280": "\u7b08", + "5281": "\u7b19", + "5282": "\u7bad", + "5283": "\u7c38", + "5284": "\u80e4", + "5285": "\u81cd", + "5286": "\u821b", + "5287": "\u827e", + "5288": "\u8318", + "5289": "\u83aa", + "5290": "\u8403", + "5291": "\u8431", + "5292": "\u848b", + "5293": "\u8597", + "5294": "\u85f9", + "5295": "\u86ce", + "5296": "\u86ef", + "5297": "\u8815", + "5298": "\u88b1", + "5299": "\u8977", + "5300": "\u89af", + "5301": "\u89c0", + "5302": "\u8a48", + "5303": "\u8aa6", + "5304": "\u8acc", + "5305": "\u8ae4", + "5306": "\u8b7d", + "5307": "\u8c50", + "5308": "\u8cce", + "5309": "\u8ce4", + "5310": "\u8d6d", + "5311": "\u8dcb", + "5312": "\u8e42", + "5313": "\u8e99", + "5314": "\u8f46", + "5315": "\u8f64", + "5316": "\u9041", + "5317": "\u9248", + "5318": "\u9249", + "5319": "\u932e", + "5320": "\u96d9", + "5321": "\u98ee", + "5322": "\u991e", + "5323": "\u9952", + "5324": "\u9957", + "5325": "\u99c8", + "5326": "\u99dd", + "5327": "\u9a57", + "5328": "\u9d44", + "5329": "\u9dd7", + "5330": "\u9eb4", + "5331": "\u9ed1", + "5332": "\ud857\udc4b", + "5333": "\u4e15", + "5334": "\u4e2a", + "5335": "\u4e99", + "5336": "\u4eb0", + "5337": "\u4efd", + "5338": "\u5047", + "5339": "\u50d6", + "5340": "\u50ed", + "5341": "\u524c", + "5342": "\u528d", + "5343": "\u52bf", + "5344": "\u5377", + "5345": "\u53c3", + "5346": "\u548b", + "5347": "\u54ab", + "5348": "\u54ea", + "5349": "\u5583", + "5350": "\u55ae", + "5351": "\u56b4", + "5352": "\u56c2", + "5353": "\u56d1", + "5354": "\u57b3", + "5355": "\u5852", + "5356": "\u58d8", + "5357": "\u5919", + "5358": "\u5934", + "5359": "\u5987", + "5360": "\u59b2", + "5361": "\u59c6", + "5362": "\u5ae3", + "5363": "\u5be5", + "5364": "\u5bf9", + "5365": "\u5c07", + "5366": "\u5c08", + "5367": "\u5d5c", + "5368": "\u5e08", + "5369": "\u5e1a", + "5370": "\u5e36", + "5371": "\u5e96", + "5372": "\u5eec", + "5373": "\u5f61", + "5374": "\u5f9e", + "5375": "\u5fb7", + "5376": "\u60fb", + "5377": "\u613f", + "5378": "\u6147", + "5379": "\u618a", + "5380": "\u61c3", + "5381": "\u61ff", + "5382": "\u6208", + "5383": "\u6230", + "5384": "\u6237", + "5385": "\u6289", + "5386": "\u62c2", + "5387": "\u62cc", + "5388": "\u62d4", + "5389": "\u6369", + "5390": "\u63ac", + "5391": "\u6451", + "5392": "\u6493", + "5393": "\u64b9", + "5394": "\u652c", + "5395": "\u6656", + "5396": "\u678c", + "5397": "\u6837", + "5398": "\u68b3", + "5399": "\u69ff", + "5400": "\u6a31", + "5401": "\u6a84", + "5402": "\u6aa2", + "5403": "\u6aaa", + "5404": "\u6aac", + "5405": "\u6ab8", + "5406": "\u6ae8", + "5407": "\u6b1d", + "5408": "\u6c9b", + "5409": "\u6cbd", + "5410": "\u6d35", + "5411": "\u6da6", + "5412": "\u6e8c", + "5413": "\u6ec9", + "5414": "\u6eef", + "5415": "\u6efe", + "5416": "\u6f11", + "5417": "\u6f32", + "5418": "\u6f6d", + "5419": "\u7165", + "5420": "\u71fc", + "5421": "\u7252", + "5422": "\u72f7", + "5423": "\u7463", + "5424": "\u7511", + "5425": "\u758b", + "5426": "\u75cd", + "5427": "\u75f0", + "5428": "\u7672", + "5429": "\u767c", + "5430": "\u76c2", + "5431": "\u775b", + "5432": "\u77dc", + "5433": "\u77e9", + "5434": "\u787c", + "5435": "\u78a9", + "5436": "\u7941", + "5437": "\u798e", + "5438": "\u79b9", + "5439": "\u7b1e", + "5440": "\u7b45", + "5441": "\u7b86", + "5442": "\u7c11", + "5443": "\u7cae", + "5444": "\u7d45", + "5445": "\u7d7d", + "5446": "\u7d93", + "5447": "\u7da0", + "5448": "\u7dac", + "5449": "\u7db8", + "5450": "\u7dd8", + "5451": "\u7e12", + "5452": "\u7e61", + "5453": "\u7e69", + "5454": "\u7e6a", + "5455": "\u7e8c", + "5456": "\u7eb8", + "5457": "\u7ec8", + "5458": "\u804a", + "5459": "\u8070", + "5460": "\u8085", + "5461": "\u80c4", + "5462": "\u820d", + "5463": "\u8229", + "5464": "\u8258", + "5465": "\u8278", + "5466": "\u83eb", + "5467": "\u8514", + "5468": "\u851a", + "5469": "\u860a", + "5470": "\u863f", + "5471": "\u86de", + "5472": "\u870a", + "5473": "\u8753", + "5474": "\u8755", + "5475": "\u87c4", + "5476": "\u87e0", + "5477": "\u884d", + "5478": "\u88dd", + "5479": "\u89bd", + "5480": "\u89bf", + "5481": "\u8a3b", + "5482": "\u8ac4", + "5483": "\u8b74", + "5484": "\u8b80", + "5485": "\u8b93", + "5486": "\u8bf7", + "5487": "\u8c6c", + "5488": "\u8c98", + "5489": "\u8d39", + "5490": "\u8d6b", + "5491": "\u8de3", + "5492": "\u8e89", + "5493": "\u8efe", + "5494": "\u8f49", + "5495": "\u8ff8", + "5496": "\u8ff9", + "5497": "\u914a", + "5498": "\u9169", + "5499": "\u91aa", + "5500": "\u923f", + "5501": "\u929c", + "5502": "\u934d", + "5503": "\u943a", + "5504": "\u945a", + "5505": "\u94bf", + "5506": "\u95bb", + "5507": "\u95ee", + "5508": "\u965e", + "5509": "\u96dc", + "5510": "\u9706", + "5511": "\u9730", + "5512": "\u97cb", + "5513": "\u985a", + "5514": "\u9986", + "5515": "\u99c1", + "5516": "\u99f1", + "5517": "\u9a55", + "5518": "\u9b51", + "5519": "\u93b9", + "5520": "\u6248", + "5521": "\u9e7c", + "5522": "\u9c24", + "5523": "\u8757", + "5524": "\u6777", + "5525": "\u66c9", + "5526": "\u9c67", + "5527": "\u9c47", + "5528": "\u9214", + "5529": "\u6eaa", + "5530": "\u65a4", + "5531": "\u734f", + "5532": "\u6670", + "5533": "\u76d2", + "5534": "\u5e5f", + "5535": "\u8f5f", + "5536": "\u8ad2", + "5537": "\u7b92", + "5538": "\u75e3", + "5539": "\u9ea9", + "5540": "\u699c", + "5541": "\u9b92", + "5542": "\u5398", + "5543": "\u8cc2", + "5544": "\u84a1", + "5545": "\u85af", + "5546": "\u6a80", + "5547": "\u8e35", + "5548": "\u5366", + "5549": "\u7962", + "5550": "\u60b8", + "5551": "\u7b48", + "5552": "\u76c3", + "5553": "\u67a1", + "5554": "\u87a2", + "5555": "\u9b41", + "5556": "\u7fb9", + "5557": "\u6bef", + "5558": "\u7bed", + "5559": "\u7621", + "5560": "\u5653", + "5561": "\u535c", + "5562": "\u7d2c", + "5563": "\u58f7", + "5564": "\u55e3", + "5565": "\u80f1", + "5566": "\u96c1", + "5567": "\u6634", + "5568": "\u6602", + "5569": "\u647a", + "5570": "\u8b02", + "5571": "\u818f", + "5572": "\u7d9c", + "5573": "\u87fb", + "5574": "\u81e5", + "5575": "\u9bab", + "5576": "\u6ad3", + "5577": "\u88df", + "5578": "\u59be", + "5579": "\u74dc", + "5580": "\u9eb5", + "5581": "\u87f2", + "5582": "\u9e78", + "5583": "\u515c", + "5584": "\u7e8f", + "5585": "\u9306", + "5586": "\u88b4", + "5587": "\u74e2", + "5588": "\u4e19", + "5589": "\u7aff", + "5590": "\u5962", + "5591": "\u852d", + "5592": "\u67ca", + "5593": "\u55ac", + "5594": "\u9921", + "5595": "\u8fc4", + "5596": "\u676d", + "5597": "\u7c95", + "5598": "\u64e2", + "5599": "\u9784", + "5600": "\u8e5f", + "5601": "\u7e55", + "5602": "\u8087", + "5603": "\u9742", + "5604": "\u907d", + "5605": "\u57c3", + "5606": "\u6813", + "5607": "\u751a", + "5608": "\u714c", + "5609": "\u67f5", + "5610": "\u51cc", + "5611": "\u853d", + "5612": "\u71c8", + "5613": "\u9949", + "5614": "\u91c7", + "5615": "\u8463", + "5616": "\u696f", + "5617": "\u57a2", + "5618": "\u6e26", + "5619": "\u6bc5", + "5620": "\u6028", + "5621": "\u5687", + "5622": "\u9e9f", + "5623": "\u67d1", + "5624": "\u6689", + "5625": "\u7dcb", + "5626": "\u75e2", + "5627": "\u6893", + "5628": "\u6e4a", + "5629": "\u901d", + "5630": "\u7aaf", + "5631": "\u5740", + "5632": "\u7e4d", + "5633": "\u63c6", + "5634": "\u60e7", + "5635": "\u5df3", + "5636": "\u58fa", + "5637": "\u7483", + "5638": "\u80b4", + "5639": "\u8098", + "5640": "\u9b8e", + "5641": "\u8a6e", + "5642": "\u514e", + "5643": "\u9aed", + "5644": "\u8471", + "5645": "\u5840", + "5646": "\u53ea", + "5647": "\u7ca5", + "5648": "\u8a23", + "5649": "\u6284", + "5650": "\u5f10", + "5651": "\u5446", + "5652": "\u8338", + "5653": "\u5ec9", + "5654": "\u7078", + "5655": "\u681e", + "5656": "\u5e25", + "5657": "\u82fa", + "5658": "\u6953", + "5659": "\u724c", + "5660": "\u7d79", + "5661": "\u68af", + "5662": "\u6234", + "5663": "\u4e98", + "5664": "\u5bb5", + "5665": "\u8b5a", + "5666": "\u5efb", + "5667": "\u9bdb", + "5668": "\u99b3", + "5669": "\u51e7", + "5670": "\u7a14", + "5671": "\u7f60", + "5672": "\u9192", + "5673": "\u75b9", + "5674": "\u7dbb", + "5675": "\u589c", + "5676": "\u9262", + "5677": "\u72d7", + "5678": "\u6912", + "5679": "\u4ed4", + "5680": "\u7cde", + "5681": "\u8d66", + "5682": "\u8404", + "5683": "\u82d4", + "5684": "\u7027", + "5685": "\u8823", + "5686": "\u59d1", + "5687": "\u8017", + "5688": "\u51db", + "5689": "\u98f4", + "5690": "\u68fa", + "5691": "\u60a6", + "5692": "\u9bad", + "5693": "\u87f9", + "5694": "\u7709", + "5695": "\u6816", + "5696": "\u9bc9", + "5697": "\u8587", + "5698": "\u541f", + "5699": "\u9591", + "5700": "\u86ee", + "5701": "\u85fb", + "5702": "\u7a9f", + "5703": "\u8c8c", + "5704": "\u5a7f", + "5705": "\u817a", + "5706": "\u75fa", + "5707": "\u9688", + "5708": "\u81fc", + "5709": "\u7d10", + "5710": "\u7dbf", + "5711": "\u69fd", + "5712": "\u9be8", + "5713": "\u7409", + "5714": "\u53c9", + "5715": "\u4ff5", + "5716": "\u7259", + "5717": "\u831c", + "5718": "\u7432", + "5719": "\u5e16", + "5720": "\u906e", + "5721": "\u6ef4", + "5722": "\u932f", + "5723": "\u907c", + "5724": "\u9bd6", + "5725": "\u59dc", + "5726": "\u8749", + "5727": "\u9813", + "5728": "\u7897", + "5729": "\u732a", + "5730": "\u9a30", + "5731": "\u5b9b", + "5732": "\u914e", + "5733": "\u71d5", + "5734": "\u9cf3", + "5735": "\u5ac9", + "5736": "\u5766", + "5737": "\u6c70", + "5738": "\u9d28", + "5739": "\u8f3f", + "5740": "\u984e", + "5741": "\u8aed", + "5742": "\u760d", + "5743": "\u6841", + "5744": "\u842c", + "5745": "\u904d", + "5746": "\u67d0", + "5747": "\u9756", + "5748": "\u58f1", + "5749": "\u971e", + "5750": "\u865a", + "5751": "\u5e06", + "5752": "\u7a6b", + "5753": "\u81b3", + "5754": "\u9ba8", + "5755": "\u6681", + "5756": "\u62d0", + "5757": "\u5b8b", + "5758": "\u51e1", + "5759": "\u6ce1", + "5760": "\u5451", + "5761": "\u9ce9", + "5762": "\u55b0", + "5763": "\u56da", + "5764": "\u59ea", + "5765": "\u584a", + "5766": "\u59ac", + "5767": "\u7d17", + "5768": "\u74f6", + "5769": "\u5c3a", + "5770": "\u77db", + "5771": "\u5ee3", + "5772": "\u9e93", + "5773": "\u84cb", + "5774": "\u6f02", + "5775": "\u6643", + "5776": "\u5f84", + "5777": "\u5146", + "5778": "\u67ff", + "5779": "\u4fa0", + "5780": "\u9b31", + "5781": "\u5bf8", + "5782": "\u638c", + "5783": "\u5b9c", + "5784": "\u8ce0", + "5785": "\u6f84", + "5786": "\u674f", + "5787": "\u59fb", + "5788": "\u53a8", + "5789": "\u95a5", + "5790": "\u68f2", + "5791": "\u4faf", + "5792": "\u731f", + "5793": "\u674e", + "5794": "\u7985", + "5795": "\u8b19", + "5796": "\u86c7", + "5797": "\u80c6", + "5798": "\u30c2", + "5799": "\u6627", + "5800": "\u971c", + "5801": "\u845b", + "5802": "\u65ac", + "5803": "\u7c60", + "5804": "\u66f9", + "5805": "\u60e8", + "5806": "\u7e2b", + "5807": "\u7070", + "5808": "\u6842", + "5809": "\u8fbb", + "5810": "\u864e", + "5811": "\u7c92", + "5812": "\u7b1b", + "5813": "\u5507", + "5814": "\u9175", + "5815": "\u80ce", + "5816": "\u722a", + "5817": "\u73e0", + "5818": "\u76fe", + "5819": "\u6bbb", + "5820": "\u9418", + "5821": "\u925b", + "5822": "\u9685", + "5823": "\u821f", + "5824": "\u9285", + "5825": "\u570b", + "5826": "\u9326", + "5827": "\u70c8", + "5828": "\u9df9", + "5829": "\u92fc", + "5830": "\u6795", + "5831": "\u5824", + "5832": "\u8a1f", + "5833": "\u51f6", + "5834": "\u673a", + "5835": "\u5eb6", + "5836": "\u5c3c", + "5837": "\u5589", + "5838": "\u6850", + "5839": "\u819d", + "5840": "\u58c7", + "5841": "\u84c4", + "5842": "\u82bd", + "5843": "\u8607", + "5844": "\u7bb8", + "5845": "\u5ce0", + "5846": "\u8c9e", + "5847": "\u7089", + "5848": "\u5ce1", + "5849": "\u7d46", + "5850": "\u6ecb", + "5851": "\u8896", + "5852": "\u74a7", + "5853": "\u5609", + "5854": "\u7f36", + "5855": "\u8679", + "5856": "\u88f8", + "5857": "\u8015", + "5858": "\u60a0", + "5859": "\u8475", + "5860": "\u642c", + "5861": "\u664b", + "5862": "\u5f26", + "5863": "\u990c", + "5864": "\u8247", + "5865": "\u4eae", + "5866": "\u816b", + "5867": "\u72fc", + "5868": "\u697c", + "5869": "\u9905", + "5870": "\u723a", + "5871": "\u53c8", + "5872": "\u4f8d", + "5873": "\u68df", + "5874": "\u596e", + "5875": "\u50e7", + "5876": "\u84ee", + "5877": "\u828b", + "5878": "\u7573", + "5879": "\u5bb4", + "5880": "\u99ff", + "5881": "\u916c", + "5882": "\u68da", + "5883": "\u5256", + "5884": "\u8cca", + "5885": "\u8870", + "5886": "\u5841", + "5887": "\u8b5c", + "5888": "\u65cb", + "5889": "\u8b90", + "5890": "\u80aa", + "5891": "\u8178", + "5892": "\u83f1", + "5893": "\u95b2", + "5894": "\u7b52", + "5895": "\u54c9", + "5896": "\u9675", + "5897": "\u5a46", + "5898": "\u6b04", + "5899": "\u9855", + "5900": "\u9042", + "5901": "\u7e1b", + "5902": "\u8ef8", + "5903": "\u585e", + "5904": "\u5b8f", + "5905": "\u7def", + "5906": "\u7434", + "5907": "\u5bb0", + "5908": "\u91dc", + "5909": "\u862d", + "5910": "\u9298", + "5911": "\u6f64", + "5912": "\u66a6", + "5913": "\u4f0e", + "5914": "\u75f4", + "5915": "\u73b2", + "5916": "\u75ab", + "5917": "\u660c", + "5918": "\u73ed", + "5919": "\u5f80", + "5920": "\u5203", + "5921": "\u6f54", + "5922": "\u6d32", + "5923": "\u5982", + "5924": "\u5cac", + "5925": "\u7950", + "5926": "\u67cf", + "5927": "\u518a", + "5928": "\u96c0", + "5929": "\u88c2", + "5930": "\u53eb", + "5931": "\u7f85", + "5932": "\u7c8b", + "5933": "\u67f1", + "5934": "\u7948", + "5935": "\u6566", + "5936": "\u30f2", + "5937": "\u5c09", + "5938": "\u3045", + "5939": "\u7db1", + "5940": "\u4e4f", + "5941": "\u6b3a", + "5942": "\u66fd", + "5943": "\u6df3", + "5944": "\u7fd4", + "5945": "\u628a", + "5946": "\u6b96", + "5947": "\u6daf", + "5948": "\u6212", + "5949": "\u5a92", + "5950": "\u7b26", + "5951": "\u9162", + "5952": "\u9177", + "5953": "\u8c9d", + "5954": "\u5cf0", + "5955": "\u5bdb", + "5956": "\u96b7", + "5957": "\u733f", + "5958": "\u764c", + "5959": "\u7dbe", + "5960": "\u6ce5", + "5961": "\u7c9b", + "5962": "\u6249", + "5963": "\u5a20", + "5964": "\u8f14", + "5965": "\u76bf", + "5966": "\u9f13", + "5967": "\u719f", + "5968": "\u6717", + "5969": "\u99d2", + "5970": "\u92ad", + "5971": "\u82d1", + "5972": "\u9396", + "5973": "\u809d", + "5974": "\u5782", + "5975": "\u5104", + "5976": "\u78c1", + "5977": "\u6d1e", + "5978": "\u95c7", + "5979": "\u8987", + "5980": "\u51a0", + "5981": "\u58b3", + "5982": "\u4e3c", + "5983": "\u5be7", + "5984": "\u77b3", + "5985": "\u7656", + "5986": "\u525b", + "5987": "\u83ca", + "5988": "\u5b22", + "5989": "\u9047", + "5990": "\u80a2", + "5991": "\u654f", + "5992": "\u5c0b", + "5993": "\u72c2", + "5994": "\u67f4", + "5995": "\u5f6b", + "5996": "\u5805", + "5997": "\u679d", + "5998": "\u7d2b", + "5999": "\u62fe", + "6000": "\u5d8b", + "6001": "\u9084", + "6002": "\u7e26", + "6003": "\u80de", + "6004": "\u6069", + "6005": "\u3043", + "6006": "\u91c8", + "6007": "\u5c3b", + "6008": "\u5eb5", + "6009": "\u5a01", + "6010": "\u5c1a", + "6011": "\u62f3", + "6012": "\u64b2", + "6013": "\u5320", + "6014": "\u6676", + "6015": "\u61a9", + "6016": "\u7965", + "6017": "\u7832", + "6018": "\u7126", + "6019": "\u5c3f", + "6020": "\u9b42", + "6021": "\u7a42", + "6022": "\u8f44", + "6023": "\u62dd", + "6024": "\u91a4", + "6025": "\u658e", + "6026": "\u621a", + "6027": "\u5de3", + "6028": "\u572d", + "6029": "\u9727", + "6030": "\u6f6e", + "6031": "\u57f9", + "6032": "\u5f81", + "6033": "\u5f25", + "6034": "\u5b5d", + "6035": "\u8150", + "6036": "\u8ca2", + "6037": "\u6ca1", + "6038": "\u68cb", + "6039": "\u5f70", + "6040": "\u5e3d", + "6041": "\u83cc", + "6042": "\u7891", + "6043": "\u6597", + "6044": "\u63fa", + "6045": "\u7cf8", + "6046": "\u9d8f", + "6047": "\u9f3b", + "6048": "\u7235", + "6049": "\u85a6", + "6050": "\u808c", + "6051": "\u5c48", + "6052": "\u7d0b", + "6053": "\u67a0", + "6054": "\u57a3", + "6055": "\u65ec", + "6056": "\u614e", + "6057": "\u968f", + "6058": "\u8ed2", + "6059": "\u4e59", + "6060": "\u7384", + "6061": "\u5200", + "6062": "\u8df5", + "6063": "\u4f0f", + "6064": "\u5642", + "6065": "\u5e84", + "6066": "\u78e8", + "6067": "\u9694", + "6068": "\u9686", + "6069": "\u7a74", + "6070": "\u76c6", + "6071": "\u8ca7", + "6072": "\u9375", + "6073": "\u5cb3", + "6074": "\u616e", + "6075": "\u5374", + "6076": "\u62f6", + "6077": "\u5f13", + "6078": "\u5373", + "6079": "\u59d3", + "6080": "\u6398", + "6081": "\u6d6a", + "6082": "\u8b72", + "6083": "\u6589", + "6084": "\u9a0e", + "6085": "\u5968", + "6086": "\u8b00", + "6087": "\u5854", + "6088": "\u6ed1", + "6089": "\u5098", + "6090": "\u96f7", + "6091": "\u4fca", + "6092": "\u8edf", + "6093": "\u8f1d", + "6094": "\u6458", + "6095": "\u6176", + "6096": "\u6c57", + "6097": "\u6fa4", + "6098": "\u7bc7", + "6099": "\u8b0e", + "6100": "\u5e7b", + "6101": "\u9903", + "6102": "\u5339", + "6103": "\u543e", + "6104": "\u93e1", + "6105": "\u68d2", + "6106": "\u6d99", + "6107": "\u8cc3", + "6108": "\u8302", + "6109": "\u609f", + "6110": "\u5504", + "6111": "\u846c", + "6112": "\u6469", + "6113": "\u7c3f", + "6114": "\u6e09", + "6115": "\u511f", + "6116": "\u50da", + "6117": "\u65ed", + "6118": "\u8102", + "6119": "\u5e8f", + "6120": "\u8cab", + "6121": "\u3049", + "6122": "\u8aa4", + "6123": "\u7ffc", + "6124": "\u5bee", + "6125": "\u6edd", + "6126": "\u6c37", + "6127": "\u91dd", + "6128": "\u96c5", + "6129": "\u502b", + "6130": "\u85e9", + "6131": "\u5237", + "6132": "\u9663", + "6133": "\u7551", + "6134": "\u5a9b", + "6135": "\u5c3d", + "6136": "\u8cdb", + "6137": "\u50b5", + "6138": "\u8aa0", + "6139": "\u716e", + "6140": "\u6843", + "6141": "\u52d8", + "6142": "\u7a32", + "6143": "\u68c4", + "6144": "\u8170", + "6145": "\u83c5", + "6146": "\u7344", + "6147": "\u67f3", + "6148": "\u7ca7", + "6149": "\u5f18", + "6150": "\u9db4", + "6151": "\u80a9", + "6152": "\u9283", + "6153": "\u6c41", + "6154": "\u706f", + "6155": "\u773c", + "6156": "\u7db2", + "6157": "\u5c01", + "6158": "\u564c", + "6159": "\u7a3f", + "6160": "\u9644", + "6161": "\u676f", + "6162": "\u6094", + "6163": "\u9ea6", + "6164": "\u6e7f", + "6165": "\u9774", + "6166": "\u307a", + "6167": "\u6b8a", + "6168": "\u62b5", + "6169": "\u790e", + "6170": "\u8c5a", + "6171": "\u9ed9", + "6172": "\u7de0", + "6173": "\u9154", + "6174": "\u4e43", + "6175": "\u71e5", + "6176": "\u934b", + "6177": "\u8c6a", + "6178": "\u8a0e", + "6179": "\u6fc3", + "6180": "\u7d05", + "6181": "\u7968", + "6182": "\u708e", + "6183": "\u76ae", + "6184": "\u7e2e", + "6185": "\u5fb9", + "6186": "\u6749", + "6187": "\u8f03", + "6188": "\u7a1a", + "6189": "\u5800", + "6190": "\u5e33", + "6191": "\u5fcd", + "6192": "\u4f2f", + "6193": "\u8a17", + "6194": "\u77e2", + "6195": "\u8a69", + "6196": "\u8feb", + "6197": "\u5076", + "6198": "\u6838", + "6199": "\u5100", + "6200": "\u53cc", + "6201": "\u5230", + "6202": "\u524a", + "6203": "\u6db2", + "6204": "\u99c6", + "6205": "\u4e80", + "6206": "\u8972", + "6207": "\u8846", + "6208": "\u5510", + "6209": "\u7cd6", + "6210": "\u5de1", + "6211": "\u7e41", + "6212": "\u81ed", + "6213": "\u708a", + "6214": "\u9670", + "6215": "\u8155", + "6216": "\u6d44", + "6217": "\u5629", + "6218": "\u54b2", + "6219": "\u76d7", + "6220": "\u8108", + "6221": "\u6ede", + "6222": "\u7267", + "6223": "\u574a", + "6224": "\u5305", + "6225": "\u81f3", + "6226": "\u679a", + "6227": "\u5049", + "6228": "\u81f4", + "6229": "\u8a13", + "6230": "\u8ca8", + "6231": "\u8033", + "6232": "\u6f22", + "6233": "\u65d7", + "6234": "\u5df1", + "6235": "\u6247", + "6236": "\u6885", + "6237": "\u63e1", + "6238": "\u6b27", + "6239": "\u8584", + "6240": "\u6065", + "6241": "\u732e", + "6242": "\u9810", + "6243": "\u4ec1", + "6244": "\u9f8d", + "6245": "\u8a70", + "6246": "\u73cd", + "6247": "\u5f69", + "6248": "\u5feb", + "6249": "\u6cbc", + "6250": "\u6bd2", + "6251": "\u4e39", + "6252": "\u53e5", + "6253": "\u9234", + "6254": "\u91e3", + "6255": "\u7e01", + "6256": "\u5fae", + "6257": "\u5999", + "6258": "\u62ec", + "6259": "\u6669", + "6260": "\u7c89", + "6261": "\u9eba", + "6262": "\u5353", + "6263": "\u570f", + "6264": "\u517c", + "6265": "\u6b20", + "6266": "\u8e0a", + "6267": "\u8133", + "6268": "\u7adc", + "6269": "\u9cf4", + "6270": "\u5f66", + "6271": "\u5ac1", + "6272": "\u9a12", + "6273": "\u9aea", + "6274": "\u8acb", + "6275": "\u5375", + "6276": "\u640d", + "6277": "\u8377", + "6278": "\u68a8", + "6279": "\u5531", + "6280": "\u5d50", + "6281": "\u5e4c", + "6282": "\u4f34", + "6283": "\u624d", + "6284": "\u961c", + "6285": "\u81d3", + "6286": "\u7363", + "6287": "\u7bb1", + "6288": "\u7956", + "6289": "\u7532", + "6290": "\u6d74", + "6291": "\u5c0a", + "6292": "\u907f", + "6293": "\u6607", + "6294": "\u718a", + "6295": "\u58c1", + "6296": "\u4e18", + "6297": "\u6790", + "6298": "\u5b6b", + "6299": "\u5e72", + "6300": "\u7687", + "6301": "\u539a", + "6302": "\u4ead", + "6303": "\u970a", + "6304": "\u7b46", + "6305": "\u627f", + "6306": "\u5747", + "6307": "\u878d", + "6308": "\u5f8b", + "6309": "\u7dd1", + "6310": "\u5426", + "6311": "\u9b3c", + "6312": "\u587e", + "6313": "\u811a", + "6314": "\u9808", + "6315": "\u90aa", + "6316": "\u888b", + "6317": "\u6e56", + "6318": "\u4e73", + "6319": "\u88d5", + "6320": "\u63ee", + "6321": "\u51cd", + "6322": "\u6ec5", + "6323": "\u4e7e", + "6324": "\u3074", + "6325": "\u7fbd", + "6326": "\u6162", + "6327": "\u5019", + "6328": "\u62e1", + "6329": "\u6fef", + "6330": "\u8cb8", + "6331": "\u7802", + "6332": "\u656c", + "6333": "\u6982", + "6334": "\u5e81", + "6335": "\u7159", + "6336": "\u57f7", + "6337": "\u5e95", + "6338": "\u88c1", + "6339": "\u558b", + "6340": "\u9e97", + "6341": "\u9732", + "6342": "\u96f2", + "6343": "\u9aa8", + "6344": "\u500d", + "6345": "\u6bbf", + "6346": "\u5947", + "6347": "\u6613", + "6348": "\u5c64", + "6349": "\u6577", + "6350": "\u5e55", + "6351": "\u6bdb", + "6352": "\u7206", + "6353": "\u6687", + "6354": "\u68b0", + "6355": "\u8cb4", + "6356": "\u96a3", + "6357": "\u8f38", + "6358": "\u3077", + "6359": "\u67c4", + "6360": "\u59eb", + "6361": "\u7bc4", + "6362": "\u63b2", + "6363": "\u5618", + "6364": "\u585a", + "6365": "\u5264", + "6366": "\u5145", + "6367": "\u4f75", + "6368": "\u5974", + "6369": "\u675f", + "6370": "\u5893", + "6371": "\u702c", + "6372": "\u520a", + "6373": "\u8863", + "6374": "\u629e", + "6375": "\u7e04", + "6376": "\u77ac", + "6377": "\u5c04", + "6378": "\u83d3", + "6379": "\u52df", + "6380": "\u4e71", + "6381": "\u8fce", + "6382": "\u62b1", + "6383": "\u6c38", + "6384": "\u7af9", + "6385": "\u9178", + "6386": "\u523a", + "6387": "\u95a3", + "6388": "\u90f7", + "6389": "\u4e5f", + "6390": "\u61b6", + "6391": "\u5263", + "6392": "\u529f", + "6393": "\u9e7f", + "6394": "\u725b", + "6395": "\u79d8", + "6396": "\u4ecf", + "6397": "\u96c4", + "6398": "\u866b", + "6399": "\u5584", + "6400": "\u5c4a", + "6401": "\u8266", + "6402": "\u7247", + "6403": "\u8907", + "6404": "\u70ba", + "6405": "\u6cf3", + "6406": "\u5b9d", + "6407": "\u6fc0", + "6408": "\u5e79", + "6409": "\u81e3", + "6410": "\u4e4b", + "6411": "\u6691", + "6412": "\u6d66", + "6413": "\u770b", + "6414": "\u7591", + "6415": "\u8a98", + "6416": "\u66b4", + "6417": "\u8056", + "6418": "\u6368", + "6419": "\u677f", + "6420": "\u685c", + "6421": "\u7834", + "6422": "\u9769", + "6423": "\u5e0c", + "6424": "\u5e45", + "6425": "\u5442", + "6426": "\u6298", + "6427": "\u8a3a", + "6428": "\u4f38", + "6429": "\u60d1", + "6430": "\u6e2c", + "6431": "\u99d0", + "6432": "\u7a93", + "6433": "\u7d00", + "6434": "\u820e", + "6435": "\u7f72", + "6436": "\u60a3", + "6437": "\u5cb8", + "6438": "\u7e3e", + "6439": "\u6e7e", + "6440": "\u5c90", + "6441": "\u6a39", + "6442": "\u7d0d", + "6443": "\u79c0", + "6444": "\u514d", + "6445": "\u8b1d", + "6446": "\u6c60", + "6447": "\u7981", + "6448": "\u80cc", + "6449": "\u8e8d", + "6450": "\u8074", + "6451": "\u6297", + "6452": "\u8c46", + "6453": "\u7a0e", + "6454": "\u594f", + "6455": "\u8349", + "6456": "\u5f3e", + "6457": "\u6075", + "6458": "\u8001", + "6459": "\u793c", + "6460": "\u89d2", + "6461": "\u7ae5", + "6462": "\u5be9", + "6463": "\u88cf", + "6464": "\u5439", + "6465": "\u7720", + "6466": "\u6b6f", + "6467": "\u62e0", + "6468": "\u5bd2", + "6469": "\u6163", + "6470": "\u89e6", + "6471": "\u98fc", + "6472": "\u8358", + "6473": "\u7fa4", + "6474": "\u8ff7", + "6475": "\u6cca", + "6476": "\u5b97", + "6477": "\u65e6", + "6478": "\u50b7", + "6479": "\u984d", + "6480": "\u5869", + "6481": "\u5238", + "6482": "\u5e8a", + "6483": "\u9759", + "6484": "\u7559", + "6485": "\u8457", + "6486": "\u6cb9", + "6487": "\u8a8c", + "6488": "\u7f6a", + "6489": "\u7d14", + "6490": "\u8179", + "6491": "\u5075", + "6492": "\u5247", + "6493": "\u58ca", + "6494": "\u672d", + "6495": "\u8f2a", + "6496": "\u6383", + "6497": "\u707d", + "6498": "\u95d8", + "6499": "\u5f31", + "6500": "\u523b", + "6501": "\u822a", + "6502": "\u7b54", + "6503": "\u6804", + "6504": "\u59ff", + "6505": "\u4ea1", + "6506": "\u7e54", + "6507": "\u6557", + "6508": "\u7ae0", + "6509": "\u5438", + "6510": "\u4ee4", + "6511": "\u9bae", + "6512": "\u88dc", + "6513": "\u5915", + "6514": "\u635c", + "6515": "\u6012", + "6516": "\u6a21", + "6517": "\u76ca", + "6518": "\u559c", + "6519": "\u83ef", + "6520": "\u7d75", + "6521": "\u7533", + "6522": "\u76e4", + "6523": "\u8efd", + "6524": "\u7a4d", + "6525": "\u6a19", + "6526": "\u968e", + "6527": "\u7701", + "6528": "\u5bc6", + "6529": "\u9805", + "6530": "\u732b", + "6531": "\u5f93", + "6532": "\u975e", + "6533": "\u5e1d", + "6534": "\u5b63", + "6535": "\u6355", + "6536": "\u515a", + "6537": "\u6211", + "6538": "\u5727", + "6539": "\u9999", + "6540": "\u7b4b", + "6541": "\u8f29", + "6542": "\u7c4d", + "6543": "\u4e01", + "6544": "\u62bc", + "6545": "\u5c3e", + "6546": "\u97d3", + "6547": "\u64cd", + "6548": "\u6697", + "6549": "\u75c7", + "6550": "\u6563", + "6551": "\u7a81", + "6552": "\u9069", + "6553": "\u96d1", + "6554": "\u8de1", + "6555": "\u53b3", + "6556": "\u4e86", + "6557": "\u9ce5", + "6558": "\u9003", + "6559": "\u8b1b", + "6560": "\u6674", + "6561": "\u5fb4", + "6562": "\u5211", + "6563": "\u99c4", + "6564": "\u5009", + "6565": "\u56f0", + "6566": "\u77ed", + "6567": "\u5a66", + "6568": "\u9063", + "6569": "\u7565", + "6570": "\u9f62", + "6571": "\u9707", + "6572": "\u6575", + "6573": "\u8535", + "6574": "\u535a", + "6575": "\u8840", + "6576": "\u6e80", + "6577": "\u5fd7", + "6578": "\u8217", + "6579": "\u5b99", + "6580": "\u90e1", + "6581": "\u90a3", + "6582": "\u5bff", + "6583": "\u907a", + "6584": "\u79cb", + "6585": "\u6975", + "6586": "\u91cc", + "6587": "\u5ec3", + "6588": "\u56e0", + "6589": "\u5178", + "6590": "\u67d3", + "6591": "\u5f92", + "6592": "\u5dfb", + "6593": "\u9802", + "6594": "\u5742", + "6595": "\u8d85", + "6596": "\u6cb3", + "6597": "\u76db", + "6598": "\u72ac", + "6599": "\u8c4a", + "6600": "\u7aef", + "6601": "\u7d39", + "6602": "\u9996", + "6603": "\u6e6f", + "6604": "\u967d", + "6605": "\u7cbe", + "6606": "\u7949", + "6607": "\u6b73", + "6608": "\u7df4", + "6609": "\u6c5f", + "6610": "\u602a", + "6611": "\u5370", + "6612": "\u7b97", + "6613": "\u7d19", + "6614": "\u6255", + "6615": "\u6c42", + "6616": "\u969c", + "6617": "\u7c21", + "6618": "\u5fa1", + "6619": "\u9014", + "6620": "\u5275", + "6621": "\u8cc0", + "6622": "\u8239", + "6623": "\u5802", + "6624": "\u83dc", + "6625": "\u30a5", + "6626": "\u52e4", + "6627": "\u75db", + "6628": "\u4e26", + "6629": "\u666f", + "6630": "\u96ea", + "6631": "\u7bc0", + "6632": "\u9451", + "6633": "\u6d5c", + "6634": "\u663c", + "6635": "\u6e05", + "6636": "\u629c", + "6637": "\u52e2", + "6638": "\u66ae", + "6639": "\u9280", + "6640": "\u76df", + "6641": "\u9b5a", + "6642": "\u7387", + "6643": "\u6d0b", + "6644": "\u5bfa", + "6645": "\u5f01", + "6646": "\u7686", + "6647": "\u5fb3", + "6648": "\u8336", + "6649": "\u7b11", + "6650": "\u6e21", + "6651": "\u5948", + "6652": "\u9806", + "6653": "\u6cc1", + "6654": "\u8ac7", + "6655": "\u821e", + "6656": "\u6848", + "6657": "\u5ca9", + "6658": "\u8ca0", + "6659": "\u65e7", + "6660": "\u8ca1", + "6661": "\u8a31", + "6662": "\u6545", + "6663": "\u51ac", + "6664": "\u6a2a", + "6665": "\u5965", + "6666": "\u8a33", + "6667": "\u6bd4", + "6668": "\u56f2", + "6669": "\u505c", + "6670": "\u7bc9", + "6671": "\u6ce2", + "6672": "\u59b9", + "6673": "\u6797", + "6674": "\u6696", + "6675": "\u7d22", + "6676": "\u8d64", + "6677": "\u7d66", + "6678": "\u672b", + "6679": "\u50ac", + "6680": "\u6b66", + "6681": "\u6d17", + "6682": "\u9045", + "6683": "\u8ff0", + "6684": "\u9ed2", + "6685": "\u72af", + "6686": "\u5de6", + "6687": "\u6e90", + "6688": "\u9b54", + "6689": "\u7d30", + "6690": "\u4e45", + "6691": "\u4e0e", + "6692": "\u6e1b", + "6693": "\u7d1a", + "6694": "\u8cbb", + "6695": "\u8d8a", + "6696": "\u5dee", + "6697": "\u59bb", + "6698": "\u9818", + "6699": "\u885b", + "6700": "\u4e38", + "6701": "\u7d61", + "6702": "\u968a", + "6703": "\u85ac", + "6704": "\u6c0f", + "6705": "\u671b", + "6706": "\u4f3c", + "6707": "\u5c31", + "6708": "\u53f3", + "6709": "\u6761", + "6710": "\u5e03", + "6711": "\u51e6", + "6712": "\u8c37", + "6713": "\u7b56", + "6714": "\u52b9", + "6715": "\u5fd8", + "6716": "\u71b1", + "6717": "\u5fa9", + "6718": "\u59c9", + "6719": "\u30cc", + "6720": "\u632f", + "6721": "\u8ab2", + "6722": "\u898f", + "6723": "\u5012", + "6724": "\u6e2f", + "6725": "\u6ce8", + "6726": "\u68ee", + "6727": "\u9632", + "6728": "\u7d99", + "6729": "\u9000", + "6730": "\u6839", + "6731": "\u706b", + "6732": "\u66ff", + "6733": "\u9678", + "6734": "\u53bb", + "6735": "\u8996", + "6736": "\u6574", + "6737": "\u6e96", + "6738": "\u5ead", + "6739": "\u30be", + "6740": "\u72ec", + "6741": "\u6483", + "6742": "\u5150", + "6743": "\u6a4b", + "6744": "\u307d", + "6745": "\u63db", + "6746": "\u5ff5", + "6747": "\u8b58", + "6748": "\u306c", + "6749": "\u6253", + "6750": "\u6d25", + "6751": "\u96e8", + "6752": "\u5e78", + "6753": "\u542b", + "6754": "\u796d", + "6755": "\u97ff", + "6756": "\u52b4", + "6757": "\u51c4", + "6758": "\u5c06", + "6759": "\u5b98", + "6760": "\u82e6", + "6761": "\u8ffd", + "6762": "\u9060", + "6763": "\u672a", + "6764": "\u8ca9", + "6765": "\u5a18", + "6766": "\u8857", + "6767": "\u66dc", + "6768": "\u7a0b", + "6769": "\u63d0", + "6770": "\u7389", + "6771": "\u5224", + "6772": "\u79fb", + "6773": "\u653b", + "6774": "\u4f4e", + "6775": "\u88c5", + "6776": "\u65ad", + "6777": "\u53ca", + "6778": "\u8a3c", + "6779": "\u8c61", + "6780": "\u5b88", + "6781": "\u9752", + "6782": "\u5bcc", + "6783": "\u623b", + "6784": "\u8a5e", + "6785": "\u5409", + "6786": "\u6295", + "6787": "\u6b74", + "6788": "\u6ca2", + "6789": "\u8f09", + "6790": "\u5177", + "6791": "\u5eab", + "6792": "\u9664", + "6793": "\u74b0", + "6794": "\u5c55", + "6795": "\u5352", + "6796": "\u4e89", + "6797": "\u5931", + "6798": "\u623f", + "6799": "\u6625", + "6800": "\u6319", + "6801": "\u6f5f", + "6802": "\u8fd4", + "6803": "\u99ac", + "6804": "\u6b32", + "6805": "\u6750", + "6806": "\u6238", + "6807": "\u56f3", + "6808": "\u5bdd", + "6809": "\u990a", + "6810": "\u713c", + "6811": "\u5c0e", + "6812": "\u5922", + "6813": "\u7c73", + "6814": "\u51b7", + "6815": "\u606f", + "6816": "\u5175", + "6817": "\u5e2d", + "6818": "\u6e08", + "6819": "\u5287", + "6820": "\u63f4", + "6821": "\u98ef", + "6822": "\u592e", + "6823": "\u967a", + "6824": "\u670d", + "6825": "\u614b", + "6826": "\u8d70", + "6827": "\u8a55", + "6828": "\u5c45", + "6829": "\u6a29", + "6830": "\u8ad6", + "6831": "\u5f1f", + "6832": "\u3085", + "6833": "\u5883", + "6834": "\u5bdf", + "6835": "\u6388", + "6836": "\u983c", + "6837": "\u6d3e", + "6838": "\u64ae", + "6839": "\u7d20", + "6840": "\u4fee", + "6841": "\u7b2c", + "6842": "\u8cea", + "6843": "\u544a", + "6844": "\u8208", + "6845": "\u79d2", + "6846": "\u5b87", + "6847": "\u8089", + "6848": "\u5144", + "6849": "\u50cf", + "6850": "\u79f0", + "6851": "\u5024", + "6852": "\u982d", + "6853": "\u9031", + "6854": "\u7763", + "6855": "\u6d88", + "6856": "\u5b85", + "6857": "\u82b8", + "6858": "\u9854", + "6859": "\u8aad", + "6860": "\u4ef2", + "6861": "\u904a", + "6862": "\u8a66", + "6863": "\u901f", + "6864": "\u9152", + "6865": "\u5bbf", + "6866": "\u96e2", + "6867": "\u677e", + "6868": "\u5897", + "6869": "\u6bba", + "6870": "\u9244", + "6871": "\u53f8", + "6872": "\u5bb3", + "6873": "\u5272", + "6874": "\u77f3", + "6875": "\u590f", + "6876": "\u7248", + "6877": "\u4f50", + "6878": "\u52a9", + "6879": "\u82f1", + "6880": "\u53f7", + "6881": "\u60f3", + "6882": "\u7ba1", + "6883": "\u6025", + "6884": "\u9803", + "6885": "\u3065", + "6886": "\u82e5", + "6887": "\u604b", + "6888": "\u9020", + "6889": "\u53f2", + "6890": "\u6cc9", + "6891": "\u91cf", + "6892": "\u88fd", + "6893": "\u5e9c", + "6894": "\u8db3", + "6895": "\u6016", + "6896": "\u738b", + "6897": "\u59d4", + "6898": "\u4e21", + "6899": "\u8fba", + "6900": "\u6b8b", + "6901": "\u9006", + "6902": "\u5099", + "6903": "\u8ecd", + "6904": "\u8b66", + "6905": "\u67fb", + "6906": "\u5217", + "6907": "\u7de8", + "6908": "\u6bb5", + "6909": "\u53cd", + "6910": "\u30bc", + "6911": "\u643a", + "6912": "\u6b69", + "6913": "\u682a", + "6914": "\u5668", + "6915": "\u5ea7", + "6916": "\u98db", + "6917": "\u4e08", + "6918": "\u82b1", + "6919": "\u4fa1", + "6920": "\u76e3", + "6921": "\u5d0e", + "6922": "\u85e4", + "6923": "\u30d8", + "6924": "\u5468", + "6925": "\u6bce", + "6926": "\u7d71", + "6927": "\u53ce", + "6928": "\u843d", + "6929": "\u661f", + "6930": "\u964d", + "6931": "\u62c5", + "6932": "\u5074", + "6933": "\u7642", + "6934": "\u5e2b", + "6935": "\u5199", + "6936": "\u985e", + "6937": "\u547d", + "6938": "\u4ecb", + "6939": "\u9858", + "6940": "\u8b77", + "6941": "\u57ce", + "6942": "\u6b7b", + "6943": "\u679c", + "6944": "\u962a", + "6945": "\u4efb", + "6946": "\u66f4", + "6947": "\u5e38", + "6948": "\u4fbf", + "6949": "\u305c", + "6950": "\u691c", + "6951": "\u904e", + "6952": "\u8cc7", + "6953": "\u50cd", + "6954": "\u8a8d", + "6955": "\u822c", + "6956": "\u793a", + "6957": "\u5ba2", + "6958": "\u7fd2", + "6959": "\u7a76", + "6960": "\u534a", + "6961": "\u9332", + "6962": "\u5b57", + "6963": "\u6614", + "6964": "\u5eb7", + "6965": "\u90ce", + "6966": "\u5f71", + "6967": "\u899a", + "6968": "\u578b", + "6969": "\u58f0", + "6970": "\u4ef6", + "6971": "\u7fa9", + "6972": "\u65bd", + "6973": "\u798f", + "6974": "\u5bb9", + "6975": "\u8def", + "6976": "\u547c", + "6977": "\u5f79", + "6978": "\u5358", + "6979": "\u4e95", + "6980": "\u72b6", + "6981": "\u5efa", + "6982": "\u7531", + "6983": "\u5c5e", + "6984": "\u52c9", + "6985": "\u571f", + "6986": "\u8449", + "6987": "\u8d77", + "6988": "\u89a7", + "6989": "\u914d", + "6990": "\u5f35", + "6991": "\u63a5", + "6992": "\u8fbc", + "6993": "\u5f85", + "6994": "\u5ba4", + "6995": "\u75c5", + "6996": "\u5e2f", + "6997": "\u5acc", + "6998": "\u5a5a", + "6999": "\u5149", + "7000": "\u500b", + "7001": "\u8077", + "7002": "\u55b6", + "7003": "\u307c", + "7004": "\u7814", + "7005": "\u8a08", + "7006": "\u76f4", + "7007": "\u96e3", + "7008": "\u305e", + "7009": "\u7d76", + "7010": "\u30e8", + "7011": "\u7167", + "7012": "\u897f", + "7013": "\u7d04", + "7014": "\u5b58", + "7015": "\u9a13", + "7016": "\u6cbb", + "7017": "\u7236", + "7018": "\u89e3", + "7019": "\u5ca1", + "7020": "\u8ee2", + "7021": "\u5546", + "7022": "\u9032", + "7023": "\u4fc2", + "7024": "\u8aac", + "7025": "\u89b3", + "7026": "\u7403", + "7027": "\u4e57", + "7028": "\u5bae", + "7029": "\u652f", + "7030": "\u5f97", + "7031": "\u541b", + "7032": "\u8b70", + "7033": "\u5065", + "7034": "\u9580", + "7035": "\u6b62", + "7036": "\u91cd", + "7037": "\u6e29", + "7038": "\u7dd2", + "7039": "\u7740", + "7040": "\u98f2", + "7041": "\u6bcd", + "7042": "\u58eb", + "7043": "\u3056", + "7044": "\u96c6", + "7045": "\u4e07", + "7046": "\u592a", + "7047": "\u7d9a", + "7048": "\u7dda", + "7049": "\u7a2e", + "7050": "\u683c", + "7051": "\u4f4d", + "7052": "\u30e6", + "7053": "\u6b4c", + "7054": "\u591c", + "7055": "\u5171", + "7056": "\u6b63", + "7057": "\u5fc5", + "7058": "\u30d2", + "7059": "\u8272", + "7060": "\u554f", + "7061": "\u518d", + "7062": "\u57df", + "7063": "\u3086", + "7064": "\u52dd", + "7065": "\u53f0", + "7066": "\u6280", + "7067": "\u65c5", + "7068": "\u5f15", + "7069": "\u7cfb", + "7070": "\u9662", + "7071": "\u60aa", + "7072": "\u57fa", + "7073": "\u795e", + "7074": "\u9650", + "7075": "\u7523", + "7076": "\u6c7a", + "7077": "\u6c11", + "7078": "\u4ea4", + "7079": "\u653f", + "7080": "\u8cde", + "7081": "\u7a7a", + "7082": "\u533b", + "7083": "\u5f7c", + "7084": "\u592b", + "7085": "\u53ef", + "7086": "\u8ab0", + "7087": "\u53e4", + "7088": "\u5e30", + "7089": "\u8853", + "7090": "\u76f8", + "7091": "\u6751", + "7092": "\u56e3", + "7093": "\u4f1d", + "7094": "\u5186", + "7095": "\u4f4f", + "7096": "\u984c", + "7097": "\u5e73", + "7098": "\u4e88", + "7099": "\u97f3", + "7100": "\u671d", + "7101": "\u6307", + "7102": "\u771f", + "7103": "\u30f4", + "7104": "\u52d9", + "7105": "\u70b9", + "7106": "\u5404", + "7107": "\u9928", + "7108": "\u5fdc", + "7109": "\u73fe", + "7110": "\u5229", + "7111": "\u5929", + "7112": "\u7b49", + "7113": "\u6728", + "7114": "\u767d", + "7115": "\u5f62", + "7116": "\u4f9b", + "7117": "\u7d4c", + "7118": "\u3047", + "7119": "\u65cf", + "7120": "\u65e9", + "7121": "\u4f8b", + "7122": "\u50d5", + "7123": "\u4e0d", + "7124": "\u5207", + "7125": "\u5357", + "7126": "\u52a0", + "7127": "\u969b", + "7128": "\u7d42", + "7129": "\u69d8", + "7130": "\u653e", + "7131": "\u548c", + "7132": "\u4f11", + "7133": "\u5dde", + "7134": "\u6c34", + "7135": "\u5354", + "7136": "\u5728", + "7137": "\u7d44", + "7138": "\u5411", + "7139": "\u5e83", + "7140": "\u8eab", + "7141": "\u754c", + "7142": "\u5de5", + "7143": "\u9078", + "7144": "\u59cb", + "7145": "\u5143", + "7146": "\u96f6", + "7147": "\u3005", + "7148": "\u89aa", + "7149": "\u7f8e", + "7150": "\u4fe1", + "7151": "\u90fd", + "7152": "\u7f6e", + "7153": "\u5c40", + "7154": "\u99c5", + "7155": "\u904b", + "7156": "\u9001", + "7157": "\u98a8", + "7158": "\u53e3", + "7159": "\u6f14", + "7160": "\u8abf", + "7161": "\u304e", + "7162": "\u512a", + "7163": "\u6b21", + "7164": "\u30a9", + "7165": "\u4ed6", + "7166": "\u5712", + "7167": "\u4fdd", + "7168": "\u7537", + "7169": "\u53c2", + "7170": "\u5c11", + "7171": "\u767e", + "7172": "\u7279", + "7173": "\u8003", + "7174": "\u7121", + "7175": "\u4e03", + "7176": "\u30e4", + "7177": "\u30ae", + "7178": "\u826f", + "7179": "\u30b6", + "7180": "\u5236", + "7181": "\u4eac", + "7182": "\u611b", + "7183": "\u58f2", + "7184": "\u80fd", + "7185": "\u539f", + "7186": "\u30b2", + "7187": "\u6709", + "7188": "\u516d", + "7189": "\u5b89", + "7190": "\u30b4", + "7191": "\u80b2", + "7192": "\u79d1", + "7193": "\u8981", + "7194": "\u6599", + "7195": "\u66f8", + "7196": "\u8a9e", + "7197": "\u8a2d", + "7198": "\u6d77", + "7199": "\u671f", + "7200": "\u6d41", + "7201": "\u78ba", + "7202": "\u30da", + "7203": "\u533a", + "7204": "\u3080", + "7205": "\u9023", + "7206": "\u8cb7", + "7207": "\u3072", + "7208": "\u3075", + "7209": "\u4ed8", + "7210": "\u753a", + "7211": "\u6d3b", + "7212": "\u60c5", + "7213": "\u6708", + "7214": "\u8868", + "7215": "\u66f2", + "7216": "\u5f37", + "7217": "\u4e16", + "7218": "\u660e", + "7219": "\u6210", + "7220": "\u30ce", + "7221": "\u30a1", + "7222": "\u6587", + "7223": "\u9055", + "7224": "\u6771", + "7225": "\u53cb", + "7226": "\u610f", + "7227": "\u529b", + "7228": "\u5f0f", + "7229": "\u6cd5", + "7230": "\u5831", + "7231": "\u54e1", + "7232": "\u5fc3", + "7233": "\u5c4b", + "7234": "\u54c1", + "7235": "\u5317", + "7236": "\u5148", + "7237": "\u5cf6", + "7238": "\u5473", + "7239": "\u5ddd", + "7240": "\u958b", + "7241": "\u5343", + "7242": "\u95a2", + "7243": "\u516b", + "7244": "\u96fb", + "7245": "\u7136", + "7246": "\u5ea6", + "7247": "\u4ffa", + "7248": "\u9054", + "7249": "\u9762", + "7250": "\u4e5d", + "7251": "\u6570", + "7252": "\u53d6", + "7253": "\u697d", + "7254": "\u91d1", + "7255": "\u6027", + "7256": "\u91ce", + "7257": "\u5225", + "7258": "\u6226", + "7259": "\u516c", + "7260": "\u6a5f", + "7261": "\u9053", + "7262": "\u76ee", + "7263": "\u8a18", + "7264": "\u3073", + "7265": "\u767a", + "7266": "\u5bfe", + "7267": "\u7acb", + "7268": "\u521d", + "7269": "\u5316", + "7270": "\u30bd", + "7271": "\u56db", + "7272": "\u30ef", + "7273": "\u7530", + "7274": "\u6301", + "7275": "\u30ac", + "7276": "\u8eca", + "7277": "\u756a", + "7278": "\u30d4", + "7279": "\u805e", + "7280": "\u56de", + "7281": "\u3041", + "7282": "\u3076", + "7283": "\u30d9", + "7284": "\u4e94", + "7285": "\u3052", + "7286": "\u5b9f", + "7287": "\u30dc", + "7288": "\u5e97", + "7289": "\u5c0f", + "7290": "\u5b9a", + "7291": "\u30e2", + "7292": "\u9577", + "7293": "\u65b0", + "7294": "\u30cf", + "7295": "\u30b1", + "7296": "\u5916", + "7297": "\u30dd", + "7298": "\u8fd1", + "7299": "\u6240", + "7300": "\u3078", + "7301": "\u770c", + "7302": "\u540c", + "7303": "\u30cd", + "7304": "\u5185", + "7305": "\u5973", + "7306": "\u30db", + "7307": "\u4f53", + "7308": "\u597d", + "7309": "\u30c4", + "7310": "\u30bb", + "7311": "\u77e5", + "7312": "\u5c71", + "7313": "\u6765", + "7314": "\u30a7", + "7315": "\u4f7f", + "7316": "\u30e7", + "7317": "\u30ba", + "7318": "\u4e3b", + "7319": "\u52d5", + "7320": "\u7406", + "7321": "\u7269", + "7322": "\u6620", + "7323": "\u8005", + "7324": "\u3050", + "7325": "\u7684", + "7326": "\u4ee3", + "7327": "\u5909", + "7328": "\u6559", + "7329": "\u793e", + "7330": "\u7528", + "7331": "\u8a71", + "7332": "\u540d", + "7333": "\u69cb", + "7334": "\u9ad8", + "7335": "\u6700", + "7336": "\u305a", + "7337": "\u30df", + "7338": "\u6821", + "7339": "\u30c0", + "7340": "\u98df", + "7341": "\u5f8c", + "7342": "\u624b", + "7343": "\u4e09", + "7344": "\u901a", + "7345": "\u611f", + "7346": "\u5408", + "7347": "\u591a", + "7348": "\u696d", + "7349": "\u5165", + "7350": "\u30a8", + "7351": "\u5834", + "7352": "\u3079", + "7353": "\u4e0a", + "7354": "\u5bb6", + "7355": "\u79c1", + "7356": "\u5e74", + "7357": "\u9593", + "7358": "\u753b", + "7359": "\u524d", + "7360": "\u4e0b", + "7361": "\u30e3", + "7362": "\u5730", + "7363": "\u4e8c", + "7364": "\u30a6", + "7365": "\u30ca", + "7366": "\u30d3", + "7367": "\u81ea", + "7368": "\u5168", + "7369": "\u30d1", + "7370": "\u7d50", + "7371": "\u30d6", + "7372": "\u30e5", + "7373": "\u5e02", + "7374": "\u30b5", + "7375": "\u6c17", + "7376": "\u65b9", + "7377": "\u30c7", + "7378": "\u5341", + "7379": "\u30ad", + "7380": "\u5f53", + "7381": "\u56fd", + "7382": "\u4f5c", + "7383": "\u30a3", + "7384": "\u90e8", + "7385": "\u30aa", + "7386": "\u30cb", + "7387": "\u30c1", + "7388": "\u30e0", + "7389": "\u30b0", + "7390": "\u30e1", + "7391": "\u3054", + "7392": "\u5b50", + "7393": "\u3070", + "7394": "\u751f", + "7395": "\u307b", + "7396": "\u3071", + "7397": "\u305b", + "7398": "\u4f55", + "7399": "\u51fa", + "7400": "\u8a00", + "7401": "\u4eca", + "7402": "\u30d0", + "7403": "\u4e8b", + "7404": "\u4e2d", + "7405": "\u30d7", + "7406": "\u6642", + "7407": "\u30b3", + "7408": "\u898b", + "7409": "\u30c6", + "7410": "\u4f1a", + "7411": "\u30de", + "7412": "\u30ab", + "7413": "\u601d", + "7414": "\u30ed", + "7415": "\u30b8", + "7416": "\u30d5", + "7417": "\u30b7", + "7418": "\u3081", + "7419": "\u30ec", + "7420": "\u30c9", + "7421": "\u5206", + "7422": "\u3087", + "7423": "\u308d", + "7424": "\u5b66", + "7425": "\u884c", + "7426": "\u30bf", + "7427": "\u5927", + "7428": "\u3064", + "7429": "\u672c", + "7430": "\u65e5", + "7431": "\u308f", + "7432": "\u4e00", + "7433": "\u30af", + "7434": "\u307f", + "7435": "\u30ea", + "7436": "\u30a2", + "7437": "\u30c3", + "7438": "\u4eba", + "7439": "\u30e9", + "7440": "\uff1f", + "7441": "\u304a", + "7442": "\u3058", + "7443": "\u30a4", + "7444": "\u30eb", + "7445": "\u30c8", + "7446": "\u3083", + "7447": "\u304d", + "7448": "\u3055", + "7449": "\u3061", + "7450": "\u3084", + "7451": "\u30b9", + "7452": "\u3069", + "7453": "\u3051", + "7454": "\u304f", + "7455": "\u3048", + "7456": "\u3092", + "7457": "\u308a", + "7458": "\u3088", + "7459": "\u3053", + "7460": "\u30f3", + "7461": "\u3060", + "7462": "\u308c", + "7463": "\u3089", + "7464": "\u306d", + "7465": "\u304c", + "7466": "\u307e", + "7467": "\u30fc", + "7468": "\u3082", + "7469": "\u305d", + "7470": "\u3057", + "7471": "\u306b", + "7472": "\u306f", + "7473": "\u308b", + "7474": "\u3059", + "7475": "\u3068", + "7476": "\u305f", + "7477": "\u3042", + "7478": "\u3066", + "7479": "\u3063", + "7480": "\u3067", + "7481": "\u304b", + "7482": "\u306a", + "7483": "\u3093", + "7484": "\u3046", + "7485": "\u306e", + "7486": "\u3001", + "7487": "\u3002", + "7488": "\u3044", + "7489": "", + "7490": "\uc774", + "7491": "\uac00", + "7492": "\uc744", + "7493": "\ub294", + "7494": "\uc5d0", + "7495": "\ub3c4", + "7496": "\uace0", + "7497": "\uc758", + "7498": "\uc9c0", + "7499": "\ub97c", + "7500": "\u2581\uadf8", + "7501": "\ub2e4", + "7502": "\uc740", + "7503": "\uae30", + "7504": "\ud55c", + "7505": "\uc5b4", + "7506": "\uc2dc", + "7507": "\uc790", + "7508": "\uc11c", + "7509": "\ub85c", + "7510": "\ud574", + "7511": "\ub9ac", + "7512": "\uc694", + "7513": "\uc0ac", + "7514": "\u2581\ubb50", + "7515": "\uc778", + "7516": "\uac8c", + "7517": "\uc5d0\uc11c", + "7518": "\u2581\uc774\uc81c", + "7519": "\uc815", + "7520": "\ud558", + "7521": "\u2581\uc5b4", + "7522": "\u2581\uac70", + "7523": "\ud558\ub294", + "7524": "\ub098", + "7525": "\ub300", + "7526": "\u2581\uc880", + "7527": "\ud558\uace0", + "7528": "\ub9cc", + "7529": "\u2581\uc218", + "7530": "\u2581\uc544", + "7531": "\uc7a5", + "7532": "\uba74", + "7533": "\uc73c\ub85c", + "7534": "\uc6d0", + "7535": "\uc57c", + "7536": "\uc8fc", + "7537": "\uacfc", + "7538": "\uc0c1", + "7539": "\uad6c", + "7540": "\uc2a4", + "7541": "\uc77c", + "7542": "\u2581\uadf8\ub7f0", + "7543": "\ub77c", + "7544": "\uc218", + "7545": "\ud560", + "7546": "\uc544", + "7547": "\ub4e4", + "7548": "\u2581\uc774\ub7f0", + "7549": "\u2581\uc608", + "7550": "\uac70", + "7551": "\u2581\uc9c0\uae08", + "7552": "\uc131", + "7553": "\u2581\ubcf4", + "7554": "\u2581\uc548", + "7555": "\ubcf4", + "7556": "\u2581\ub610", + "7557": "\ub3d9", + "7558": "\uc18c", + "7559": "\uc2e0", + "7560": "\u2581\uc788\ub294", + "7561": "\uc2ed", + "7562": "\u2581\uac83", + "7563": "\uac04", + "7564": "\uc81c", + "7565": "\ub294\ub370", + "7566": "\uac74", + "7567": "\u2581\ub300", + "7568": "\ubd80", + "7569": "\ud654", + "7570": "\uc804", + "7571": "\u2581\uc804", + "7572": "\u2581\uc774\ub807\uac8c", + "7573": "\u2581\uc77c", + "7574": "\u2581\uadfc\ub370", + "7575": "\ub4e4\uc774", + "7576": "\u2581\uadf8\ub798\uc11c", + "7577": "\ub370", + "7578": "\ud588", + "7579": "\uce58", + "7580": "\uc120", + "7581": "\ub4dc", + "7582": "\u2581\ub9ce\uc774", + "7583": "\uc138", + "7584": "\uc9c4", + "7585": "\uc5f0", + "7586": "\uc5ec", + "7587": "\uad00", + "7588": "\ubd84", + "7589": "\u2581\ub124", + "7590": "\ub9c8", + "7591": "\uc624", + "7592": "\ubbf8", + "7593": "\uc704", + "7594": "\uc8e0", + "7595": "\uc2b5\ub2c8\ub2e4", + "7596": "\uacc4", + "7597": "\uc2dd", + "7598": "\ubb34", + "7599": "\uc788", + "7600": "\ubb38", + "7601": "\ub2f9", + "7602": "\uc7ac", + "7603": "\ub144", + "7604": "\uccb4", + "7605": "\u2581\ub098", + "7606": "\uc640", + "7607": "\uc6b0", + "7608": "\ub77c\uace0", + "7609": "\uc2e4", + "7610": "\u2581\ub54c", + "7611": "\ub2e8", + "7612": "\ud1b5", + "7613": "\uc601", + "7614": "\u2581\uc8fc", + "7615": "\uc801", + "7616": "\uba85", + "7617": "\u2581\uc54a", + "7618": "\u2581\ub9d0", + "7619": "\u2581\uc624", + "7620": "\u2581\uc788\ub2e4", + "7621": "\ud574\uc11c", + "7622": "\ub824", + "7623": "\u2581\uc5b4\ub5a4", + "7624": "\ubc29", + "7625": "\uc0b0", + "7626": "\u2581\uc6b0\ub9ac", + "7627": "\ucc28", + "7628": "\u2581\uc800", + "7629": "\ubb3c", + "7630": "\ub2c8", + "7631": "\u2581\uc544\ub2c8", + "7632": "\u2581\ub354", + "7633": "\u2581\uc0ac", + "7634": "\ubc18", + "7635": "\ub2c8\ub2e4", + "7636": "\uc810", + "7637": "\u2581\ube44", + "7638": "\ud2b8", + "7639": "\u2581\uc74c", + "7640": "\uc6a9", + "7641": "\uc5c5", + "7642": "\uacbd", + "7643": "\uc0dd", + "7644": "\uc801\uc73c\ub85c", + "7645": "\uacf5", + "7646": "\u2581\ub0b4", + "7647": "\u2581\uadf8\ub9ac\uace0", + "7648": "\uad6d", + "7649": "\ub7ec", + "7650": "\uc548", + "7651": "\ube44", + "7652": "\uae4c\uc9c0", + "7653": "\ub2c8\uae4c", + "7654": "\uae08", + "7655": "\uc6b4", + "7656": "\u2581\uc774\uac8c", + "7657": "\u2581\uacf5", + "7658": "\ub0b4", + "7659": "\ud68c", + "7660": "\u2581\uc798", + "7661": "\ud558\uac8c", + "7662": "\ud589", + "7663": "\uc870", + "7664": "\ubaa8", + "7665": "\uac10", + "7666": "\uac00\uc9c0\uace0", + "7667": "\u2581\ub9c9", + "7668": "\uc9d1", + "7669": "\ub41c", + "7670": "\uac83", + "7671": "\ubc1c", + "7672": "\ud559", + "7673": "\uc2ec", + "7674": "\ub358", + "7675": "\ubc31", + "7676": "\u2581\uc720", + "7677": "\ub77c\ub294", + "7678": "\ub0a8", + "7679": "\u2581\ub54c\ubb38\uc5d0", + "7680": "\u2581\uadf8\ub7ec\ub2c8\uae4c", + "7681": "\ub418\ub294", + "7682": "\uc785\ub2c8\ub2e4", + "7683": "\ud0c0", + "7684": "\uad50", + "7685": "\u2581\ub4e4\uc5b4", + "7686": "\u2581\uc5c6", + "7687": "\uc5b4\uc694", + "7688": "\ubc95", + "7689": "\uc801\uc778", + "7690": "\uc5ed", + "7691": "\u2581\uc0dd\uac01", + "7692": "\ub9e4", + "7693": "\ubbfc", + "7694": "\ud55c\ub2e4", + "7695": "\u2581\uac19\uc740", + "7696": "\u2581\uadf8\ub0e5", + "7697": "\ubc30", + "7698": "\ub974", + "7699": "\u2581\ub418", + "7700": "\ubd80\ubd84", + "7701": "\uc721", + "7702": "\u2581\uc598\uae30", + "7703": "\ud638", + "7704": "\ud504", + "7705": "\ub0a0", + "7706": "\u2581\ubabb", + "7707": "\u2581\uc0ac\uc2e4", + "7708": "\uac70\ub4e0\uc694", + "7709": "\ucc9c", + "7710": "\ub4f1", + "7711": "\u2581\uc5b4\ub5bb\uac8c", + "7712": "\u2581\uc81c", + "7713": "\uc9c0\ub9cc", + "7714": "\ud788", + "7715": "\u2581\uc81c\uac00", + "7716": "\u2581\uadf8\ub807\uac8c", + "7717": "\ub354", + "7718": "\uad8c", + "7719": "\ud558\uba74", + "7720": "\ucd9c", + "7721": "\ub2e4\uace0", + "7722": "\ub2ec", + "7723": "\uaca0", + "7724": "\uc791", + "7725": "\uc785", + "7726": "\u2581\uc800\ub294", + "7727": "\ud574\uc57c", + "7728": "\u2581\ubd80", + "7729": "\u2581\uc9c4\uc9dc", + "7730": "\ud45c", + "7731": "\uc9c1", + "7732": "\uc591", + "7733": "\u2581\ubc14", + "7734": "\ud569\ub2c8\ub2e4", + "7735": "\uc0b4", + "7736": "\ub825", + "7737": "\uc5c8", + "7738": "\ud588\ub2e4", + "7739": "\u2581\ub108\ubb34", + "7740": "\u2581\uac00\uc7a5", + "7741": "\u2581\uc870", + "7742": "\ud314", + "7743": "\uc911", + "7744": "\ub2d8", + "7745": "\u2581\ub0b4\uac00", + "7746": "\uc720", + "7747": "\ub798", + "7748": "\ubc84", + "7749": "\ubc88", + "7750": "\uac1c", + "7751": "\ud6c4", + "7752": "\uc796\uc544\uc694", + "7753": "\ud558\uc9c0", + "7754": "\ud53c", + "7755": "\uc885", + "7756": "\ub124", + "7757": "\ud604", + "7758": "\u2581\uc788\uc2b5\ub2c8\ub2e4", + "7759": "\u2581\uc88b", + "7760": "\ub9de", + "7761": "\ub09c", + "7762": "\uac19", + "7763": "\u2581\uad49\uc7a5\ud788", + "7764": "\u2581\uc911", + "7765": "\ucd94", + "7766": "\uc6d4", + "7767": "\uc5d0\ub294", + "7768": "\uccad", + "7769": "\uc18d", + "7770": "\u2581\uc0ac\ub78c", + "7771": "\ubc1b", + "7772": "\uc9c8", + "7773": "\ub178", + "7774": "\ud615", + "7775": "\u2581\uac78", + "7776": "\uad70", + "7777": "\uc600", + "7778": "\ud30c", + "7779": "\ub514", + "7780": "\ubcf8", + "7781": "\ub3fc", + "7782": "\ub108", + "7783": "\u2581\uadf8\ub7ec\uba74", + "7784": "\u2581\ubd88", + "7785": "\u2581\ub450", + "7786": "\u2581\uc624\ub298", + "7787": "\u2581\uac1c", + "7788": "\ucd5c", + "7789": "\u2581\uc0bc", + "7790": "\ud06c", + "7791": "\ub410", + "7792": "\ud3b8", + "7793": "\ucabd", + "7794": "\ud310", + "7795": "\ub54c", + "7796": "\u2581\ub418\uac8c", + "7797": "\u2581\ub098\ub294", + "7798": "\ubd80\ud130", + "7799": "\ub791", + "7800": "\u2581\uadf8\uac70", + "7801": "\u2581\ub300\ud574\uc11c", + "7802": "\u2581\uc815\ub3c4", + "7803": "\ub808", + "7804": "\u2581\uae40", + "7805": "\u2581\uc774\uac70", + "7806": "\u2581\uc788\uace0", + "7807": "\u2581\uac15", + "7808": "\u2581\ub300\ud55c", + "7809": "\uc73c\uba74", + "7810": "\u2581\uadf8\uac8c", + "7811": "\u2581\ubb38\uc81c", + "7812": "\ud3ec", + "7813": "\ubaa9", + "7814": "\uacb0", + "7815": "\uc900", + "7816": "\ud0dc", + "7817": "\u2581\ud558\ub098", + "7818": "\uc678", + "7819": "\uc528", + "7820": "\uc796\uc544", + "7821": "\uc784", + "7822": "\uce60", + "7823": "\uc5f4", + "7824": "\ubcc0", + "7825": "\ub41c\ub2e4", + "7826": "\uc608\uc694", + "7827": "\ud0a4", + "7828": "\ubc15", + "7829": "\u2581\uadf8\ub807", + "7830": "\uae4c", + "7831": "\u2581\ub9d0\uc500", + "7832": "\u2581\uc870\uae08", + "7833": "\ud130", + "7834": "\u2581\uc6b0\ub9ac\uac00", + "7835": "\uc57d", + "7836": "\uc778\ub370", + "7837": "\uae34", + "7838": "\ub9ce", + "7839": "\ud558\uae30", + "7840": "\ub4e0", + "7841": "\u2581\uc57d\uac04", + "7842": "\u2581\uc788\uc5c8", + "7843": "\u2581\ub420", + "7844": "\uaca9", + "7845": "\uc6cc", + "7846": "\ub4e4\uc740", + "7847": "\ud558\ub2e4", + "7848": "\u2581\ub2e4\ub978", + "7849": "\uba39", + "7850": "\u2581\uc815\ub9d0", + "7851": "\u2581\uc65c", + "7852": "\uba74\uc11c", + "7853": "\uc220", + "7854": "\ud569", + "7855": "\uc99d", + "7856": "\u2581\uacc4\uc18d", + "7857": "\uce74", + "7858": "\u2581\uacbd\uc6b0", + "7859": "\ud3c9", + "7860": "\ub0d0", + "7861": "\uc774\ub2e4", + "7862": "\ubd24", + "7863": "\ub4e4\uc744", + "7864": "\uc11d", + "7865": "\uac01", + "7866": "\ubcf4\ub2e4", + "7867": "\ubd84\ub4e4", + "7868": "\uadfc", + "7869": "\ub9b0", + "7870": "\ubcfc", + "7871": "\uae09", + "7872": "\uc54c", + "7873": "\uc124", + "7874": "\uc558", + "7875": "\ub418", + "7876": "\ucd08", + "7877": "\uc4f0", + "7878": "\uc74c", + "7879": "\u2581\ub098\uc624", + "7880": "\uc73c", + "7881": "\uc62c", + "7882": "\uc838", + "7883": "\ucc45", + "7884": "\ud655", + "7885": "\uac08", + "7886": "\ub3c8", + "7887": "\u2581\uc788\ub294\ub370", + "7888": "\ubcf5", + "7889": "\uc751", + "7890": "\ub418\uace0", + "7891": "\uc904", + "7892": "\u2581\ub9ce\uc740", + "7893": "\ub839", + "7894": "\ud5a5", + "7895": "\uac70\uc8e0", + "7896": "\u2581\ubcf4\uba74", + "7897": "\ub8e8", + "7898": "\uc5b8", + "7899": "\uc808", + "7900": "\uc5d0\uc11c\ub294", + "7901": "\ud2f0", + "7902": "\u2581\ud55c\uad6d", + "7903": "\ud1a0", + "7904": "\ud55c\ud14c", + "7905": "\u2581\ub9de\uc544", + "7906": "\uc5d0\uac8c", + "7907": "\u2581\uadf8\ub7f0\ub370", + "7908": "\ub2e4\ub294", + "7909": "\u2581\uc0c1\ud669", + "7910": "\u2581\uadf8\ub7ec", + "7911": "\uc774\ub77c\uace0", + "7912": "\ub8cc", + "7913": "\uc774\ub098", + "7914": "\u2581\uc5ec\uae30", + "7915": "\ubc14", + "7916": "\u2581\uc544\uc774", + "7917": "\uc560", + "7918": "\ub300\ub85c", + "7919": "\u2581\uac70\uae30", + "7920": "\u2581\uc88b\uc544", + "7921": "\ucc38", + "7922": "\uace0\uc694", + "7923": "\uadf8", + "7924": "\uba74\uc740", + "7925": "\uc0bc", + "7926": "\uad6c\uc694", + "7927": "\ub984", + "7928": "\ucc98\ub7fc", + "7929": "\ub2f4", + "7930": "\u2581\uc788\uc744", + "7931": "\u2581\uc88b\uc740", + "7932": "\ud488", + "7933": "\uc800", + "7934": "\uc2b9", + "7935": "\u2581\ubbf8\uad6d", + "7936": "\u2581\uac19\uc560", + "7937": "\ud558\uc2dc", + "7938": "\ubcd1", + "7939": "\ud658", + "7940": "\u2581\ud544\uc694", + "7941": "\u2581\uc0ac\ub78c\ub4e4", + "7942": "\uc9c0\ub294", + "7943": "\uc545", + "7944": "\u2581\ud55c\ubc88", + "7945": "\u2581\uc778\uc81c", + "7946": "\ub860", + "7947": "\uc21c", + "7948": "\uc628", + "7949": "\ucc98", + "7950": "\uc84c", + "7951": "\uc168", + "7952": "\u2581\uadf8\ub54c", + "7953": "\ub450", + "7954": "\uac14", + "7955": "\uc190", + "7956": "\uc6b8", + "7957": "\ubc8c", + "7958": "\ucf54", + "7959": "\u2581\uadf8\ub2c8\uae4c", + "7960": "\ucde8", + "7961": "\u2581\uc788\uc5b4", + "7962": "\uc804\uc5d0", + "7963": "\u2581\uac83\uc774", + "7964": "\ub204", + "7965": "\u2581\uc0ac\ub78c\ub4e4\uc774", + "7966": "\u2581\uc790\uae30", + "7967": "\ub838", + "7968": "\u2581\uc544\ub2c8\ub77c", + "7969": "\uc608", + "7970": "\ud22c", + "7971": "\uc2b5\ub2c8\uae4c", + "7972": "\u2581\uc77c\ub2e8", + "7973": "\u2581\uc5c6\ub294", + "7974": "\ud070", + "7975": "\u2581\uc0dd\uac01\uc744", + "7976": "\ub978", + "7977": "\uc0c8", + "7978": "\uae38", + "7979": "\ud0dd", + "7980": "\ud50c", + "7981": "\uac81", + "7982": "\u2581\uc694\uc998", + "7983": "\u2581\uadf8\ub7fc", + "7984": "\uba38", + "7985": "\u2581\ubb54\uac00", + "7986": "\uc2f6", + "7987": "\uac80", + "7988": "\uba54", + "7989": "\uac70\ub098", + "7990": "\ub780", + "7991": "\ub3c5", + "7992": "\uba87", + "7993": "\uc654", + "7994": "\ubd81", + "7995": "\ud588\uc2b5\ub2c8\ub2e4", + "7996": "\u2581\uadf8\ub798", + "7997": "\uacf3", + "7998": "\uc871", + "7999": "\uaca8", + "8000": "\ube60", + "8001": "\u2581\ubcf4\ub2c8\uae4c", + "8002": "\u2581\uc815\ubd80", + "8003": "\ub9dd", + "8004": "\uc788\ub294", + "8005": "\ub098\uc694", + "8006": "\uc6c0", + "8007": "\u2581\uc0ac\ub78c\uc774", + "8008": "\u2581\uc598\uae30\ub97c", + "8009": "\ub193", + "8010": "\ud2b9", + "8011": "\u2581\uac83\ub3c4", + "8012": "\u2581\uc774\uc57c\uae30", + "8013": "\uad11", + "8014": "\uba70", + "8015": "\uac15", + "8016": "\ud558\uba74\uc11c", + "8017": "\ub85d", + "8018": "\ub9d0", + "8019": "\ube0c", + "8020": "\u2581\uad00\ub828", + "8021": "\u2581\uc2dc\uc791", + "8022": "\uae00", + "8023": "\ud588\ub358", + "8024": "\u2581\uacbd\uc81c", + "8025": "\uc644", + "8026": "\uaca0\ub2e4", + "8027": "\uaca0\uc2b5\ub2c8\ub2e4", + "8028": "\u2581\uce5c\uad6c", + "8029": "\u2581\uad6d\ubbfc", + "8030": "\u2581\uadf8\uac83", + "8031": "\ubd10", + "8032": "\ud65c", + "8033": "\ub192", + "8034": "\u2581\uc788\uc5b4\uc694", + "8035": "\uc774\ub77c\ub294", + "8036": "\u2581\ub2e4\uc2dc", + "8037": "\u2581\uc5ec\ub7ec", + "8038": "\ucd1d", + "8039": "\uc7a1", + "8040": "\ub2a5", + "8041": "\ud56d", + "8042": "\ub958", + "8043": "\uaddc", + "8044": "\ub530", + "8045": "\ucc44", + "8046": "\uc874", + "8047": "\ub9bd", + "8048": "\uce5c", + "8049": "\uc7c1", + "8050": "\ub298", + "8051": "\ubc94", + "8052": "\ubcc4", + "8053": "\uce21", + "8054": "\ud14c", + "8055": "\ucca0", + "8056": "\ub531", + "8057": "\uc5d4", + "8058": "\uc5b5", + "8059": "\ub05d", + "8060": "\ub77d", + "8061": "\ub9b4", + "8062": "\ucc3d", + "8063": "\uadf9", + "8064": "\uc918", + "8065": "\ud611", + "8066": "\ud328", + "8067": "\ucee4", + "8068": "\uc55e", + "8069": "\ub3cc", + "8070": "\ucda9", + "8071": "\uc0c9", + "8072": "\ub208", + "8073": "\uc154", + "8074": "\uc775", + "8075": "\uc811", + "8076": "\uc1a1", + "8077": "\ud798", + "8078": "\ub0ac", + "8079": "\uafb8", + "8080": "\uaed8", + "8081": "\uc2f8", + "8082": "\ub420", + "8083": "\ub2f5", + "8084": "\ud5d8", + "8085": "\uce68", + "8086": "\uc728", + "8087": "\ub7fd", + "8088": "\ud398", + "8089": "\uac78", + "8090": "\ub4a4", + "8091": "\ud76c", + "8092": "\ucf1c", + "8093": "\ubab0", + "8094": "\ud600", + "8095": "\uc368", + "8096": "\ud300", + "8097": "\uc158", + "8098": "\ucd95", + "8099": "\ub7c9", + "8100": "\ud63c", + "8101": "\uccd0", + "8102": "\ub4dd", + "8103": "\ubc00", + "8104": "\ubca0", + "8105": "\ud669", + "8106": "\ud3ed", + "8107": "\ub140", + "8108": "\uc27d", + "8109": "\ud2c0", + "8110": "\ud6a8", + "8111": "\uace8", + "8112": "\ubd88", + "8113": "\ub78c", + "8114": "\ub840", + "8115": "\ub290", + "8116": "\uc988", + "8117": "\ube14", + "8118": "\ub180", + "8119": "\ud568", + "8120": "\ub5a8", + "8121": "\ud154", + "8122": "\ud5c8", + "8123": "\ub17c", + "8124": "\uad81", + "8125": "\ub9bc", + "8126": "\ud0c4", + "8127": "\ub7f4", + "8128": "\uacac", + "8129": "\uc529", + "8130": "\ub7f0", + "8131": "\ub5a0", + "8132": "\ub118", + "8133": "\ud480", + "8134": "\uc8fd", + "8135": "\uc8c4", + "8136": "\uc2b5", + "8137": "\ud575", + "8138": "\uadc0", + "8139": "\uc61b", + "8140": "\uc724", + "8141": "\ud64d", + "8142": "\ub07c", + "8143": "\ub18d", + "8144": "\ub828", + "8145": "\uac16", + "8146": "\uccab", + "8147": "\ub458", + "8148": "\ud639", + "8149": "\uc5e0", + "8150": "\uc9d5", + "8151": "\ud6c8", + "8152": "\ud0c8", + "8153": "\ucf00", + "8154": "\uaf2d", + "8155": "\ub960", + "8156": "\ud601", + "8157": "\uc12f", + "8158": "\ucc29", + "8159": "\ud734", + "8160": "\ubd09", + "8161": "\ud074", + "8162": "\uc5fc", + "8163": "\ubc1d", + "8164": "\ud48d", + "8165": "\uc2ac", + "8166": "\ubd99", + "8167": "\uace1", + "8168": "\uc5bc", + "8169": "\ucc0d", + "8170": "\ubfd0", + "8171": "\uc9dc", + "8172": "\ubab8", + "8173": "\uc7a0", + "8174": "\ub123", + "8175": "\ub79c", + "8176": "\uc26c", + "8177": "\ub4ef", + "8178": "\ub110", + "8179": "\ub790", + "8180": "\ubc0f", + "8181": "\uc655", + "8182": "\ud37c", + "8183": "\uc555", + "8184": "\ub9db", + "8185": "\ub0ae", + "8186": "\ud0c1", + "8187": "\uc561", + "8188": "\uc92c", + "8189": "\ucc2c", + "8190": "\uc9f8", + "8191": "\ud544", + "8192": "\uc288", + "8193": "\uc13c", + "8194": "\ub099", + "8195": "\ud3d0", + "8196": "\ub73b", + "8197": "\uac11", + "8198": "\uc695", + "8199": "\ud3f0", + "8200": "\uc77d", + "8201": "\uc5c6", + "8202": "\ud2bc", + "8203": "\uc6c3", + "8204": "\ub9e8", + "8205": "\uce35", + "8206": "\ucc3e", + "8207": "\uc219", + "8208": "\ud1f4", + "8209": "\uad74", + "8210": "\uade0", + "8211": "\uc6e0", + "8212": "\ud765", + "8213": "\ub150", + "8214": "\ub4e3", + "8215": "\uc637", + "8216": "\ub0c8", + "8217": "\ud754", + "8218": "\ud61c", + "8219": "\uc554", + "8220": "\uac1d", + "8221": "\uc5d8", + "8222": "\ub274", + "8223": "\uae68", + "8224": "\ubb58", + "8225": "\ub04c", + "8226": "\ub6f0", + "8227": "\uc2eb", + "8228": "\ub054", + "8229": "\uce90", + "8230": "\ub2d0", + "8231": "\uacbc", + "8232": "\uc559", + "8233": "\ud750", + "8234": "\ub7b5", + "8235": "\ubcbd", + "8236": "\uc598", + "8237": "\ub9c9", + "8238": "\uaebc", + "8239": "\uc9d3", + "8240": "\uc990", + "8241": "\ucc30", + "8242": "\uba3c", + "8243": "\uc4f8", + "8244": "\ub355", + "8245": "\uae50", + "8246": "\ubc25", + "8247": "\uc5c7", + "8248": "\ub728", + "8249": "\ucdb0", + "8250": "\ubd05", + "8251": "\ubbff", + "8252": "\ub807", + "8253": "\uce59", + "8254": "\ub0b8", + "8255": "\ubc24", + "8256": "\uc9dd", + "8257": "\uc5bb", + "8258": "\uc794", + "8259": "\uc058", + "8260": "\ud0ac", + "8261": "\ud138", + "8262": "\uc6b1", + "8263": "\uac12", + "8264": "\ub9c1", + "8265": "\uc88c", + "8266": "\ube4c", + "8267": "\ucee8", + "8268": "\ub86d", + "8269": "\ud5cc", + "8270": "\uac54", + "8271": "\ucfe0", + "8272": "\ud305", + "8273": "\ube7c", + "8274": "\uc228", + "8275": "\uce20", + "8276": "\uc5c4", + "8277": "\ud614", + "8278": "\ub545", + "8279": "\uafc8", + "8280": "\ub2e5", + "8281": "\ub0bc", + "8282": "\uacf1", + "8283": "\uc0b6", + "8284": "\ub9e5", + "8285": "\uc98c", + "8286": "\uc606", + "8287": "\uc54a", + "8288": "\uad34", + "8289": "\ube48", + "8290": "\ud134", + "8291": "\uc625", + "8292": "\uc6e8", + "8293": "\ud0a8", + "8294": "\ub9ad", + "8295": "\ud32c", + "8296": "\ud5e4", + "8297": "\ud718", + "8298": "\uc12d", + "8299": "\uba40", + "8300": "\uce6d", + "8301": "\uc05c", + "8302": "\ub0a9", + "8303": "\ub1cc", + "8304": "\ucc99", + "8305": "\uad73", + "8306": "\uc37c", + "8307": "\ub4ed", + "8308": "\uaef4", + "8309": "\ub9e1", + "8310": "\uc1fc", + "8311": "\uace4", + "8312": "\ube68", + "8313": "\ucce4", + "8314": "\uc568", + "8315": "\ucbe4", + "8316": "\ub313", + "8317": "\ub179", + "8318": "\uc989", + "8319": "\ucf58", + "8320": "\ub2a6", + "8321": "\ube5b", + "8322": "\ud608", + "8323": "\ubf51", + "8324": "\uae4a", + "8325": "\ub538", + "8326": "\uc4f4", + "8327": "\uaf43", + "8328": "\ud39c", + "8329": "\ub04a", + "8330": "\ud3b4", + "8331": "\ub78d", + "8332": "\ud648", + "8333": "\ub0c9", + "8334": "\ud508", + "8335": "\ub220", + "8336": "\ud0d5", + "8337": "\ubc11", + "8338": "\uae54", + "8339": "\ub8b0", + "8340": "\ucc0c", + "8341": "\ub800", + "8342": "\ud551", + "8343": "\ud758", + "8344": "\uce7c", + "8345": "\ucb49", + "8346": "\uc2f1", + "8347": "\uc2b7", + "8348": "\ub35c", + "8349": "\uafd4", + "8350": "\ubb54", + "8351": "\ub799", + "8352": "\uc0ad", + "8353": "\ud478", + "8354": "\ub86f", + "8355": "\ub529", + "8356": "\ucf69", + "8357": "\ud68d", + "8358": "\ubd07", + "8359": "\ud150", + "8360": "\ub8f9", + "8361": "\ub9f9", + "8362": "\ud31d", + "8363": "\uc2fc", + "8364": "\uc878", + "8365": "\ubca4", + "8366": "\ub044", + "8367": "\ub80c", + "8368": "\ub7ab", + "8369": "\ubba4", + "8370": "\ud610", + "8371": "\ud0b9", + "8372": "\uc313", + "8373": "\uc635", + "8374": "\ud78c", + "8375": "\ucf13", + "8376": "\ud380", + "8377": "\ud3f4", + "8378": "\uc820", + "8379": "\ubc97", + "8380": "\ucd09", + "8381": "\uace7", + "8382": "\uacaa", + "8383": "\ub113", + "8384": "\uc783", + "8385": "\ubca8", + "8386": "\ub1a8", + "8387": "\ubd04", + "8388": "\ubb3b", + "8389": "\ub784", + "8390": "\uba58", + "8391": "\uceec", + "8392": "\ud761", + "8393": "\ucd98", + "8394": "\uba4b", + "8395": "\ucd0c", + "8396": "\ub378", + "8397": "\uc12c", + "8398": "\ucc59", + "8399": "\ucf30", + "8400": "\uae5d", + "8401": "\ud578", + "8402": "\ud649", + "8403": "\ucd78", + "8404": "\ucf5c", + "8405": "\uaf64", + "8406": "\uc9d0", + "8407": "\uc22b", + "8408": "\uc998", + "8409": "\ub454", + "8410": "\ucef5", + "8411": "\uc194", + "8412": "\uae0d", + "8413": "\ub7ac", + "8414": "\ud3fc", + "8415": "\ub141", + "8416": "\ub5bb", + "8417": "\ud329", + "8418": "\ub5a1", + "8419": "\uaf2c", + "8420": "\ud799", + "8421": "\uc0c0", + "8422": "\uca54", + "8423": "\uaf08", + "8424": "\ud0d0", + "8425": "\ucea0", + "8426": "\uc2b4", + "8427": "\ubfcc", + "8428": "\uc9da", + "8429": "\uc1c4", + "8430": "\ubb18", + "8431": "\ub9bf", + "8432": "\uc564", + "8433": "\ud640", + "8434": "\uc14b", + "8435": "\ud1a1", + "8436": "\uc130", + "8437": "\uc78a", + "8438": "\ub465", + "8439": "\ub2eb", + "8440": "\ucda4", + "8441": "\ube59", + "8442": "\ubaac", + "8443": "\uaf3c", + "8444": "\ub7a8", + "8445": "\ube75", + "8446": "\uc2a8", + "8447": "\ub7fc", + "8448": "\ud3bc", + "8449": "\uc140", + "8450": "\ub864", + "8451": "\ub82c", + "8452": "\ud0d1", + "8453": "\uc384", + "8454": "\ub137", + "8455": "\ub4b7", + "8456": "\uc5d1", + "8457": "\ub7ed", + "8458": "\ucef4", + "8459": "\ub611", + "8460": "\ub2dd", + "8461": "\ud5ec", + "8462": "\ucca8", + "8463": "\ub904", + "8464": "\ub51c", + "8465": "\uae5c", + "8466": "\ud2f1", + "8467": "\ub744", + "8468": "\uc0e4", + "8469": "\ube45", + "8470": "\ub834", + "8471": "\uc81d", + "8472": "\uca4c", + "8473": "\uc300", + "8474": "\ubc0d", + "8475": "\ud751", + "8476": "\ub194", + "8477": "\uac77", + "8478": "\uba78", + "8479": "\ud1a4", + "8480": "\uc5fd", + "8481": "\ud050", + "8482": "\ucd2c", + "8483": "\ucb64", + "8484": "\ud29c", + "8485": "\ud790", + "8486": "\uc88b", + "8487": "\ub959", + "8488": "\ube5a", + "8489": "\ucf8c", + "8490": "\uafc0", + "8491": "\ud540", + "8492": "\ub871", + "8493": "\ub2cc", + "8494": "\ub5bc", + "8495": "\uba48", + "8496": "\ub550", + "8497": "\ud034", + "8498": "\uae40", + "8499": "\ub985", + "8500": "\ub048", + "8501": "\ub20c", + "8502": "\uc30d", + "8503": "\ubb35", + "8504": "\ub534", + "8505": "\uc789", + "8506": "\ubbc0", + "8507": "\ub369", + "8508": "\uc6c5", + "8509": "\uc5c9", + "8510": "\ub0ab", + "8511": "\ud280", + "8512": "\uc67c", + "8513": "\ub0c4", + "8514": "\uaf3d", + "8515": "\uacb8", + "8516": "\ubc16", + "8517": "\ub10c", + "8518": "\uc148", + "8519": "\ub0e5", + "8520": "\uafbc", + "8521": "\ub188", + "8522": "\uba4d", + "8523": "\ubabd", + "8524": "\ubd95", + "8525": "\uacb9", + "8526": "\ubc34", + "8527": "\uc950", + "8528": "\ub080", + "8529": "\ubb50", + "8530": "\ub8f0", + "8531": "\ub809", + "8532": "\ub561", + "8533": "\ub69c", + "8534": "\ub8f8", + "8535": "\ubcbc", + "8536": "\ub2c9", + "8537": "\ub9d8", + "8538": "\ud15c", + "8539": "\ub2f7", + "8540": "\ub518", + "8541": "\ub0ad", + "8542": "\uc11e", + "8543": "\uc6ec", + "8544": "\uce78", + "8545": "\uc787", + "8546": "\uc881", + "8547": "\ub989", + "8548": "\uc571", + "8549": "\ub9fa", + "8550": "\ud6fc", + "8551": "\ubed4", + "8552": "\uac24", + "8553": "\ub010", + "8554": "\ud6cc", + "8555": "\ub084", + "8556": "\ub36e", + "8557": "\uc3d8", + "8558": "\ub057", + "8559": "\ubf08", + "8560": "\ucabc", + "8561": "\uc798", + "8562": "\ubb36", + "8563": "\ub154", + "8564": "\ud53d", + "8565": "\ucca9", + "8566": "\ucef8", + "8567": "\ub2ed", + "8568": "\uc735", + "8569": "\uc1e0", + "8570": "\ud2f4", + "8571": "\ub374", + "8572": "\uc90d", + "8573": "\ud14d", + "8574": "\ubdd4", + "8575": "\uc6f9", + "8576": "\uc0f5", + "8577": "\ub2ff", + "8578": "\ubdf0", + "8579": "\ub367", + "8580": "\ubbf9", + "8581": "\ub364", + "8582": "\ub42c", + "8583": "\uc796", + "8584": "\uacfd", + "8585": "\uad04", + "8586": "\uad1c", + "8587": "\ub8ec", + "8588": "\ub987", + "8589": "\ubd59", + "8590": "\ub760", + "8591": "\ub8e1", + "8592": "\ub155", + "8593": "\ub4ec", + "8594": "\ubabb", + "8595": "\uaf34", + "8596": "\ub69d", + "8597": "\ub801", + "8598": "\ucc2e", + "8599": "\ub610", + "8600": "\ub96d", + "8601": "\uc15c", + "8602": "\ud31f", + "8603": "\ud33d", + "8604": "\ubed0", + "8605": "\uc2f9", + "8606": "\ud0d3", + "8607": "\ub451", + "8608": "\ud1b1", + "8609": "\ucf64", + "8610": "\ub6b1", + "8611": "\uae0b", + "8612": "\uba64", + "8613": "\ub9d9", + "8614": "\uc824", + "8615": "\uc708", + "8616": "\uac90", + "8617": "\ud587", + "8618": "\ube57", + "8619": "\uacf0", + "8620": "\uae65", + "8621": "\uba5c", + "8622": "\ub0af", + "8623": "\uc639", + "8624": "\ucfe8", + "8625": "\ubcf6", + "8626": "\uc232", + "8627": "\ub365", + "8628": "\ubc1f", + "8629": "\ud2f8", + "8630": "\ud2c8", + "8631": "\ub52a", + "8632": "\uae4e", + "8633": "\uac89", + "8634": "\uce69", + "8635": "\ub480", + "8636": "\uc717", + "8637": "\uc090", + "8638": "\uc575", + "8639": "\ub125", + "8640": "\uafe8", + "8641": "\ubb49", + "8642": "\uc22d", + "8643": "\ud321", + "8644": "\ubc45", + "8645": "\ud56b", + "8646": "\ud749", + "8647": "\uce94", + "8648": "\ub46c", + "8649": "\ub540", + "8650": "\uc53b", + "8651": "\ud6a1", + "8652": "\ucfc4", + "8653": "\ubc2d", + "8654": "\uc369", + "8655": "\ub014", + "8656": "\uac31", + "8657": "\ubc38", + "8658": "\ud514", + "8659": "\uc880", + "8660": "\ud1a8", + "8661": "\uc580", + "8662": "\ube61", + "8663": "\uaecf", + "8664": "\ub258", + "8665": "\ub2ee", + "8666": "\ub1e8", + "8667": "\ud131", + "8668": "\ub304", + "8669": "\ud760", + "8670": "\ub7ad", + "8671": "\uc78e", + "8672": "\ub835", + "8673": "\ube8f", + "8674": "\ucad9", + "8675": "\ub0c5", + "8676": "\uc557", + "8677": "\uca4d", + "8678": "\ud770", + "8679": "\uad49", + "8680": "\ud584", + "8681": "\uada4", + "8682": "\uae43", + "8683": "\uc90c", + "8684": "\uc0d8", + "8685": "\ub55c", + "8686": "\uc0cc", + "8687": "\ucdc4", + "8688": "\ud0f1", + "8689": "\ud0a5", + "8690": "\ubc85", + "8691": "\uc3e0", + "8692": "\uc74d", + "8693": "\ubccd", + "8694": "\ud230", + "8695": "\uca0c", + "8696": "\ube10", + "8697": "\ub3d5", + "8698": "\ud140", + "8699": "\uc9e4", + "8700": "\ucef7", + "8701": "\uc0bd", + "8702": "\uaf42", + "8703": "\ub837", + "8704": "\uc139", + "8705": "\ud54f", + "8706": "\ud5e8", + "8707": "\uad7d", + "8708": "\ub8e9", + "8709": "\ucda5", + "8710": "\ub2e6", + "8711": "\ub7a9", + "8712": "\ud47c", + "8713": "\uc660", + "8714": "\ub3cb", + "8715": "\ud30d", + "8716": "\ucc14", + "8717": "\ub72c", + "8718": "\ud5f7", + "8719": "\ubaab", + "8720": "\ud399", + "8721": "\ubd93", + "8722": "\ud0e0", + "8723": "\ub72f", + "8724": "\uc149", + "8725": "\uad90", + "8726": "\ub625", + "8727": "\uae41", + "8728": "\ud0d4", + "8729": "\uba55", + "8730": "\uc816", + "8731": "\ub4c0", + "8732": "\ud4e8", + "8733": "\ub9f5", + "8734": "\uc587", + "8735": "\uc068", + "8736": "\uaecd", + "8737": "\uac13", + "8738": "\ub109", + "8739": "\uc9f1", + "8740": "\uce6b", + "8741": "\ud759", + "8742": "\uaf49", + "8743": "\uc501", + "8744": "\ub428", + "8745": "\ud3a0", + "8746": "\ubf40", + "8747": "\uac07", + "8748": "\uc465", + "8749": "\ud5d0", + "8750": "\ub299", + "8751": "\uc500", + "8752": "\ubc40", + "8753": "\ub618", + "8754": "\uc370", + "8755": "\ud23c", + "8756": "\ub95c", + "8757": "\ub86c", + "8758": "\ubed7", + "8759": "\ud301", + "8760": "\ud48b", + "8761": "\uc274", + "8762": "\ucea1", + "8763": "\ub584", + "8764": "\uc0f7", + "8765": "\uc539", + "8766": "\uc7a3", + "8767": "\uc3f4", + "8768": "\ubb47", + "8769": "\uc270", + "8770": "\ub81b", + "8771": "\uc65c", + "8772": "\ud729", + "8773": "\uc36c", + "8774": "\uc5ce", + "8775": "\ud5db", + "8776": "\ubfd4", + "8777": "\uc27c", + "8778": "\uc813", + "8779": "\ub729", + "8780": "\uc719", + "8781": "\uc29b", + "8782": "\uc170", + "8783": "\uc19f", + "8784": "\uc9e0", + "8785": "\ud6d4", + "8786": "\uc6f0", + "8787": "\uc634", + "8788": "\ud384", + "8789": "\uaf41", + "8790": "\ub730", + "8791": "\ubf55", + "8792": "\ub2ac", + "8793": "\ucc1c", + "8794": "\ud391", + "8795": "\ubbac", + "8796": "\uccbc", + "8797": "\ud241", + "8798": "\ub5b4", + "8799": "\ub284", + "8800": "\ub291", + "8801": "\ucf08", + "8802": "\ud0b4", + "8803": "\uc3d9", + "8804": "\uce98", + "8805": "\uad7c", + "8806": "\ud22d", + "8807": "\ub968", + "8808": "\ub6f8", + "8809": "\uc5ff", + "8810": "\uc610", + "8811": "\ubd90", + "8812": "\uc7ad", + "8813": "\ub315", + "8814": "\uafc9", + "8815": "\ucf67", + "8816": "\ud479", + "8817": "\uc70c", + "8818": "\ucffc", + "8819": "\uac9f", + "8820": "\uc060", + "8821": "\uc5ee", + "8822": "\ud69f", + "8823": "\uad7f", + "8824": "\uae61", + "8825": "\ub2d9", + "8826": "\uc2e3", + "8827": "\ucf55", + "8828": "\ubc43", + "8829": "\uc5b9", + "8830": "\uc9ec", + "8831": "\ud234", + "8832": "\ubee5", + "8833": "\ud07c", + "8834": "\uc290", + "8835": "\uc7a6", + "8836": "\ud720", + "8837": "\uc19c", + "8838": "\ud38c", + "8839": "\uca61", + "8840": "\ud5dd", + "8841": "\ud1b0", + "8842": "\uc0d0", + "8843": "\uae01", + "8844": "\ud31c", + "8845": "\ube54", + "8846": "\ub3d7", + "8847": "\uac2f", + "8848": "\ucf04", + "8849": "\ub7ff", + "8850": "\ub301", + "8851": "\ub310", + "8852": "\uc570", + "8853": "\ud0ed", + "8854": "\ube90", + "8855": "\ub308", + "8856": "\ub2db", + "8857": "\ud6c5", + "8858": "\ube7d", + "8859": "\ub12c", + "8860": "\uc9d6", + "8861": "\ub460", + "8862": "\uce84", + "8863": "\ub461", + "8864": "\ucac4", + "8865": "\ub528", + "8866": "\ubca1", + "8867": "\uc7a4", + "8868": "\ud004", + "8869": "\ubfdc", + "8870": "\uac2d", + "8871": "\ub3d4", + "8872": "\ucf70", + "8873": "\uc653", + "8874": "\uc96c", + "8875": "\ub314", + "8876": "\ub5b3", + "8877": "\ub0b1", + "8878": "\ud168", + "8879": "\ubcd5", + "8880": "\ub38c", + "8881": "\ud30e", + "8882": "\uad88", + "8883": "\ud0b5", + "8884": "\uadc4", + "8885": "\ucc10", + "8886": "\ucc1d", + "8887": "\uae4d", + "8888": "\ub7b4", + "8889": "\ud145", + "8890": "\ube80", + "8891": "\ud325", + "8892": "\ubd48", + "8893": "\uba67", + "8894": "\uc52c", + "8895": "\ubc99", + "8896": "\ubcb3", + "8897": "\uc1a5", + "8898": "\uc82f", + "8899": "\ub6f4", + "8900": "\ucb48", + "8901": "\ub810", + "8902": "\uc250", + "8903": "\uaec4", + "8904": "\uc584", + "8905": "\uc5e3", + "8906": "\uc324", + "8907": "\uc3dc", + "8908": "\ucff5", + "8909": "\ud0b7", + "8910": "\uc648", + "8911": "\ub764", + "8912": "\ube74", + "8913": "\ube84", + "8914": "\uafc7", + "8915": "\ub525", + "8916": "\ub544", + "8917": "\ub818", + "8918": "\ud54d", + "8919": "\uc378", + "8920": "\ub5a4", + "8921": "\ub215", + "8922": "\ub9f7", + "8923": "\ucc3c", + "8924": "\uc308", + "8925": "\ub2a0", + "8926": "\uc999", + "8927": "\uaf30", + "8928": "\ub371", + "8929": "\uc20d", + "8930": "\uc5ca", + "8931": "\uc0bf", + "8932": "\uaf80", + "8933": "\ub9e3", + "8934": "\uc7bc", + "8935": "\uac40", + "8936": "\ud018", + "8937": "\uc464", + "8938": "\ubfb0", + "8939": "\uca50", + "8940": "\ubb44", + "8941": "\uade4", + "8942": "\ub797", + "8943": "\uca0b", + "8944": "\ucee5", + "8945": "\uaff0", + "8946": "\uc123", + "8947": "\uc379", + "8948": "\ud3c8", + "8949": "\ud0ec", + "8950": "\ub2aa", + "8951": "\ub738", + "8952": "\uce89", + "8953": "\ubab9", + "8954": "\uc298", + "8955": "\ub975", + "8956": "\uc4f1", + "8957": "\ud6d7", + "8958": "\ub9cf", + "8959": "\ud15d", + "8960": "\ub51b", + "8961": "\ucc39", + "8962": "\ubb8c", + "8963": "\uce04", + "8964": "\ud3ad", + "8965": "\ube64", + "8966": "\ub08c", + "8967": "\ubed8", + "8968": "\uc6c1", + "8969": "\uafb9", + "8970": "\ub205", + "8971": "\uc6cd", + "8972": "\ud4f0", + "8973": "\uca5c", + "8974": "\uc21f", + "8975": "\uac94", + "8976": "\ud038", + "8977": "\ucf65", + "8978": "\ub8fb", + "8979": "\ub515", + "8980": "\ube91", + "8981": "\uc167", + "8982": "\uceeb", + "8983": "\uc388", + "8984": "\ubf18", + "8985": "\ub128", + "8986": "\ucc4c", + "8987": "\ud3ab", + "8988": "\ud390", + "8989": "\ucbd4", + "8990": "\ud5c9", + "8991": "\ud2ac", + "8992": "\ub9f4", + "8993": "\uc58c", + "8994": "\ud143", + "8995": "\uc69c", + "8996": "\ubed1", + "8997": "\uce85", + "8998": "\ubc0b", + "8999": "\uc574", + "9000": "\ud295", + "9001": "\ub214", + "9002": "\uc0f9", + "9003": "\ubc09", + "9004": "\uac71", + "9005": "\uae45", + "9006": "\uc408", + "9007": "\ud3c4", + "9008": "\uc204", + "9009": "\ub4c8", + "9010": "\ubee3", + "9011": "\ubc27", + "9012": "\uac20", + "9013": "\ud401", + "9014": "\uaf5d", + "9015": "\uce30", + "9016": "\ucfe1", + "9017": "\ucfe4", + "9018": "\ucf10", + "9019": "\uaecc", + "9020": "\uce75", + "9021": "\ub6a4", + "9022": "\ucc60", + "9023": "\uadd3", + "9024": "\ub11b", + "9025": "\ud6d1", + "9026": "\ud37d", + "9027": "\uc329", + "9028": "\uae7c", + "9029": "\ucea3", + "9030": "\ubea8", + "9031": "\ud6e8", + "9032": "\ub78f", + "9033": "\uccb8", + "9034": "\uc0ec", + "9035": "\ucb10", + "9036": "\uae6c", + "9037": "\uca84", + "9038": "\uc5cc", + "9039": "\ub07d", + "9040": "\uc9f0", + "9041": "\uac38", + "9042": "\uadc8", + "9043": "\ub385", + "9044": "\ub748", + "9045": "\uac4d", + "9046": "\ub08d", + "9047": "\ucf85", + "9048": "\ud0e4", + "9049": "\ud17c", + "9050": "\ub311", + "9051": "\ucc3b", + "9052": "\uad18", + "9053": "\uc73d", + "9054": "\uc309", + "9055": "\ub527", + "9056": "\ub7a0", + "9057": "\ucc21", + "9058": "\uafcb", + "9059": "\ub00c", + "9060": "\ubb63", + "9061": "\ub524", + "9062": "\ud64b", + "9063": "\ub8fd", + "9064": "\ud338", + "9065": "\ud5e5", + "9066": "\uaebd", + "9067": "\uafcd", + "9068": "\ud058", + "9069": "\ucef9", + "9070": "\uac1b", + "9071": "\uae70", + "9072": "\uc314", + "9073": "\ub775", + "9074": "\ud6e4", + "9075": "\uc53d", + "9076": "\ud141", + "9077": "\ub93c", + "9078": "\uc20f", + "9079": "\uc9ed", + "9080": "\ubbc4", + "9081": "\uc258", + "9082": "\ud33b", + "9083": "\uaed0", + "9084": "\uacf6", + "9085": "\ub400", + "9086": "\uc14c", + "9087": "\uca98", + "9088": "\uc641", + "9089": "\uc90f", + "9090": "\ucdcc", + "9091": "\ud330", + "9092": "\uc9ca", + "9093": "\uc60c", + "9094": "\uc37d", + "9095": "\uc83c", + "9096": "\ud719", + "9097": "\uba71", + "9098": "\uc2a5", + "9099": "\ucf20", + "9100": "\ub217", + "9101": "\uc954", + "9102": "\uc2ef", + "9103": "\ub796", + "9104": "\ubcb5", + "9105": "\uc0db", + "9106": "\uc7c8", + "9107": "\ucb50", + "9108": "\ucda7", + "9109": "\ub5d0", + "9110": "\ucc57", + "9111": "\uc178", + "9112": "\uc6dc", + "9113": "\ubc08", + "9114": "\ud248", + "9115": "\ud57c", + "9116": "\ubf48", + "9117": "\uc5e5", + "9118": "\ubd4c", + "9119": "\uaf48", + "9120": "\uc330", + "9121": "\ubd5c", + "9122": "\uaf3f", + "9123": "\ube73", + "9124": "\uc7b0", + "9125": "\ud2a0", + "9126": "\uc605", + "9127": "\uaec0", + "9128": "\uc37b", + "9129": "\uc538", + "9130": "\ucac0", + "9131": "\ub5c0", + "9132": "\ubfe1", + "9133": "\uc886", + "9134": "\uc42c", + "9135": "\ub761", + "9136": "\uc0e8", + "9137": "\uad82", + "9138": "\uac30", + "9139": "\ube55", + "9140": "\uccc7", + "9141": "\ub554", + "9142": "\uba69", + "9143": "\uba4e", + "9144": "\ubb88", + "9145": "\ub0c7", + "9146": "\ud000", + "9147": "\ud035", + "9148": "\ub4d0", + "9149": "\ud2bf", + "9150": "\uc573", + "9151": "\ud2a4", + "9152": "\ub3db", + "9153": "\uc607", + "9154": "\ud6e0", + "9155": "\ub5b5", + "9156": "\ubcd0", + "9157": "\uae7d", + "9158": "\uad9c", + "9159": "\uc211", + "9160": "\ubbc8", + "9161": "\ubd40", + "9162": "\ucf24", + "9163": "\ub289", + "9164": "\ucc48", + "9165": "\uc22f", + "9166": "\ubc28", + "9167": "\ucad1", + "9168": "\ub380", + "9169": "\ud5f9", + "9170": "\ub819", + "9171": "\ub138", + "9172": "\ub15c", + "9173": "\uc29d", + "9174": "\uc3ed", + "9175": "\ud54c", + "9176": "\uc58f", + "9177": "\ub9f8", + "9178": "\uc7bd", + "9179": "\ubf41", + "9180": "\uc468", + "9181": "\uc698", + "9182": "\ub119", + "9183": "\ub9ec", + "9184": "\ud188", + "9185": "\uba84", + "9186": "\uad38", + "9187": "\ubafc", + "9188": "\ucad2", + "9189": "\ub158", + "9190": "\uc958", + "9191": "\uac84", + "9192": "\uae60", + "9193": "\ubeb4", + "9194": "\ub135", + "9195": "\ubfcd", + "9196": "\ub020", + "9197": "\ud3a9", + "9198": "\uc174", + "9199": "\ucc58", + "9200": "\ub189", + "9201": "\ucd10", + "9202": "\uc5e1", + "9203": "\ub381", + "9204": "\uc0a5", + "9205": "\ucf78", + "9206": "\uc8e4", + "9207": "\ucf71", + "9208": "\ub74c", + "9209": "\ubccf", + "9210": "\uac2c", + "9211": "\ub0b5", + "9212": "\uc6a4", + "9213": "\ucc55", + "9214": "\uae7b", + "9215": "\uca30", + "9216": "\ub1fd", + "9217": "\uc38c", + "9218": "\ube8c", + "9219": "\uac85", + "9220": "\uc705", + "9221": "\uce87", + "9222": "\uc7b4", + "9223": "\uceac", + "9224": "\uc714", + "9225": "\ub768", + "9226": "\ub11c", + "9227": "\ubb4d", + "9228": "\ubd87", + "9229": "\ucea5", + "9230": "\ub383", + "9231": "\uc7c0", + "9232": "\ub700", + "9233": "\ubdf4", + "9234": "\uc58d", + "9235": "\ub404", + "9236": "\ud03c", + "9237": "\ud144", + "9238": "\ubdf8", + "9239": "\ub844", + "9240": "\uc82d", + "9241": "\uc730", + "9242": "\ud6d9", + "9243": "\ubd2c", + "9244": "\ud667", + "9245": "\ucd28", + "9246": "\uae31", + "9247": "\ub5b0", + "9248": "\ubb45", + "9249": "\ud5c0", + "9250": "\uafce", + "9251": "\uba49", + "9252": "\ucd25", + "9253": "\ucea4", + "9254": "\ud590", + "9255": "\uc6e1", + "9256": "\uccb5", + "9257": "\ud38d", + "9258": "\uc530", + "9259": "\uc200", + "9260": "\uce24", + "9261": "\uc229", + "9262": "\uc19d", + "9263": "\uc398", + "9264": "\ud789", + "9265": "\ubeec", + "9266": "\ud73c", + "9267": "\ub0d4", + "9268": "\uc0dc", + "9269": "\ub134", + "9270": "\uc094", + "9271": "\ud5f8", + "9272": "\uac9c", + "9273": "\uc234", + "9274": "\uc82c", + "9275": "\uacc8", + "9276": "\ub11d", + "9277": "\ub588", + "9278": "\uc894", + "9279": "\ud5f4", + "9280": "\uc61c", + "9281": "\uc974", + "9282": "\uc9fc", + "9283": "\uac17", + "9284": "\ub599", + "9285": "\ub9df", + "9286": "\uba53", + "9287": "\uca5d", + "9288": "\uc74f", + "9289": "\ud339", + "9290": "\uc289", + "9291": "\uaf10", + "9292": "\uc0fe", + "9293": "\uc3bc", + "9294": "\ud3ff", + "9295": "\ud489", + "9296": "\uacd8", + "9297": "\uc479", + "9298": "\uac8b", + "9299": "\ub8d4", + "9300": "\ud690", + "9301": "\ubc0e", + "9302": "\ud71c", + "9303": "\ub0e0", + "9304": "\ud5d9", + "9305": "\uaea0", + "9306": "\ubf09", + "9307": "\uc883", + "9308": "\uc0e5", + "9309": "\ub4f8", + "9310": "\ub81d", + "9311": "\ubbd0", + "9312": "\uadff", + "9313": "\ub0d8", + "9314": "\ub40f", + "9315": "\uc6e9", + "9316": "\uca08", + "9317": "\ucb58", + "9318": "\ub463", + "9319": "\ub3e0", + "9320": "\ud06d", + "9321": "\uc0d9", + "9322": "\ud56c", + "9323": "\uc53c", + "9324": "\uc619", + "9325": "\uc394", + "9326": "\uca09", + "9327": "\ub5f4", + "9328": "\ub9dc", + "9329": "\uc14d", + "9330": "\ud3a8", + "9331": "\uc9f9", + "9332": "\ub614", + "9333": "\uacbb", + "9334": "\uc8c8", + "9335": "\ub664", + "9336": "\ucc1f", + "9337": "\uaedc", + "9338": "\ud23d", + "9339": "\uae5f", + "9340": "\uc84d", + "9341": "\ub878", + "9342": "\ud2c9", + "9343": "\ubc0c", + "9344": "\uad54", + "9345": "\uaf07", + "9346": "\ub543", + "9347": "\ub81c", + "9348": "\ub877", + "9349": "\ub879", + "9350": "\uc315", + "9351": "\ucb2c", + "9352": "\uc651", + "9353": "\ud79d", + "9354": "\ud460", + "9355": "\uae37", + "9356": "\uba04", + "9357": "\uae84", + "9358": "\uc2f0", + "9359": "\uc6df", + "9360": "\ud763", + "9361": "\ubba8", + "9362": "\ubfb1", + "9363": "\uc248", + "9364": "\uc814", + "9365": "\ud081", + "9366": "\uc345", + "9367": "\uc0a3", + "9368": "\uacef", + "9369": "\uc0b5", + "9370": "\ub139", + "9371": "\ucb14", + "9372": "\uae79", + "9373": "\uaf4c", + "9374": "\ud1b3", + "9375": "\ud3c5", + "9376": "\ucff1", + "9377": "\uc8d7", + "9378": "\uce61", + "9379": "\ucacd", + "9380": "\ubca7", + "9381": "\ub620", + "9382": "\ub594", + "9383": "\ud207", + "9384": "\uc79b", + "9385": "\uc098", + "9386": "\uc448", + "9387": "\ubb00", + "9388": "\uc18e", + "9389": "\ub5cd", + "9390": "\uaf79", + "9391": "\uc62f", + "9392": "\ub2a1", + "9393": "\ubd91", + "9394": "\uad1e", + "9395": "\uac02", + "9396": "\ube7b", + "9397": "\uc88d", + "9398": "\uc36a", + "9399": "\uc318", + "9400": "\ucc3f", + "9401": "\uacd7", + "9402": "\ud711", + "9403": "\ucc27", + "9404": "\ub754", + "9405": "\ub5ab", + "9406": "\uc80b", + "9407": "\uc62d", + "9408": "\ud613", + "9409": "\uc27f", + "9410": "\uc6f8", + "9411": "\ud25c", + "9412": "\ud0c9", + "9413": "\ubb90", + "9414": "\ub5cf", + "9415": "\ucad8", + "9416": "\uc54e", + "9417": "\ub2fb", + "9418": "\uca44", + "9419": "\ub701", + "9420": "\uc59c", + "9421": "\uc1f3", + "9422": "\ub055", + "9423": "\ud651", + "9424": "\ud0ef", + "9425": "\ucbe7", + "9426": "\ub269", + "9427": "\ud284", + "9428": "\ud744", + "9429": "\ub260", + "9430": "\uc737", + "9431": "\ubb3d", + "9432": "\ud0f0", + "9433": "\uc091", + "9434": "\uac58", + "9435": "\ud585", + "9436": "\ube8d", + "9437": "\uacea", + "9438": "\ud683", + "9439": "\ud2d4", + "9440": "\uc9e2", + "9441": "\ubc9b", + "9442": "\ubfc5", + "9443": "\uc5f7", + "9444": "\ub091", + "9445": "\uc100", + "9446": "\uac09", + "9447": "\uafe9", + "9448": "\uc733", + "9449": "\uc251", + "9450": "\ud2cb", + "9451": "\uc74a", + "9452": "\ub4b9", + "9453": "\uad2d", + "9454": "\ubf1b", + "9455": "\ub739", + "9456": "\uc887", + "9457": "\ub01c", + "9458": "\ube70", + "9459": "\ub3a0", + "9460": "\uc7bf", + "9461": "\ub10b", + "9462": "\ud288", + "9463": "\ub4e6", + "9464": "\ud565", + "9465": "\ub755", + "9466": "\uc2ad", + "9467": "\uc0c5", + "9468": "\uc410", + "9469": "\ub059", + "9470": "\ud515", + "9471": "\uc231", + "9472": "\uca0d", + "9473": "\ub2f3", + "9474": "\ub36b", + "9475": "\ubd50", + "9476": "\uc069", + "9477": "\ub01d", + "9478": "\uad0c", + "9479": "\uc597", + "9480": "\ucb59", + "9481": "\ubf50", + "9482": "\ubee4", + "9483": "\uc595", + "9484": "\uc30c", + "9485": "\ub5c4", + "9486": "\ubc9a", + "9487": "\uc0f4", + "9488": "\ubc49", + "9489": "\ucacc", + "9490": "\uc9d9", + "9491": "\uad76", + "9492": "\ud769", + "9493": "\ub25c", + "9494": "\ucd18", + "9495": "\ub0a1", + "9496": "\uc50c", + "9497": "\ub234", + "9498": "\ucc22", + "9499": "\ud320", + "9500": "\uaed1", + "9501": "\uc5bd", + "9502": "\uad75", + "9503": "\ud07d", + "9504": "\uc553", + "9505": "\ub918", + "9506": "\uacc1", + "9507": "\uc7e4", + "9508": "\ubd89", + "9509": "\ub053", + "9510": "\ub9d1", + "9511": "\ub98e", + "9512": "\ub09a", + "9513": "\ucd1b", + "9514": "\uaebe", + "9515": "\uac1a", + "9516": "\ucc54", + "9517": "\ubb61", + "9518": "\ub560", + "9519": "\ub6ab", + "9520": "\uc633", + "9521": "\ucad3", + "9522": "\uc3df", + "9523": "\uc62e", + "9524": "\ub35f", + "9525": "\ub0b3", + "9526": "\uc549", + "9527": "\uc80a", + "9528": "\uc9e7", + "9529": "\ub429", + "9530": "\ucf2f", + "9531": "\ud290", + "9532": "", + "9533": "ene", + "9534": "\u2581ble", + "9535": "ikk", + "9536": "opp", + "9537": "\u2581Han", + "9538": "\u2581Den", + "9539": "unn", + "9540": "\u2581han", + "9541": "asjon", + "9542": "\u2581word", + "9543": "\u2581werd", + "9544": "", + "9545": "eg", + "9546": "\u2581ikkje", + "9547": "\u2581bok", + "9548": "lik", + "9549": "\u2581eit", + "9550": "s\u00e5", + "9551": "kk", + "9552": "\u2581nok", + "9553": "\u2581god", + "9554": "\u2581lese", + "9555": "dde", + "9556": "inga", + "9557": "\u2581denn", + "9558": "inn", + "9559": "kkje", + "9560": "dig", + "9561": "tid", + "9562": "\u2581b\u00f8ke", + "9563": "ord", + "9564": "\u2581tru", + "9565": "skje", + "9566": "\u2581sei", + "9567": "ller", + "9568": "\u2581fle", + "9569": "skriv", + "9570": "\u2581heil", + "9571": "wy", + "9572": "\u015a", + "9573": "\u0141", + "9574": "\u0179", + "9575": "\u017b", + "9576": "car", + "9577": "t\u00e3o", + "9578": "ia", + "9579": "\u2581foi", + "9580": "ito", + "9581": "ram", + "9582": "fa", + "9583": "\u2581meu", + "9584": "\u00e7a", + "9585": "\u2581dois", + "9586": "a\u00e7\u00e3o", + "9587": "\u2581ter", + "9588": "n\u00e7a", + "9589": "\u2581compra", + "9590": "\u2581mil", + "9591": "\u2581minha", + "9592": "\u2581passa", + "9593": "\u2581casa", + "9594": "\u00c3", + "9595": "\u00b7", + "9596": "", + "9597": "das", + "9598": "\u2581s\u00e3o", + "9599": "\u2581Pa", + "9600": "tura", + "9601": "\u2581ser", + "9602": "\u2581Ele", + "9603": "forma", + "9604": "\u2581Esta", + "9605": "\u00f5es", + "9606": "\u2581pelo", + "9607": "tua", + "9608": "\u2581pela", + "9609": "mar", + "9610": "\u2581Foi", + "9611": "\u2581foram", + "9612": "este", + "9613": "\u2581Um", + "9614": "\u2581S\u00e3o", + "9615": "\u2581entre", + "9616": "fun", + "9617": "agem", + "9618": "gua", + "9619": "\u2581Brasil", + "9620": "\u2581grande", + "9621": "icos", + "9622": "\u2581cidade", + "9623": "inda", + "9624": "\u2581Este", + "9625": "\u2581maior", + "9626": "\u2581brasileiro", + "9627": "\u2581munic\u00edpio", + "9628": "\u2581nome", + "9629": "\u2581encontra", + "9630": "amb\u00e9m", + "9631": "\u2581Sua", + "9632": "\u2581tr\u00eas", + "9633": "\u2581\u0421", + "9634": "\u2581\u0410", + "9635": "\u2581\u041a", + "9636": "\u0431\u0435", + "9637": "\u2581\u041e", + "9638": "\u0441\u0435", + "9639": "\u2581\u041f", + "9640": "\u2581\u043c\u043d\u0435", + "9641": "\u2581\u043e\u043d", + "9642": "\u0446\u0430", + "9643": "\u043d\u0438\u0435", + "9644": "\u0436\u0430", + "9645": "\u0441\u0442\u044c", + "9646": "\u043f\u0443", + "9647": "\u043c\u044b", + "9648": "\u0441\u043a\u0430", + "9649": "\u0441\u0430", + "9650": "\u2581\u0442\u0435\u0431\u044f", + "9651": "\u0433\u0438", + "9652": "\u2581\u0444\u0438\u043b\u044c\u043c", + "9653": "\u0442\u0440\u0435", + "9654": "\u0433\u0440\u0430", + "9655": "\u043c\u0435\u0440", + "9656": "\u0448\u0430", + "9657": "\u2581\u0412\u043a\u043b\u044e\u0447\u0438", + "9658": "\u043b\u0441\u044f", + "9659": "\u0449\u0438", + "9660": "\u2581\u0441\u0435\u0437\u043e\u043d", + "9661": "\u2581\u041a\u0430\u043a", + "9662": "\u2581\u0441\u043c\u043e\u0442\u0440\u0435\u0448\u043a\u0435", + "9663": "\u2581\u0421\u0431\u0435\u0440", + "9664": "\u2581\u0422\u0432", + "9665": "\u2581\u041d\u0435", + "9666": "\u2581\u0414\u0436\u043e\u0439", + "9667": "\u2581\u043e\u0434\u0438\u043d", + "9668": "\u2581\u0410\u0444\u0438\u043d\u0430", + "9669": "\u2581\u041c\u0430", + "9670": "\u2581\u0441\u0435\u043c\u044c", + "9671": "\u2581\u0422\u0430", + "9672": "\u2581\u0421\u0430\u043b\u044e\u0442", + "9673": "\u0431\u043e\u043b\u044c\u0448", + "9674": "\u0441\u043a\u0438\u0439", + "9675": "\u2581\u043f\u044f\u0442\u044c", + "9676": "\u2581\u0441\u0435\u0440\u0438\u0430\u043b", + "9677": "\u2581\u0447\u0435\u0442\u044b\u0440\u0435", + "9678": "\u043a\u043b\u044e\u0447", + "9679": "\u2581\u0448\u0435\u0441\u0442\u044c", + "9680": "\u0438\u0442\u0441\u044f", + "9681": "\u2581\u0432\u043e\u0441\u0435\u043c\u044c", + "9682": "\u2581\u0432\u043e\u043e\u0431\u0449\u0435", + "9683": "\u2581\u041f\u043e\u043a\u0430\u0436\u0438", + "9684": "\u2581\u043f\u043e\u0442\u043e\u043c\u0443", + "9685": "\u2581\u0434\u0432\u0430\u0434\u0446\u0430\u0442\u044c", + "9686": "\u2581\u043a\u0430\u043d\u0430\u043b", + "9687": "\u2581\u0432\u043a\u043b\u044e\u0447\u0438", + "9688": "\u2581\u0440\u0430\u0431\u043e\u0442", + "9689": "\u2581\u043a\u0430\u0440\u0442", + "9690": "\u0438\u0448\u044c", + "9691": "\u2581\u0434\u0435\u043d\u044c", + "9692": "\u042b", + "9693": "ska", + "9694": "var", + "9695": "", + "9696": "\u2581\u0e32", + "9697": "\u2581\u0e19", + "9698": "\u2581\u0e23", + "9699": "\u2581\u0e01", + "9700": "\u2581\u0e2d", + "9701": "\u0e40", + "9702": "\u2581\u0e48", + "9703": "\u2581\u0e07", + "9704": "\u0e31", + "9705": "\u2581\u0e21", + "9706": "\u2581\u0e49", + "9707": "\u2581\u0e22", + "9708": "\u2581\u0e35", + "9709": "\u2581\u0e25", + "9710": "\u2581\u0e27", + "9711": "\u2581\u0e14", + "9712": "\u2581\u0e17", + "9713": "\u2581\u0e2a", + "9714": "\u2581\u0e15", + "9715": "\u2581\u0e34", + "9716": "\u2581\u0e1a", + "9717": "\u2581\u0e1b", + "9718": "\u2581\u0e30", + "9719": "\u2581\u0e2b", + "9720": "\u0e41", + "9721": "\u2581\u0e04", + "9722": "\u2581\u0e08", + "9723": "\u2581\u0e02", + "9724": "\u0e43", + "9725": "\u0e44", + "9726": "\u0e37", + "9727": "\u2581\u0e1e", + "9728": "\u2581\u0e0a", + "9729": "\u2581\u0e47", + "9730": "\u2581\u0e39", + "9731": "\u2581\u0e38", + "9732": "\u2581\u0e4c", + "9733": "\u0e42", + "9734": "\u0e4d", + "9735": "\u2581\u0e36", + "9736": "\u2581\u0e28", + "9737": "\u2581\u0e16", + "9738": "\u2581\u0e0b", + "9739": "\u0e1c", + "9740": "\u2581\u0e20", + "9741": "\u2581\u0e29", + "9742": "\u2581\u0e13", + "9743": "\u2581\u0e18", + "9744": "\u2581\u0e0d", + "9745": "\u0e32", + "9746": "\u0e19", + "9747": "\u2581\u0e1f", + "9748": "\u0e23", + "9749": "\u0e01", + "9750": "\u0e2d", + "9751": "\u0e48", + "9752": "\u0e07", + "9753": "\u0e21", + "9754": "\u0e49", + "9755": "\u0e09", + "9756": "\u0e22", + "9757": "\u2581\u0e10", + "9758": "\u0e35", + "9759": "\u0e25", + "9760": "\u0e27", + "9761": "\u0e14", + "9762": "\u0e17", + "9763": "\u2581\u0e1d", + "9764": "\u0e2a", + "9765": "\u0e15", + "9766": "\u0e34", + "9767": "\u0e1a", + "9768": "\u2581\u0e2e", + "9769": "\u0e1b", + "9770": "\u0e30", + "9771": "\u0e2b", + "9772": "\u0e24", + "9773": "\u0e04", + "9774": "\u0e08", + "9775": "\u2581\u0e0f", + "9776": "\u0e12", + "9777": "\u0e02", + "9778": "\u0e1e", + "9779": "\u0e0a", + "9780": "\u0e47", + "9781": "\u0e39", + "9782": "\u0e38", + "9783": "\u0e4c", + "9784": "\u0e4a", + "9785": "\u2581\u0e2c", + "9786": "\u2581\u0e0e", + "9787": "\u0e11", + "9788": "\u0e36", + "9789": "\u0e28", + "9790": "\u0e16", + "9791": "\u0e0b", + "9792": "\u0e20", + "9793": "\u2581\u0e4b", + "9794": "\u0e29", + "9795": "\u0e13", + "9796": "\u0e18", + "9797": "\u0e0d", 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"9846": "\u011e", + "9847": "", + "9848": "\u2581\u987b", + "9849": "\u2581\u8d28", + "9850": "\u2581\u6237", + "9851": "\u2581\u4e91", + "9852": "\u2581\u697c", + "9853": "\u2581\u77f3", + "9854": "\u2581\u5ba1", + "9855": "\u2581\u663e", + "9856": "\u2581\u7559", + "9857": "\u2581\u5c3d", + "9858": "\u2581\u96f7", + "9859": "\u2581\u6597", + "9860": "\u2581\u667a", + "9861": "\u2581\u6740", + "9862": "\u2581\u62ec", + "9863": "\u2581\u6267", + "9864": "\u2581\u6548", + "9865": "\u2581\u9752", + "9866": "\u2581\u5584", + "9867": "\u2581\u793c", + "9868": "\u2581\u9760", + "9869": "\u2581\u674e", + "9870": "\u2581\u9ec4", + "9871": "\u2581\u54cd", + "9872": "\u2581\u8425", + "9873": "\u2581\u8865", + "9874": "\u2581\u52bf", + "9875": "\u2581\u8db3", + "9876": "\u2581\u6781", + "9877": "\u2581\u6c5f", + "9878": "\u2581\u7701", + "9879": "\u2581\u9999", + "9880": "\u2581\u7a76", + "9881": "\u2581\u8ffd", + "9882": "\u2581\u7ef4", + "9883": "\u2581\u7fa4", + "9884": "\u2581\u5347", + 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"\u7751", + "10123": "\u7ee5", + "10124": "\u8ddb", + "10125": "\u9122", + "10126": "\u5639", + "10127": "\u5d02", + "10128": "\u642a", + "10129": "\u655d", + "10130": "\u8c49", + "10131": "\u8d45", + "10132": "\u98d2", + "10133": "\u5c91", + "10134": "\u7ba9", + "10135": "\u87a8", + "10136": "\u6e0c", + "10137": "\u961a", + "10138": "\u998a", + "10139": "\u704f", + "10140": "\u70b7", + "10141": "\u712f", + "10142": "\u752c", + "10143": "\u8748", + "10144": "\u55e4", + "10145": "\u5cb7", + "10146": "\u62bf", + "10147": "\u6d9e", + "10148": "\u75b8", + "10149": "\u779f", + "10150": "\u7eb0", + "10151": "\u701b", + "10152": "\u75c9", + "10153": "\u7601", + "10154": "\u8368", + "10155": "\u88c6", + "10156": "\u9e51", + "10157": "\u5b6c", + "10158": "\u7c0b", + "10159": "\u7ec9", + "10160": "\u8331", + "10161": "\u839c", + "10162": "\u86d4", + "10163": "\u6800", + "10164": "\u72f0", + "10165": "\u78a3", + "10166": "\u909d", + "10167": "\u94c6", + "10168": "\u6cad", + "10169": "\u80e5", + "10170": "\u858f", + "10171": "\u8941", + "10172": "\u8f76", + "10173": "\u9537", + "10174": "\u504c", + "10175": "\u57c2", + "10176": "\u6035", + "10177": "\u6cd4", + "10178": "\u80db", + "10179": "\u5482", + "10180": "\u5676", + "10181": "\u5d27", + "10182": "\u623e", + "10183": "\u781d", + "10184": "\u8d2e", + "10185": "\u6715", + "10186": "\u6773", + "10187": "\u705e", + "10188": "\u7a37", + "10189": "\u8e2e", + "10190": "\u9506", + "10191": "\u542e", + "10192": "\u6525", + "10193": "\u6bd3", + "10194": "\u6ca3", + "10195": "\u85ff", + "10196": "\u88f1", + "10197": "\u4fda", + "10198": "\u51bd", + "10199": "\u77ec", + "10200": "\u852b", + "10201": "\u998f", + "10202": "\u7812", + "10203": "\u8983", + "10204": "\u8e09", + "10205": "\u949c", + "10206": "\u57a4", + "10207": "\u6dde", + "10208": "\u891a", + "10209": "\u8e52", + "10210": "\u8e69", + "10211": "\u90dc", + "10212": "\u6c68", + "10213": "\u7548", + "10214": "\u75e8", + "10215": "\u7823", + "10216": "\u785a", + "10217": "\u8c1f", + "10218": "\u9528", + "10219": "\u5773", + "10220": "\u57ad", + "10221": "\u5b51", + "10222": "\u5d4b", + "10223": "\u5d99", + "10224": "\u664c", + "10225": "\u6654", + "10226": "\u684e", + "10227": "\u6c85", + "10228": "\u6dc5", + "10229": "\u6ed8", + "10230": "\u714a", + "10231": "\u7284", + "10232": "\u7ea8", + "10233": "\u8188", + "10234": "\u9563", + "10235": "\u510b", + "10236": "\u51c7", + "10237": "\u5d03", + "10238": "\u5fe4", + "10239": "\u6004", + "10240": "\u6a28", + "10241": "\u7430", + "10242": "\u75fc", + "10243": "\u8238", + "10244": "\u853a", + "10245": "\u87cb", + "10246": "\u94a8", + "10247": "\u94e8", + "10248": "\u9cb3", + "10249": "\u9edd", + "10250": "\u4f91", + "10251": "\u5d06", + "10252": "\u69ab", + "10253": "\u72b8", + "10254": "\u742c", + "10255": "\u7eeb", + "10256": "\u8d48", + "10257": "\u909b", + "10258": "\u9995", + "10259": "\u9a77", + "10260": "\u56cd", + "10261": "\u57a1", + "10262": "\u59dd", + "10263": "\u6414", + "10264": "\u6ddd", + "10265": "\u6f78", + "10266": "\u70c3", + "10267": "\u73b3", + "10268": "\u73ee", + "10269": "\u768b", + "10270": "\u8174", + "10271": "\u8dec", + "10272": "\u9ca0", + "10273": "\u9f2c", + "10274": "\u4f22", + "10275": "\u5043", + "10276": "\u5d4a", + "10277": "\u60b1", + "10278": "\u63e9", + "10279": "\u6636", + "10280": "\u6ceb", + "10281": "\u6da0", + "10282": "\u6e6b", + "10283": "\u784c", + "10284": "\u7aa8", + "10285": "\u7ed4", + "10286": "\u7fb8", + "10287": "\u8148", + "10288": "\u8671", + "10289": "\u8d30", + "10290": "\u8db5", + "10291": "\u948e", + "10292": "\u94f7", + "10293": "\u4f2b", + "10294": "\u57a9", + "10295": "\u57dd", + "10296": "\u59af", + "10297": "\u5a09", + "10298": "\u626a", + "10299": "\u63ae", + "10300": "\u6d2e", + "10301": "\u6d43", + "10302": "\u7173", + "10303": "\u737e", + "10304": "\u73f2", + "10305": "\u7583", + "10306": "\u7800", + "10307": "\u7b71", + "10308": "\u7da6", + "10309": "\u826e", + "10310": "\u8306", + "10311": "\u891b", + "10312": "\u8bd3", + "10313": "\u8c94", + "10314": "\u902f", + "10315": "\u90e7", + "10316": "\u539d", + "10317": "\u56d4", + "10318": "\u584d", + "10319": "\u5889", + "10320": "\u5a9e", + "10321": "\u5f9c", + "10322": "\u6387", + "10323": "\u63b8", + "10324": "\u665e", + "10325": "\u66b9", + "10326": "\u6cee", + "10327": "\u6e9f", + "10328": "\u6f5e", + "10329": "\u7287", + "10330": "\u749f", + "10331": "\u7747", + "10332": "\u82cb", + "10333": "\u83c0", + "10334": "\u8473", + "10335": "\u8dda", + "10336": "\u90c5", + "10337": "\u94b4", + "10338": "\u9f39", + "10339": "\u4edf", + "10340": "\u4f97", + "10341": "\u4ffe", + "10342": "\u53c1", + "10343": "\u573b", + "10344": "\u5785", + "10345": "\u59a4", + "10346": "\u65cc", + "10347": "\u67b3", + "10348": "\u6954", + "10349": "\u6978", + "10350": "\u6e86", + "10351": "\u6fc2", + "10352": "\u77f8", + "10353": "\u7efb", + "10354": "\u7f31", + "10355": "\u8153", + "10356": "\u84e5", + "10357": "\u8c11", + "10358": "\u8c15", + "10359": "\u8e31", + "10360": "\u9099", + "10361": "\u94af", + "10362": "\u9512", + "10363": "\u95f3", + "10364": "\u9621", + "10365": "\u98a2", + "10366": "\u9a90", + "10367": "\u9cad", + "10368": "\u9cb7", + "10369": "\u9e5e", + "10370": "\u52ad", + "10371": "\u5575", + "10372": "\u5d47", + "10373": "\u5eb9", + "10374": "\u62da", + "10375": "\u65fb", + "10376": "\u67de", + "10377": "\u6a2f", + "10378": "\u6e8f", + "10379": "\u6f8d", + "10380": "\u740f", + "10381": "\u7762", + "10382": "\u7837", + "10383": "\u795a", + "10384": "\u7afd", + "10385": "\u82e1", + "10386": "\u8347", + "10387": "\u8385", + "10388": "\u8572", + "10389": "\u8731", + "10390": "\u87ca", + "10391": "\u88e8", + "10392": "\u89d0", + "10393": "\u8bc3", + "10394": "\u8c27", + "10395": "\u9095", + "10396": "\u90d3", + "10397": "\u9170", + "10398": "\u94d6", + "10399": "\u94df", + "10400": "\u954c", + "10401": "\u9606", + "10402": "\u9615", + "10403": "\u96d2", + "10404": "\u9701", + "10405": "\u9acb", + "10406": "\u9c85", + "10407": "\u9c91", + "10408": "\u9ca2", + "10409": "\u9eb8", + "10410": "\u523d", + "10411": "\u5511", + "10412": "\u559f", + "10413": "\u55ea", + "10414": "\u5658", + "10415": "\u56f9", + "10416": "\u572a", + "10417": "\u579a", + "10418": "\u57f8", + "10419": "\u5807", + "10420": "\u5aeb", + "10421": "\u5b17", + "10422": "\u5b5b", + "10423": "\u5b73", + "10424": "\u5cc1", + "10425": "\u5d6c", + "10426": "\u5f0b", + "10427": "\u60bb", + "10428": "\u625e", + "10429": "\u6448", + "10430": "\u64ba", + "10431": "\u64d8", + "10432": "\u6710", + "10433": "\u680e", + "10434": "\u6c8f", + "10435": "\u6d60", + "10436": "\u6de6", + "10437": "\u6e11", + "10438": "\u6f4b", + "10439": "\u7094", + "10440": "\u7117", + "10441": "\u7118", + "10442": "\u7168", + "10443": "\u7424", + "10444": "\u742e", + "10445": "\u7477", + "10446": "\u759d", + "10447": "\u75bd", + "10448": "\u7aa0", + "10449": "\u7cbc", + "10450": "\u7ebe", + "10451": "\u7f19", + "10452": "\u7f54", + "10453": "\u816d", + "10454": "\u830c", + "10455": "\u832f", + "10456": "\u8360", + "10457": "\u8438", + "10458": "\u8788", + "10459": "\u8872", + "10460": "\u8c2f", + "10461": "\u8e3a", + "10462": "\u8f6b", + "10463": "\u90b3", + "10464": "\u90ef", + "10465": "\u94e3", + "10466": "\u94e9", + "10467": "\u94f0", + "10468": "\u9532", + "10469": "\u9616", + "10470": "\u9708", + "10471": "\u9aa0", + "10472": "\u9ecd", + "10473": "\u4dae", + "10474": "\u4ee1", + "10475": "\u5053", + "10476": "\u520d", + "10477": "\u525c", + "10478": "\u5416", + "10479": "\u549d", + "10480": "\u54bb", + "10481": "\u54c2", + "10482": "\u5537", + "10483": "\u5581", + "10484": "\u55c4", + "10485": "\u562d", + "10486": "\u5659", + "10487": "\u5739", + "10488": "\u5769", + "10489": "\u57c7", + "10490": "\u57d5", + "10491": "\u57da", + "10492": "\u59ab", + "10493": "\u5a0c", + "10494": "\u5ada", + "10495": "\u5b71", + "10496": "\u5b93", + "10497": "\u5c05", + "10498": "\u5d9d", + "10499": "\u5f2d", + "10500": "\u6006", + "10501": "\u603f", + "10502": "\u6041", + "10503": "\u6078", + "10504": "\u6266", + "10505": "\u678b", + "10506": "\u690b", + "10507": "\u6a3e", + "10508": "\u6bc2", + "10509": "\u6c4a", + "10510": "\u6c69", + "10511": "\u6ce0", + "10512": "\u6d39", + "10513": "\u6d48", + "10514": "\u7113", + "10515": "\u727e", + "10516": "\u73b9", + "10517": "\u73d9", + "10518": "\u75a3", + "10519": "\u75b4", + "10520": "\u7633", + "10521": "\u772c", + "10522": "\u77fd", + "10523": "\u79e3", + "10524": "\u7b33", + "10525": "\u7be6", + "10526": "\u7c7c", + "10527": "\u7cb2", + "10528": "\u7ec0", + "10529": "\u7ecb", + "10530": "\u82a9", + "10531": "\u84e6", + "10532": "\u8821", + "10533": "\u8934", + "10534": "\u8a3e", + "10535": "\u8ba3", + "10536": "\u8bd8", + "10537": "\u8dba", + "10538": "\u8e2f", + "10539": "\u8e5a", + "10540": "\u8e85", + "10541": "\u8f78", + "10542": "\u9021", + "10543": "\u9150", + "10544": "\u9487", + "10545": "\u94b2", + "10546": "\u94e7", + "10547": "\u9509", + "10548": "\u951f", + "10549": "\u95e9", + "10550": "\u9697", + "10551": "\u9880", + "10552": "\u98e7", + "10553": "\u9ac2", + "10554": "\u9b49", + "10555": "\u9cdf", + "10556": "\u9e22", + "10557": "\uff21", + "10558": "\u9980", + "10559": "\u966c", + "10560": "\u8914", + "10561": "\u7596", + "10562": "\u68c2", + "10563": "\u6677", + "10564": "\u643d", + "10565": "\u9011", + "10566": "\u82f7", + "10567": "\u783c", + "10568": "\u76c5", + "10569": "\u746d", + "10570": "\u61b7", + "10571": "\u5fff", + "10572": "\u5c50", + "10573": "\u5c15", + "10574": "\u586c", + "10575": "\u500c", + "10576": "\u8df9", + "10577": "\u845a", + "10578": "\u6b93", + "10579": "\u51bc", + "10580": "\u50ee", + "10581": "\u8f73", + "10582": "\u8df6", + "10583": "\u8dce", + "10584": "\u8c85", + "10585": "\u831b", + "10586": "\u73fa", + "10587": "\u67d2", + "10588": "\u4f76", + "10589": "\u94e1", + "10590": "\u7cb3", + "10591": "\u71ee", + "10592": "\u67b0", + "10593": "\u547b", + "10594": "\u9534", + "10595": "\u5cd2", + "10596": "\u551b", + "10597": "\u9c9f", + "10598": "\u9a9d", + "10599": "\u975b", + "10600": "\u8db8", + "10601": "\u8019", + "10602": "\u78b4", + "10603": "\u71ca", + "10604": "\u6dd6", + "10605": "\u948f", + "10606": "\u886e", + "10607": "\u7428", + "10608": "\u5f89", + "10609": "\u5501", + "10610": "\u80d7", + "10611": "\u7ecc", + "10612": "\u5a4a", + "10613": "\u54ad", + "10614": "\u9a85", + "10615": "\u794e", + "10616": "\u7663", + "10617": "\u72d2", + "10618": "\u90ba", + "10619": "\u87c0", + "10620": "\u7a1e", + "10621": "\u6e4e", + "10622": "\u659b", + "10623": "\u688f", + "10624": "\u679e", + "10625": "\u9549", + "10626": "\u7bb4", + "10627": "\u7166", + "10628": "\u55d4", + "10629": "\u82e3", + "10630": "\u7fca", + "10631": "\u765c", + "10632": "\u8e7c", + "10633": "\u86c6", + "10634": "\u7441", + "10635": "\u6600", + "10636": "\u9a9e", + "10637": "\u77fe", + "10638": "\u749e", + "10639": "\u6849", + "10640": "\u5d58", + "10641": "\u5662", + "10642": "\u8bb4", + "10643": "\u7691", + "10644": "\u73c9", + "10645": "\u835a", + "10646": "\u7fce", + "10647": "\u5a75", + "10648": "\u8d53", + "10649": "\u7f30", + "10650": "\u7f28", + "10651": "\u7620", + "10652": "\u61cb", + "10653": "\u789c", + "10654": "\u70e9", + "10655": "\u5b37", + "10656": "\u5472", + "10657": "\u9e4c", + "10658": "\u9604", + "10659": "\u9555", + "10660": "\u7b60", + "10661": "\u7080", + "10662": "\u6c1f", + "10663": "\u5729", + "10664": "\u71a0", + "10665": "\u6f2a", + "10666": "\u6b46", + "10667": "\u64c0", + "10668": "\u9a9c", + "10669": "\u956d", + "10670": "\u8d4a", + "10671": "\u83c1", + "10672": "\u7bea", + "10673": "\u7708", + "10674": "\u5ffb", + "10675": "\u5b40", + "10676": "\u85dc", + "10677": "\u70f7", + "10678": "\u5bb8", + "10679": "\u504e", + "10680": "\u9539", + "10681": "\u94c9", + "10682": "\u8913", + "10683": "\u768e", + "10684": "\u72b7", + "10685": "\u7292", + "10686": "\u55d6", + "10687": "\u9e5c", + "10688": "\u950c", + "10689": "\u73cf", + "10690": "\u85d3", + "10691": "\u8dc4", + "10692": "\u69ad", + "10693": "\u5ad4", + "10694": "\u5a23", + "10695": "\u8d3b", + "10696": "\u870d", + "10697": "\u7f04", + "10698": "\u7738", + "10699": "\u7719", + "10700": "\u9e6b", + "10701": "\u8734", + "10702": "\u81ba", + "10703": "\u762a", + "10704": "\u6c93", + "10705": "\u6593", + "10706": "\u64de", + "10707": "\u5d2e", + "10708": "\u9541", + "10709": "\u7eab", + "10710": "\u789a", + "10711": "\u6862", + "10712": "\u98da", + "10713": "\u840b", + "10714": "\u7131", + "10715": "\u6a35", + "10716": "\u576f", + "10717": "\u5636", + "10718": "\u954a", + "10719": "\u8869", + "10720": "\u86f9", + "10721": "\u83a0", + "10722": "\u783e", + "10723": "\u6e0d", + "10724": "\u6be1", + "10725": "\u65ef", + "10726": "\u579b", + "10727": "\u9530", + "10728": "\u915a", + "10729": "\u9ccd", + "10730": "\u9968", + "10731": "\u94c0", + "10732": "\u5ccb", + "10733": "\u9a9b", + "10734": "\u8169", + "10735": "\u754a", + "10736": "\u5530", + "10737": "\u4ede", + "10738": "\u9609", + "10739": "\u72de", + "10740": "\u6631", + "10741": "\u6421", + "10742": "\u8f67", + "10743": "\u81e7", + "10744": "\u7a95", + "10745": "\u781a", + "10746": "\u70ca", + "10747": "\u6963", + "10748": "\u5fe1", + "10749": "\u9e42", + "10750": "\u6868", + "10751": "\u645e", + "10752": "\u612b", + "10753": "\u949b", + "10754": "\u797a", + "10755": "\u8e76", + "10756": "\u6043", + "10757": "\u5477", + "10758": "\u7b06", + "10759": "\u62a1", + "10760": "\u5ff1", + "10761": "\u5b05", + "10762": "\u520e", + "10763": "\u94b5", + "10764": "\u8ba7", + "10765": "\u86c0", + "10766": "\u6748", + "10767": "\u992e", + "10768": "\u948a", + "10769": "\u7f0e", + "10770": "\u954d", + "10771": "\u89ce", + "10772": "\u5a67", + "10773": "\u98a7", + "10774": "\u989a", + "10775": "\u874c", + "10776": "\u810d", + "10777": "\u55f2", + "10778": "\u5323", + "10779": "\u9f8a", + "10780": "\u82de", + "10781": "\u9cab", + "10782": "\u8e8f", + "10783": "\u8885", + "10784": "\u7ee2", + "10785": "\u5a7a", + "10786": "\u94ff", + "10787": "\u86b1", + "10788": "\u7bd1", + "10789": "\u94e4", + "10790": "\u8113", + "10791": "\u5ab2", + "10792": "\u94c4", + "10793": "\u7bab", + "10794": "\u5a06", + "10795": "\u4f58", + "10796": "\u90b0", + "10797": "\u83ba", + "10798": "\u7f22", + "10799": "\u6410", + "10800": "\u916f", + "10801": "\u8426", + "10802": "\u6f3e", + "10803": "\u6c7e", + "10804": "\u6bfd", + "10805": "\u8902", + "10806": "\u6684", + "10807": "\u9e3e", + "10808": "\u9b1f", + "10809": "\u7f07", + "10810": "\u7bd3", + "10811": "\u6cfe", + "10812": "\u8d73", + "10813": "\u8146", + "10814": "\u9cdd", + "10815": "\u97ec", + "10816": "\u950f", + "10817": "\u8be9", + "10818": "\u79f8", + "10819": "\u622c", + "10820": "\u89d1", + "10821": "\u8559", + "10822": "\u9e6d", + "10823": "\u86a4", + "10824": "\u828a", + "10825": "\u780c", + "10826": "\u7352", + "10827": "\u6b87", + "10828": "\u5942", + "10829": "\u94a3", + "10830": "\u8191", + "10831": "\u7cd7", + "10832": "\u76f9", + "10833": "\u73a5", + "10834": "\u9083", + "10835": "\u8713", + "10836": "\u71ce", + "10837": "\u5567", + "10838": "\u7f44", + "10839": "\u873b", + "10840": "\u776c", + "10841": "\u732c", + "10842": "\u9984", + "10843": "\u7696", + "10844": "\u5140", + "10845": "\u970e", + "10846": "\u84d3", + "10847": "\u7634", + "10848": "\u75eb", + "10849": "\u9550", + "10850": "\u8936", + "10851": "\u8dfa", + "10852": "\u70ec", + "10853": "\u6cd3", + "10854": "\u9535", + "10855": "\u8bb7", + "10856": "\u86aa", + "10857": "\u79c6", + "10858": "\u6cde", + "10859": "\u9cd7", + "10860": "\u8725", + "10861": "\u7085", + "10862": "\u65ee", + "10863": "\u6382", + "10864": "\u58d1", + "10865": "\u54a3", + "10866": "\u9b47", + "10867": "\u7898", + "10868": "\u7699", + "10869": "\u5bd0", + "10870": "\u7600", + "10871": "\u6005", + "10872": "\u869d", + "10873": "\u8398", + "10874": "\u5bf0", + "10875": "\u832c", + "10876": "\u51a2", + "10877": "\u9cde", + "10878": "\u9573", + "10879": "\u8f8d", + "10880": "\u9a8b", + "10881": "\u85b0", + "10882": "\u7b75", + "10883": "\u76ce", + "10884": "\u6988", + "10885": "\u5498", + "10886": "\u4fac", + "10887": "\u8f98", + "10888": "\u812f", + "10889": "\u695e", + "10890": "\u997d", + "10891": "\u82ef", + "10892": "\u9ab0", + "10893": "\u970f", + "10894": "\u8722", + "10895": "\u6d54", + "10896": "\u631b", + "10897": "\u5a04", + "10898": "\u60b4", + "10899": "\u5a55", + "10900": "\u55b3", + "10901": "\u557e", + "10902": "\u8baa", + "10903": "\u5e1b", + "10904": "\u5b7a", + "10905": "\u8bcb", + "10906": "\u8bff", + "10907": "\u78fa", + "10908": "\u7693", + "10909": "\u62f4", + "10910": "\u709c", + "10911": "\u5e44", + "10912": "\u5d3d", + "10913": "\u50a5", + "10914": "\u9cc5", + "10915": "\u94ee", + "10916": "\u6da7", + "10917": "\u94be", + "10918": "\u819b", + "10919": "\u7d0a", + "10920": "\u75e7", + "10921": "\u728a", + "10922": "\u6d5a", + "10923": "\u9163", + "10924": "\u6479", + "10925": "\u5e62", + "10926": "\u5ced", + "10927": "\u59e3", + "10928": "\u5406", + "10929": "\u7ead", + "10930": "\u8301", + "10931": "\u6ec7", + "10932": "\u4f57", + "10933": "\u9035", + "10934": "\u6e4d", + "10935": "\u8c29", + "10936": "\u836b", + "10937": "\u7a96", + "10938": "\u715c", + "10939": "\u9955", + "10940": "\u9062", + "10941": "\u67ad", + "10942": "\u60e6", + "10943": "\u8f7c", + "10944": "\u7bf1", + "10945": "\u7abf", + "10946": "\u795b", + "10947": "\u54fd", + "10948": "\u9e43", + "10949": "\u7c41", + "10950": "\u69b7", + "10951": "\u6635", + "10952": "\u5657", + "10953": "\u8925", + "10954": "\u7638", + "10955": "\u6cef", + "10956": "\u5b5c", + "10957": "\u8bb9", + "10958": "\u8537", + "10959": "\u729f", + "10960": "\u5c96", + "10961": "\u9791", + "10962": "\u91c9", + "10963": "\u8e4b", + "10964": "\u7c91", + "10965": "\u6d93", + "10966": "\u6cf7", + "10967": "\u9c88", + "10968": "\u988a", + "10969": "\u6d19", + "10970": "\u952d", + "10971": "\u7116", + "10972": "\u60ec", + "10973": "\u9a6e", + "10974": "\u998b", + "10975": "\u6995", + "10976": "\u996f", + "10977": "\u9776", + "10978": "\u9542", + "10979": "\u6cb1", + "10980": "\u6452", + "10981": "\u54d0", + "10982": "\u9eef", + "10983": "\u8be7", + "10984": "\u64ac", + "10985": "\u94d0", + "10986": "\u83cf", + "10987": "\u5671", + "10988": "\u82ae", + "10989": "\u739f", + "10990": "\u6dae", + "10991": "\u94c2", + "10992": "\u80ed", + "10993": "\u7459", + "10994": "\u5c79", + "10995": "\u55dd", + "10996": "\u9cd6", + "10997": "\u9602", + "10998": "\u5693", + "10999": "\u86a3", + "11000": "\u7c7d", + "11001": "\u7095", + "11002": "\u568f", + "11003": "\u8fe9", + "11004": "\u9981", + "11005": "\u72c8", + "11006": "\u631d", + "11007": "\u95f5", + "11008": "\u8c0f", + "11009": "\u7bc6", + "11010": "\u75a1", + "11011": "\u6dfc", + "11012": "\u631e", + "11013": "\u61e6", + "11014": "\u6059", + "11015": "\u5f5d", + "11016": "\u5958", + "11017": "\u4f36", + "11018": "\u6dcc", + "11019": "\u9ccc", + "11020": "\u80ef", + "11021": "\u6c74", + "11022": "\u9497", + "11023": "\u8de4", + "11024": "\u68e3", + "11025": "\u6657", + "11026": "\u5fd1", + "11027": "\u56f1", + "11028": "\u7405", + "11029": "\u5f99", + "11030": "\u7f9a", + "11031": "\u6a90", + "11032": "\u853c", + "11033": "\u8334", + "11034": "\u9997", + "11035": "\u8c1b", + "11036": "\u7444", + "11037": "\u6866", + "11038": "\u64b5", + "11039": "\u9e25", + "11040": "\u87b3", + "11041": "\u7edb", + "11042": "\u7ea3", + "11043": "\u7a57", + "11044": "\u69bb", + "11045": "\u6942", + "11046": "\u607a", + "11047": "\u592f", + "11048": "\u54ee", + "11049": "\u9e2f", + "11050": "\u60fa", + "11051": "\u9131", + "11052": "\u8f84", + "11053": "\u567c", + "11054": "\u53ae", + "11055": "\u533e", + "11056": "\u5014", + "11057": "\u7736", + "11058": "\u6829", + "11059": "\u664f", + "11060": "\u55d2", + "11061": "\u4f7c", + "11062": "\u6376", + "11063": "\u9a81", + "11064": "\u9504", + "11065": "\u80eb", + "11066": "\u9977", + "11067": "\u7b8d", + "11068": "\u70e8", + "11069": "\u8892", + "11070": "\u7578", + "11071": "\u60ee", + "11072": "\u7357", + "11073": "\u6ed5", + "11074": "\u5e3c", + "11075": "\u74a8", + "11076": "\u667e", + "11077": "\u8df7", + "11078": "\u62a8", + "11079": "\u74ee", + "11080": "\u82c7", + "11081": "\u621b", + "11082": "\u8e6c", + "11083": "\u556c", + "11084": "\u4f5f", + "11085": "\u5c9a", + "11086": "\u5b1b", + "11087": "\u956f", + "11088": "\u7f81", + "11089": "\u98d3", + "11090": "\u905b", + "11091": "\u6e85", + "11092": "\u9522", + "11093": "\u8386", + "11094": "\u63b3", + "11095": "\u7172", + "11096": "\u9698", + "11097": "\u6f4d", + "11098": "\u8be3", + "11099": "\u5c49", + "11100": "\u5b5a", + "11101": "\u4f70", + "11102": "\u9a6f", + "11103": "\u66a8", + "11104": "\u4fd1", + "11105": "\u835f", + "11106": "\u5cea", + "11107": "\u9890", + "11108": "\u919b", + "11109": "\u62e3", + "11110": "\u87d2", + "11111": "\u6ca5", + "11112": "\u6096", + "11113": "\u9ae6", + "11114": "\u63b7", + "11115": "\u4ee8", + "11116": "\u998d", + "11117": "\u94e0", + "11118": "\u75ca", + "11119": "\u6fd1", + "11120": "\u5623", + "11121": "\u8693", + "11122": "\u7830", + "11123": "\u8dc6", + "11124": "\u6d52", + "11125": "\u5ce5", + "11126": "\u4ea2", + "11127": "\u7329", + "11128": "\u6c76", + "11129": "\u79ba", + "11130": "\u73d1", + "11131": "\u53fc", + "11132": "\u8638", + "11133": "\u9e20", + "11134": "\u7fe9", + "11135": "\u7f24", + "11136": "\u7c27", + "11137": "\u747e", + "11138": "\u552c", + "11139": "\u748b", + "11140": "\u68a7", + "11141": "\u75f1", + "11142": "\u9a6d", + "11143": "\u741b", + "11144": "\u6c2a", + "11145": "\u84bf", + "11146": "\u78f7", + "11147": "\u949d", + "11148": "\u8fab", + "11149": "\u84df", + "11150": "\u7cb1", + "11151": "\u67b8", + "11152": "\u8717", + "11153": "\u7a98", + "11154": "\u9975", + "11155": "\u5228", + "11156": "\u7629", + "11157": "\u54c6", + "11158": "\u88f4", + "11159": "\u804b", + "11160": "\u7316", + "11161": "\u80e7", + "11162": "\u609a", + "11163": "\u8884", + "11164": "\u8364", + "11165": "\u80fa", + "11166": "\u6805", + "11167": "\u5fd2", + "11168": "\u9611", + "11169": "\u8f97", + "11170": "\u8e1d", + "11171": "\u6fd2", + "11172": "\u6d31", + "11173": "\u6a71", + "11174": "\u9a7f", + "11175": "\u7b5d", + "11176": "\u85c9", + "11177": "\u7ede", + "11178": "\u6bcb", + "11179": "\u80f0", + "11180": "\u70fd", + "11181": "\u701a", + "11182": "\u8f99", + "11183": "\u5ae6", + "11184": "\u6f7c", + "11185": "\u6e0e", + "11186": "\u6e32", + "11187": "\u55f7", + "11188": "\u7a20", + "11189": "\u5ad6", + "11190": "\u622e", + "11191": "\u6b83", + "11192": "\u9a78", + "11193": "\u8d58", + "11194": "\u56b7", + "11195": "\u5a34", + "11196": "\u5586", + "11197": "\u8327", + "11198": "\u7f2a", + "11199": "\u9e49", + "11200": "\u9abc", + "11201": "\u7f15", + "11202": "\u5dcd", + "11203": "\u9e66", + "11204": "\u8d43", + "11205": "\u8715", + "11206": "\u6ea5", + "11207": "\u7b03", + "11208": "\u952f", + "11209": "\u94b0", + "11210": "\u9a79", + "11211": "\u8c82", + "11212": "\u766b", + "11213": "\u759a", + "11214": "\u8708", + "11215": "\u5412", + "11216": "\u9704", + "11217": "\u968d", + "11218": "\u9e33", + "11219": "\u7eca", + "11220": "\u6da1", + "11221": "\u5e37", + "11222": "\u94db", + "11223": "\u4fea", + "11224": "\u9716", + "11225": "\u8517", + "11226": "\u692d", + "11227": "\u6e89", + "11228": "\u5ce6", + "11229": "\u5a05", + "11230": "\u532e", + "11231": "\u6994", + "11232": "\u4fd0", + "11233": "\u541d", + "11234": "\u8bec", + "11235": "\u97ed", + "11236": "\u4fde", + "11237": "\u70ef", + "11238": "\u574d", + "11239": "\u7599", + "11240": "\u6cae", + "11241": "\u7750", + "11242": "\u6c55", + "11243": "\u50a3", + "11244": "\u9885", + "11245": "\u865e", + "11246": "\u9619", + "11247": "\u7487", + "11248": "\u8bdf", + "11249": "\u659f", + "11250": "\u816e", + "11251": "\u70af", + "11252": "\u6b7c", + "11253": "\u90f8", + "11254": "\u75f9", + "11255": "\u66e6", + "11256": "\u64c2", + "11257": "\u9525", + "11258": "\u8eac", + "11259": "\u772f", + "11260": "\u8c4c", + "11261": "\u8bfd", + "11262": "\u60eb", + "11263": "\u9e4a", + "11264": "\u854a", + "11265": "\u6151", + "11266": "\u7ec5", + "11267": "\u64d2", + "11268": "\u6342", + "11269": "\u7efd", + "11270": "\u5b70", + "11271": "\u6664", + "11272": "\u5d2d", + "11273": "\u6f62", + "11274": "\u5e42", + "11275": "\u62e7", + "11276": "\u80ae", + "11277": "\u9176", + "11278": "\u6c2e", + "11279": "\u566c", + "11280": "\u9893", + "11281": "\u821c", + "11282": "\u683e", + "11283": "\u9523", + "11284": "\u86e4", + "11285": "\u9ac5", + "11286": "\u95eb", + "11287": "\u6cf5", + "11288": "\u996a", + "11289": "\u6002", + "11290": "\u814c", + "11291": "\u9cb8", + "11292": "\u752d", + "11293": "\u57a6", + "11294": "\u5180", + "11295": "\u78c5", + "11296": "\u5f29", + "11297": "\u796f", + "11298": "\u68ad", + "11299": "\u6615", + "11300": "\u4fa5", + "11301": "\u6123", + "11302": "\u77aa", + "11303": "\u6da4", + "11304": "\u68f1", + "11305": "\u7eef", + "11306": "\u6f9c", + "11307": "\u59d7", + "11308": "\u85d5", + "11309": "\u973e", + "11310": "\u9502", + "11311": "\u9540", + "11312": "\u6c79", + "11313": "\u9ca4", + "11314": "\u6e43", + "11315": "\u7c07", + "11316": "\u6e3a", + "11317": "\u9074", + "11318": "\u4e4d", + "11319": "\u6273", + "11320": "\u8018", + "11321": "\u9102", + "11322": "\u75ae", + "11323": "\u9ab7", + "11324": "\u8680", + "11325": "\u8042", + "11326": "\u75a4", + "11327": "\u6de4", + "11328": "\u5777", + "11329": "\u79fd", + "11330": "\u77a9", + "11331": "\u97f6", + "11332": "\u94a7", + "11333": "\u87d1", + "11334": "\u8335", + "11335": "\u829c", + "11336": "\u620c", + "11337": "\u52b5", + "11338": "\u5520", + "11339": "\u7eee", + "11340": "\u6d4a", + "11341": "\u6f13", + "11342": "\u6ba1", + "11343": "\u7728", + "11344": "\u60ed", + "11345": "\u502a", + "11346": "\u715e", + "11347": "\u6ed4", + "11348": "\u5018", + "11349": "\u67ab", + "11350": "\u6f88", + "11351": "\u5b7d", + "11352": "\u96f3", + "11353": "\u6c28", + "11354": "\u7ef0", + "11355": "\u8f95", + "11356": "\u9551", + "11357": "\u7184", + "11358": "\u6064", + "11359": "\u631a", + "11360": "\u98a4", + "11361": "\u778c", + "11362": "\u56e7", + "11363": "\u8bb3", + "11364": "\u75ea", + "11365": "\u70c1", + "11366": "\u7f94", + "11367": "\u79c3", + "11368": "\u6177", + "11369": "\u5c94", + "11370": "\u6f33", + "11371": "\u75de", + "11372": "\u5f64", + "11373": "\u69a8", + "11374": "\u76cf", + "11375": "\u6c90", + "11376": "\u68e0", + "11377": "\u5d34", + "11378": "\u575e", + "11379": "\u5429", + "11380": "\u6808", + "11381": "\u67e0", + "11382": "\u6556", + "11383": "\u4f88", + "11384": "\u7faf", + "11385": "\u6e1d", + "11386": "\u7ef7", + "11387": "\u7eb6", + "11388": "\u7cef", + "11389": "\u8354", + "11390": "\u6dc6", + "11391": "\u9661", + "11392": "\u4fcf", + "11393": "\u58a9", + "11394": "\u7cbd", + "11395": "\u67ec", + "11396": "\u5600", + "11397": "\u53a5", + "11398": "\u5254", + "11399": "\u903e", + "11400": "\u7fb2", + "11401": "\u8beb", + "11402": "\u7f00", + "11403": "\u5768", + "11404": "\u8d42", + "11405": "\u603c", + "11406": "\u5669", + "11407": "\u9647", + "11408": "\u94a6", + "11409": "\u94a0", + "11410": "\u5527", + "11411": "\u51ff", + "11412": "\u55e1", + "11413": "\u5431", + "11414": "\u5349", + "11415": "\u5455", + "11416": "\u6c5b", + "11417": "\u5f08", + "11418": "\u79e7", + "11419": "\u7cd9", + "11420": "\u7115", + "11421": "\u6da9", + "11422": "\u7d6e", + "11423": "\u7490", + "11424": "\u6d95", + "11425": "\u75b5", + "11426": "\u8110", + "11427": "\u6c13", + "11428": "\u7fbf", + "11429": "\u8c24", + "11430": "\u8759", + "11431": "\u904f", + "11432": "\u8760", + "11433": "\u7076", + "11434": "\u6789", + "11435": "\u54a9", + "11436": "\u61f5", + "11437": "\u5a6a", + "11438": "\u60d5", + "11439": "\u8bc5", + "11440": "\u5580", + "11441": "\u6320", + "11442": "\u9753", + "11443": "\u90dd", + "11444": "\u6cfb", + "11445": "\u97e7", + "11446": "\u618b", + "11447": "\u94dd", + "11448": "\u777f", + "11449": "\u5189", + "11450": "\u7a8d", + "11451": "\u78be", + "11452": "\u60f6", + "11453": "\u6f47", + "11454": "\u5dc5", + "11455": "\u9668", + "11456": "\u73ba", + "11457": "\u8d63", + "11458": "\u9c8d", + "11459": "\u54d7", + "11460": "\u7ca4", + "11461": "\u5a25", + "11462": "\u56e4", + "11463": "\u7011", + "11464": "\u68d5", + "11465": "\u53fd", + "11466": "\u710a", + "11467": "\u9e3d", + "11468": "\u6292", + "11469": "\u527f", + "11470": "\u82df", + "11471": "\u915d", + "11472": "\u8046", + "11473": "\u7845", + "11474": "\u7779", + "11475": "\u8782", + "11476": "\u6252", + "11477": "\u4eb5", + "11478": "\u9508", + "11479": "\u4e10", + "11480": "\u731d", + "11481": "\u964b", + "11482": "\u8845", + "11483": "\u599e", + "11484": "\u5478", + "11485": "\u7f1a", + "11486": "\u9a87", + "11487": "\u9f9a", + "11488": "\u5241", + "11489": "\u73ae", + "11490": "\u7785", + "11491": "\u4fd8", + "11492": "\u6986", + "11493": "\u5a76", + "11494": "\u761f", + "11495": "\u655b", + "11496": "\u8747", + "11497": "\u4fed", + "11498": "\u9556", + "11499": "\u9a8f", + "11500": "\u51f3", + "11501": "\u501a", + "11502": "\u5578", + "11503": "\u7b77", + "11504": "\u7ef8", + "11505": "\u6caa", + "11506": "\u886b", + "11507": "\u7455", + "11508": "\u6d3d", + "11509": "\u89c5", + "11510": "\u818a", + "11511": "\u4f6c", + "11512": "\u7f2e", + "11513": "\u63ba", + "11514": "\u80f3", + "11515": "\u7682", + "11516": "\u90a2", + "11517": "\u7ed2", + "11518": "\u78b1", + "11519": "\u7aa5", + "11520": "\u66a7", + "11521": "\u61c8", + "11522": "\u69df", + "11523": "\u56a3", + "11524": "\u7caa", + "11525": "\u9499", + "11526": "\u846b", + "11527": "\u5201", + "11528": "\u54d2", + "11529": "\u90b9", + "11530": "\u6a61", + "11531": "\u8165", + "11532": "\u9985", + "11533": "\u77f6", + "11534": "\u9cc4", + "11535": "\u545b", + "11536": "\u61ac", + "11537": "\u76b1", + "11538": "\u55b1", + "11539": "\u960e", + "11540": "\u55e6", + "11541": "\u96ef", + "11542": "\u5570", + "11543": "\u7a9c", + "11544": "\u9992", + "11545": "\u655e", + "11546": "\u8d41", + "11547": "\u7980", + "11548": "\u6402", + "11549": "\u5288", + "11550": "\u8038", + "11551": "\u8574", + "11552": "\u7bf7", + "11553": "\u8c41", + "11554": "\u8214", + "11555": "\u6bd9", + "11556": "\u7aa6", + "11557": "\u565c", + "11558": "\u8a79", + "11559": "\u762b", + "11560": "\u5f6a", + "11561": "\u6380", + "11562": "\u94f2", + "11563": "\u987d", + "11564": "\u7be1", + "11565": "\u4e53", + "11566": "\u9600", + "11567": "\u5a1f", + "11568": "\u946b", + "11569": "\u5e1c", + "11570": "\u4e2b", + "11571": "\u9ad3", + "11572": "\u6ca6", + "11573": "\u53e8", + "11574": "\u9576", + "11575": "\u55d3", + "11576": "\u8bf2", + "11577": "\u548f", + "11578": "\u997a", + "11579": "\u9e26", + "11580": "\u6984", + "11581": "\u5e90", + "11582": "\u864f", + "11583": "\u9a86", + "11584": "\u874e", + "11585": "\u54d4", + "11586": "\u8f7f", + "11587": "\u63cd", + "11588": "\u61a8", + "11589": "\u4f84", + "11590": "\u9165", + "11591": "\u8e39", + "11592": "\u6a44", + "11593": "\u7eba", + "11594": "\u516e", + "11595": "\u70db", + "11596": "\u60af", + "11597": "\u8783", + "11598": "\u8424", + "11599": "\u53a2", + "11600": "\u6ca7", + "11601": "\u5543", + "11602": "\u8f9c", + "11603": "\u7f55", + "11604": "\u9972", + "11605": "\u8c1c", + "11606": "\u5364", + "11607": "\u6d47", + "11608": "\u57d4", + "11609": "\u7426", + "11610": "\u8469", + "11611": "\u6073", + "11612": "\u7b0b", + "11613": "\u5490", + "11614": "\u5c7f", + "11615": "\u949e", + "11616": "\u8bc0", + "11617": "\u96cf", + "11618": "\u63b0", + "11619": "\u9610", + "11620": "\u5c4e", + "11621": "\u5495", + "11622": "\u6467", + "11623": "\u9ecf", + "11624": "\u6441", + "11625": "\u6055", + "11626": "\u7f09", + "11627": "\u6e24", + "11628": "\u7eac", + "11629": "\u64b8", + "11630": "\u840d", + "11631": "\u6512", + "11632": "\u64ce", + "11633": "\u7741", + "11634": "\u70b3", + "11635": "\u4e52", + "11636": "\u7ad6", + "11637": "\u7f14", + "11638": "\u4ed1", + "11639": "\u95f8", + "11640": "\u8be1", + "11641": "\u5564", + "11642": "\u7410", + "11643": "\u8682", + "11644": "\u8774", + "11645": "\u5955", + "11646": "\u8c34", + "11647": "\u63fd", + "11648": "\u53ee", + "11649": "\u7ece", + "11650": "\u77eb", + "11651": "\u6363", + "11652": "\u6b47", + "11653": "\u888d", + "11654": "\u8c0d", + "11655": "\u67a3", + "11656": "\u55b5", + "11657": "\u9ca8", + "11658": "\u8bcf", + "11659": "\u5960", + "11660": "\u5029", + "11661": "\u8e6d", + "11662": "\u64a9", + "11663": "\u7fd8", + "11664": "\u4fa8", + "11665": "\u8f90", + "11666": "\u7792", + "11667": "\u7130", + "11668": "\u9965", + "11669": "\u54a6", + "11670": "\u889c", + "11671": "\u634d", + "11672": "\u6a0a", + "11673": "\u95fd", + "11674": "\u94f8", + "11675": "\u58f6", + "11676": "\u8611", + "11677": "\u7f38", + "11678": "\u90b5", + "11679": "\u76d4", + "11680": "\u7096", + "11681": "\u6f8e", + "11682": "\u8c2c", + "11683": "\u6dc7", + "11684": "\u94c5", + "11685": "\u5d1b", + "11686": "\u803f", + "11687": "\u63e3", + "11688": "\u7504", + "11689": "\u575d", + "11690": "\u4ea9", + "11691": "\u9631", + "11692": "\u96a7", + "11693": "\u7538", + "11694": "\u5c27", + "11695": "\u78d5", + "11696": "\u6233", + "11697": "\u6ee4", + "11698": "\u8bb6", + "11699": "\u7574", + "11700": "\u917f", + "11701": "\u8206", + "11702": "\u5c82", + "11703": "\u5ac2", + "11704": "\u707f", + "11705": "\u886c", + "11706": "\u75d8", + "11707": "\u8393", + "11708": "\u549a", + "11709": "\u5fcf", + "11710": "\u9882", + "11711": "\u9521", + "11712": "\u563b", + "11713": "\u5188", + "11714": "\u7ee3", + "11715": "\u8d31", + "11716": "\u7eb1", + "11717": "\u96cd", + "11718": "\u98d9", + "11719": "\u7737", + "11720": "\u7784", + "11721": "\u5195", + "11722": "\u5ed6", + "11723": "\u62e2", + "11724": "\u6390", + "11725": "\u6d51", + "11726": "\u69c3", + "11727": "\u9489", + "11728": "\u6487", + "11729": "\u9a74", + "11730": "\u6ee5", + "11731": "\u88f9", + "11732": "\u545c", + "11733": "\u5e10", + "11734": "\u7aed", + "11735": "\u8d3f", + "11736": "\u6d46", + "11737": "\u8116", + "11738": "\u5306", + "11739": "\u9a7c", + "11740": "\u859b", + "11741": "\u9b44", + "11742": "\u8bf5", + "11743": "\u5792", + "11744": "\u7f05", + "11745": "\u8e66", + "11746": "\u9709", + "11747": "\u63ea", + "11748": "\u5784", + "11749": "\u5300", + "11750": "\u7ea4", + "11751": "\u6405", + "11752": "\u574e", + "11753": "\u7a3b", + "11754": "\u6869", + "11755": "\u73ab", + "11756": "\u8367", + "11757": "\u7a91", + "11758": "\u54d1", + "11759": "\u6413", + "11760": "\u94ed", + "11761": "\u5151", + "11762": "\u8086", + "11763": "\u5494", + "11764": "\u575f", + "11765": "\u56ca", + "11766": "\u9a70", + "11767": "\u77a7", + "11768": "\u58e4", + "11769": "\u5bde", + "11770": "\u9887", + "11771": "\u62ce", + "11772": "\u65f7", + "11773": "\u8721", + "11774": "\u7fa1", + "11775": "\u5594", + "11776": "\u6d85", + "11777": "\u94a5", + "11778": "\u7199", + "11779": "\u6495", + "11780": "\u70eb", + "11781": "\u9a73", + "11782": "\u7f06", + "11783": "\u8e48", + "11784": "\u77bb", + "11785": "\u7470", + "11786": "\u8854", + "11787": "\u803b", + "11788": "\u8681", + "11789": "\u95fa", + "11790": "\u6346", + "11791": "\u9877", + "11792": "\u5858", + "11793": "\u7476", + "11794": "\u8c2d", + "11795": "\u83b9", + "11796": "\u743c", + "11797": "\u62e6", + "11798": "\u7a46", + "11799": "\u83e0", + "11800": "\u54aa", + "11801": "\u68f5", + "11802": "\u8bbd", + "11803": "\u5ae9", + "11804": "\u8bdb", + "11805": "\u57ae", + "11806": "\u5499", + "11807": "\u9e64", + "11808": "\u74f7", + "11809": "\u9e70", + "11810": "\u5021", + "11811": "\u5471", + "11812": "\u964c", + "11813": "\u6084", + "11814": "\u70d8", + "11815": "\u62f1", + "11816": "\u62ef", + "11817": "\u8231", + "11818": "\u71b9", + "11819": "\u5de9", + "11820": "\u6d4f", + "11821": "\u7529", + "11822": "\u9888", + "11823": "\u5c61", + "11824": "\u62fd", + "11825": "\u584c", + "11826": "\u8d2c", + "11827": "\u8822", + "11828": "\u82ac", + "11829": "\u7ef5", + "11830": "\u5308", + "11831": "\u640f", + "11832": "\u8d4e", + "11833": "\u658b", + "11834": "\u8c10", + "11835": "\u852c", + "11836": "\u800d", + "11837": "\u789f", + "11838": "\u83c7", + "11839": "\u4e1b", + "11840": "\u5de2", + "11841": "\u5e18", + "11842": "\u83bd", + "11843": "\u5bc7", + "11844": "\u88d9", + "11845": "\u8c6b", + "11846": "\u64c5", + "11847": "\u4f63", + "11848": "\u567b", + "11849": "\u9976", + "11850": "\u6e17", + "11851": "\u953b", + "11852": "\u8be0", + "11853": "\u8482", + "11854": "\u52fa", + "11855": "\u96b6", + "11856": "\u5a77", + "11857": "\u8d9f", + "11858": "\u6401", + "11859": "\u561f", + "11860": "\u5760", + "11861": "\u594e", + "11862": "\u814a", + "11863": "\u6cfc", + "11864": "\u532a", + "11865": "\u9510", + "11866": "\u54e9", + "11867": "\u8270", + "11868": "\u5428", + "11869": "\u8c23", + "11870": "\u59ec", + "11871": "\u4fa3", + "11872": "\u6fa1", + "11873": "\u69db", + "11874": "\u8346", + "11875": "\u72e0", + "11876": "\u6e23", + "11877": "\u9655", + "11878": "\u638f", + "11879": "\u5f17", + "11880": "\u8c0a", + "11881": "\u9881", + "11882": "\u6500", + "11883": "\u6124", + "11884": "\u5992", + "11885": "\u94a9", + "11886": "\u80c0", + "11887": "\u625b", + "11888": "\u6254", + "11889": "\u51d1", + "11890": "\u70ab", + "11891": "\u57ab", + "11892": "\u94ae", + "11893": "\u5783", + "11894": "\u9e45", + "11895": "\u6127", + "11896": "\u50f5", + "11897": "\u6e34", + "11898": "\u632a", + "11899": "\u8c05", + "11900": "\u94c3", + "11901": "\u7b3c", + "11902": "\u8dea", + "11903": "\u745c", + "11904": "\u6e83", + "11905": "\u60ac", + "11906": "\u8d3e", + "11907": "\u6b79", + "11908": "\u9f7f", + "11909": "\u8d81", + "11910": "\u63a9", + "11911": "\u8bbc", + "11912": "\u8d29", + "11913": "\u6ee9", + "11914": "\u9524", + "11915": "\u76ef", + "11916": "\u6251", + "11917": "\u727a", + "11918": "\u58f3", + "11919": "\u573e", + "11920": "\u52cb", + "11921": "\u54fc", + "11922": "\u763e", + "11923": "\u82cd", + "11924": "\u59ae", + "11925": "\u9896", + "11926": "\u9614", + "11927": "\u718f", + "11928": "\u778e", + "11929": "\u6e0a", + "11930": "\u5764", + "11931": "\u9e23", + "11932": "\u6108", + "11933": "\u900a", + "11934": "\u817b", + "11935": "\u9a84", + "11936": "\u8d1e", + "11937": "\u5524", + "11938": "\u97f5", + "11939": "\u5a74", + "11940": "\u6cbe", + "11941": "\u97e6", + "11942": "\u98a0", + "11943": "\u68cd", + "11944": "\u4e54", + "11945": "\u5c4c", + "11946": "\u8083", + "11947": "\u80c1", + "11948": "\u5f6d", + "11949": "\u78ca", + "11950": "\u556a", + "11951": "\u53a6", + "11952": "\u742a", + "11953": "\u7ef3", + "11954": "\u59ca", + "11955": "\u9a9a", + "11956": "\u7eb2", + "11957": "\u8f96", + "11958": "\u867e", + "11959": "\u8c0e", + "11960": "\u8881", + "11961": "\u7f20", + "11962": "\u7f50", + "11963": "\u5be8", + "11964": "\u5e9e", + "11965": "\u95ef", + "11966": "\u5a07", + "11967": "\u8e72", + "11968": "\u53ed", + "11969": "\u5e15", + "11970": "\u8427", + "11971": "\u5401", + "11972": "\u745f", + "11973": "\u6c1b", + "11974": "\u838e", + "11975": "\u6454", + "11976": "\u76fc", + "11977": "\u5ab3", + "11978": "\u95f7", + "11979": "\u635e", + "11980": "\u4ff1", + "11981": "\u9e3f", + "11982": "\u9e4f", + "11983": "\u9988", + "11984": "\u7545", + "11985": "\u8c26", + "11986": "\u5509", + "11987": "\u62a0", + "11988": "\u8fc8", + "11989": "\u7b5b", + "11990": "\u8d3a", + "11991": "\u841d", + "11992": "\u8c28", + "11993": "\u7ebd", + "11994": "\u7239", + "11995": "\u80be", + "11996": "\u9aa4", + "11997": "\u51af", + "11998": "\u626d", + "11999": "\u5587", + "12000": "\u7816", + "12001": "\u8bde", + "12002": "\u65a9", + "12003": "\u72ee", + "12004": "\u7f62", + "12005": "\u8bf1", + "12006": "\u5492", + "12007": "\u7855", + "12008": "\u7f1d", + "12009": "\u6345", + "12010": "\u9a71", + "12011": "\u55bb", + "12012": "\u76d0", + "12013": "\u8fbd", + "12014": "\u54c4", + "12015": "\u9171", + "12016": "\u62e8", + "12017": "\u53f9", + "12018": "\u60e9", + "12019": "\u6cdb", + "12020": "\u5986", + "12021": "\u9601", + "12022": "\u6ee8", + "12023": "\u4fa6", + "12024": "\u6021", + "12025": "\u5978", + "12026": "\u5733", + "12027": "\u7b28", + "12028": "\u8eba", + "12029": "\u5179", + "12030": "\u6b67", + "12031": "\u4ed7", + "12032": "\u7fc5", + "12033": "\u7a9d", + "12034": "\u7ff0", + "12035": "\u5c97", + "12036": "\u88e4", + "12037": "\u7ed8", + "12038": "\u8bc8", + "12039": "\u9971", + "12040": "\u8b6c", + "12041": "\u8f69", + "12042": "\u8d2b", + "12043": "\u77e3", + "12044": "\u6323", + "12045": "\u67ef", + "12046": "\u8dc3", + "12047": "\u9493", + "12048": "\u63ed", + "12049": "\u6361", + "12050": "\u59e8", + "12051": "\u81c2", + "12052": "\u8db4", + "12053": "\u98d8", + "12054": "\u4eff", + "12055": "\u8f74", + "12056": "\u5939", + "12057": "\u758f", + "12058": "\u7838", + "12059": "\u94bb", + "12060": "\u54a7", + "12061": "\u80bf", + "12062": "\u997f", + "12063": "\u626f", + "12064": "\u7eb9", + "12065": "\u644a", + "12066": "\u4f2a", + "12067": "\u8c31", + "12068": "\u8d2f", + "12069": "\u809a", + "12070": "\u7f34", + "12071": "\u8361", + "12072": "\u629b", + "12073": "\u80a0", + "12074": "\u5415", + "12075": "\u5cad", + "12076": "\u78b3", + "12077": "\u90bb", + "12078": "\u9a7b", + "12079": "\u9e2d", + "12080": "\u629a", + "12081": "\u5154", + "12082": "\u7ea0", + "12083": "\u9f9f", + "12084": "\u71ac", + "12085": "\u5435", + "12086": "\u6d53", + "12087": "\u503e", + "12088": "\u5395", + "12089": "\u6d82", + "12090": "\u4fe9", + "12091": "\u9093", + "12092": "\u96fe", + "12093": "\u7eb5", + "12094": "\u5367", + "12095": "\u80a4", + "12096": "\u4e27", + "12097": "\u80f6", + "12098": "\u80d6", + "12099": "\u6377", + "12100": "\u6db5", + "12101": "\u8d60", + "12102": "\u8d4c", + "12103": "\u90ae", + "12104": "\u6655", + "12105": "\u7bee", + "12106": "\u5362", + "12107": "\u7ed1", + "12108": "\u575b", + "12109": "\u7978", + "12110": "\u83b2", + "12111": "\u6760", + "12112": "\u730e", + "12113": "\u8f70", + "12114": "\u53e0", + "12115": "\u5c38", + "12116": "\u67dc", + "12117": "\u5821", + "12118": "\u5242", + "12119": "\u607c", + "12120": "\u5220", + "12121": "\u594b", + "12122": "\u6296", + "12123": "\u70e4", + "12124": "\u5f7b", + "12125": "\u9189", + "12126": "\u950b", + "12127": "\u7cdf", + "12128": "\u6746", + "12129": "\u4f1e", + "12130": "\u7eb7", + "12131": "\u538c", + "12132": "\u6846", + "12133": "\u680f", + "12134": "\u4f69", + "12135": "\u529d", + "12136": "\u901b", + "12137": "\u918b", + "12138": "\u8be6", + "12139": "\u8273", + "12140": "\u70bc", + "12141": "\u522e", + "12142": "\u6062", + "12143": "\u5938", + "12144": "\u9012", + "12145": "\u739b", + "12146": "\u5c18", + "12147": "\u8d50", + "12148": "\u8fdf", + "12149": "\u83f2", + "12150": "\u8d4b", + "12151": "\u75af", + "12152": "\u7efc", + "12153": "\u8350", + "12154": "\u6cea", + "12155": "\u5c34", + "12156": "\u507f", + "12157": "\u6324", + "12158": "\u50a8", + "12159": "\u8fc1", + "12160": "\u9677", + "12161": "\u9a76", + "12162": "\u8230", + "12163": "\u5457", + "12164": "\u72b9", + "12165": "\u52b2", + "12166": "\u624e", + "12167": "\u518c", + "12168": "\u7275", + "12169": "\u7b79", + "12170": "\u50bb", + "12171": "\u8f89", + "12172": "\u6668", + "12173": "\u4ed3", + "12174": "\u8e22", + "12175": "\u9970", + "12176": "\u7f69", + "12177": "\u51bb", + "12178": "\u7ed5", + "12179": "\u55b7", + "12180": "\u7eea", + "12181": "\u8d54", + "12182": "\u780d", + "12183": "\u8d21", + "12184": "\u8e29", + "12185": "\u6491", + "12186": "\u4fa7", + "12187": "\u95f2", + "12188": "\u8fa9", + "12189": "\u6b49", + "12190": "\u5baa", + "12191": "\u94dc", + "12192": "\u94fe", + "12193": "\u6c27", + "12194": "\u817e", + "12195": "\u9f84", + "12196": "\u5a31", + "12197": "\u8f86", + "12198": "\u8d2a", + "12199": "\u89c8", + "12200": "\u5899", + "12201": "\u9274", + "12202": "\u5561", + "12203": "\u8109", + "12204": "\u5413", + "12205": "\u72f1", + "12206": "\u517d", + "12207": "\u7a97", + "12208": "\u5f2f", + "12209": "\u70ae", + "12210": "\u54a8", + "12211": "\u5fe7", + "12212": "\u96d5", + "12213": "\u5ba0", + "12214": "\u5c2c", + "12215": "\u6e14", + "12216": "\u806a", + "12217": "\u77ff", + "12218": "\u94fa", + "12219": "\u684c", + "12220": "\u6bc1", + "12221": "\u6735", + "12222": "\u88ad", + "12223": "\u6270", + "12224": "\u5a1c", + "12225": "\u9526", + "12226": "\u6321", + "12227": "\u680b", + "12228": "\u903b", + "12229": "\u90d1", + "12230": "\u568e", + "12231": "\u51ef", + "12232": "\u8f68", + "12233": "\u5e99", + "12234": "\u51ed", + "12235": "\u62df", + "12236": "\u5c1d", + "12237": "\u5565", + "12238": "\u55e8", + "12239": "\u6cfd", + "12240": "\u731c", + "12241": "\u5085", + "12242": "\u5141", + "12243": "\u95f9", + "12244": "\u9ed8", + "12245": "\u7a77", + "12246": "\u5466", + "12247": "\u7f13", + "12248": "\u9e1f", + "12249": "\u7f29", + "12250": "\u8d38", + "12251": "\u8eb2", + "12252": "\u8d4f", + "12253": "\u626c", + "12254": "\u7cd5", + "12255": "\u649e", + "12256": "\u8d37", + "12257": "\u593a", + "12258": "\u8212", + "12259": "\u5fc6", + "12260": "\u6d01", + "12261": "\u61d2", + "12262": "\u6c47", + "12263": "\u8f85", + "12264": "\u62d6", + "12265": "\u8bd1", + "12266": "\u788e", + "12267": "\u4f19", + "12268": "\u4eea", + "12269": "\u5496", + "12270": "\u6e10", + "12271": "\u8d24", + "12272": "\u810f", + "12273": "\u996e", + "12274": "\u6478", + "12275": "\u9080", + "12276": "\u8f88", + "12277": "\u563f", + "12278": "\u6653", + "12279": "\u62e5", + "12280": "\u9897", + "12281": "\u5a03", + "12282": "\u5e05", + "12283": "\u8d56", + "12284": "\u62c6", + "12285": "\u5e9f", + "12286": "\u70c2", + "12287": "\u9605", + "12288": "\u9a91", + "12289": "\u6c61", + "12290": "\u63d2", + "12291": "\u8fea", + "12292": "\u82f9", + "12293": "\u8bca", + "12294": "\u8d26", + "12295": "\u6682", + "12296": "\u7a23", + "12297": "\u9a7e", + "12298": "\u62fc", + "12299": "\u987f", + "12300": "\u9a82", + "12301": "\u8bfa", + "12302": "\u6c89", + "12303": "\u5582", + "12304": "\u5bbe", + "12305": "\u62ac", + "12306": "\u503a", + "12307": "\u51e4", + "12308": "\u8d8b", + "12309": "\u5385", + "12310": "\u7237", + "12311": "\u6865", + "12312": "\u6444", + "12313": "\u6269", + "12314": "\u9505", + "12315": "\u8ba2", + "12316": "\u9501", + "12317": "\u4e4c", + "12318": "\u4e30", + "12319": "\u9738", + "12320": "\u4f26", + "12321": "\u626b", + "12322": "\u8bda", + "12323": "\u9c9c", + "12324": "\u9057", + "12325": "\u9f50", + "12326": "\u6446", + "12327": "\u5434", + "12328": "\u9690", + "12329": "\u7840", + "12330": "\u5bbd", + "12331": "\u5706", + "12332": "\u78b0", + "12333": "\u60ef", + "12334": "\u4ecd", + "12335": "\u60ca", + "12336": "\u654c", + "12337": "\u997c", + "12338": "\u6325", + "12339": "\u6770", + "12340": "\u9c81", + "12341": "\u7ee9", + "12342": "\u62a2", + "12343": "\u8d3c", + "12344": "\u5e86", + "12345": "\u6c64", + "12346": "\u560e", + "12347": "\u8d1d", + "12348": "\u5f03", + "12349": "\u6316", + "12350": "\u955c", + "12351": "\u558a", + "12352": "\u5269", + "12353": "\u5077", + "12354": "\u9635", + "12355": "\u989c", + "12356": "\u8363", + "12357": "\u7f5a", + "12358": "\u54df", + "12359": "\u8f91", + "12360": "\u9634", + "12361": "\u7eaf", + "12362": "\u7b7e", + "12363": "\u6eda", + "12364": "\u84dd", + "12365": "\u7f18", + "12366": "\u8be2", + "12367": "\u6d89", + "12368": "\u9a97", + "12369": "\u7ade", + "12370": "\u8dcc", + "12371": "\u5761", + "12372": "\u8bbf", + "12373": "\u707e", + "12374": "\u95ed", + "12375": "\u9875", + "12376": "\u94a2", + "12377": "\u4f30", + "12378": "\u82cf", + "12379": "\u5e01", + "12380": "\u5251", + "12381": "\u5e93", + "12382": "\u706d", + "12383": "\u6302", + "12384": "\u8fdd", + "12385": "\u552e", + "12386": "\u5b81", + "12387": "\u6263", + "12388": "\u575a", + "12389": "\u6768", + "12390": "\u8d62", + "12391": "\u4e1d", + "12392": "\u55bd", + "12393": "\u67aa", + "12394": "\u8d5a", + "12395": "\u5708", + "12396": "\u7eb3", + "12397": "\u8d34", + "12398": "\u7597", + "12399": "\u5389", + "12400": "\u8f6f", + "12401": "\u6c9f", + "12402": "\u8bd7", + "12403": "\u8d5e", + "12404": "\u70df", + "12405": "\u8d25", + "12406": "\u8651", + "12407": "\u65c1", + "12408": "\u635f", + "12409": "\u54af", + "12410": "\u6742", + "12411": "\u7f3a", + "12412": "\u5976", + "12413": "\u5c9b", + "12414": "\u4e61", + "12415": "\u7ec7", + "12416": "\u70e7", + "12417": "\u989d", + "12418": "\u51c0", + "12419": "\u952e", + "12420": "\u9547", + "12421": "\u8138", + "12422": "\u7a33", + "12423": "\u6863", + "12424": "\u8f7d", + "12425": "\u5979", + "12426": "\u7a0d", + "12427": "\u8bf8", + "12428": "\u7f16", + "12429": "\u8d75", + "12430": "\u7334", + "12431": "\u6447", + "12432": "\u5170", + "12433": "\u54b1", + "12434": "\u4ec5", + "12435": "\u5218", + "12436": "\u8c0b", + "12437": "\u7adf", + "12438": "\u542f", + "12439": "\u68a6", + "12440": "\u4f1f", + "12441": "\u4e34", + "12442": "\u7edc", + "12443": "\u5b59", + "12444": "\u97e9", + "12445": "\u8f6e", + "12446": "\u6da8", + "12447": "\u5bfb", + "12448": "\u9500", + "12449": "\u8bef", + "12450": "\u5382", + "12451": "\u91ca", + "12452": "\u7ecd", + "12453": "\u4e8f", + "12454": "\u9636", + "12455": "\u8bad", + "12456": "\u8d2d", + "12457": "\u95ea", + "12458": "\u641c", + "12459": "\u9646", + "12460": "\u52b3", + "12461": "\u4e3d", + "12462": "\u5f39", + "12463": "\u6076", + "12464": "\u53bf", + "12465": "\u7801", + "12466": "\u4e22", + "12467": "\u5f02", + "12468": "\u8d27", + "12469": "\u6bd5", + "12470": "\u9891", + "12471": "\u8428", + "12472": "\u6293", + "12473": "\u5956", + "12474": "\u7b14", + "12475": "\u6000", + "12476": "\u8f93", + "12477": "\u6811", + "12478": "\u7eaa", + "12479": "\u996d", + "12480": "\u70e6", + "12481": "\u7eff", + "12482": "\u51b0", + "12483": "\u80dc", + "12484": "\u62e9", + "12485": "\u7238", + "12486": "\u51fb", + "12487": "\u95fb", + "12488": "\u574f", + "12489": "\u94c1", + "12490": "\u83b7", + "12491": "\u987e", + "12492": "\u56f4", + "12493": "\u8d23", + "12494": "\u60a8", + "12495": "\u9002", + "12496": "\u5f52", + "12497": "\u8bc4", + "12498": "\u76d8", + "12499": "\u9e21", + "12500": "\u5e7a", + "12501": "\u804c", + "12502": "\u79ef", + "12503": "\u827a", + "12504": "\u9488", + "12505": "\u8d76", + "12506": "\u8111", + "12507": "\u5174", + "12508": "\u8d22", + "12509": "\u519c", + "12510": "\u7d27", + "12511": "\u987a", + "12512": "\u56ed", + "12513": "\u6d4b", + "12514": "\u8baf", + "12515": "\u5f55", + "12516": "\u8d35", + "12517": "\u538b", + "12518": "\u94f6", + "12519": "\u8303", + "12520": "\u9648", + "12521": "\u5267", + "12522": "\u7ec3", + "12523": "\u76d1", + "12524": "\u534f", + "12525": "\u51cf", + "12526": "\u8bcd", + "12527": "\u5450", + "12528": "\u4f18", + "12529": "\u949f", + "12530": "\u5c81", + "12531": "\u4e25", + "12532": "\u7ec6", + "12533": "\u6c49", + "12534": "\u8d1f", + "12535": "\u76d6", + "12536": "\u836f", + "12537": "\u4e9a", + "12538": "\u9876", + "12539": "\u4f24", + "12540": "\u5c42", + "12541": "\u70ed", + "12542": "\u8f7b", + "12543": "\u68c0", + "12544": "\u5c14", + "12545": "\u7075", + "12546": "\u4ebf", + "12547": "\u7ef4", + "12548": "\u6781", + "12549": "\u8865", + "12550": "\u8425", + "12551": "\u54cd", + "12552": "\u9760", + "12553": "\u6548", + "12554": "\u6267", + "12555": "\u6740", + "12556": "\u663e", + "12557": "\u5ba1", + "12558": "\u8d28", + "12559": "\u987b", + "12560": "\u6784", + "12561": "\u5723", + "12562": "\u8c13", + "12563": "\u5356", + "12564": "\u54e5", + "12565": "\u4eb2", + "12566": "\u6d4e", + "12567": "\u7edd", + "12568": "\u9c7c", + "12569": "\u9669", + "12570": "\u8bfb", + "12571": "\u8bfe", + "12572": "\u7f57", + "12573": "\u867d", + "12574": "\u98de", + "12575": "\u5b69", + "12576": "\u5361", + "12577": "\u536b", + "12578": "\u503c", + "12579": "\u62a4", + "12580": "\u8c08", + "12581": "\u6015", + "12582": "\u9a8c", + "12583": "\u8d5b", + "12584": "\u620f", + "12585": "\u7ee7", + "12586": "\u8054", + "12587": "\u9633", + "12588": "\u5212", + "12589": "\u521b", + "12590": "\u665a", + "12591": "\u589e", + "12592": "\u8bc9", + "12593": "\u8bd5", + "12594": "\u8bc6", + "12595": "\u8dd1", + "12596": "\u9884", + "12597": "\u73af", + "12598": "\u8bb8", + "12599": "\u61c2", + "12600": "\u6001", + "12601": "\u9879", + "12602": "\u56e2", + "12603": "\u5bab", + "12604": "\u5907", + "12605": "\u79bb", + "12606": "\u9f99", + "12607": "\u8ba8", + "12608": "\u9645", + "12609": "\u7b80", + "12610": "\u517b", + "12611": "\u5bfc", + "12612": "\u4e3e", + "12613": "\u5757", + "12614": "\u961f", + "12615": "\u8fde", + "12616": "\u672f", + "12617": "\u5386", + "12618": "\u56fe", + "12619": "\u5219", + "12620": "\u8bc1", + "12621": "\u8bed", + "12622": "\u62dc", + "12623": "\u4e13", + "12624": "\u7ea2", + "12625": "\u6362", + "12626": "\u4f17", + "12627": "\u6b65", + "12628": "\u7ea7", + "12629": "\u6743", + "12630": "\u4e60", + "12631": "\u67e5", + "12632": "\u590d", + "12633": "\u513f", + "12634": "\u51b5", + "12635": "\u51b3", + "12636": "\u9886", + "12637": "\u8fbe", + "12638": "\u6807", + "12639": "\u6b22", + "12640": "\u7ec4", + "12641": "\u641e", + "12642": "\u7c7b", + "12643": "\u7eed", + "12644": "\u53e6", + "12645": "\u5988", + "12646": "\u5e7f", + "12647": "\u534e", + "12648": "\u4e50", + "12649": "\u89c4", + "12650": "\u4f20", + "12651": "\u786e", + "12652": "\u8282", + "12653": "\u4e49", + "12654": "\u561e", + "12655": "\u9519", + "12656": "\u7ea6", + "12657": "\u89c6", + "12658": "\u519b", + "12659": "\u54c7", + "12660": "\u6218", + "12661": "\u5f3a", + "12662": "\u8bae", + "12663": "\u6536", + "12664": "\u89c2", + "12665": "\u8c01", + "12666": "\u4ef7", + "12667": "\u8f6c", + "12668": "\u8fd0", + "12669": "\u62ff", + "12670": "\u52a1", + "12671": "\u6389", + "12672": "\u5e76", + "12673": "\u7f51", + "12674": "\u8fdc", + "12675": "\u6ee1", + "12676": "\u7ebf", + "12677": "\u96be", + "12678": "\u603b", + "12679": "\u94b1", + "12680": "\u7edf", + "12681": "\u5e2e", + "12682": "\u8ba1", + "12683": "\u98ce", + "12684": "\u95e8", + "12685": "\u7231", + "12686": "\u5f20", + "12687": "\u5440", + "12688": "\u9a6c", + "12689": "\u627e", + "12690": "\u6c14", + "12691": "\u529e", + "12692": "\u8bbe", + "12693": "\u5e26", + "12694": "\u4e70", + "12695": "\u5904", + "12696": "\u62a5", + "12697": "\u9009", + "12698": "\u8ba4", + "12699": "\u8bba", + "12700": "\u4e66", + "12701": "\u89c1", + "12702": "\u8f66", + "12703": "\u7ed3", + "12704": "\u5355", + "12705": "\u8bb0", + "12706": "\u6bcf", + "12707": "\u591f", + "12708": "\u8c03", + "12709": "\u4ea7", + "12710": "\u542c", + "12711": "\u5566", + "12712": "\u8c22", + "12713": "\u8bf6", + "12714": "\u5458", + "12715": "\u55ef", + "12716": "\u8f83", + "12717": "\u7535", + "12718": "\u8d44", + "12719": "\u53d8", + "12720": "\u65e0", + "12721": "\u522b", + "12722": "\u573a", + "12723": "\u54ce", + "12724": "\u5417", + "12725": "\u8ba9", + "12726": "\u8be5", + "12727": "\u4ece", + "12728": "\u5427", + "12729": "\u4e1a", + "12730": "\u9898", + "12731": "\u600e", + "12732": "\u95f4", + "12733": "\u4e1c", + "12734": "\u561b", + "12735": "\u5e94", + "12736": "\u957f", + "12737": "\u8fdb", + "12738": "\u521a", + "12739": "\u52a8", + "12740": "\u5173", + "12741": "\u8fb9", + "12742": "\u89c9", + "12743": "\u800c", + "12744": "\u53d1", + "12745": "\u7ecf", + "12746": "\u8bdd", + "12747": "\u79cd", + "12748": "\u8bb2", + "12749": "\u5f00", + "12750": "\u5b83", + "12751": "\u5b9e", + "12752": "\u7ed9", + "12753": "\u505a", + "12754": "\u8ddf", + "12755": "\u73b0", + "12756": "\u8fc7", + "12757": "\u5443", + "12758": "\u5f88", + "12759": "\u54e6", + "12760": "\u65f6", + "12761": "\u8fd8", + "12762": "\u5462", + "12763": "\u8bf4", + "12764": "\u4e3a", + "12765": "\u4e48", + "12766": "\u4eec", + "12767": "\u554a", + "12768": "\u4f60", + "12769": "\u8fd9", + "12770": "\u3da7", + "12771": "\u4f5a", + "12772": "\u4f5d", + "12773": "\u4fdf", + "12774": "\u5048", + "12775": "\u507b", + "12776": "\u52d0", + "12777": "\u530f", + "12778": "\u5372", + "12779": "\u540b", + "12780": "\u54d5", + "12781": "\u5533", + "12782": "\u5572", + "12783": "\u5576", + "12784": "\u55eb", + "12785": "\u55ec", + "12786": "\u560f", + "12787": "\u56d7", + "12788": "\u56eb", + "12789": "\u56ef", + "12790": "\u56f5", + "12791": "\u5704", + "12792": "\u576d", + "12793": "\u578c", + "12794": "\u5803", + "12795": "\u5914", + "12796": "\u5941", + "12797": "\u59aa", + "12798": "\u5a08", + "12799": "\u5ad2", + "12800": "\u5d5b", + "12801": "\u5e54", + "12802": "\u5fea", + "12803": "\u602b", + "12804": "\u60ad", + "12805": "\u61d1", + "12806": "\u620d", + "12807": "\u6217", + "12808": "\u6427", + "12809": "\u6555", + "12810": "\u65d6", + "12811": "\u661d", + "12812": "\u668c", + "12813": "\u66be", + "12814": "\u66c8", + "12815": "\u67a5", + "12816": "\u67c3", + "12817": "\u680c", + "12818": "\u6874", + "12819": "\u6877", + "12820": "\u6901", + "12821": "\u691f", + "12822": "\u6924", + "12823": "\u6934", + "12824": "\u6989", + "12825": "\u69ed", + "12826": "\u6a50", + "12827": "\u6a97", + "12828": "\u6b38", + "12829": "\u6b59", + "12830": "\u6b81", + "12831": "\u6b9a", + "12832": "\u6c1a", + "12833": "\u6c24", + "12834": "\u6c32", + "12835": "\u6cd6", + "12836": "\u6cfa", + "12837": "\u6d3a", + "12838": "\u6d50", + "12839": "\u6d91", + "12840": "\u6ef9", + "12841": "\u6f2d", + "12842": "\u6f46", + "12843": "\u6fa7", + "12844": "\u6fb6", + "12845": "\u70c0", + "12846": "\u71b5", + "12847": "\u7260", + "12848": "\u7301", + "12849": "\u7339", + "12850": "\u736d", + "12851": "\u7391", + "12852": "\u73e5", + "12853": "\u7622", + "12854": "\u765e", + "12855": "\u77cd", + "12856": "\u782d", + "12857": "\u7852", + "12858": "\u7856", + "12859": "\u78f4", + "12860": "\u7a39", + "12861": "\u7b38", + "12862": "\u7bfc", + "12863": "\u7ea1", + "12864": "\u7f1b", + "12865": "\u7f2c", + "12866": "\u7fa7", + "12867": "\u8004", + "12868": "\u800b", + "12869": "\u801c", + "12870": "\u802a", + "12871": "\u80b1", + "12872": "\u81ec", + "12873": "\u824b", + "12874": "\u827f", + "12875": "\u8297", + "12876": "\u82be", + "12877": "\u8333", + "12878": "\u833c", + "12879": "\u835b", + "12880": "\u8378", + "12881": "\u83d8", + "12882": "\u84af", + "12883": "\u84c1", + "12884": "\u85b7", + "12885": "\u85e0", + "12886": "\u86ac", + "12887": "\u86b4", + "12888": "\u86c9", + "12889": "\u877d", + "12890": "\u87c6", + "12891": "\u880a", + "12892": "\u89e5", + "12893": "\u8be4", + "12894": "\u8bf9", + "12895": "\u8c16", + "12896": "\u8c18", + "12897": "\u8c2e", + "12898": "\u8c36", + "12899": "\u8c47", + "12900": "\u8c62", + "12901": "\u8c89", + "12902": "\u8d32", + "12903": "\u8d49", + "12904": "\u8e41", + "12905": "\u8e49", + "12906": "\u8f8a", + "12907": "\u900b", + "12908": "\u9051", + "12909": "\u90c7", + "12910": "\u915e", + "12911": "\u9490", + "12912": "\u9492", + "12913": "\u94bc", + "12914": "\u94cb", + "12915": "\u94cd", + "12916": "\u94d1", + "12917": "\u9511", + "12918": "\u954f", + "12919": "\u9554", + "12920": "\u95fe", + "12921": "\u9649", + "12922": "\u972a", + "12923": "\u9751", + "12924": "\u97eb", + "12925": "\u98a6", + "12926": "\u990d", + "12927": "\u9974", + "12928": "\u9991", + "12929": "\u9a88", + "12930": "\u9a93", + "12931": "\u9aba", + "12932": "\u9acc", + "12933": "\u9aef", + "12934": "\u9b5f", + "12935": "\u9cbc", + "12936": "\u9cc3", + "12937": "\u9e29", + "12938": "\u9e2a", + "12939": "\u9e2b", + "12940": "\u9e41", + "12941": "\u9e67", + "12942": "\u9e73", + "12943": "\uff0c", + "12944": "", + "12945": "\u2581t", + "12946": "\u2581\u0111", + "12947": "nh", + "12948": "\u2581th", + "12949": "\u2581ch", + "12950": "\u2581nh", + "12951": "\u2581kh", + "12952": "\u2581ng", + "12953": "\u2581g", + "12954": "\u00f4ng", + "12955": "\u2581ph", + "12956": "\u2581r", + "12957": "\u2581gi", + "12958": "\u1eddi", + "12959": "\u00ean", + "12960": "\u2581c\u00e1", + "12961": "\u2581v\u00e0", + "12962": "\u2581c\u00f3", + "12963": "i\u1ec7", + "12964": "\u1ed9t", + "12965": "\u2581kh\u00f4ng", + "12966": "\u00f4i", + "12967": "i\u1ebf", + "12968": "\u2581m\u1ed9t", + "12969": "\u1edbi", + "12970": "\u1ee7a", + "12971": "\u2581c\u1ee7a", + "12972": "\u2581x", + "12973": "\u01b0\u1eddi", + "12974": "\u01b0\u1ee3", + "12975": "\u00ecnh", + "12976": "\u1ea5t", + "12977": "\u1ea1i", + "12978": "uy", + "12979": "\u00e0y", + "12980": "\u2581ng\u01b0\u1eddi", + "12981": "ong", + "12982": "anh", + "12983": "\u01b0\u1ee3c", + "12984": "i\u1ec1", + "12985": "\u2581\u0111\u01b0\u1ee3c", + "12986": "\u2581n\u00f3", + "12987": "\u1eefng", + "12988": "\u2581cho", + "12989": "\u1ea5y", + "12990": "\u2581nh\u01b0", + "12991": "\u2581ngh", + "12992": "\u2581m\u00e0", + "12993": "\u2581t\u00f4i", + "12994": "\u01b0\u01a1", + "12995": "\u1ea3i", + "12996": "\u2581nh\u1eefng", + "12997": "\u2581th\u00ec", + "12998": "\u00e2y", + "12999": "ao", + "13000": "\u2581\u0111\u00e3", + "13001": "\u1ea7n", + "13002": "\u2581c\u00e1i", + "13003": "\u2581\u0111\u00f3", + "13004": "\u2581\u0111i", + "13005": "\u2581v\u1edbi", + "13006": "\u01b0\u1edb", + "13007": "\u2581trong", + "13008": "\u2581c\u00e1c", + "13009": "i\u1ec1u", + "13010": "\u2581n\u00e0y", + "13011": "\u0169ng", + "13012": "\u00fang", + "13013": "\u0103m", + "13014": "\u1ed3i", + "13015": "\u1ea1n", + "13016": "\u2581anh", + "13017": "\u01b0", + "13018": "\u1ebf", + "13019": "\u1ea1", + "13020": "\u1ed9", + "13021": "\u1edd", + "13022": "\u1ea3", + "13023": "\u1ea5", + "13024": "\u1ed1", + "13025": "\u1edb", + "13026": "\u1ec7", + "13027": "\u1ec1", + "13028": "\u1ec3", + "13029": "\u01a1", + "13030": "\u1ee7", + "13031": "\u1ead", + "13032": "\u1ee3", + "13033": "\u1ea7", + "13034": "\u1ecb", + "13035": "\u1eef", + "13036": "\u1ee9", + "13037": "\u1ef1", + "13038": "\u1ecd", + "13039": "\u1ed3", + "13040": "\u1edf", + "13041": "\u1eaf", + "13042": "\u1eeb", + "13043": "\u1ee5", + "13044": "\u0169", + "13045": "\u1ed5", + "13046": "\u1eb7", + "13047": "\u1ebd", + "13048": "\u1eb1", + "13049": "\u0110", + "13050": "\u1ec9", + "13051": "\u1ecf", + "13052": "\u1eed", + "13053": "\u0129", + "13054": "\u1ed7", + "13055": "\u1eab", + "13056": "\u1eb9", + "13057": "\u1ea9", + "13058": "\u1ec5", + "13059": "\u1ebb", + "13060": "\u1eb3", + "13061": "\u1ef9", + "13062": "\u1ee1", + "13063": "\u1ef3", + "13064": "\u1ef7", + "13065": "\u1eb5", + "13066": "\u1ede", + "13067": "\u1ef5", + "13068": "\u1ea4", + "13069": "\u00dd", + "13070": "\u1eea", + "13071": "\u0102", + "13072": "\u1edc", + "13073": "\u1ea2", + "13074": "\u1ed2", + "13075": "\u01a0", + "13076": "\u01af", + "13077": "\u1ee8", + "13078": "\u1ed0", + "13079": "\u1eda", + "13080": "\u1ee6", + "13081": "\u1ea8", + "13082": "\u1eae", + "13083": "\u1ed4", + "13084": "\u1ef6", + "13085": "\u1ebe", + "13086": "\u1ef2" +} \ No newline at end of file diff --git a/multilingual/560ms/decoder.mlmodelc/analytics/coremldata.bin b/multilingual/560ms/decoder.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..646ff8b67c27f7e1035df022ec9e0691346d2780 --- /dev/null +++ b/multilingual/560ms/decoder.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9c32520b84ded2c698000854a77228adf394db522b5a3c25f7737415aae7ed0d +size 243 diff --git a/multilingual/560ms/decoder.mlmodelc/coremldata.bin b/multilingual/560ms/decoder.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..b50e1931d2e3334018b12d034c8ff8a57896a265 --- /dev/null +++ b/multilingual/560ms/decoder.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fa812bb65dd2a3bef6acf584b2abd5d0f26f4d09afaccf6c5dfd41e630d0fd1b +size 433 diff --git a/multilingual/560ms/decoder.mlmodelc/model.mil b/multilingual/560ms/decoder.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..697235107988a50dadcf7b2334d72723c3d73048 --- /dev/null +++ b/multilingual/560ms/decoder.mlmodelc/model.mil @@ -0,0 +1,64 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.5.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] +{ + func main(tensor c_in, tensor h_in, tensor token, tensor token_length) { + int32 y_axis_0 = const()[name = string("y_axis_0"), val = int32(0)]; + int32 y_batch_dims_0 = const()[name = string("y_batch_dims_0"), val = int32(0)]; + bool y_validate_indices_0 = const()[name = string("y_validate_indices_0"), val = bool(false)]; + tensor module_prediction_embed_weight_to_fp16 = const()[name = string("module_prediction_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + string token_to_int16_dtype_0 = const()[name = string("token_to_int16_dtype_0"), val = string("int16")]; + tensor token_to_int16 = cast(dtype = token_to_int16_dtype_0, x = token)[name = string("cast_8")]; + tensor y_cast_fp16_cast_uint16 = gather(axis = y_axis_0, batch_dims = y_batch_dims_0, indices = token_to_int16, validate_indices = y_validate_indices_0, x = module_prediction_embed_weight_to_fp16)[name = string("y_cast_fp16_cast_uint16")]; + tensor input_3_perm_0 = const()[name = string("input_3_perm_0"), val = tensor([1, 0, 2])]; + int32 split_0_num_splits_0 = const()[name = string("split_0_num_splits_0"), val = int32(2)]; + int32 split_0_axis_0 = const()[name = string("split_0_axis_0"), val = int32(0)]; + string h_in_to_fp16_dtype_0 = const()[name = string("h_in_to_fp16_dtype_0"), val = string("fp16")]; + tensor h_in_to_fp16 = cast(dtype = h_in_to_fp16_dtype_0, x = h_in)[name = string("cast_7")]; + tensor split_0_cast_fp16_0, tensor split_0_cast_fp16_1 = split(axis = split_0_axis_0, num_splits = split_0_num_splits_0, x = h_in_to_fp16)[name = string("split_0_cast_fp16")]; + int32 split_1_num_splits_0 = const()[name = string("split_1_num_splits_0"), val = int32(2)]; + int32 split_1_axis_0 = const()[name = string("split_1_axis_0"), val = int32(0)]; + string c_in_to_fp16_dtype_0 = const()[name = string("c_in_to_fp16_dtype_0"), val = string("fp16")]; + tensor c_in_to_fp16 = cast(dtype = c_in_to_fp16_dtype_0, x = c_in)[name = string("cast_6")]; + tensor split_1_cast_fp16_0, tensor split_1_cast_fp16_1 = split(axis = split_1_axis_0, num_splits = split_1_num_splits_0, x = c_in_to_fp16)[name = string("split_1_cast_fp16")]; + tensor input_lstm_layer_0_lstm_h0_squeeze_axes_0 = const()[name = string("input_lstm_layer_0_lstm_h0_squeeze_axes_0"), val = tensor([0])]; + tensor input_lstm_layer_0_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_lstm_layer_0_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_0)[name = string("input_lstm_layer_0_lstm_h0_squeeze_cast_fp16")]; + tensor input_lstm_layer_0_lstm_c0_squeeze_axes_0 = const()[name = string("input_lstm_layer_0_lstm_c0_squeeze_axes_0"), val = tensor([0])]; + tensor input_lstm_layer_0_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_lstm_layer_0_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_0)[name = string("input_lstm_layer_0_lstm_c0_squeeze_cast_fp16")]; + string input_lstm_layer_0_direction_0 = const()[name = string("input_lstm_layer_0_direction_0"), val = string("forward")]; + bool input_lstm_layer_0_output_sequence_0 = const()[name = string("input_lstm_layer_0_output_sequence_0"), val = bool(true)]; + string input_lstm_layer_0_recurrent_activation_0 = const()[name = string("input_lstm_layer_0_recurrent_activation_0"), val = string("sigmoid")]; + string input_lstm_layer_0_cell_activation_0 = const()[name = string("input_lstm_layer_0_cell_activation_0"), val = string("tanh")]; + string input_lstm_layer_0_activation_0 = const()[name = string("input_lstm_layer_0_activation_0"), val = string("tanh")]; + tensor concat_1_to_fp16 = const()[name = string("concat_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16752768)))]; + tensor concat_2_to_fp16 = const()[name = string("concat_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20029632)))]; + tensor concat_0_to_fp16 = const()[name = string("concat_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23306496)))]; + tensor input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = y_cast_fp16_cast_uint16)[name = string("transpose_2")]; + tensor input_lstm_layer_0_cast_fp16_0, tensor input_lstm_layer_0_cast_fp16_1, tensor input_lstm_layer_0_cast_fp16_2 = lstm(activation = input_lstm_layer_0_activation_0, bias = concat_0_to_fp16, cell_activation = input_lstm_layer_0_cell_activation_0, direction = input_lstm_layer_0_direction_0, initial_c = input_lstm_layer_0_lstm_c0_squeeze_cast_fp16, initial_h = input_lstm_layer_0_lstm_h0_squeeze_cast_fp16, output_sequence = input_lstm_layer_0_output_sequence_0, recurrent_activation = input_lstm_layer_0_recurrent_activation_0, weight_hh = concat_2_to_fp16, weight_ih = concat_1_to_fp16, x = input_3_cast_fp16)[name = string("input_lstm_layer_0_cast_fp16")]; + tensor input_lstm_h0_squeeze_axes_0 = const()[name = string("input_lstm_h0_squeeze_axes_0"), val = tensor([0])]; + tensor input_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_1)[name = string("input_lstm_h0_squeeze_cast_fp16")]; + tensor input_lstm_c0_squeeze_axes_0 = const()[name = string("input_lstm_c0_squeeze_axes_0"), val = tensor([0])]; + tensor input_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_1)[name = string("input_lstm_c0_squeeze_cast_fp16")]; + string input_direction_0 = const()[name = string("input_direction_0"), val = string("forward")]; + bool input_output_sequence_0 = const()[name = string("input_output_sequence_0"), val = bool(true)]; + string input_recurrent_activation_0 = const()[name = string("input_recurrent_activation_0"), val = string("sigmoid")]; + string input_cell_activation_0 = const()[name = string("input_cell_activation_0"), val = string("tanh")]; + string input_activation_0 = const()[name = string("input_activation_0"), val = string("tanh")]; + tensor concat_4_to_fp16 = const()[name = string("concat_4_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23311680)))]; + tensor concat_5_to_fp16 = const()[name = string("concat_5_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26588544)))]; + tensor concat_3_to_fp16 = const()[name = string("concat_3_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29865408)))]; + tensor input_cast_fp16_0, tensor input_cast_fp16_1, tensor input_cast_fp16_2 = lstm(activation = input_activation_0, bias = concat_3_to_fp16, cell_activation = input_cell_activation_0, direction = input_direction_0, initial_c = input_lstm_c0_squeeze_cast_fp16, initial_h = input_lstm_h0_squeeze_cast_fp16, output_sequence = input_output_sequence_0, recurrent_activation = input_recurrent_activation_0, weight_hh = concat_5_to_fp16, weight_ih = concat_4_to_fp16, x = input_lstm_layer_0_cast_fp16_0)[name = string("input_cast_fp16")]; + int32 obj_3_axis_0 = const()[name = string("obj_3_axis_0"), val = int32(0)]; + tensor obj_3_cast_fp16 = stack(axis = obj_3_axis_0, values = (input_lstm_layer_0_cast_fp16_1, input_cast_fp16_1))[name = string("obj_3_cast_fp16")]; + string obj_3_cast_fp16_to_fp32_dtype_0 = const()[name = string("obj_3_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + int32 obj_axis_0 = const()[name = string("obj_axis_0"), val = int32(0)]; + tensor obj_cast_fp16 = stack(axis = obj_axis_0, values = (input_lstm_layer_0_cast_fp16_2, input_cast_fp16_2))[name = string("obj_cast_fp16")]; + string obj_cast_fp16_to_fp32_dtype_0 = const()[name = string("obj_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 2, 0])]; + string transpose_0_cast_fp16_to_fp32_dtype_0 = const()[name = string("transpose_0_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor transpose_0_cast_fp16 = transpose(perm = transpose_0_perm_0, x = input_cast_fp16_0)[name = string("transpose_1")]; + tensor decoder_out = cast(dtype = transpose_0_cast_fp16_to_fp32_dtype_0, x = transpose_0_cast_fp16)[name = string("cast_3")]; + tensor c_out = cast(dtype = obj_cast_fp16_to_fp32_dtype_0, x = obj_cast_fp16)[name = string("cast_4")]; + tensor h_out = cast(dtype = obj_3_cast_fp16_to_fp32_dtype_0, x = obj_3_cast_fp16)[name = string("cast_5")]; + tensor token_length_tmp = identity(x = token_length)[name = string("token_length_tmp")]; + } -> (decoder_out, h_out, c_out); +} \ No newline at end of file diff --git a/multilingual/560ms/decoder.mlmodelc/weights/weight.bin b/multilingual/560ms/decoder.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..8b094b9de3ad890a887d85c198a8ee88800323fa --- /dev/null +++ b/multilingual/560ms/decoder.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2e49d029739602d265719f0040fcf5328c007a1ed62d0c6ea621e0b2aaeb9a64 +size 29870592 diff --git a/multilingual/560ms/decoder.mlpackage/Data/com.apple.CoreML/model.mlmodel b/multilingual/560ms/decoder.mlpackage/Data/com.apple.CoreML/model.mlmodel new file mode 100644 index 0000000000000000000000000000000000000000..3b86cf52f95837fc5e90f3e8cf6373bde60c5ad1 --- /dev/null +++ b/multilingual/560ms/decoder.mlpackage/Data/com.apple.CoreML/model.mlmodel @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c26ee345b7763ed9f217561572b1386719956de5b58e9174f5586926b4ab85c5 +size 10360 diff --git a/multilingual/560ms/decoder.mlpackage/Data/com.apple.CoreML/weights/weight.bin b/multilingual/560ms/decoder.mlpackage/Data/com.apple.CoreML/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..8b094b9de3ad890a887d85c198a8ee88800323fa --- /dev/null +++ b/multilingual/560ms/decoder.mlpackage/Data/com.apple.CoreML/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2e49d029739602d265719f0040fcf5328c007a1ed62d0c6ea621e0b2aaeb9a64 +size 29870592 diff --git a/multilingual/560ms/decoder.mlpackage/Manifest.json b/multilingual/560ms/decoder.mlpackage/Manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..1ac968d8fcfbdfdead1150534e3677d2f045db51 --- /dev/null +++ b/multilingual/560ms/decoder.mlpackage/Manifest.json @@ -0,0 +1,18 @@ +{ + "fileFormatVersion": "1.0.0", + "itemInfoEntries": { + "542DC13B-08DF-47C7-AAAA-C2F9DE67BB37": { + "author": "com.apple.CoreML", + "description": "CoreML Model Weights", + "name": "weights", + "path": "com.apple.CoreML/weights" + }, + "8B23B00A-4F60-49E4-B460-719FB6B05887": { + "author": "com.apple.CoreML", + "description": "CoreML Model Specification", + "name": "model.mlmodel", + "path": "com.apple.CoreML/model.mlmodel" + } + }, + "rootModelIdentifier": "8B23B00A-4F60-49E4-B460-719FB6B05887" +} diff --git a/multilingual/560ms/decoder_joint.mlmodelc/analytics/coremldata.bin b/multilingual/560ms/decoder_joint.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..11215e473da8d2cc24d47de983947493613791d7 --- /dev/null +++ b/multilingual/560ms/decoder_joint.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3b314763ced4d2bd27484b8ec2a9c60939b724f8ab60b32d29ad0c03f6192599 +size 243 diff --git a/multilingual/560ms/decoder_joint.mlmodelc/coremldata.bin b/multilingual/560ms/decoder_joint.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..a036c0b966fa4c57af9b0d7699bfb5e37c53f4d4 --- /dev/null +++ b/multilingual/560ms/decoder_joint.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:729baa5678fde0b9fa3e46044cb8eafcb96249bff4c306740e1c40ce326b7101 +size 454 diff --git a/multilingual/560ms/decoder_joint.mlmodelc/model.mil b/multilingual/560ms/decoder_joint.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..e0c611c93d86fefc0f5758164fe03174da291441 --- /dev/null +++ b/multilingual/560ms/decoder_joint.mlmodelc/model.mil @@ -0,0 +1,83 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.5.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] +{ + func main(tensor c_in, tensor encoder, tensor h_in, tensor token, tensor token_length) { + int32 y_axis_0 = const()[name = string("y_axis_0"), val = int32(0)]; + int32 y_batch_dims_0 = const()[name = string("y_batch_dims_0"), val = int32(0)]; + bool y_validate_indices_0 = const()[name = string("y_validate_indices_0"), val = bool(false)]; + tensor decoder_module_prediction_embed_weight_to_fp16 = const()[name = string("decoder_module_prediction_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + string token_to_int16_dtype_0 = const()[name = string("token_to_int16_dtype_0"), val = string("int16")]; + tensor token_to_int16 = cast(dtype = token_to_int16_dtype_0, x = token)[name = string("cast_9")]; + tensor y_cast_fp16_cast_uint16 = gather(axis = y_axis_0, batch_dims = y_batch_dims_0, indices = token_to_int16, validate_indices = y_validate_indices_0, x = decoder_module_prediction_embed_weight_to_fp16)[name = string("y_cast_fp16_cast_uint16")]; + tensor input_3_perm_0 = const()[name = string("input_3_perm_0"), val = tensor([1, 0, 2])]; + int32 split_0_num_splits_0 = const()[name = string("split_0_num_splits_0"), val = int32(2)]; + int32 split_0_axis_0 = const()[name = string("split_0_axis_0"), val = int32(0)]; + string h_in_to_fp16_dtype_0 = const()[name = string("h_in_to_fp16_dtype_0"), val = string("fp16")]; + tensor h_in_to_fp16 = cast(dtype = h_in_to_fp16_dtype_0, x = h_in)[name = string("cast_8")]; + tensor split_0_cast_fp16_0, tensor split_0_cast_fp16_1 = split(axis = split_0_axis_0, num_splits = split_0_num_splits_0, x = h_in_to_fp16)[name = string("split_0_cast_fp16")]; + int32 split_1_num_splits_0 = const()[name = string("split_1_num_splits_0"), val = int32(2)]; + int32 split_1_axis_0 = const()[name = string("split_1_axis_0"), val = int32(0)]; + string c_in_to_fp16_dtype_0 = const()[name = string("c_in_to_fp16_dtype_0"), val = string("fp16")]; + tensor c_in_to_fp16 = cast(dtype = c_in_to_fp16_dtype_0, x = c_in)[name = string("cast_7")]; + tensor split_1_cast_fp16_0, tensor split_1_cast_fp16_1 = split(axis = split_1_axis_0, num_splits = split_1_num_splits_0, x = c_in_to_fp16)[name = string("split_1_cast_fp16")]; + tensor input_5_lstm_layer_0_lstm_h0_squeeze_axes_0 = const()[name = string("input_5_lstm_layer_0_lstm_h0_squeeze_axes_0"), val = tensor([0])]; + tensor input_5_lstm_layer_0_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_5_lstm_layer_0_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_0)[name = string("input_5_lstm_layer_0_lstm_h0_squeeze_cast_fp16")]; + tensor input_5_lstm_layer_0_lstm_c0_squeeze_axes_0 = const()[name = string("input_5_lstm_layer_0_lstm_c0_squeeze_axes_0"), val = tensor([0])]; + tensor input_5_lstm_layer_0_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_5_lstm_layer_0_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_0)[name = string("input_5_lstm_layer_0_lstm_c0_squeeze_cast_fp16")]; + string input_5_lstm_layer_0_direction_0 = const()[name = string("input_5_lstm_layer_0_direction_0"), val = string("forward")]; + bool input_5_lstm_layer_0_output_sequence_0 = const()[name = string("input_5_lstm_layer_0_output_sequence_0"), val = bool(true)]; + string input_5_lstm_layer_0_recurrent_activation_0 = const()[name = string("input_5_lstm_layer_0_recurrent_activation_0"), val = string("sigmoid")]; + string input_5_lstm_layer_0_cell_activation_0 = const()[name = string("input_5_lstm_layer_0_cell_activation_0"), val = string("tanh")]; + string input_5_lstm_layer_0_activation_0 = const()[name = string("input_5_lstm_layer_0_activation_0"), val = string("tanh")]; + tensor concat_1_to_fp16 = const()[name = string("concat_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16752768)))]; + tensor concat_2_to_fp16 = const()[name = string("concat_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20029632)))]; + tensor concat_0_to_fp16 = const()[name = string("concat_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23306496)))]; + tensor input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = y_cast_fp16_cast_uint16)[name = string("transpose_4")]; + tensor input_5_lstm_layer_0_cast_fp16_0, tensor input_5_lstm_layer_0_cast_fp16_1, tensor input_5_lstm_layer_0_cast_fp16_2 = lstm(activation = input_5_lstm_layer_0_activation_0, bias = concat_0_to_fp16, cell_activation = input_5_lstm_layer_0_cell_activation_0, direction = input_5_lstm_layer_0_direction_0, initial_c = input_5_lstm_layer_0_lstm_c0_squeeze_cast_fp16, initial_h = input_5_lstm_layer_0_lstm_h0_squeeze_cast_fp16, output_sequence = input_5_lstm_layer_0_output_sequence_0, recurrent_activation = input_5_lstm_layer_0_recurrent_activation_0, weight_hh = concat_2_to_fp16, weight_ih = concat_1_to_fp16, x = input_3_cast_fp16)[name = string("input_5_lstm_layer_0_cast_fp16")]; + tensor input_5_lstm_h0_squeeze_axes_0 = const()[name = string("input_5_lstm_h0_squeeze_axes_0"), val = tensor([0])]; + tensor input_5_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_5_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_1)[name = string("input_5_lstm_h0_squeeze_cast_fp16")]; + tensor input_5_lstm_c0_squeeze_axes_0 = const()[name = string("input_5_lstm_c0_squeeze_axes_0"), val = tensor([0])]; + tensor input_5_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_5_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_1)[name = string("input_5_lstm_c0_squeeze_cast_fp16")]; + string input_5_direction_0 = const()[name = string("input_5_direction_0"), val = string("forward")]; + bool input_5_output_sequence_0 = const()[name = string("input_5_output_sequence_0"), val = bool(true)]; + string input_5_recurrent_activation_0 = const()[name = string("input_5_recurrent_activation_0"), val = string("sigmoid")]; + string input_5_cell_activation_0 = const()[name = string("input_5_cell_activation_0"), val = string("tanh")]; + string input_5_activation_0 = const()[name = string("input_5_activation_0"), val = string("tanh")]; + tensor concat_4_to_fp16 = const()[name = string("concat_4_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23311680)))]; + tensor concat_5_to_fp16 = const()[name = string("concat_5_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26588544)))]; + tensor concat_3_to_fp16 = const()[name = string("concat_3_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29865408)))]; + tensor input_5_cast_fp16_0, tensor input_5_cast_fp16_1, tensor input_5_cast_fp16_2 = lstm(activation = input_5_activation_0, bias = concat_3_to_fp16, cell_activation = input_5_cell_activation_0, direction = input_5_direction_0, initial_c = input_5_lstm_c0_squeeze_cast_fp16, initial_h = input_5_lstm_h0_squeeze_cast_fp16, output_sequence = input_5_output_sequence_0, recurrent_activation = input_5_recurrent_activation_0, weight_hh = concat_5_to_fp16, weight_ih = concat_4_to_fp16, x = input_5_lstm_layer_0_cast_fp16_0)[name = string("input_5_cast_fp16")]; + int32 obj_3_axis_0 = const()[name = string("obj_3_axis_0"), val = int32(0)]; + tensor obj_3_cast_fp16 = stack(axis = obj_3_axis_0, values = (input_5_lstm_layer_0_cast_fp16_1, input_5_cast_fp16_1))[name = string("obj_3_cast_fp16")]; + string obj_3_cast_fp16_to_fp32_dtype_0 = const()[name = string("obj_3_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + int32 obj_axis_0 = const()[name = string("obj_axis_0"), val = int32(0)]; + tensor obj_cast_fp16 = stack(axis = obj_axis_0, values = (input_5_lstm_layer_0_cast_fp16_2, input_5_cast_fp16_2))[name = string("obj_cast_fp16")]; + string obj_cast_fp16_to_fp32_dtype_0 = const()[name = string("obj_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor transpose_1_perm_0 = const()[name = string("transpose_1_perm_0"), val = tensor([1, 0, 2])]; + tensor input_7_perm_0 = const()[name = string("input_7_perm_0"), val = tensor([0, 2, 1])]; + string encoder_to_fp16_dtype_0 = const()[name = string("encoder_to_fp16_dtype_0"), val = string("fp16")]; + tensor joint_module_enc_weight_to_fp16 = const()[name = string("joint_module_enc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29870592)))]; + tensor joint_module_enc_bias_to_fp16 = const()[name = string("joint_module_enc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31181376)))]; + tensor encoder_to_fp16 = cast(dtype = encoder_to_fp16_dtype_0, x = encoder)[name = string("cast_4")]; + tensor input_7_cast_fp16 = transpose(perm = input_7_perm_0, x = encoder_to_fp16)[name = string("transpose_2")]; + tensor linear_0_cast_fp16 = linear(bias = joint_module_enc_bias_to_fp16, weight = joint_module_enc_weight_to_fp16, x = input_7_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor joint_module_pred_weight_to_fp16 = const()[name = string("joint_module_pred_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31182720)))]; + tensor joint_module_pred_bias_to_fp16 = const()[name = string("joint_module_pred_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32001984)))]; + tensor transpose_1_cast_fp16 = transpose(perm = transpose_1_perm_0, x = input_5_cast_fp16_0)[name = string("transpose_3")]; + tensor linear_1_cast_fp16 = linear(bias = joint_module_pred_bias_to_fp16, weight = joint_module_pred_weight_to_fp16, x = transpose_1_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_79_axes_0 = const()[name = string("op_79_axes_0"), val = tensor([2])]; + tensor var_79_cast_fp16 = expand_dims(axes = var_79_axes_0, x = linear_0_cast_fp16)[name = string("op_79_cast_fp16")]; + tensor var_80_axes_0 = const()[name = string("op_80_axes_0"), val = tensor([1])]; + tensor var_80_cast_fp16 = expand_dims(axes = var_80_axes_0, x = linear_1_cast_fp16)[name = string("op_80_cast_fp16")]; + tensor input_11_cast_fp16 = add(x = var_79_cast_fp16, y = var_80_cast_fp16)[name = string("input_11_cast_fp16")]; + tensor input_13_cast_fp16 = relu(x = input_11_cast_fp16)[name = string("input_13_cast_fp16")]; + tensor joint_module_joint_net_2_weight_to_fp16 = const()[name = string("joint_module_joint_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32003328)))]; + tensor joint_module_joint_net_2_bias_to_fp16 = const()[name = string("joint_module_joint_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48756032)))]; + tensor linear_2_cast_fp16 = linear(bias = joint_module_joint_net_2_bias_to_fp16, weight = joint_module_joint_net_2_weight_to_fp16, x = input_13_cast_fp16)[name = string("linear_2_cast_fp16")]; + string linear_2_cast_fp16_to_fp32_dtype_0 = const()[name = string("linear_2_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor logits = cast(dtype = linear_2_cast_fp16_to_fp32_dtype_0, x = linear_2_cast_fp16)[name = string("cast_3")]; + tensor c_out = cast(dtype = obj_cast_fp16_to_fp32_dtype_0, x = obj_cast_fp16)[name = string("cast_5")]; + tensor h_out = cast(dtype = obj_3_cast_fp16_to_fp32_dtype_0, x = obj_3_cast_fp16)[name = string("cast_6")]; + tensor token_length_tmp = identity(x = token_length)[name = string("token_length_tmp")]; + } -> (logits, h_out, c_out); +} \ No newline at end of file diff --git a/multilingual/560ms/decoder_joint.mlmodelc/weights/weight.bin b/multilingual/560ms/decoder_joint.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..c9ff4a76fa3fff489b780debbcd78bdc261f1489 --- /dev/null +++ b/multilingual/560ms/decoder_joint.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f283edec035d616e9e1372419dd9bfd8a2de2c92a70b23f6a0f5ed93366ebb03 +size 48782272 diff --git a/multilingual/560ms/decoder_joint.mlpackage/Data/com.apple.CoreML/model.mlmodel b/multilingual/560ms/decoder_joint.mlpackage/Data/com.apple.CoreML/model.mlmodel new file mode 100644 index 0000000000000000000000000000000000000000..abae2ff390063679ad4ac25d546acf5853a04d16 --- /dev/null +++ b/multilingual/560ms/decoder_joint.mlpackage/Data/com.apple.CoreML/model.mlmodel @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:507c3a291a78a11f62b898c64e611016f518d1af658b2aa55c054e0a1029f7ea +size 13746 diff --git a/multilingual/560ms/decoder_joint.mlpackage/Data/com.apple.CoreML/weights/weight.bin b/multilingual/560ms/decoder_joint.mlpackage/Data/com.apple.CoreML/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..c9ff4a76fa3fff489b780debbcd78bdc261f1489 --- /dev/null +++ b/multilingual/560ms/decoder_joint.mlpackage/Data/com.apple.CoreML/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f283edec035d616e9e1372419dd9bfd8a2de2c92a70b23f6a0f5ed93366ebb03 +size 48782272 diff --git a/multilingual/560ms/decoder_joint.mlpackage/Manifest.json b/multilingual/560ms/decoder_joint.mlpackage/Manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..baad94a8850f47a141e9357ae3ea3cee9981fcc9 --- /dev/null +++ b/multilingual/560ms/decoder_joint.mlpackage/Manifest.json @@ -0,0 +1,18 @@ +{ + "fileFormatVersion": "1.0.0", + "itemInfoEntries": { + "627E3113-852A-47BD-981E-FAB26C6AB6D0": { + "author": "com.apple.CoreML", + "description": "CoreML Model Weights", + "name": "weights", + "path": "com.apple.CoreML/weights" + }, + "7EEB6F52-3184-47E3-98CD-28268604F7F1": { + "author": "com.apple.CoreML", + "description": "CoreML Model Specification", + "name": "model.mlmodel", + "path": "com.apple.CoreML/model.mlmodel" + } + }, + "rootModelIdentifier": "7EEB6F52-3184-47E3-98CD-28268604F7F1" +} diff --git a/multilingual/560ms/decoder_joint_noencproj.mlmodelc/analytics/coremldata.bin b/multilingual/560ms/decoder_joint_noencproj.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..ea284470a3fb674426cdea416f38e1e49cfb8994 --- /dev/null +++ b/multilingual/560ms/decoder_joint_noencproj.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c4e3c25c06d72ba514e93ae2ea0dd313f057622a3fb70f65de3bee4b80b3946b +size 243 diff --git a/multilingual/560ms/decoder_joint_noencproj.mlmodelc/coremldata.bin b/multilingual/560ms/decoder_joint_noencproj.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..5876c180574a6a160cf59c709341a44cf45df1bf --- /dev/null +++ b/multilingual/560ms/decoder_joint_noencproj.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c9ea28113904cfeca8e5684fb8b54358eb48338f26c894739e3bd076848dcadd +size 519 diff --git a/multilingual/560ms/decoder_joint_noencproj.mlmodelc/model.mil b/multilingual/560ms/decoder_joint_noencproj.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..0bb38a83354a8f578eda404248b0895118b55ad5 --- /dev/null +++ b/multilingual/560ms/decoder_joint_noencproj.mlmodelc/model.mil @@ -0,0 +1,91 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.10.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] +{ + func main(tensor c_in, tensor encoder_proj, tensor h_in, tensor token, tensor token_length) { + int32 y_batch_dims_0 = const()[name = string("y_batch_dims_0"), val = int32(0)]; + bool y_validate_indices_0 = const()[name = string("y_validate_indices_0"), val = bool(false)]; + tensor decoder_module_prediction_embed_weight_to_fp16 = const()[name = string("decoder_module_prediction_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + string token_to_int16_dtype_0 = const()[name = string("token_to_int16_dtype_0"), val = string("int16")]; + string cast_1_dtype_0 = const()[name = string("cast_1_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor token_to_int16 = cast(dtype = token_to_int16_dtype_0, x = token)[name = string("cast_10")]; + tensor cast_1 = cast(dtype = cast_1_dtype_0, x = token_to_int16)[name = string("cast_9")]; + tensor greater_equal_0 = greater_equal(x = cast_1, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(13088)]; + tensor add_2 = add(x = cast_1, y = slice_by_index_0)[name = string("add_2")]; + tensor select_0 = select(a = cast_1, b = add_2, cond = greater_equal_0)[name = string("select_0")]; + int32 y_cast_fp16_cast_uint16_axis_0 = const()[name = string("y_cast_fp16_cast_uint16_axis_0"), val = int32(0)]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_8")]; + tensor y_cast_fp16_cast_uint16_cast_uint16 = gather(axis = y_cast_fp16_cast_uint16_axis_0, batch_dims = y_batch_dims_0, indices = select_0_to_int16, validate_indices = y_validate_indices_0, x = decoder_module_prediction_embed_weight_to_fp16)[name = string("y_cast_fp16_cast_uint16_cast_uint16")]; + tensor input_3_perm_0 = const()[name = string("input_3_perm_0"), val = tensor([1, 0, 2])]; + int32 split_0_num_splits_0 = const()[name = string("split_0_num_splits_0"), val = int32(2)]; + int32 split_0_axis_0 = const()[name = string("split_0_axis_0"), val = int32(0)]; + string h_in_to_fp16_dtype_0 = const()[name = string("h_in_to_fp16_dtype_0"), val = string("fp16")]; + tensor h_in_to_fp16 = cast(dtype = h_in_to_fp16_dtype_0, x = h_in)[name = string("cast_7")]; + tensor split_0_cast_fp16_0, tensor split_0_cast_fp16_1 = split(axis = split_0_axis_0, num_splits = split_0_num_splits_0, x = h_in_to_fp16)[name = string("split_0_cast_fp16")]; + int32 split_1_num_splits_0 = const()[name = string("split_1_num_splits_0"), val = int32(2)]; + int32 split_1_axis_0 = const()[name = string("split_1_axis_0"), val = int32(0)]; + string c_in_to_fp16_dtype_0 = const()[name = string("c_in_to_fp16_dtype_0"), val = string("fp16")]; + tensor c_in_to_fp16 = cast(dtype = c_in_to_fp16_dtype_0, x = c_in)[name = string("cast_6")]; + tensor split_1_cast_fp16_0, tensor split_1_cast_fp16_1 = split(axis = split_1_axis_0, num_splits = split_1_num_splits_0, x = c_in_to_fp16)[name = string("split_1_cast_fp16")]; + tensor input_5_lstm_layer_0_lstm_h0_squeeze_axes_0 = const()[name = string("input_5_lstm_layer_0_lstm_h0_squeeze_axes_0"), val = tensor([0])]; + tensor input_5_lstm_layer_0_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_5_lstm_layer_0_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_0)[name = string("input_5_lstm_layer_0_lstm_h0_squeeze_cast_fp16")]; + tensor input_5_lstm_layer_0_lstm_c0_squeeze_axes_0 = const()[name = string("input_5_lstm_layer_0_lstm_c0_squeeze_axes_0"), val = tensor([0])]; + tensor input_5_lstm_layer_0_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_5_lstm_layer_0_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_0)[name = string("input_5_lstm_layer_0_lstm_c0_squeeze_cast_fp16")]; + string input_5_lstm_layer_0_direction_0 = const()[name = string("input_5_lstm_layer_0_direction_0"), val = string("forward")]; + bool input_5_lstm_layer_0_output_sequence_0 = const()[name = string("input_5_lstm_layer_0_output_sequence_0"), val = bool(true)]; + string input_5_lstm_layer_0_recurrent_activation_0 = const()[name = string("input_5_lstm_layer_0_recurrent_activation_0"), val = string("sigmoid")]; + string input_5_lstm_layer_0_cell_activation_0 = const()[name = string("input_5_lstm_layer_0_cell_activation_0"), val = string("tanh")]; + string input_5_lstm_layer_0_activation_0 = const()[name = string("input_5_lstm_layer_0_activation_0"), val = string("tanh")]; + tensor concat_1_to_fp16 = const()[name = string("concat_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16752768)))]; + tensor concat_2_to_fp16 = const()[name = string("concat_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20029632)))]; + tensor concat_0_to_fp16 = const()[name = string("concat_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23306496)))]; + tensor input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = y_cast_fp16_cast_uint16_cast_uint16)[name = string("transpose_3")]; + tensor input_5_lstm_layer_0_cast_fp16_0, tensor input_5_lstm_layer_0_cast_fp16_1, tensor input_5_lstm_layer_0_cast_fp16_2 = lstm(activation = input_5_lstm_layer_0_activation_0, bias = concat_0_to_fp16, cell_activation = input_5_lstm_layer_0_cell_activation_0, direction = input_5_lstm_layer_0_direction_0, initial_c = input_5_lstm_layer_0_lstm_c0_squeeze_cast_fp16, initial_h = input_5_lstm_layer_0_lstm_h0_squeeze_cast_fp16, output_sequence = input_5_lstm_layer_0_output_sequence_0, recurrent_activation = input_5_lstm_layer_0_recurrent_activation_0, weight_hh = concat_2_to_fp16, weight_ih = concat_1_to_fp16, x = input_3_cast_fp16)[name = string("input_5_lstm_layer_0_cast_fp16")]; + tensor input_5_lstm_h0_squeeze_axes_0 = const()[name = string("input_5_lstm_h0_squeeze_axes_0"), val = tensor([0])]; + tensor input_5_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_5_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_1)[name = string("input_5_lstm_h0_squeeze_cast_fp16")]; + tensor input_5_lstm_c0_squeeze_axes_0 = const()[name = string("input_5_lstm_c0_squeeze_axes_0"), val = tensor([0])]; + tensor input_5_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_5_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_1)[name = string("input_5_lstm_c0_squeeze_cast_fp16")]; + string input_5_direction_0 = const()[name = string("input_5_direction_0"), val = string("forward")]; + bool input_5_output_sequence_0 = const()[name = string("input_5_output_sequence_0"), val = bool(true)]; + string input_5_recurrent_activation_0 = const()[name = string("input_5_recurrent_activation_0"), val = string("sigmoid")]; + string input_5_cell_activation_0 = const()[name = string("input_5_cell_activation_0"), val = string("tanh")]; + string input_5_activation_0 = const()[name = string("input_5_activation_0"), val = string("tanh")]; + tensor concat_4_to_fp16 = const()[name = string("concat_4_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23311680)))]; + tensor concat_5_to_fp16 = const()[name = string("concat_5_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26588544)))]; + tensor concat_3_to_fp16 = const()[name = string("concat_3_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29865408)))]; + tensor input_5_cast_fp16_0, tensor input_5_cast_fp16_1, tensor input_5_cast_fp16_2 = lstm(activation = input_5_activation_0, bias = concat_3_to_fp16, cell_activation = input_5_cell_activation_0, direction = input_5_direction_0, initial_c = input_5_lstm_c0_squeeze_cast_fp16, initial_h = input_5_lstm_h0_squeeze_cast_fp16, output_sequence = input_5_output_sequence_0, recurrent_activation = input_5_recurrent_activation_0, weight_hh = concat_5_to_fp16, weight_ih = concat_4_to_fp16, x = input_5_lstm_layer_0_cast_fp16_0)[name = string("input_5_cast_fp16")]; + int32 obj_3_axis_0 = const()[name = string("obj_3_axis_0"), val = int32(0)]; + tensor obj_3_cast_fp16 = stack(axis = obj_3_axis_0, values = (input_5_lstm_layer_0_cast_fp16_1, input_5_cast_fp16_1))[name = string("obj_3_cast_fp16")]; + string obj_3_cast_fp16_to_fp32_dtype_0 = const()[name = string("obj_3_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + int32 obj_axis_0 = const()[name = string("obj_axis_0"), val = int32(0)]; + tensor obj_cast_fp16 = stack(axis = obj_axis_0, values = (input_5_lstm_layer_0_cast_fp16_2, input_5_cast_fp16_2))[name = string("obj_cast_fp16")]; + string obj_cast_fp16_to_fp32_dtype_0 = const()[name = string("obj_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor transpose_1_perm_0 = const()[name = string("transpose_1_perm_0"), val = tensor([1, 0, 2])]; + tensor joint_module_pred_weight_to_fp16 = const()[name = string("joint_module_pred_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29870592)))]; + tensor joint_module_pred_bias_to_fp16 = const()[name = string("joint_module_pred_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30689856)))]; + tensor transpose_1_cast_fp16 = transpose(perm = transpose_1_perm_0, x = input_5_cast_fp16_0)[name = string("transpose_2")]; + tensor linear_0_cast_fp16 = linear(bias = joint_module_pred_bias_to_fp16, weight = joint_module_pred_weight_to_fp16, x = transpose_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor f_axes_0 = const()[name = string("f_axes_0"), val = tensor([2])]; + string encoder_proj_to_fp16_dtype_0 = const()[name = string("encoder_proj_to_fp16_dtype_0"), val = string("fp16")]; + tensor encoder_proj_to_fp16 = cast(dtype = encoder_proj_to_fp16_dtype_0, x = encoder_proj)[name = string("cast_3")]; + tensor f_cast_fp16 = expand_dims(axes = f_axes_0, x = encoder_proj_to_fp16)[name = string("f_cast_fp16")]; + tensor g_axes_0 = const()[name = string("g_axes_0"), val = tensor([1])]; + tensor g_cast_fp16 = expand_dims(axes = g_axes_0, x = linear_0_cast_fp16)[name = string("g_cast_fp16")]; + tensor input_9_cast_fp16 = add(x = f_cast_fp16, y = g_cast_fp16)[name = string("input_9_cast_fp16")]; + tensor input_11_cast_fp16 = relu(x = input_9_cast_fp16)[name = string("input_11_cast_fp16")]; + tensor joint_module_joint_net_2_weight_to_fp16 = const()[name = string("joint_module_joint_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30691200)))]; + tensor joint_module_joint_net_2_bias_to_fp16 = const()[name = string("joint_module_joint_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47443904)))]; + tensor linear_1_cast_fp16 = linear(bias = joint_module_joint_net_2_bias_to_fp16, weight = joint_module_joint_net_2_weight_to_fp16, x = input_11_cast_fp16)[name = string("linear_1_cast_fp16")]; + int32 var_83 = const()[name = string("op_83"), val = int32(-1)]; + tensor var_85_softmax_cast_fp16 = softmax(axis = var_83, x = linear_1_cast_fp16)[name = string("op_85_softmax_cast_fp16")]; + fp32 var_85_epsilon_0 = const()[name = string("op_85_epsilon_0"), val = fp32(0x1p-149)]; + tensor var_85_cast_fp16 = log(epsilon = var_85_epsilon_0, x = var_85_softmax_cast_fp16)[name = string("op_85_cast_fp16")]; + string var_85_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_85_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor logits = cast(dtype = var_85_cast_fp16_to_fp32_dtype_0, x = var_85_cast_fp16)[name = string("cast_2")]; + tensor c_out = cast(dtype = obj_cast_fp16_to_fp32_dtype_0, x = obj_cast_fp16)[name = string("cast_4")]; + tensor h_out = cast(dtype = obj_3_cast_fp16_to_fp32_dtype_0, x = obj_3_cast_fp16)[name = string("cast_5")]; + tensor token_length_tmp = identity(x = token_length)[name = string("token_length_tmp")]; + } -> (logits, h_out, c_out); +} \ No newline at end of file diff --git a/multilingual/560ms/decoder_joint_noencproj.mlmodelc/weights/weight.bin b/multilingual/560ms/decoder_joint_noencproj.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..a04dee90b1d0140bff13a5c28dd8d3b4054a8679 --- /dev/null +++ b/multilingual/560ms/decoder_joint_noencproj.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version 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sha256:714e860f3f55f94b849a8c2bbcc07637dddaaef2002cb885bdc6dae31b7f7237 +size 632 diff --git a/multilingual/560ms/encoder.mlmodelc/model.mil b/multilingual/560ms/encoder.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..d6f0179d3761ce7d81034c775fc7550c16626d14 --- /dev/null +++ b/multilingual/560ms/encoder.mlmodelc/model.mil @@ -0,0 +1,4434 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}})] +{ + func main(tensor cache_channel, tensor cache_len, tensor cache_time, tensor mel, tensor mel_length, tensor prompt_id) { + tensor value_3_perm_0 = const()[name = string("value_3_perm_0"), val = tensor([1, 0, 2, 3])]; + string cache_channel_to_fp16_dtype_0 = const()[name = string("cache_channel_to_fp16_dtype_0"), val = string("fp16")]; + tensor value_5_perm_0 = const()[name = string("value_5_perm_0"), val = tensor([1, 0, 2, 3])]; + string cache_time_to_fp16_dtype_0 = const()[name = string("cache_time_to_fp16_dtype_0"), val = string("fp16")]; + int32 var_59 = const()[name = string("op_59"), val = int32(-1)]; + int32 var_68 = const()[name = string("op_68"), val = int32(1)]; + tensor x_1_perm_0 = const()[name = string("x_1_perm_0"), val = tensor([0, 2, 1])]; + string mel_to_fp16_dtype_0 = const()[name = string("mel_to_fp16_dtype_0"), val = string("fp16")]; + tensor tensor_1_axes_0 = const()[name = string("tensor_1_axes_0"), val = tensor([1])]; + tensor mel_to_fp16 = cast(dtype = mel_to_fp16_dtype_0, x = mel)[name = string("cast_21")]; + tensor x_1_cast_fp16 = transpose(perm = x_1_perm_0, x = mel_to_fp16)[name = string("transpose_367")]; + tensor tensor_1_cast_fp16 = expand_dims(axes = tensor_1_axes_0, x = x_1_cast_fp16)[name = string("tensor_1_cast_fp16")]; + tensor expand_dims_0 = const()[name = string("expand_dims_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor var_137_axes_0 = const()[name = string("op_137_axes_0"), val = tensor([1])]; + tensor var_137 = expand_dims(axes = var_137_axes_0, x = mel_length)[name = string("op_137")]; + tensor time_mask_1 = less(x = expand_dims_0, y = var_137)[name = string("time_mask_1")]; + tensor var_139_axes_0 = const()[name = string("op_139_axes_0"), val = tensor([-1])]; + tensor var_139 = expand_dims(axes = var_139_axes_0, x = time_mask_1)[name = string("op_139")]; + tensor var_141_reps_0 = const()[name = string("op_141_reps_0"), val = tensor([1, 1, 128])]; + tensor var_141 = tile(reps = var_141_reps_0, x = var_139)[name = string("op_141")]; + tensor var_147_axes_0 = const()[name = string("op_147_axes_0"), val = tensor([1])]; + string mask_1_to_fp16_dtype_0 = const()[name = string("mask_1_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_141_to_fp16 = cast(dtype = mask_1_to_fp16_dtype_0, x = var_141)[name = string("cast_20")]; + tensor var_147_cast_fp16 = expand_dims(axes = var_147_axes_0, x = var_141_to_fp16)[name = string("op_147_cast_fp16")]; + tensor input_1_cast_fp16 = mul(x = tensor_1_cast_fp16, y = var_147_cast_fp16)[name = string("input_1_cast_fp16")]; + tensor input_3_pad_0 = const()[name = string("input_3_pad_0"), val = tensor([0, 0, 0, 0, 2, 1, 2, 1])]; + string input_3_mode_0 = const()[name = string("input_3_mode_0"), val = string("constant")]; + fp16 const_9_to_fp16 = const()[name = string("const_9_to_fp16"), val = fp16(0x0p+0)]; + tensor input_3_cast_fp16 = pad(constant_val = const_9_to_fp16, mode = input_3_mode_0, pad = input_3_pad_0, x = input_1_cast_fp16)[name = string("input_3_cast_fp16")]; + string tensor_3_pad_type_0 = const()[name = string("tensor_3_pad_type_0"), val = string("valid")]; + tensor tensor_3_strides_0 = const()[name = string("tensor_3_strides_0"), val = tensor([2, 2])]; + tensor tensor_3_pad_0 = const()[name = string("tensor_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor tensor_3_dilations_0 = const()[name = string("tensor_3_dilations_0"), val = tensor([1, 1])]; + int32 tensor_3_groups_0 = const()[name = string("tensor_3_groups_0"), val = int32(1)]; + tensor encoder_pre_encode_conv_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2816))))[name = string("encoder_pre_encode_conv_0_weight_to_fp16_quantized")]; + tensor encoder_pre_encode_conv_0_bias_to_fp16 = const()[name = string("encoder_pre_encode_conv_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3392)))]; + tensor tensor_3_cast_fp16 = conv(bias = encoder_pre_encode_conv_0_bias_to_fp16, dilations = tensor_3_dilations_0, groups = tensor_3_groups_0, pad = tensor_3_pad_0, pad_type = tensor_3_pad_type_0, strides = tensor_3_strides_0, weight = encoder_pre_encode_conv_0_weight_to_fp16_quantized, x = input_3_cast_fp16)[name = string("tensor_3_cast_fp16")]; + string current_lengths_1_to_fp16_dtype_0 = const()[name = string("current_lengths_1_to_fp16_dtype_0"), val = string("fp16")]; + fp16 var_160_promoted_to_fp16 = const()[name = string("op_160_promoted_to_fp16"), val = fp16(0x1p+1)]; + tensor mel_length_to_fp16 = cast(dtype = current_lengths_1_to_fp16_dtype_0, x = mel_length)[name = string("cast_19")]; + tensor var_161_cast_fp16 = add(x = mel_length_to_fp16, y = var_160_promoted_to_fp16)[name = string("op_161_cast_fp16")]; + fp16 var_162_promoted_to_fp16 = const()[name = string("op_162_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_163_cast_fp16 = add(x = var_161_cast_fp16, y = var_162_promoted_to_fp16)[name = string("op_163_cast_fp16")]; + fp16 var_164_promoted_to_fp16 = const()[name = string("op_164_promoted_to_fp16"), val = fp16(0x1.8p+1)]; + tensor var_165_cast_fp16 = sub(x = var_163_cast_fp16, y = var_164_promoted_to_fp16)[name = string("op_165_cast_fp16")]; + fp16 var_56_promoted_to_fp16 = const()[name = string("op_56_promoted_to_fp16"), val = fp16(0x1p+1)]; + tensor floor_div_0_cast_fp16 = floor_div(x = var_165_cast_fp16, y = var_56_promoted_to_fp16)[name = string("floor_div_0_cast_fp16")]; + fp16 var_167_promoted_to_fp16 = const()[name = string("op_167_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor current_lengths_3_cast_fp16 = add(x = floor_div_0_cast_fp16, y = var_167_promoted_to_fp16)[name = string("current_lengths_3_cast_fp16")]; + string lengths_19_dtype_0 = const()[name = string("lengths_19_dtype_0"), val = string("int32")]; + tensor expand_dims_1 = const()[name = string("expand_dims_1"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3968)))]; + tensor var_176_axes_0 = const()[name = string("op_176_axes_0"), val = tensor([1])]; + tensor current_lengths_3_cast_fp16_to_int32 = cast(dtype = lengths_19_dtype_0, x = current_lengths_3_cast_fp16)[name = string("cast_18")]; + tensor var_176 = expand_dims(axes = var_176_axes_0, x = current_lengths_3_cast_fp16_to_int32)[name = string("op_176")]; + tensor time_mask_3 = less(x = expand_dims_1, y = var_176)[name = string("time_mask_3")]; + tensor var_178_axes_0 = const()[name = string("op_178_axes_0"), val = tensor([-1])]; + tensor var_178 = expand_dims(axes = var_178_axes_0, x = time_mask_3)[name = string("op_178")]; + tensor var_180_reps_0 = const()[name = string("op_180_reps_0"), val = tensor([1, 1, 65])]; + tensor var_180 = tile(reps = var_180_reps_0, x = var_178)[name = string("op_180")]; + tensor var_186_axes_0 = const()[name = string("op_186_axes_0"), val = tensor([1])]; + string mask_3_to_fp16_dtype_0 = const()[name = string("mask_3_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_180_to_fp16 = cast(dtype = mask_3_to_fp16_dtype_0, x = var_180)[name = string("cast_17")]; + tensor var_186_cast_fp16 = expand_dims(axes = var_186_axes_0, x = var_180_to_fp16)[name = string("op_186_cast_fp16")]; + tensor expanded_mask_3_reps_0 = const()[name = string("expanded_mask_3_reps_0"), val = tensor([1, 256, 1, 1])]; + tensor expanded_mask_3_cast_fp16 = tile(reps = expanded_mask_3_reps_0, x = var_186_cast_fp16)[name = string("expanded_mask_3_cast_fp16")]; + tensor input_5_cast_fp16 = mul(x = tensor_3_cast_fp16, y = expanded_mask_3_cast_fp16)[name = string("input_5_cast_fp16")]; + tensor tensor_5_cast_fp16 = relu(x = input_5_cast_fp16)[name = string("tensor_5_cast_fp16")]; + tensor input_7_cast_fp16 = mul(x = tensor_5_cast_fp16, y = expanded_mask_3_cast_fp16)[name = string("input_7_cast_fp16")]; + tensor input_9_pad_0 = const()[name = string("input_9_pad_0"), val = tensor([0, 0, 0, 0, 2, 1, 2, 1])]; + string input_9_mode_0 = const()[name = string("input_9_mode_0"), val = string("constant")]; + fp16 const_23_to_fp16 = const()[name = string("const_23_to_fp16"), val = fp16(0x0p+0)]; + tensor input_9_cast_fp16 = pad(constant_val = const_23_to_fp16, mode = input_9_mode_0, pad = input_9_pad_0, x = input_7_cast_fp16)[name = string("input_9_cast_fp16")]; + string tensor_7_pad_type_0 = const()[name = string("tensor_7_pad_type_0"), val = string("valid")]; + tensor tensor_7_strides_0 = const()[name = string("tensor_7_strides_0"), val = tensor([2, 2])]; + int32 tensor_7_groups_0 = const()[name = string("tensor_7_groups_0"), val = int32(256)]; + tensor tensor_7_pad_0 = const()[name = string("tensor_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor tensor_7_dilations_0 = const()[name = string("tensor_7_dilations_0"), val = tensor([1, 1])]; + tensor encoder_pre_encode_conv_2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6592))))[name = string("encoder_pre_encode_conv_2_weight_to_fp16_quantized")]; + tensor encoder_pre_encode_conv_2_bias_to_fp16 = const()[name = string("encoder_pre_encode_conv_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7168)))]; + tensor tensor_7_cast_fp16 = conv(bias = encoder_pre_encode_conv_2_bias_to_fp16, dilations = tensor_7_dilations_0, groups = tensor_7_groups_0, pad = tensor_7_pad_0, pad_type = tensor_7_pad_type_0, strides = tensor_7_strides_0, weight = encoder_pre_encode_conv_2_weight_to_fp16_quantized, x = input_9_cast_fp16)[name = string("tensor_7_cast_fp16")]; + fp16 var_208_promoted_to_fp16 = const()[name = string("op_208_promoted_to_fp16"), val = fp16(0x1p+1)]; + tensor var_209_cast_fp16 = add(x = current_lengths_3_cast_fp16, y = var_208_promoted_to_fp16)[name = string("op_209_cast_fp16")]; + fp16 var_210_promoted_to_fp16 = const()[name = string("op_210_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_211_cast_fp16 = add(x = var_209_cast_fp16, y = var_210_promoted_to_fp16)[name = string("op_211_cast_fp16")]; + fp16 var_212_promoted_to_fp16 = const()[name = string("op_212_promoted_to_fp16"), val = fp16(0x1.8p+1)]; + tensor var_213_cast_fp16 = sub(x = var_211_cast_fp16, y = var_212_promoted_to_fp16)[name = string("op_213_cast_fp16")]; + fp16 var_56_promoted_1_to_fp16 = const()[name = string("op_56_promoted_1_to_fp16"), val = fp16(0x1p+1)]; + tensor floor_div_1_cast_fp16 = floor_div(x = var_213_cast_fp16, y = var_56_promoted_1_to_fp16)[name = string("floor_div_1_cast_fp16")]; + fp16 var_215_promoted_to_fp16 = const()[name = string("op_215_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor current_lengths_5_cast_fp16 = add(x = floor_div_1_cast_fp16, y = var_215_promoted_to_fp16)[name = string("current_lengths_5_cast_fp16")]; + string lengths_21_dtype_0 = const()[name = string("lengths_21_dtype_0"), val = string("int32")]; + tensor expand_dims_2 = const()[name = string("expand_dims_2"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7744)))]; + tensor var_224_axes_0 = const()[name = string("op_224_axes_0"), val = tensor([1])]; + tensor current_lengths_5_cast_fp16_to_int32 = cast(dtype = lengths_21_dtype_0, x = current_lengths_5_cast_fp16)[name = string("cast_16")]; + tensor var_224 = expand_dims(axes = var_224_axes_0, x = current_lengths_5_cast_fp16_to_int32)[name = string("op_224")]; + tensor time_mask_5 = less(x = expand_dims_2, y = var_224)[name = string("time_mask_5")]; + tensor var_226_axes_0 = const()[name = string("op_226_axes_0"), val = tensor([-1])]; + tensor var_226 = expand_dims(axes = var_226_axes_0, x = time_mask_5)[name = string("op_226")]; + tensor var_228_reps_0 = const()[name = string("op_228_reps_0"), val = tensor([1, 1, 33])]; + tensor var_228 = tile(reps = var_228_reps_0, x = var_226)[name = string("op_228")]; + tensor var_234_axes_0 = const()[name = string("op_234_axes_0"), val = tensor([1])]; + string mask_5_to_fp16_dtype_0 = const()[name = string("mask_5_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_228_to_fp16 = cast(dtype = mask_5_to_fp16_dtype_0, x = var_228)[name = string("cast_15")]; + tensor var_234_cast_fp16 = expand_dims(axes = var_234_axes_0, x = var_228_to_fp16)[name = string("op_234_cast_fp16")]; + tensor expanded_mask_7_reps_0 = const()[name = string("expanded_mask_7_reps_0"), val = tensor([1, 256, 1, 1])]; + tensor expanded_mask_7_cast_fp16 = tile(reps = expanded_mask_7_reps_0, x = var_234_cast_fp16)[name = string("expanded_mask_7_cast_fp16")]; + tensor input_11_cast_fp16 = mul(x = tensor_7_cast_fp16, y = expanded_mask_7_cast_fp16)[name = string("input_11_cast_fp16")]; + string tensor_9_pad_type_0 = const()[name = string("tensor_9_pad_type_0"), val = string("valid")]; + tensor tensor_9_strides_0 = const()[name = string("tensor_9_strides_0"), val = tensor([1, 1])]; + tensor tensor_9_pad_0 = const()[name = string("tensor_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor tensor_9_dilations_0 = const()[name = string("tensor_9_dilations_0"), val = tensor([1, 1])]; + int32 tensor_9_groups_0 = const()[name = string("tensor_9_groups_0"), val = int32(1)]; + tensor encoder_pre_encode_conv_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73536))))[name = string("encoder_pre_encode_conv_3_weight_to_fp16_quantized")]; + tensor encoder_pre_encode_conv_3_bias_to_fp16 = const()[name = string("encoder_pre_encode_conv_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(74112)))]; + tensor tensor_9_cast_fp16 = conv(bias = encoder_pre_encode_conv_3_bias_to_fp16, dilations = tensor_9_dilations_0, groups = tensor_9_groups_0, pad = tensor_9_pad_0, pad_type = tensor_9_pad_type_0, strides = tensor_9_strides_0, weight = encoder_pre_encode_conv_3_weight_to_fp16_quantized, x = input_11_cast_fp16)[name = string("tensor_9_cast_fp16")]; + tensor input_13_cast_fp16 = mul(x = tensor_9_cast_fp16, y = expanded_mask_7_cast_fp16)[name = string("input_13_cast_fp16")]; + tensor tensor_11_cast_fp16 = relu(x = input_13_cast_fp16)[name = string("tensor_11_cast_fp16")]; + tensor input_15_cast_fp16 = mul(x = tensor_11_cast_fp16, y = expanded_mask_7_cast_fp16)[name = string("input_15_cast_fp16")]; + tensor input_17_pad_0 = const()[name = string("input_17_pad_0"), val = tensor([0, 0, 0, 0, 2, 1, 2, 1])]; + string input_17_mode_0 = const()[name = string("input_17_mode_0"), val = string("constant")]; + fp16 const_41_to_fp16 = const()[name = string("const_41_to_fp16"), val = fp16(0x0p+0)]; + tensor input_17_cast_fp16 = pad(constant_val = const_41_to_fp16, mode = input_17_mode_0, pad = input_17_pad_0, x = input_15_cast_fp16)[name = string("input_17_cast_fp16")]; + string tensor_13_pad_type_0 = const()[name = string("tensor_13_pad_type_0"), val = string("valid")]; + tensor tensor_13_strides_0 = const()[name = string("tensor_13_strides_0"), val = tensor([2, 2])]; + int32 tensor_13_groups_0 = const()[name = string("tensor_13_groups_0"), val = int32(256)]; + tensor tensor_13_pad_0 = const()[name = string("tensor_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor tensor_13_dilations_0 = const()[name = string("tensor_13_dilations_0"), val = tensor([1, 1])]; + tensor encoder_pre_encode_conv_5_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(74688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77056))))[name = string("encoder_pre_encode_conv_5_weight_to_fp16_quantized")]; + tensor encoder_pre_encode_conv_5_bias_to_fp16 = const()[name = string("encoder_pre_encode_conv_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77632)))]; + tensor tensor_13_cast_fp16 = conv(bias = encoder_pre_encode_conv_5_bias_to_fp16, dilations = tensor_13_dilations_0, groups = tensor_13_groups_0, pad = tensor_13_pad_0, pad_type = tensor_13_pad_type_0, strides = tensor_13_strides_0, weight = encoder_pre_encode_conv_5_weight_to_fp16_quantized, x = input_17_cast_fp16)[name = string("tensor_13_cast_fp16")]; + fp16 var_271_promoted_to_fp16 = const()[name = string("op_271_promoted_to_fp16"), val = fp16(0x1p+1)]; + tensor var_272_cast_fp16 = add(x = current_lengths_5_cast_fp16, y = var_271_promoted_to_fp16)[name = string("op_272_cast_fp16")]; + fp16 var_273_promoted_to_fp16 = const()[name = string("op_273_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor var_274_cast_fp16 = add(x = var_272_cast_fp16, y = var_273_promoted_to_fp16)[name = string("op_274_cast_fp16")]; + fp16 var_275_promoted_to_fp16 = const()[name = string("op_275_promoted_to_fp16"), val = fp16(0x1.8p+1)]; + tensor var_276_cast_fp16 = sub(x = var_274_cast_fp16, y = var_275_promoted_to_fp16)[name = string("op_276_cast_fp16")]; + fp16 var_56_promoted_2_to_fp16 = const()[name = string("op_56_promoted_2_to_fp16"), val = fp16(0x1p+1)]; + tensor floor_div_2_cast_fp16 = floor_div(x = var_276_cast_fp16, y = var_56_promoted_2_to_fp16)[name = string("floor_div_2_cast_fp16")]; + fp16 var_278_promoted_to_fp16 = const()[name = string("op_278_promoted_to_fp16"), val = fp16(0x1p+0)]; + tensor current_lengths_cast_fp16 = add(x = floor_div_2_cast_fp16, y = var_278_promoted_to_fp16)[name = string("current_lengths_cast_fp16")]; + string lengths_dtype_0 = const()[name = string("lengths_dtype_0"), val = string("int32")]; + tensor expand_dims_3 = const()[name = string("expand_dims_3"), val = tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8]])]; + tensor var_287_axes_0 = const()[name = string("op_287_axes_0"), val = tensor([1])]; + tensor current_lengths_cast_fp16_to_int32 = cast(dtype = lengths_dtype_0, x = current_lengths_cast_fp16)[name = string("cast_14")]; + tensor var_287 = expand_dims(axes = var_287_axes_0, x = current_lengths_cast_fp16_to_int32)[name = string("op_287")]; + tensor time_mask = less(x = expand_dims_3, y = var_287)[name = string("time_mask")]; + tensor var_289_axes_0 = const()[name = string("op_289_axes_0"), val = tensor([-1])]; + tensor var_289 = expand_dims(axes = var_289_axes_0, x = time_mask)[name = string("op_289")]; + tensor var_291_reps_0 = const()[name = string("op_291_reps_0"), val = tensor([1, 1, 17])]; + tensor var_291 = tile(reps = var_291_reps_0, x = var_289)[name = string("op_291")]; + tensor var_297_axes_0 = const()[name = string("op_297_axes_0"), val = tensor([1])]; + string mask_7_to_fp16_dtype_0 = const()[name = string("mask_7_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_291_to_fp16 = cast(dtype = mask_7_to_fp16_dtype_0, x = var_291)[name = string("cast_13")]; + tensor var_297_cast_fp16 = expand_dims(axes = var_297_axes_0, x = var_291_to_fp16)[name = string("op_297_cast_fp16")]; + tensor expanded_mask_13_reps_0 = const()[name = string("expanded_mask_13_reps_0"), val = tensor([1, 256, 1, 1])]; + tensor expanded_mask_13_cast_fp16 = tile(reps = expanded_mask_13_reps_0, x = var_297_cast_fp16)[name = string("expanded_mask_13_cast_fp16")]; + tensor input_19_cast_fp16 = mul(x = tensor_13_cast_fp16, y = expanded_mask_13_cast_fp16)[name = string("input_19_cast_fp16")]; + string tensor_15_pad_type_0 = const()[name = string("tensor_15_pad_type_0"), val = string("valid")]; + tensor tensor_15_strides_0 = const()[name = string("tensor_15_strides_0"), val = tensor([1, 1])]; + tensor tensor_15_pad_0 = const()[name = string("tensor_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor tensor_15_dilations_0 = const()[name = string("tensor_15_dilations_0"), val = tensor([1, 1])]; + int32 tensor_15_groups_0 = const()[name = string("tensor_15_groups_0"), val = int32(1)]; + tensor encoder_pre_encode_conv_6_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78208))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(143808))))[name = string("encoder_pre_encode_conv_6_weight_to_fp16_quantized")]; + tensor encoder_pre_encode_conv_6_bias_to_fp16 = const()[name = string("encoder_pre_encode_conv_6_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144384)))]; + tensor tensor_15_cast_fp16 = conv(bias = encoder_pre_encode_conv_6_bias_to_fp16, dilations = tensor_15_dilations_0, groups = tensor_15_groups_0, pad = tensor_15_pad_0, pad_type = tensor_15_pad_type_0, strides = tensor_15_strides_0, weight = encoder_pre_encode_conv_6_weight_to_fp16_quantized, x = input_19_cast_fp16)[name = string("tensor_15_cast_fp16")]; + tensor input_21_cast_fp16 = mul(x = tensor_15_cast_fp16, y = expanded_mask_13_cast_fp16)[name = string("input_21_cast_fp16")]; + tensor tensor_cast_fp16 = relu(x = input_21_cast_fp16)[name = string("tensor_cast_fp16")]; + tensor x_3_cast_fp16 = mul(x = tensor_cast_fp16, y = expanded_mask_13_cast_fp16)[name = string("x_3_cast_fp16")]; + tensor var_331_perm_0 = const()[name = string("op_331_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_332 = const()[name = string("op_332"), val = tensor([1, 9, -1])]; + tensor var_331_cast_fp16 = transpose(perm = var_331_perm_0, x = x_3_cast_fp16)[name = string("transpose_366")]; + tensor input_23_cast_fp16 = reshape(shape = var_332, x = var_331_cast_fp16)[name = string("input_23_cast_fp16")]; + tensor encoder_pre_encode_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144960))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4601472))))[name = string("encoder_pre_encode_out_weight_to_fp16_quantized")]; + tensor encoder_pre_encode_out_bias_to_fp16 = const()[name = string("encoder_pre_encode_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4603584)))]; + tensor linear_0_cast_fp16 = linear(bias = encoder_pre_encode_out_bias_to_fp16, weight = encoder_pre_encode_out_weight_to_fp16_quantized, x = input_23_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_342_begin_0 = const()[name = string("op_342_begin_0"), val = tensor([0, 2, 0])]; + tensor var_342_end_0 = const()[name = string("op_342_end_0"), val = tensor([1, 9, 1024])]; + tensor var_342_end_mask_0 = const()[name = string("op_342_end_mask_0"), val = tensor([true, true, true])]; + tensor var_342_cast_fp16 = slice_by_index(begin = var_342_begin_0, end = var_342_end_0, end_mask = var_342_end_mask_0, x = linear_0_cast_fp16)[name = string("op_342_cast_fp16")]; + int32 var_344 = const()[name = string("op_344"), val = int32(2)]; + tensor var_345 = sub(x = current_lengths_cast_fp16_to_int32, y = var_344)[name = string("op_345")]; + string var_345_promoted_to_fp16_dtype_0 = const()[name = string("op_345_promoted_to_fp16_dtype_0"), val = string("fp16")]; + fp16 var_62_promoted_to_fp16 = const()[name = string("op_62_promoted_to_fp16"), val = fp16(0x0p+0)]; + fp16 const_61_to_fp16 = const()[name = string("const_61_to_fp16"), val = fp16(inf)]; + tensor var_345_to_fp16 = cast(dtype = var_345_promoted_to_fp16_dtype_0, x = var_345)[name = string("cast_12")]; + tensor clip_0_cast_fp16 = clip(alpha = var_62_promoted_to_fp16, beta = const_61_to_fp16, x = var_345_to_fp16)[name = string("clip_0_cast_fp16")]; + tensor max_audio_length_1 = const()[name = string("max_audio_length_1"), val = tensor([7])]; + fp16 var_361_promoted_to_fp16 = const()[name = string("op_361_promoted_to_fp16"), val = fp16(0x1.5p+5)]; + tensor padding_length_cast_fp16 = add(x = clip_0_cast_fp16, y = var_361_promoted_to_fp16)[name = string("padding_length_cast_fp16")]; + int32 const_63 = const()[name = string("const_63"), val = int32(-1)]; + tensor var_363 = mul(x = cache_len, y = const_63)[name = string("op_363")]; + int32 var_364 = const()[name = string("op_364"), val = int32(42)]; + tensor offset = add(x = var_363, y = var_364)[name = string("offset")]; + tensor var_404_axes_0 = const()[name = string("op_404_axes_0"), val = tensor([-1])]; + tensor var_404_cast_fp16 = expand_dims(axes = var_404_axes_0, x = padding_length_cast_fp16)[name = string("op_404_cast_fp16")]; + tensor var_403_promoted_to_fp16 = const()[name = string("op_403_promoted_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4605696)))]; + tensor pad_mask_1_cast_fp16 = less(x = var_403_promoted_to_fp16, y = var_404_cast_fp16)[name = string("pad_mask_1_cast_fp16")]; + tensor expand_dims_5 = const()[name = string("expand_dims_5"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4605888)))]; + tensor var_410_axes_0 = const()[name = string("op_410_axes_0"), val = tensor([-1])]; + tensor var_410 = expand_dims(axes = var_410_axes_0, x = offset)[name = string("op_410")]; + tensor pad_mask_off = greater_equal(x = expand_dims_5, y = var_410)[name = string("pad_mask_off")]; + tensor pad_mask_3 = logical_and(x = pad_mask_off, y = pad_mask_1_cast_fp16)[name = string("pad_mask_3")]; + tensor var_413_axes_0 = const()[name = string("op_413_axes_0"), val = tensor([1])]; + tensor var_413 = expand_dims(axes = var_413_axes_0, x = pad_mask_3)[name = string("op_413")]; + tensor var_414 = const()[name = string("op_414"), val = tensor([1, 49, 1])]; + tensor pad_mask_for_att_mask_1 = tile(reps = var_414, x = var_413)[name = string("pad_mask_for_att_mask_1")]; + tensor var_416_perm_0 = const()[name = string("op_416_perm_0"), val = tensor([0, 2, 1])]; + tensor var_416 = transpose(perm = var_416_perm_0, x = pad_mask_for_att_mask_1)[name = string("transpose_365")]; + tensor pad_mask_for_att_mask = logical_and(x = pad_mask_for_att_mask_1, y = var_416)[name = string("pad_mask_for_att_mask")]; + tensor const_71 = const()[name = string("const_71"), val = tensor([[[true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true]]])]; + tensor att_mask_9 = logical_and(x = pad_mask_for_att_mask, y = const_71)[name = string("att_mask_9")]; + tensor att_mask = logical_not(x = att_mask_9)[name = string("att_mask")]; + tensor pad_mask_5 = logical_not(x = pad_mask_3)[name = string("pad_mask_5")]; + tensor pad_mask_begin_0 = const()[name = string("pad_mask_begin_0"), val = tensor([0, 42])]; + tensor pad_mask_end_0 = const()[name = string("pad_mask_end_0"), val = tensor([1, 49])]; + tensor pad_mask_end_mask_0 = const()[name = string("pad_mask_end_mask_0"), val = tensor([true, true])]; + tensor pad_mask = slice_by_index(begin = pad_mask_begin_0, end = pad_mask_end_0, end_mask = pad_mask_end_mask_0, x = pad_mask_5)[name = string("pad_mask")]; + tensor mask_9_begin_0 = const()[name = string("mask_9_begin_0"), val = tensor([0, 42, 0])]; + tensor mask_9_end_0 = const()[name = string("mask_9_end_0"), val = tensor([1, 49, 49])]; + tensor mask_9_end_mask_0 = const()[name = string("mask_9_end_mask_0"), val = tensor([true, true, true])]; + tensor mask_9 = slice_by_index(begin = mask_9_begin_0, end = mask_9_end_0, end_mask = mask_9_end_mask_0, x = att_mask)[name = string("mask_9")]; + tensor cache_1_begin_0 = const()[name = string("cache_1_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor cache_1_end_0 = const()[name = string("cache_1_end_0"), val = tensor([1, 1, 42, 1024])]; + tensor cache_1_end_mask_0 = const()[name = string("cache_1_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_1_squeeze_mask_0 = const()[name = string("cache_1_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_channel_to_fp16 = cast(dtype = cache_channel_to_fp16_dtype_0, x = cache_channel)[name = string("cast_11")]; + tensor value_3_cast_fp16 = transpose(perm = value_3_perm_0, x = cache_channel_to_fp16)[name = string("transpose_364")]; + tensor cache_1_cast_fp16 = slice_by_index(begin = cache_1_begin_0, end = cache_1_end_0, end_mask = cache_1_end_mask_0, squeeze_mask = cache_1_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_1_cast_fp16")]; + tensor cache_3_begin_0 = const()[name = string("cache_3_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor cache_3_end_0 = const()[name = string("cache_3_end_0"), val = tensor([1, 1, 1024, 8])]; + tensor cache_3_end_mask_0 = const()[name = string("cache_3_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_3_squeeze_mask_0 = const()[name = string("cache_3_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_time_to_fp16 = cast(dtype = cache_time_to_fp16_dtype_0, x = cache_time)[name = string("cast_10")]; + tensor value_5_cast_fp16 = transpose(perm = value_5_perm_0, x = cache_time_to_fp16)[name = string("transpose_363")]; + tensor cache_3_cast_fp16 = slice_by_index(begin = cache_3_begin_0, end = cache_3_end_0, end_mask = cache_3_end_mask_0, squeeze_mask = cache_3_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_3_cast_fp16")]; + tensor input_27_axes_0 = const()[name = string("input_27_axes_0"), val = tensor([-1])]; + tensor encoder_layers_0_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_0_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4606208)))]; + tensor encoder_layers_0_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_0_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4608320)))]; + fp16 var_42_to_fp16 = const()[name = string("op_42_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_27_cast_fp16 = layer_norm(axes = input_27_axes_0, beta = encoder_layers_0_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_0_norm_feed_forward1_weight_to_fp16, x = var_342_cast_fp16)[name = string("input_27_cast_fp16")]; + tensor encoder_layers_0_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4610432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8804800))))[name = string("encoder_layers_0_feed_forward1_linear1_weight_to_fp16_quantized")]; + tensor encoder_layers_0_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_0_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8813056)))]; + tensor linear_1_cast_fp16 = linear(bias = encoder_layers_0_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_0_feed_forward1_linear1_weight_to_fp16_quantized, x = input_27_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor input_31_cast_fp16 = silu(x = linear_1_cast_fp16)[name = string("input_31_cast_fp16")]; + tensor encoder_layers_0_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8821312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13015680))))[name = string("encoder_layers_0_feed_forward1_linear2_weight_to_fp16_quantized")]; + tensor encoder_layers_0_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_0_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13017792)))]; + tensor linear_2_cast_fp16 = linear(bias = encoder_layers_0_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_0_feed_forward1_linear2_weight_to_fp16_quantized, x = input_31_cast_fp16)[name = string("linear_2_cast_fp16")]; + fp16 var_455_to_fp16 = const()[name = string("op_455_to_fp16"), val = fp16(0x1p-1)]; + tensor var_456_cast_fp16 = mul(x = linear_2_cast_fp16, y = var_455_to_fp16)[name = string("op_456_cast_fp16")]; + tensor input_37_cast_fp16 = add(x = var_342_cast_fp16, y = var_456_cast_fp16)[name = string("input_37_cast_fp16")]; + tensor key_1_axes_0 = const()[name = string("key_1_axes_0"), val = tensor([-1])]; + tensor encoder_layers_0_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_0_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13019904)))]; + tensor encoder_layers_0_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_0_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13022016)))]; + tensor key_1_cast_fp16 = layer_norm(axes = key_1_axes_0, beta = encoder_layers_0_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_0_norm_self_att_weight_to_fp16, x = input_37_cast_fp16)[name = string("key_1_cast_fp16")]; + bool input_39_interleave_0 = const()[name = string("input_39_interleave_0"), val = bool(false)]; + tensor input_39_cast_fp16 = concat(axis = var_68, interleave = input_39_interleave_0, values = (cache_1_cast_fp16, key_1_cast_fp16))[name = string("input_39_cast_fp16")]; + tensor var_478_begin_0 = const()[name = string("op_478_begin_0"), val = tensor([0, 7, 0])]; + tensor var_478_end_0 = const()[name = string("op_478_end_0"), val = tensor([1, 42, 1024])]; + tensor var_478_end_mask_0 = const()[name = string("op_478_end_mask_0"), val = tensor([true, true, true])]; + tensor var_478_cast_fp16 = slice_by_index(begin = var_478_begin_0, end = var_478_end_0, end_mask = var_478_end_mask_0, x = cache_1_cast_fp16)[name = string("op_478_cast_fp16")]; + bool var_484_interleave_0 = const()[name = string("op_484_interleave_0"), val = bool(false)]; + tensor var_484_cast_fp16 = concat(axis = var_68, interleave = var_484_interleave_0, values = (var_478_cast_fp16, key_1_cast_fp16))[name = string("op_484_cast_fp16")]; + tensor encoder_layers_0_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13024128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14072768))))[name = string("encoder_layers_0_self_attn_linear_q_weight_to_fp16_quantized")]; + tensor encoder_layers_0_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_0_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14074880)))]; + tensor linear_3_cast_fp16 = linear(bias = encoder_layers_0_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_0_self_attn_linear_q_weight_to_fp16_quantized, x = key_1_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_489 = const()[name = string("op_489"), val = tensor([1, -1, 8, 128])]; + tensor q_1_cast_fp16 = reshape(shape = var_489, x = linear_3_cast_fp16)[name = string("q_1_cast_fp16")]; + tensor encoder_layers_0_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14076992))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15125632))))[name = string("encoder_layers_0_self_attn_linear_k_weight_to_fp16_quantized")]; + tensor encoder_layers_0_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_0_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15127744)))]; + tensor linear_4_cast_fp16 = linear(bias = encoder_layers_0_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_0_self_attn_linear_k_weight_to_fp16_quantized, x = input_39_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_494 = const()[name = string("op_494"), val = tensor([1, -1, 8, 128])]; + tensor k_1_cast_fp16 = reshape(shape = var_494, x = linear_4_cast_fp16)[name = string("k_1_cast_fp16")]; + tensor encoder_layers_0_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15129856))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16178496))))[name = string("encoder_layers_0_self_attn_linear_v_weight_to_fp16_quantized")]; + tensor encoder_layers_0_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_0_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16180608)))]; + tensor linear_5_cast_fp16 = linear(bias = encoder_layers_0_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_0_self_attn_linear_v_weight_to_fp16_quantized, x = input_39_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_499 = const()[name = string("op_499"), val = tensor([1, -1, 8, 128])]; + tensor v_1_cast_fp16 = reshape(shape = var_499, x = linear_5_cast_fp16)[name = string("v_1_cast_fp16")]; + tensor value_9_perm_0 = const()[name = string("value_9_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_0_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_0_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16182720)))]; + tensor var_512_cast_fp16 = add(x = q_1_cast_fp16, y = encoder_layers_0_self_attn_pos_bias_u_to_fp16)[name = string("op_512_cast_fp16")]; + tensor encoder_layers_0_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_0_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16184832)))]; + tensor var_514_cast_fp16 = add(x = q_1_cast_fp16, y = encoder_layers_0_self_attn_pos_bias_v_to_fp16)[name = string("op_514_cast_fp16")]; + tensor q_with_bias_v_1_perm_0 = const()[name = string("q_with_bias_v_1_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_7_transpose_x_0 = const()[name = string("x_7_transpose_x_0"), val = bool(false)]; + bool x_7_transpose_y_0 = const()[name = string("x_7_transpose_y_0"), val = bool(false)]; + tensor op_516_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16186944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16286336))))[name = string("op_516_to_fp16_quantized")]; + tensor q_with_bias_v_1_cast_fp16 = transpose(perm = q_with_bias_v_1_perm_0, x = var_514_cast_fp16)[name = string("transpose_362")]; + tensor x_7_cast_fp16 = matmul(transpose_x = x_7_transpose_x_0, transpose_y = x_7_transpose_y_0, x = q_with_bias_v_1_cast_fp16, y = op_516_to_fp16_quantized)[name = string("x_7_cast_fp16")]; + tensor x_9_pad_0 = const()[name = string("x_9_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_9_mode_0 = const()[name = string("x_9_mode_0"), val = string("constant")]; + fp16 const_79_to_fp16 = const()[name = string("const_79_to_fp16"), val = fp16(0x0p+0)]; + tensor x_9_cast_fp16 = pad(constant_val = const_79_to_fp16, mode = x_9_mode_0, pad = x_9_pad_0, x = x_7_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor var_524 = const()[name = string("op_524"), val = tensor([1, 8, -1, 7])]; + tensor x_11_cast_fp16 = reshape(shape = var_524, x = x_9_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor var_528_begin_0 = const()[name = string("op_528_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_528_end_0 = const()[name = string("op_528_end_0"), val = tensor([1, 8, 98, 7])]; + tensor var_528_end_mask_0 = const()[name = string("op_528_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_528_cast_fp16 = slice_by_index(begin = var_528_begin_0, end = var_528_end_0, end_mask = var_528_end_mask_0, x = x_11_cast_fp16)[name = string("op_528_cast_fp16")]; + tensor var_529 = const()[name = string("op_529"), val = tensor([1, 8, 7, 97])]; + tensor matrix_bd_1_cast_fp16 = reshape(shape = var_529, x = var_528_cast_fp16)[name = string("matrix_bd_1_cast_fp16")]; + bool matrix_ac_1_transpose_x_0 = const()[name = string("matrix_ac_1_transpose_x_0"), val = bool(false)]; + bool matrix_ac_1_transpose_y_0 = const()[name = string("matrix_ac_1_transpose_y_0"), val = bool(false)]; + tensor transpose_96_perm_0 = const()[name = string("transpose_96_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_97_perm_0 = const()[name = string("transpose_97_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_97 = transpose(perm = transpose_97_perm_0, x = k_1_cast_fp16)[name = string("transpose_360")]; + tensor transpose_96 = transpose(perm = transpose_96_perm_0, x = var_512_cast_fp16)[name = string("transpose_361")]; + tensor matrix_ac_1_cast_fp16 = matmul(transpose_x = matrix_ac_1_transpose_x_0, transpose_y = matrix_ac_1_transpose_y_0, x = transpose_96, y = transpose_97)[name = string("matrix_ac_1_cast_fp16")]; + tensor matrix_bd_3_begin_0 = const()[name = string("matrix_bd_3_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_3_end_0 = const()[name = string("matrix_bd_3_end_0"), val = tensor([1, 8, 7, 49])]; + tensor matrix_bd_3_end_mask_0 = const()[name = string("matrix_bd_3_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_3_cast_fp16 = slice_by_index(begin = matrix_bd_3_begin_0, end = matrix_bd_3_end_0, end_mask = matrix_bd_3_end_mask_0, x = matrix_bd_1_cast_fp16)[name = string("matrix_bd_3_cast_fp16")]; + tensor var_538_cast_fp16 = add(x = matrix_ac_1_cast_fp16, y = matrix_bd_3_cast_fp16)[name = string("op_538_cast_fp16")]; + fp16 _inversed_scores_1_y_0_to_fp16 = const()[name = string("_inversed_scores_1_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_1_cast_fp16 = mul(x = var_538_cast_fp16, y = _inversed_scores_1_y_0_to_fp16)[name = string("_inversed_scores_1_cast_fp16")]; + tensor mask_11_axes_0 = const()[name = string("mask_11_axes_0"), val = tensor([1])]; + tensor mask_11 = expand_dims(axes = mask_11_axes_0, x = mask_9)[name = string("mask_11")]; + fp16 var_45_to_fp16 = const()[name = string("op_45_to_fp16"), val = fp16(-0x1.388p+13)]; + tensor scores_3_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_1_cast_fp16, cond = mask_11)[name = string("scores_3_cast_fp16")]; + tensor var_544_cast_fp16 = softmax(axis = var_59, x = scores_3_cast_fp16)[name = string("op_544_cast_fp16")]; + fp16 var_44_to_fp16 = const()[name = string("op_44_to_fp16"), val = fp16(0x0p+0)]; + tensor input_41_cast_fp16 = select(a = var_44_to_fp16, b = var_544_cast_fp16, cond = mask_11)[name = string("input_41_cast_fp16")]; + bool x_13_transpose_x_0 = const()[name = string("x_13_transpose_x_0"), val = bool(false)]; + bool x_13_transpose_y_0 = const()[name = string("x_13_transpose_y_0"), val = bool(false)]; + tensor value_9_cast_fp16 = transpose(perm = value_9_perm_0, x = v_1_cast_fp16)[name = string("transpose_359")]; + tensor x_13_cast_fp16 = matmul(transpose_x = x_13_transpose_x_0, transpose_y = x_13_transpose_y_0, x = input_41_cast_fp16, y = value_9_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor var_548_perm_0 = const()[name = string("op_548_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_549 = const()[name = string("op_549"), val = tensor([1, -1, 1024])]; + tensor var_548_cast_fp16 = transpose(perm = var_548_perm_0, x = x_13_cast_fp16)[name = string("transpose_358")]; + tensor input_43_cast_fp16 = reshape(shape = var_549, x = var_548_cast_fp16)[name = string("input_43_cast_fp16")]; + tensor encoder_layers_0_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16286656))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17335296))))[name = string("encoder_layers_0_self_attn_linear_out_weight_to_fp16_quantized")]; + tensor encoder_layers_0_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_0_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17337408)))]; + tensor linear_7_cast_fp16 = linear(bias = encoder_layers_0_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_0_self_attn_linear_out_weight_to_fp16_quantized, x = input_43_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor input_47_cast_fp16 = add(x = input_37_cast_fp16, y = linear_7_cast_fp16)[name = string("input_47_cast_fp16")]; + tensor x_17_axes_0 = const()[name = string("x_17_axes_0"), val = tensor([-1])]; + tensor encoder_layers_0_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_0_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17339520)))]; + tensor encoder_layers_0_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_0_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17341632)))]; + tensor x_17_cast_fp16 = layer_norm(axes = x_17_axes_0, beta = encoder_layers_0_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_0_norm_conv_weight_to_fp16, x = input_47_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor input_49_perm_0 = const()[name = string("input_49_perm_0"), val = tensor([0, 2, 1])]; + string input_51_pad_type_0 = const()[name = string("input_51_pad_type_0"), val = string("valid")]; + tensor input_51_strides_0 = const()[name = string("input_51_strides_0"), val = tensor([1])]; + tensor input_51_pad_0 = const()[name = string("input_51_pad_0"), val = tensor([0, 0])]; + tensor input_51_dilations_0 = const()[name = string("input_51_dilations_0"), val = tensor([1])]; + int32 input_51_groups_0 = const()[name = string("input_51_groups_0"), val = int32(1)]; + tensor encoder_layers_0_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17343744))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19440960))))[name = string("encoder_layers_0_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_49_cast_fp16 = transpose(perm = input_49_perm_0, x = x_17_cast_fp16)[name = string("transpose_357")]; + tensor input_51_cast_fp16 = conv(dilations = input_51_dilations_0, groups = input_51_groups_0, pad = input_51_pad_0, pad_type = input_51_pad_type_0, strides = input_51_strides_0, weight = encoder_layers_0_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_49_cast_fp16)[name = string("input_51_cast_fp16")]; + int32 x_19_split_num_splits_0 = const()[name = string("x_19_split_num_splits_0"), val = int32(2)]; + int32 x_19_split_axis_0 = const()[name = string("x_19_split_axis_0"), val = int32(1)]; + tensor x_19_split_cast_fp16_0, tensor x_19_split_cast_fp16_1 = split(axis = x_19_split_axis_0, num_splits = x_19_split_num_splits_0, x = input_51_cast_fp16)[name = string("x_19_split_cast_fp16")]; + tensor x_19_split_1_sigmoid_cast_fp16 = sigmoid(x = x_19_split_cast_fp16_1)[name = string("x_19_split_1_sigmoid_cast_fp16")]; + tensor x_19_cast_fp16 = mul(x = x_19_split_cast_fp16_0, y = x_19_split_1_sigmoid_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor var_575_axes_0 = const()[name = string("op_575_axes_0"), val = tensor([1])]; + tensor var_575 = expand_dims(axes = var_575_axes_0, x = pad_mask)[name = string("op_575")]; + tensor input_53_cast_fp16 = select(a = var_44_to_fp16, b = x_19_cast_fp16, cond = var_575)[name = string("input_53_cast_fp16")]; + bool new_x_3_interleave_0 = const()[name = string("new_x_3_interleave_0"), val = bool(false)]; + tensor new_x_3_cast_fp16 = concat(axis = var_59, interleave = new_x_3_interleave_0, values = (cache_3_cast_fp16, input_53_cast_fp16))[name = string("new_x_3_cast_fp16")]; + tensor var_588_begin_0 = const()[name = string("op_588_begin_0"), val = tensor([0, 0, 7])]; + tensor var_588_end_0 = const()[name = string("op_588_end_0"), val = tensor([1, 1024, 15])]; + tensor var_588_end_mask_0 = const()[name = string("op_588_end_mask_0"), val = tensor([true, true, true])]; + tensor var_588_cast_fp16 = slice_by_index(begin = var_588_begin_0, end = var_588_end_0, end_mask = var_588_end_mask_0, x = new_x_3_cast_fp16)[name = string("op_588_cast_fp16")]; + string x_21_pad_type_0 = const()[name = string("x_21_pad_type_0"), val = string("valid")]; + int32 x_21_groups_0 = const()[name = string("x_21_groups_0"), val = int32(1024)]; + tensor x_21_strides_0 = const()[name = string("x_21_strides_0"), val = tensor([1])]; + tensor x_21_pad_0 = const()[name = string("x_21_pad_0"), val = tensor([0, 0])]; + tensor x_21_dilations_0 = const()[name = string("x_21_dilations_0"), val = tensor([1])]; + tensor encoder_layers_0_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19445120))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19454400))))[name = string("encoder_layers_0_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_21_cast_fp16 = conv(dilations = x_21_dilations_0, groups = x_21_groups_0, pad = x_21_pad_0, pad_type = x_21_pad_type_0, strides = x_21_strides_0, weight = encoder_layers_0_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_3_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor input_55_perm_0 = const()[name = string("input_55_perm_0"), val = tensor([0, 2, 1])]; + tensor x_23_axes_0 = const()[name = string("x_23_axes_0"), val = tensor([-1])]; + tensor encoder_layers_0_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_0_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19456512)))]; + tensor encoder_layers_0_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_0_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19458624)))]; + tensor input_55_cast_fp16 = transpose(perm = input_55_perm_0, x = x_21_cast_fp16)[name = string("transpose_356")]; + tensor x_23_cast_fp16 = layer_norm(axes = x_23_axes_0, beta = encoder_layers_0_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_0_conv_batch_norm_weight_to_fp16, x = input_55_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor input_57_perm_0 = const()[name = string("input_57_perm_0"), val = tensor([0, 2, 1])]; + tensor input_57_cast_fp16 = transpose(perm = input_57_perm_0, x = x_23_cast_fp16)[name = string("transpose_355")]; + tensor input_59_cast_fp16 = silu(x = input_57_cast_fp16)[name = string("input_59_cast_fp16")]; + string x_25_pad_type_0 = const()[name = string("x_25_pad_type_0"), val = string("valid")]; + tensor x_25_strides_0 = const()[name = string("x_25_strides_0"), val = tensor([1])]; + tensor x_25_pad_0 = const()[name = string("x_25_pad_0"), val = tensor([0, 0])]; + tensor x_25_dilations_0 = const()[name = string("x_25_dilations_0"), val = tensor([1])]; + int32 x_25_groups_0 = const()[name = string("x_25_groups_0"), val = int32(1)]; + tensor encoder_layers_0_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19460736))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20509376))))[name = string("encoder_layers_0_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_25_cast_fp16 = conv(dilations = x_25_dilations_0, groups = x_25_groups_0, pad = x_25_pad_0, pad_type = x_25_pad_type_0, strides = x_25_strides_0, weight = encoder_layers_0_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_59_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor input_61_perm_0 = const()[name = string("input_61_perm_0"), val = tensor([0, 2, 1])]; + tensor input_61_cast_fp16 = transpose(perm = input_61_perm_0, x = x_25_cast_fp16)[name = string("transpose_354")]; + tensor input_63_cast_fp16 = add(x = input_47_cast_fp16, y = input_61_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor input_65_axes_0 = const()[name = string("input_65_axes_0"), val = tensor([-1])]; + tensor encoder_layers_0_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_0_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20511488)))]; + tensor encoder_layers_0_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_0_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20513600)))]; + tensor input_65_cast_fp16 = layer_norm(axes = input_65_axes_0, beta = encoder_layers_0_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_0_norm_feed_forward2_weight_to_fp16, x = input_63_cast_fp16)[name = string("input_65_cast_fp16")]; + tensor encoder_layers_0_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20515712))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24710080))))[name = string("encoder_layers_0_feed_forward2_linear1_weight_to_fp16_quantized")]; + tensor encoder_layers_0_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_0_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24718336)))]; + tensor linear_8_cast_fp16 = linear(bias = encoder_layers_0_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_0_feed_forward2_linear1_weight_to_fp16_quantized, x = input_65_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor input_69_cast_fp16 = silu(x = linear_8_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor encoder_layers_0_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24726592))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28920960))))[name = string("encoder_layers_0_feed_forward2_linear2_weight_to_fp16_quantized")]; + tensor encoder_layers_0_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_0_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28923072)))]; + tensor linear_9_cast_fp16 = linear(bias = encoder_layers_0_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_0_feed_forward2_linear2_weight_to_fp16_quantized, x = input_69_cast_fp16)[name = string("linear_9_cast_fp16")]; + fp16 var_631_to_fp16 = const()[name = string("op_631_to_fp16"), val = fp16(0x1p-1)]; + tensor var_632_cast_fp16 = mul(x = linear_9_cast_fp16, y = var_631_to_fp16)[name = string("op_632_cast_fp16")]; + tensor input_75_cast_fp16 = add(x = input_63_cast_fp16, y = var_632_cast_fp16)[name = string("input_75_cast_fp16")]; + tensor input_77_axes_0 = const()[name = string("input_77_axes_0"), val = tensor([-1])]; + tensor encoder_layers_0_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_0_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28925184)))]; + tensor encoder_layers_0_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_0_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28927296)))]; + tensor input_77_cast_fp16 = layer_norm(axes = input_77_axes_0, beta = encoder_layers_0_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_0_norm_out_weight_to_fp16, x = input_75_cast_fp16)[name = string("input_77_cast_fp16")]; + tensor cache_5_begin_0 = const()[name = string("cache_5_begin_0"), val = tensor([1, 0, 0, 0])]; + tensor cache_5_end_0 = const()[name = string("cache_5_end_0"), val = tensor([2, 1, 42, 1024])]; + tensor cache_5_end_mask_0 = const()[name = string("cache_5_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_5_squeeze_mask_0 = const()[name = string("cache_5_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_5_cast_fp16 = slice_by_index(begin = cache_5_begin_0, end = cache_5_end_0, end_mask = cache_5_end_mask_0, squeeze_mask = cache_5_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_5_cast_fp16")]; + tensor cache_7_begin_0 = const()[name = string("cache_7_begin_0"), val = tensor([1, 0, 0, 0])]; + tensor cache_7_end_0 = const()[name = string("cache_7_end_0"), val = tensor([2, 1, 1024, 8])]; + tensor cache_7_end_mask_0 = const()[name = string("cache_7_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_7_squeeze_mask_0 = const()[name = string("cache_7_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_7_cast_fp16 = slice_by_index(begin = cache_7_begin_0, end = cache_7_end_0, end_mask = cache_7_end_mask_0, squeeze_mask = cache_7_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_7_cast_fp16")]; + tensor input_79_axes_0 = const()[name = string("input_79_axes_0"), val = tensor([-1])]; + tensor encoder_layers_1_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_1_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28929408)))]; + tensor encoder_layers_1_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_1_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28931520)))]; + tensor input_79_cast_fp16 = layer_norm(axes = input_79_axes_0, beta = encoder_layers_1_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_1_norm_feed_forward1_weight_to_fp16, x = input_77_cast_fp16)[name = string("input_79_cast_fp16")]; + tensor encoder_layers_1_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28933632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33128000))))[name = string("encoder_layers_1_feed_forward1_linear1_weight_to_fp16_quantized")]; + tensor encoder_layers_1_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_1_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33136256)))]; + tensor linear_10_cast_fp16 = linear(bias = encoder_layers_1_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_1_feed_forward1_linear1_weight_to_fp16_quantized, x = input_79_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor input_83_cast_fp16 = silu(x = linear_10_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor encoder_layers_1_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33144512))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37338880))))[name = string("encoder_layers_1_feed_forward1_linear2_weight_to_fp16_quantized")]; + tensor encoder_layers_1_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_1_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37340992)))]; + tensor linear_11_cast_fp16 = linear(bias = encoder_layers_1_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_1_feed_forward1_linear2_weight_to_fp16_quantized, x = input_83_cast_fp16)[name = string("linear_11_cast_fp16")]; + fp16 var_668_to_fp16 = const()[name = string("op_668_to_fp16"), val = fp16(0x1p-1)]; + tensor var_669_cast_fp16 = mul(x = linear_11_cast_fp16, y = var_668_to_fp16)[name = string("op_669_cast_fp16")]; + tensor input_89_cast_fp16 = add(x = input_77_cast_fp16, y = var_669_cast_fp16)[name = string("input_89_cast_fp16")]; + tensor key_3_axes_0 = const()[name = string("key_3_axes_0"), val = tensor([-1])]; + tensor encoder_layers_1_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_1_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37343104)))]; + tensor encoder_layers_1_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_1_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37345216)))]; + tensor key_3_cast_fp16 = layer_norm(axes = key_3_axes_0, beta = encoder_layers_1_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_1_norm_self_att_weight_to_fp16, x = input_89_cast_fp16)[name = string("key_3_cast_fp16")]; + bool input_91_interleave_0 = const()[name = string("input_91_interleave_0"), val = bool(false)]; + tensor input_91_cast_fp16 = concat(axis = var_68, interleave = input_91_interleave_0, values = (cache_5_cast_fp16, key_3_cast_fp16))[name = string("input_91_cast_fp16")]; + tensor var_691_begin_0 = const()[name = string("op_691_begin_0"), val = tensor([0, 7, 0])]; + tensor var_691_end_0 = const()[name = string("op_691_end_0"), val = tensor([1, 42, 1024])]; + tensor var_691_end_mask_0 = const()[name = string("op_691_end_mask_0"), val = tensor([true, true, true])]; + tensor var_691_cast_fp16 = slice_by_index(begin = var_691_begin_0, end = var_691_end_0, end_mask = var_691_end_mask_0, x = cache_5_cast_fp16)[name = string("op_691_cast_fp16")]; + bool var_697_interleave_0 = const()[name = string("op_697_interleave_0"), val = bool(false)]; + tensor var_697_cast_fp16 = concat(axis = var_68, interleave = var_697_interleave_0, values = (var_691_cast_fp16, key_3_cast_fp16))[name = string("op_697_cast_fp16")]; + tensor encoder_layers_1_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37347328))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38395968))))[name = string("encoder_layers_1_self_attn_linear_q_weight_to_fp16_quantized")]; + tensor encoder_layers_1_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_1_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38398080)))]; + tensor linear_12_cast_fp16 = linear(bias = encoder_layers_1_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_1_self_attn_linear_q_weight_to_fp16_quantized, x = key_3_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_702 = const()[name = string("op_702"), val = tensor([1, -1, 8, 128])]; + tensor q_7_cast_fp16 = reshape(shape = var_702, x = linear_12_cast_fp16)[name = string("q_7_cast_fp16")]; + tensor encoder_layers_1_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38400192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39448832))))[name = string("encoder_layers_1_self_attn_linear_k_weight_to_fp16_quantized")]; + tensor encoder_layers_1_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_1_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39450944)))]; + tensor linear_13_cast_fp16 = linear(bias = encoder_layers_1_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_1_self_attn_linear_k_weight_to_fp16_quantized, x = input_91_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_707 = const()[name = string("op_707"), val = tensor([1, -1, 8, 128])]; + tensor k_5_cast_fp16 = reshape(shape = var_707, x = linear_13_cast_fp16)[name = string("k_5_cast_fp16")]; + tensor encoder_layers_1_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39453056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40501696))))[name = string("encoder_layers_1_self_attn_linear_v_weight_to_fp16_quantized")]; + tensor encoder_layers_1_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_1_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40503808)))]; + tensor linear_14_cast_fp16 = linear(bias = encoder_layers_1_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_1_self_attn_linear_v_weight_to_fp16_quantized, x = input_91_cast_fp16)[name = string("linear_14_cast_fp16")]; + tensor var_712 = const()[name = string("op_712"), val = tensor([1, -1, 8, 128])]; + tensor v_3_cast_fp16 = reshape(shape = var_712, x = linear_14_cast_fp16)[name = string("v_3_cast_fp16")]; + tensor value_11_perm_0 = const()[name = string("value_11_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_1_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_1_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40505920)))]; + tensor var_725_cast_fp16 = add(x = q_7_cast_fp16, y = encoder_layers_1_self_attn_pos_bias_u_to_fp16)[name = string("op_725_cast_fp16")]; + tensor encoder_layers_1_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_1_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40508032)))]; + tensor var_727_cast_fp16 = add(x = q_7_cast_fp16, y = encoder_layers_1_self_attn_pos_bias_v_to_fp16)[name = string("op_727_cast_fp16")]; + tensor q_with_bias_v_3_perm_0 = const()[name = string("q_with_bias_v_3_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_33_transpose_x_0 = const()[name = string("x_33_transpose_x_0"), val = bool(false)]; + bool x_33_transpose_y_0 = const()[name = string("x_33_transpose_y_0"), val = bool(false)]; + tensor op_729_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40510144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40609536))))[name = string("op_729_to_fp16_quantized")]; + tensor q_with_bias_v_3_cast_fp16 = transpose(perm = q_with_bias_v_3_perm_0, x = var_727_cast_fp16)[name = string("transpose_353")]; + tensor x_33_cast_fp16 = matmul(transpose_x = x_33_transpose_x_0, transpose_y = x_33_transpose_y_0, x = q_with_bias_v_3_cast_fp16, y = op_729_to_fp16_quantized)[name = string("x_33_cast_fp16")]; + tensor x_35_pad_0 = const()[name = string("x_35_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_35_mode_0 = const()[name = string("x_35_mode_0"), val = string("constant")]; + fp16 const_92_to_fp16 = const()[name = string("const_92_to_fp16"), val = fp16(0x0p+0)]; + tensor x_35_cast_fp16 = pad(constant_val = const_92_to_fp16, mode = x_35_mode_0, pad = x_35_pad_0, x = x_33_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor var_737 = const()[name = string("op_737"), val = tensor([1, 8, -1, 7])]; + tensor x_37_cast_fp16 = reshape(shape = var_737, x = x_35_cast_fp16)[name = string("x_37_cast_fp16")]; + tensor var_741_begin_0 = const()[name = string("op_741_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_741_end_0 = const()[name = string("op_741_end_0"), val = tensor([1, 8, 98, 7])]; + tensor var_741_end_mask_0 = const()[name = string("op_741_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_741_cast_fp16 = slice_by_index(begin = var_741_begin_0, end = var_741_end_0, end_mask = var_741_end_mask_0, x = x_37_cast_fp16)[name = string("op_741_cast_fp16")]; + tensor var_742 = const()[name = string("op_742"), val = tensor([1, 8, 7, 97])]; + tensor matrix_bd_5_cast_fp16 = reshape(shape = var_742, x = var_741_cast_fp16)[name = string("matrix_bd_5_cast_fp16")]; + bool matrix_ac_3_transpose_x_0 = const()[name = string("matrix_ac_3_transpose_x_0"), val = bool(false)]; + bool matrix_ac_3_transpose_y_0 = const()[name = string("matrix_ac_3_transpose_y_0"), val = bool(false)]; + tensor transpose_98_perm_0 = const()[name = string("transpose_98_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_99_perm_0 = const()[name = string("transpose_99_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_99 = transpose(perm = transpose_99_perm_0, x = k_5_cast_fp16)[name = string("transpose_351")]; + tensor transpose_98 = transpose(perm = transpose_98_perm_0, x = var_725_cast_fp16)[name = string("transpose_352")]; + tensor matrix_ac_3_cast_fp16 = matmul(transpose_x = matrix_ac_3_transpose_x_0, transpose_y = matrix_ac_3_transpose_y_0, x = transpose_98, y = transpose_99)[name = string("matrix_ac_3_cast_fp16")]; + tensor matrix_bd_7_begin_0 = const()[name = string("matrix_bd_7_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_7_end_0 = const()[name = string("matrix_bd_7_end_0"), val = tensor([1, 8, 7, 49])]; + tensor matrix_bd_7_end_mask_0 = const()[name = string("matrix_bd_7_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_7_cast_fp16 = slice_by_index(begin = matrix_bd_7_begin_0, end = matrix_bd_7_end_0, end_mask = matrix_bd_7_end_mask_0, x = matrix_bd_5_cast_fp16)[name = string("matrix_bd_7_cast_fp16")]; + tensor var_751_cast_fp16 = add(x = matrix_ac_3_cast_fp16, y = matrix_bd_7_cast_fp16)[name = string("op_751_cast_fp16")]; + fp16 _inversed_scores_5_y_0_to_fp16 = const()[name = string("_inversed_scores_5_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_5_cast_fp16 = mul(x = var_751_cast_fp16, y = _inversed_scores_5_y_0_to_fp16)[name = string("_inversed_scores_5_cast_fp16")]; + tensor scores_7_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_5_cast_fp16, cond = mask_11)[name = string("scores_7_cast_fp16")]; + tensor var_757_cast_fp16 = softmax(axis = var_59, x = scores_7_cast_fp16)[name = string("op_757_cast_fp16")]; + tensor input_93_cast_fp16 = select(a = var_44_to_fp16, b = var_757_cast_fp16, cond = mask_11)[name = string("input_93_cast_fp16")]; + bool x_39_transpose_x_0 = const()[name = string("x_39_transpose_x_0"), val = bool(false)]; + bool x_39_transpose_y_0 = const()[name = string("x_39_transpose_y_0"), val = bool(false)]; + tensor value_11_cast_fp16 = transpose(perm = value_11_perm_0, x = v_3_cast_fp16)[name = string("transpose_350")]; + tensor x_39_cast_fp16 = matmul(transpose_x = x_39_transpose_x_0, transpose_y = x_39_transpose_y_0, x = input_93_cast_fp16, y = value_11_cast_fp16)[name = string("x_39_cast_fp16")]; + tensor var_761_perm_0 = const()[name = string("op_761_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_762 = const()[name = string("op_762"), val = tensor([1, -1, 1024])]; + tensor var_761_cast_fp16 = transpose(perm = var_761_perm_0, x = x_39_cast_fp16)[name = string("transpose_349")]; + tensor input_95_cast_fp16 = reshape(shape = var_762, x = var_761_cast_fp16)[name = string("input_95_cast_fp16")]; + tensor encoder_layers_1_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40609856))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41658496))))[name = string("encoder_layers_1_self_attn_linear_out_weight_to_fp16_quantized")]; + tensor encoder_layers_1_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_1_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41660608)))]; + tensor linear_16_cast_fp16 = linear(bias = encoder_layers_1_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_1_self_attn_linear_out_weight_to_fp16_quantized, x = input_95_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor input_99_cast_fp16 = add(x = input_89_cast_fp16, y = linear_16_cast_fp16)[name = string("input_99_cast_fp16")]; + tensor x_43_axes_0 = const()[name = string("x_43_axes_0"), val = tensor([-1])]; + tensor encoder_layers_1_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_1_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41662720)))]; + tensor encoder_layers_1_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_1_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41664832)))]; + tensor x_43_cast_fp16 = layer_norm(axes = x_43_axes_0, beta = encoder_layers_1_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_1_norm_conv_weight_to_fp16, x = input_99_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor input_101_perm_0 = const()[name = string("input_101_perm_0"), val = tensor([0, 2, 1])]; + string input_103_pad_type_0 = const()[name = string("input_103_pad_type_0"), val = string("valid")]; + tensor input_103_strides_0 = const()[name = string("input_103_strides_0"), val = tensor([1])]; + tensor input_103_pad_0 = const()[name = string("input_103_pad_0"), val = tensor([0, 0])]; + tensor input_103_dilations_0 = const()[name = string("input_103_dilations_0"), val = tensor([1])]; + int32 input_103_groups_0 = const()[name = string("input_103_groups_0"), val = int32(1)]; + tensor encoder_layers_1_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41666944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43764160))))[name = string("encoder_layers_1_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_101_cast_fp16 = transpose(perm = input_101_perm_0, x = x_43_cast_fp16)[name = string("transpose_348")]; + tensor input_103_cast_fp16 = conv(dilations = input_103_dilations_0, groups = input_103_groups_0, pad = input_103_pad_0, pad_type = input_103_pad_type_0, strides = input_103_strides_0, weight = encoder_layers_1_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_101_cast_fp16)[name = string("input_103_cast_fp16")]; + int32 x_45_split_num_splits_0 = const()[name = string("x_45_split_num_splits_0"), val = int32(2)]; + int32 x_45_split_axis_0 = const()[name = string("x_45_split_axis_0"), val = int32(1)]; + tensor x_45_split_cast_fp16_0, tensor x_45_split_cast_fp16_1 = split(axis = x_45_split_axis_0, num_splits = x_45_split_num_splits_0, x = input_103_cast_fp16)[name = string("x_45_split_cast_fp16")]; + tensor x_45_split_1_sigmoid_cast_fp16 = sigmoid(x = x_45_split_cast_fp16_1)[name = string("x_45_split_1_sigmoid_cast_fp16")]; + tensor x_45_cast_fp16 = mul(x = x_45_split_cast_fp16_0, y = x_45_split_1_sigmoid_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor input_105_cast_fp16 = select(a = var_44_to_fp16, b = x_45_cast_fp16, cond = var_575)[name = string("input_105_cast_fp16")]; + bool new_x_7_interleave_0 = const()[name = string("new_x_7_interleave_0"), val = bool(false)]; + tensor new_x_7_cast_fp16 = concat(axis = var_59, interleave = new_x_7_interleave_0, values = (cache_7_cast_fp16, input_105_cast_fp16))[name = string("new_x_7_cast_fp16")]; + tensor var_801_begin_0 = const()[name = string("op_801_begin_0"), val = tensor([0, 0, 7])]; + tensor var_801_end_0 = const()[name = string("op_801_end_0"), val = tensor([1, 1024, 15])]; + tensor var_801_end_mask_0 = const()[name = string("op_801_end_mask_0"), val = tensor([true, true, true])]; + tensor var_801_cast_fp16 = slice_by_index(begin = var_801_begin_0, end = var_801_end_0, end_mask = var_801_end_mask_0, x = new_x_7_cast_fp16)[name = string("op_801_cast_fp16")]; + string x_47_pad_type_0 = const()[name = string("x_47_pad_type_0"), val = string("valid")]; + int32 x_47_groups_0 = const()[name = string("x_47_groups_0"), val = int32(1024)]; + tensor x_47_strides_0 = const()[name = string("x_47_strides_0"), val = tensor([1])]; + tensor x_47_pad_0 = const()[name = string("x_47_pad_0"), val = tensor([0, 0])]; + tensor x_47_dilations_0 = const()[name = string("x_47_dilations_0"), val = tensor([1])]; + tensor encoder_layers_1_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43768320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43777600))))[name = string("encoder_layers_1_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_47_cast_fp16 = conv(dilations = x_47_dilations_0, groups = x_47_groups_0, pad = x_47_pad_0, pad_type = x_47_pad_type_0, strides = x_47_strides_0, weight = encoder_layers_1_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_7_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor input_107_perm_0 = const()[name = string("input_107_perm_0"), val = tensor([0, 2, 1])]; + tensor x_49_axes_0 = const()[name = string("x_49_axes_0"), val = tensor([-1])]; + tensor encoder_layers_1_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_1_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43779712)))]; + tensor encoder_layers_1_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_1_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43781824)))]; + tensor input_107_cast_fp16 = transpose(perm = input_107_perm_0, x = x_47_cast_fp16)[name = string("transpose_347")]; + tensor x_49_cast_fp16 = layer_norm(axes = x_49_axes_0, beta = encoder_layers_1_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_1_conv_batch_norm_weight_to_fp16, x = input_107_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor input_109_perm_0 = const()[name = string("input_109_perm_0"), val = tensor([0, 2, 1])]; + tensor input_109_cast_fp16 = transpose(perm = input_109_perm_0, x = x_49_cast_fp16)[name = string("transpose_346")]; + tensor input_111_cast_fp16 = silu(x = input_109_cast_fp16)[name = string("input_111_cast_fp16")]; + string x_51_pad_type_0 = const()[name = string("x_51_pad_type_0"), val = string("valid")]; + tensor x_51_strides_0 = const()[name = string("x_51_strides_0"), val = tensor([1])]; + tensor x_51_pad_0 = const()[name = string("x_51_pad_0"), val = tensor([0, 0])]; + tensor x_51_dilations_0 = const()[name = string("x_51_dilations_0"), val = tensor([1])]; + int32 x_51_groups_0 = const()[name = string("x_51_groups_0"), val = int32(1)]; + tensor encoder_layers_1_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43783936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44832576))))[name = string("encoder_layers_1_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_51_cast_fp16 = conv(dilations = x_51_dilations_0, groups = x_51_groups_0, pad = x_51_pad_0, pad_type = x_51_pad_type_0, strides = x_51_strides_0, weight = encoder_layers_1_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_111_cast_fp16)[name = string("x_51_cast_fp16")]; + tensor input_113_perm_0 = const()[name = string("input_113_perm_0"), val = tensor([0, 2, 1])]; + tensor input_113_cast_fp16 = transpose(perm = input_113_perm_0, x = x_51_cast_fp16)[name = string("transpose_345")]; + tensor input_115_cast_fp16 = add(x = input_99_cast_fp16, y = input_113_cast_fp16)[name = string("input_115_cast_fp16")]; + tensor input_117_axes_0 = const()[name = string("input_117_axes_0"), val = tensor([-1])]; + tensor encoder_layers_1_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_1_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44834688)))]; + tensor encoder_layers_1_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_1_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44836800)))]; + tensor input_117_cast_fp16 = layer_norm(axes = input_117_axes_0, beta = encoder_layers_1_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_1_norm_feed_forward2_weight_to_fp16, x = input_115_cast_fp16)[name = string("input_117_cast_fp16")]; + tensor encoder_layers_1_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44838912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49033280))))[name = string("encoder_layers_1_feed_forward2_linear1_weight_to_fp16_quantized")]; + tensor encoder_layers_1_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_1_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49041536)))]; + tensor linear_17_cast_fp16 = linear(bias = encoder_layers_1_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_1_feed_forward2_linear1_weight_to_fp16_quantized, x = input_117_cast_fp16)[name = string("linear_17_cast_fp16")]; + tensor input_121_cast_fp16 = silu(x = linear_17_cast_fp16)[name = string("input_121_cast_fp16")]; + tensor encoder_layers_1_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49049792))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53244160))))[name = string("encoder_layers_1_feed_forward2_linear2_weight_to_fp16_quantized")]; + tensor encoder_layers_1_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_1_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53246272)))]; + tensor linear_18_cast_fp16 = linear(bias = encoder_layers_1_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_1_feed_forward2_linear2_weight_to_fp16_quantized, x = input_121_cast_fp16)[name = string("linear_18_cast_fp16")]; + fp16 var_844_to_fp16 = const()[name = string("op_844_to_fp16"), val = fp16(0x1p-1)]; + tensor var_845_cast_fp16 = mul(x = linear_18_cast_fp16, y = var_844_to_fp16)[name = string("op_845_cast_fp16")]; + tensor input_127_cast_fp16 = add(x = input_115_cast_fp16, y = var_845_cast_fp16)[name = string("input_127_cast_fp16")]; + tensor input_129_axes_0 = const()[name = string("input_129_axes_0"), val = tensor([-1])]; + tensor encoder_layers_1_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_1_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53248384)))]; + tensor encoder_layers_1_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_1_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53250496)))]; + tensor input_129_cast_fp16 = layer_norm(axes = input_129_axes_0, beta = encoder_layers_1_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_1_norm_out_weight_to_fp16, x = input_127_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor cache_9_begin_0 = const()[name = string("cache_9_begin_0"), val = tensor([2, 0, 0, 0])]; + tensor cache_9_end_0 = const()[name = string("cache_9_end_0"), val = tensor([3, 1, 42, 1024])]; + tensor cache_9_end_mask_0 = const()[name = string("cache_9_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_9_squeeze_mask_0 = const()[name = string("cache_9_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_9_cast_fp16 = slice_by_index(begin = cache_9_begin_0, end = cache_9_end_0, end_mask = cache_9_end_mask_0, squeeze_mask = cache_9_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_9_cast_fp16")]; + tensor cache_11_begin_0 = const()[name = string("cache_11_begin_0"), val = tensor([2, 0, 0, 0])]; + tensor cache_11_end_0 = const()[name = string("cache_11_end_0"), val = tensor([3, 1, 1024, 8])]; + tensor cache_11_end_mask_0 = const()[name = string("cache_11_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_11_squeeze_mask_0 = const()[name = string("cache_11_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_11_cast_fp16 = slice_by_index(begin = cache_11_begin_0, end = cache_11_end_0, end_mask = cache_11_end_mask_0, squeeze_mask = cache_11_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_11_cast_fp16")]; + tensor input_131_axes_0 = const()[name = string("input_131_axes_0"), val = tensor([-1])]; + tensor encoder_layers_2_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_2_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53252608)))]; + tensor encoder_layers_2_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_2_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53254720)))]; + tensor input_131_cast_fp16 = layer_norm(axes = input_131_axes_0, beta = encoder_layers_2_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_2_norm_feed_forward1_weight_to_fp16, x = input_129_cast_fp16)[name = string("input_131_cast_fp16")]; + tensor encoder_layers_2_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53256832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57451200))))[name = string("encoder_layers_2_feed_forward1_linear1_weight_to_fp16_quantized")]; + tensor encoder_layers_2_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_2_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57459456)))]; + tensor linear_19_cast_fp16 = linear(bias = encoder_layers_2_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_2_feed_forward1_linear1_weight_to_fp16_quantized, x = input_131_cast_fp16)[name = string("linear_19_cast_fp16")]; + tensor input_135_cast_fp16 = silu(x = linear_19_cast_fp16)[name = string("input_135_cast_fp16")]; + tensor encoder_layers_2_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57467712))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61662080))))[name = string("encoder_layers_2_feed_forward1_linear2_weight_to_fp16_quantized")]; + tensor encoder_layers_2_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_2_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61664192)))]; + tensor linear_20_cast_fp16 = linear(bias = encoder_layers_2_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_2_feed_forward1_linear2_weight_to_fp16_quantized, x = input_135_cast_fp16)[name = string("linear_20_cast_fp16")]; + fp16 var_881_to_fp16 = const()[name = string("op_881_to_fp16"), val = fp16(0x1p-1)]; + tensor var_882_cast_fp16 = mul(x = linear_20_cast_fp16, y = var_881_to_fp16)[name = string("op_882_cast_fp16")]; + tensor input_141_cast_fp16 = add(x = input_129_cast_fp16, y = var_882_cast_fp16)[name = string("input_141_cast_fp16")]; + tensor key_5_axes_0 = const()[name = string("key_5_axes_0"), val = tensor([-1])]; + tensor encoder_layers_2_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_2_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61666304)))]; + tensor encoder_layers_2_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_2_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61668416)))]; + tensor key_5_cast_fp16 = layer_norm(axes = key_5_axes_0, beta = encoder_layers_2_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_2_norm_self_att_weight_to_fp16, x = input_141_cast_fp16)[name = string("key_5_cast_fp16")]; + bool input_143_interleave_0 = const()[name = string("input_143_interleave_0"), val = bool(false)]; + tensor input_143_cast_fp16 = concat(axis = var_68, interleave = input_143_interleave_0, values = (cache_9_cast_fp16, key_5_cast_fp16))[name = string("input_143_cast_fp16")]; + tensor var_904_begin_0 = const()[name = string("op_904_begin_0"), val = tensor([0, 7, 0])]; + tensor var_904_end_0 = const()[name = string("op_904_end_0"), val = tensor([1, 42, 1024])]; + tensor var_904_end_mask_0 = const()[name = string("op_904_end_mask_0"), val = tensor([true, true, true])]; + tensor var_904_cast_fp16 = slice_by_index(begin = var_904_begin_0, end = var_904_end_0, end_mask = var_904_end_mask_0, x = cache_9_cast_fp16)[name = string("op_904_cast_fp16")]; + bool var_910_interleave_0 = const()[name = string("op_910_interleave_0"), val = bool(false)]; + tensor var_910_cast_fp16 = concat(axis = var_68, interleave = var_910_interleave_0, values = (var_904_cast_fp16, key_5_cast_fp16))[name = string("op_910_cast_fp16")]; + tensor encoder_layers_2_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61670528))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62719168))))[name = string("encoder_layers_2_self_attn_linear_q_weight_to_fp16_quantized")]; + tensor encoder_layers_2_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_2_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62721280)))]; + tensor linear_21_cast_fp16 = linear(bias = encoder_layers_2_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_2_self_attn_linear_q_weight_to_fp16_quantized, x = key_5_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_915 = const()[name = string("op_915"), val = tensor([1, -1, 8, 128])]; + tensor q_13_cast_fp16 = reshape(shape = var_915, x = linear_21_cast_fp16)[name = string("q_13_cast_fp16")]; + tensor encoder_layers_2_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62723392))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63772032))))[name = string("encoder_layers_2_self_attn_linear_k_weight_to_fp16_quantized")]; + tensor encoder_layers_2_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_2_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63774144)))]; + tensor linear_22_cast_fp16 = linear(bias = encoder_layers_2_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_2_self_attn_linear_k_weight_to_fp16_quantized, x = input_143_cast_fp16)[name = string("linear_22_cast_fp16")]; + tensor var_920 = const()[name = string("op_920"), val = tensor([1, -1, 8, 128])]; + tensor k_9_cast_fp16 = reshape(shape = var_920, x = linear_22_cast_fp16)[name = string("k_9_cast_fp16")]; + tensor encoder_layers_2_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63776256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64824896))))[name = string("encoder_layers_2_self_attn_linear_v_weight_to_fp16_quantized")]; + tensor encoder_layers_2_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_2_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64827008)))]; + tensor linear_23_cast_fp16 = linear(bias = encoder_layers_2_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_2_self_attn_linear_v_weight_to_fp16_quantized, x = input_143_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor var_925 = const()[name = string("op_925"), val = tensor([1, -1, 8, 128])]; + tensor v_5_cast_fp16 = reshape(shape = var_925, x = linear_23_cast_fp16)[name = string("v_5_cast_fp16")]; + tensor value_13_perm_0 = const()[name = string("value_13_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_2_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_2_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64829120)))]; + tensor var_938_cast_fp16 = add(x = q_13_cast_fp16, y = encoder_layers_2_self_attn_pos_bias_u_to_fp16)[name = string("op_938_cast_fp16")]; + tensor encoder_layers_2_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_2_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64831232)))]; + tensor var_940_cast_fp16 = add(x = q_13_cast_fp16, y = encoder_layers_2_self_attn_pos_bias_v_to_fp16)[name = string("op_940_cast_fp16")]; + tensor q_with_bias_v_5_perm_0 = const()[name = string("q_with_bias_v_5_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_59_transpose_x_0 = const()[name = string("x_59_transpose_x_0"), val = bool(false)]; + bool x_59_transpose_y_0 = const()[name = string("x_59_transpose_y_0"), val = bool(false)]; + tensor op_942_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64833344))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64932736))))[name = string("op_942_to_fp16_quantized")]; + tensor q_with_bias_v_5_cast_fp16 = transpose(perm = q_with_bias_v_5_perm_0, x = var_940_cast_fp16)[name = string("transpose_344")]; + tensor x_59_cast_fp16 = matmul(transpose_x = x_59_transpose_x_0, transpose_y = x_59_transpose_y_0, x = q_with_bias_v_5_cast_fp16, y = op_942_to_fp16_quantized)[name = string("x_59_cast_fp16")]; + tensor x_61_pad_0 = const()[name = string("x_61_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_61_mode_0 = const()[name = string("x_61_mode_0"), val = string("constant")]; + fp16 const_105_to_fp16 = const()[name = string("const_105_to_fp16"), val = fp16(0x0p+0)]; + tensor x_61_cast_fp16 = pad(constant_val = const_105_to_fp16, mode = x_61_mode_0, pad = x_61_pad_0, x = x_59_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor var_950 = const()[name = string("op_950"), val = tensor([1, 8, -1, 7])]; + tensor x_63_cast_fp16 = reshape(shape = var_950, x = x_61_cast_fp16)[name = string("x_63_cast_fp16")]; + tensor var_954_begin_0 = const()[name = string("op_954_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_954_end_0 = const()[name = string("op_954_end_0"), val = tensor([1, 8, 98, 7])]; + tensor var_954_end_mask_0 = const()[name = string("op_954_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_954_cast_fp16 = slice_by_index(begin = var_954_begin_0, end = var_954_end_0, end_mask = var_954_end_mask_0, x = x_63_cast_fp16)[name = string("op_954_cast_fp16")]; + tensor var_955 = const()[name = string("op_955"), val = tensor([1, 8, 7, 97])]; + tensor matrix_bd_9_cast_fp16 = reshape(shape = var_955, x = var_954_cast_fp16)[name = string("matrix_bd_9_cast_fp16")]; + bool matrix_ac_5_transpose_x_0 = const()[name = string("matrix_ac_5_transpose_x_0"), val = bool(false)]; + bool matrix_ac_5_transpose_y_0 = const()[name = string("matrix_ac_5_transpose_y_0"), val = bool(false)]; + tensor transpose_100_perm_0 = const()[name = string("transpose_100_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_101_perm_0 = const()[name = string("transpose_101_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_101 = transpose(perm = transpose_101_perm_0, x = k_9_cast_fp16)[name = string("transpose_342")]; + tensor transpose_100 = transpose(perm = transpose_100_perm_0, x = var_938_cast_fp16)[name = string("transpose_343")]; + tensor matrix_ac_5_cast_fp16 = matmul(transpose_x = matrix_ac_5_transpose_x_0, transpose_y = matrix_ac_5_transpose_y_0, x = transpose_100, y = transpose_101)[name = string("matrix_ac_5_cast_fp16")]; + tensor matrix_bd_11_begin_0 = const()[name = string("matrix_bd_11_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_11_end_0 = const()[name = string("matrix_bd_11_end_0"), val = tensor([1, 8, 7, 49])]; + tensor matrix_bd_11_end_mask_0 = const()[name = string("matrix_bd_11_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_11_cast_fp16 = slice_by_index(begin = matrix_bd_11_begin_0, end = matrix_bd_11_end_0, end_mask = matrix_bd_11_end_mask_0, x = matrix_bd_9_cast_fp16)[name = string("matrix_bd_11_cast_fp16")]; + tensor var_964_cast_fp16 = add(x = matrix_ac_5_cast_fp16, y = matrix_bd_11_cast_fp16)[name = string("op_964_cast_fp16")]; + fp16 _inversed_scores_9_y_0_to_fp16 = const()[name = string("_inversed_scores_9_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_9_cast_fp16 = mul(x = var_964_cast_fp16, y = _inversed_scores_9_y_0_to_fp16)[name = string("_inversed_scores_9_cast_fp16")]; + tensor scores_11_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_9_cast_fp16, cond = mask_11)[name = string("scores_11_cast_fp16")]; + tensor var_970_cast_fp16 = softmax(axis = var_59, x = scores_11_cast_fp16)[name = string("op_970_cast_fp16")]; + tensor input_145_cast_fp16 = select(a = var_44_to_fp16, b = var_970_cast_fp16, cond = mask_11)[name = string("input_145_cast_fp16")]; + bool x_65_transpose_x_0 = const()[name = string("x_65_transpose_x_0"), val = bool(false)]; + bool x_65_transpose_y_0 = const()[name = string("x_65_transpose_y_0"), val = bool(false)]; + tensor value_13_cast_fp16 = transpose(perm = value_13_perm_0, x = v_5_cast_fp16)[name = string("transpose_341")]; + tensor x_65_cast_fp16 = matmul(transpose_x = x_65_transpose_x_0, transpose_y = x_65_transpose_y_0, x = input_145_cast_fp16, y = value_13_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor var_974_perm_0 = const()[name = string("op_974_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_975 = const()[name = string("op_975"), val = tensor([1, -1, 1024])]; + tensor var_974_cast_fp16 = transpose(perm = var_974_perm_0, x = x_65_cast_fp16)[name = string("transpose_340")]; + tensor input_147_cast_fp16 = reshape(shape = var_975, x = var_974_cast_fp16)[name = string("input_147_cast_fp16")]; + tensor encoder_layers_2_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64933056))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65719552))))[name = string("encoder_layers_2_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_2_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_2_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65719744)))]; + tensor linear_25_cast_fp16 = linear(bias = encoder_layers_2_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_2_self_attn_linear_out_weight_to_fp16_palettized, x = input_147_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor input_151_cast_fp16 = add(x = input_141_cast_fp16, y = linear_25_cast_fp16)[name = string("input_151_cast_fp16")]; + tensor x_69_axes_0 = const()[name = string("x_69_axes_0"), val = tensor([-1])]; + tensor encoder_layers_2_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_2_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65721856)))]; + tensor encoder_layers_2_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_2_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65723968)))]; + tensor x_69_cast_fp16 = layer_norm(axes = x_69_axes_0, beta = encoder_layers_2_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_2_norm_conv_weight_to_fp16, x = input_151_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor input_153_perm_0 = const()[name = string("input_153_perm_0"), val = tensor([0, 2, 1])]; + string input_155_pad_type_0 = const()[name = string("input_155_pad_type_0"), val = string("valid")]; + tensor input_155_strides_0 = const()[name = string("input_155_strides_0"), val = tensor([1])]; + tensor input_155_pad_0 = const()[name = string("input_155_pad_0"), val = tensor([0, 0])]; + tensor input_155_dilations_0 = const()[name = string("input_155_dilations_0"), val = tensor([1])]; + int32 input_155_groups_0 = const()[name = string("input_155_groups_0"), val = int32(1)]; + tensor encoder_layers_2_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65726080))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67823296))))[name = string("encoder_layers_2_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_153_cast_fp16 = transpose(perm = input_153_perm_0, x = x_69_cast_fp16)[name = string("transpose_339")]; + tensor input_155_cast_fp16 = conv(dilations = input_155_dilations_0, groups = input_155_groups_0, pad = input_155_pad_0, pad_type = input_155_pad_type_0, strides = input_155_strides_0, weight = encoder_layers_2_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_153_cast_fp16)[name = string("input_155_cast_fp16")]; + int32 x_71_split_num_splits_0 = const()[name = string("x_71_split_num_splits_0"), val = int32(2)]; + int32 x_71_split_axis_0 = const()[name = string("x_71_split_axis_0"), val = int32(1)]; + tensor x_71_split_cast_fp16_0, tensor x_71_split_cast_fp16_1 = split(axis = x_71_split_axis_0, num_splits = x_71_split_num_splits_0, x = input_155_cast_fp16)[name = string("x_71_split_cast_fp16")]; + tensor x_71_split_1_sigmoid_cast_fp16 = sigmoid(x = x_71_split_cast_fp16_1)[name = string("x_71_split_1_sigmoid_cast_fp16")]; + tensor x_71_cast_fp16 = mul(x = x_71_split_cast_fp16_0, y = x_71_split_1_sigmoid_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor input_157_cast_fp16 = select(a = var_44_to_fp16, b = x_71_cast_fp16, cond = var_575)[name = string("input_157_cast_fp16")]; + bool new_x_11_interleave_0 = const()[name = string("new_x_11_interleave_0"), val = bool(false)]; + tensor new_x_11_cast_fp16 = concat(axis = var_59, interleave = new_x_11_interleave_0, values = (cache_11_cast_fp16, input_157_cast_fp16))[name = string("new_x_11_cast_fp16")]; + tensor var_1014_begin_0 = const()[name = string("op_1014_begin_0"), val = tensor([0, 0, 7])]; + tensor var_1014_end_0 = const()[name = string("op_1014_end_0"), val = tensor([1, 1024, 15])]; + tensor var_1014_end_mask_0 = const()[name = string("op_1014_end_mask_0"), val = tensor([true, true, true])]; + tensor var_1014_cast_fp16 = slice_by_index(begin = var_1014_begin_0, end = var_1014_end_0, end_mask = var_1014_end_mask_0, x = new_x_11_cast_fp16)[name = string("op_1014_cast_fp16")]; + string x_73_pad_type_0 = const()[name = string("x_73_pad_type_0"), val = string("valid")]; + int32 x_73_groups_0 = const()[name = string("x_73_groups_0"), val = int32(1024)]; + tensor x_73_strides_0 = const()[name = string("x_73_strides_0"), val = tensor([1])]; + tensor x_73_pad_0 = const()[name = string("x_73_pad_0"), val = tensor([0, 0])]; + tensor x_73_dilations_0 = const()[name = string("x_73_dilations_0"), val = tensor([1])]; + tensor encoder_layers_2_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67827456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67836736))))[name = string("encoder_layers_2_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_73_cast_fp16 = conv(dilations = x_73_dilations_0, groups = x_73_groups_0, pad = x_73_pad_0, pad_type = x_73_pad_type_0, strides = x_73_strides_0, weight = encoder_layers_2_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_11_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor input_159_perm_0 = const()[name = string("input_159_perm_0"), val = tensor([0, 2, 1])]; + tensor x_75_axes_0 = const()[name = string("x_75_axes_0"), val = tensor([-1])]; + tensor encoder_layers_2_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_2_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67838848)))]; + tensor encoder_layers_2_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_2_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67840960)))]; + tensor input_159_cast_fp16 = transpose(perm = input_159_perm_0, x = x_73_cast_fp16)[name = string("transpose_338")]; + tensor x_75_cast_fp16 = layer_norm(axes = x_75_axes_0, beta = encoder_layers_2_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_2_conv_batch_norm_weight_to_fp16, x = input_159_cast_fp16)[name = string("x_75_cast_fp16")]; + tensor input_161_perm_0 = const()[name = string("input_161_perm_0"), val = tensor([0, 2, 1])]; + tensor input_161_cast_fp16 = transpose(perm = input_161_perm_0, x = x_75_cast_fp16)[name = string("transpose_337")]; + tensor input_163_cast_fp16 = silu(x = input_161_cast_fp16)[name = string("input_163_cast_fp16")]; + string x_77_pad_type_0 = const()[name = string("x_77_pad_type_0"), val = string("valid")]; + tensor x_77_strides_0 = const()[name = string("x_77_strides_0"), val = tensor([1])]; + tensor x_77_pad_0 = const()[name = string("x_77_pad_0"), val = tensor([0, 0])]; + tensor x_77_dilations_0 = const()[name = string("x_77_dilations_0"), val = tensor([1])]; + int32 x_77_groups_0 = const()[name = string("x_77_groups_0"), val = int32(1)]; + tensor encoder_layers_2_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67843072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68891712))))[name = string("encoder_layers_2_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_77_cast_fp16 = conv(dilations = x_77_dilations_0, groups = x_77_groups_0, pad = x_77_pad_0, pad_type = x_77_pad_type_0, strides = x_77_strides_0, weight = encoder_layers_2_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_163_cast_fp16)[name = string("x_77_cast_fp16")]; + tensor input_165_perm_0 = const()[name = string("input_165_perm_0"), val = tensor([0, 2, 1])]; + tensor input_165_cast_fp16 = transpose(perm = input_165_perm_0, x = x_77_cast_fp16)[name = string("transpose_336")]; + tensor input_167_cast_fp16 = add(x = input_151_cast_fp16, y = input_165_cast_fp16)[name = string("input_167_cast_fp16")]; + tensor input_169_axes_0 = const()[name = string("input_169_axes_0"), val = tensor([-1])]; + tensor encoder_layers_2_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_2_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68893824)))]; + tensor encoder_layers_2_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_2_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68895936)))]; + tensor input_169_cast_fp16 = layer_norm(axes = input_169_axes_0, beta = encoder_layers_2_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_2_norm_feed_forward2_weight_to_fp16, x = input_167_cast_fp16)[name = string("input_169_cast_fp16")]; + tensor encoder_layers_2_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68898048))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72043840))))[name = string("encoder_layers_2_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_2_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_2_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72044032)))]; + tensor linear_26_cast_fp16 = linear(bias = encoder_layers_2_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_2_feed_forward2_linear1_weight_to_fp16_palettized, x = input_169_cast_fp16)[name = string("linear_26_cast_fp16")]; + tensor input_173_cast_fp16 = silu(x = linear_26_cast_fp16)[name = string("input_173_cast_fp16")]; + tensor encoder_layers_2_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72052288))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75198080))))[name = string("encoder_layers_2_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_2_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_2_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75198272)))]; + tensor linear_27_cast_fp16 = linear(bias = encoder_layers_2_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_2_feed_forward2_linear2_weight_to_fp16_palettized, x = input_173_cast_fp16)[name = string("linear_27_cast_fp16")]; + fp16 var_1057_to_fp16 = const()[name = string("op_1057_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1058_cast_fp16 = mul(x = linear_27_cast_fp16, y = var_1057_to_fp16)[name = string("op_1058_cast_fp16")]; + tensor input_179_cast_fp16 = add(x = input_167_cast_fp16, y = var_1058_cast_fp16)[name = string("input_179_cast_fp16")]; + tensor input_181_axes_0 = const()[name = string("input_181_axes_0"), val = tensor([-1])]; + tensor encoder_layers_2_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_2_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75200384)))]; + tensor encoder_layers_2_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_2_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75202496)))]; + tensor input_181_cast_fp16 = layer_norm(axes = input_181_axes_0, beta = encoder_layers_2_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_2_norm_out_weight_to_fp16, x = input_179_cast_fp16)[name = string("input_181_cast_fp16")]; + tensor cache_13_begin_0 = const()[name = string("cache_13_begin_0"), val = tensor([3, 0, 0, 0])]; + tensor cache_13_end_0 = const()[name = string("cache_13_end_0"), val = tensor([4, 1, 42, 1024])]; + tensor cache_13_end_mask_0 = const()[name = string("cache_13_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_13_squeeze_mask_0 = const()[name = string("cache_13_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_13_cast_fp16 = slice_by_index(begin = cache_13_begin_0, end = cache_13_end_0, end_mask = cache_13_end_mask_0, squeeze_mask = cache_13_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_13_cast_fp16")]; + tensor cache_15_begin_0 = const()[name = string("cache_15_begin_0"), val = tensor([3, 0, 0, 0])]; + tensor cache_15_end_0 = const()[name = string("cache_15_end_0"), val = tensor([4, 1, 1024, 8])]; + tensor cache_15_end_mask_0 = const()[name = string("cache_15_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_15_squeeze_mask_0 = const()[name = string("cache_15_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_15_cast_fp16 = slice_by_index(begin = cache_15_begin_0, end = cache_15_end_0, end_mask = cache_15_end_mask_0, squeeze_mask = cache_15_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_15_cast_fp16")]; + tensor input_183_axes_0 = const()[name = string("input_183_axes_0"), val = tensor([-1])]; + tensor encoder_layers_3_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_3_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75204608)))]; + tensor encoder_layers_3_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_3_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75206720)))]; + tensor input_183_cast_fp16 = layer_norm(axes = input_183_axes_0, beta = encoder_layers_3_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_3_norm_feed_forward1_weight_to_fp16, x = input_181_cast_fp16)[name = string("input_183_cast_fp16")]; + tensor encoder_layers_3_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75208832))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78354624))))[name = string("encoder_layers_3_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_3_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_3_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78354816)))]; + tensor linear_28_cast_fp16 = linear(bias = encoder_layers_3_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_3_feed_forward1_linear1_weight_to_fp16_palettized, x = input_183_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor input_187_cast_fp16 = silu(x = linear_28_cast_fp16)[name = string("input_187_cast_fp16")]; + tensor encoder_layers_3_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78363072))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81508864))))[name = string("encoder_layers_3_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_3_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_3_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81509056)))]; + tensor linear_29_cast_fp16 = linear(bias = encoder_layers_3_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_3_feed_forward1_linear2_weight_to_fp16_palettized, x = input_187_cast_fp16)[name = string("linear_29_cast_fp16")]; + fp16 var_1094_to_fp16 = const()[name = string("op_1094_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1095_cast_fp16 = mul(x = linear_29_cast_fp16, y = var_1094_to_fp16)[name = string("op_1095_cast_fp16")]; + tensor input_193_cast_fp16 = add(x = input_181_cast_fp16, y = var_1095_cast_fp16)[name = string("input_193_cast_fp16")]; + tensor key_7_axes_0 = const()[name = string("key_7_axes_0"), val = tensor([-1])]; + tensor encoder_layers_3_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_3_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81511168)))]; + tensor encoder_layers_3_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_3_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81513280)))]; + tensor key_7_cast_fp16 = layer_norm(axes = key_7_axes_0, beta = encoder_layers_3_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_3_norm_self_att_weight_to_fp16, x = input_193_cast_fp16)[name = string("key_7_cast_fp16")]; + bool input_195_interleave_0 = const()[name = string("input_195_interleave_0"), val = bool(false)]; + tensor input_195_cast_fp16 = concat(axis = var_68, interleave = input_195_interleave_0, values = (cache_13_cast_fp16, key_7_cast_fp16))[name = string("input_195_cast_fp16")]; + tensor var_1117_begin_0 = const()[name = string("op_1117_begin_0"), val = tensor([0, 7, 0])]; + tensor var_1117_end_0 = const()[name = string("op_1117_end_0"), val = tensor([1, 42, 1024])]; + tensor var_1117_end_mask_0 = const()[name = string("op_1117_end_mask_0"), val = tensor([true, true, true])]; + tensor var_1117_cast_fp16 = slice_by_index(begin = var_1117_begin_0, end = var_1117_end_0, end_mask = var_1117_end_mask_0, x = cache_13_cast_fp16)[name = string("op_1117_cast_fp16")]; + bool var_1123_interleave_0 = const()[name = string("op_1123_interleave_0"), val = bool(false)]; + tensor var_1123_cast_fp16 = concat(axis = var_68, interleave = var_1123_interleave_0, values = (var_1117_cast_fp16, key_7_cast_fp16))[name = string("op_1123_cast_fp16")]; + tensor encoder_layers_3_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81515392))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82301888))))[name = string("encoder_layers_3_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_3_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_3_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82302080)))]; + tensor linear_30_cast_fp16 = linear(bias = encoder_layers_3_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_3_self_attn_linear_q_weight_to_fp16_palettized, x = key_7_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor var_1128 = const()[name = string("op_1128"), val = tensor([1, -1, 8, 128])]; + tensor q_19_cast_fp16 = reshape(shape = var_1128, x = linear_30_cast_fp16)[name = string("q_19_cast_fp16")]; + tensor encoder_layers_3_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82304192))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83090688))))[name = string("encoder_layers_3_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_3_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_3_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83090880)))]; + tensor linear_31_cast_fp16 = linear(bias = encoder_layers_3_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_3_self_attn_linear_k_weight_to_fp16_palettized, x = input_195_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor var_1133 = const()[name = string("op_1133"), val = tensor([1, -1, 8, 128])]; + tensor k_13_cast_fp16 = reshape(shape = var_1133, x = linear_31_cast_fp16)[name = string("k_13_cast_fp16")]; + tensor encoder_layers_3_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83092992))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83879488))))[name = string("encoder_layers_3_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_3_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_3_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83879680)))]; + tensor linear_32_cast_fp16 = linear(bias = encoder_layers_3_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_3_self_attn_linear_v_weight_to_fp16_palettized, x = input_195_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor var_1138 = const()[name = string("op_1138"), val = tensor([1, -1, 8, 128])]; + tensor v_7_cast_fp16 = reshape(shape = var_1138, x = linear_32_cast_fp16)[name = string("v_7_cast_fp16")]; + tensor value_15_perm_0 = const()[name = string("value_15_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_3_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_3_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83881792)))]; + tensor var_1151_cast_fp16 = add(x = q_19_cast_fp16, y = encoder_layers_3_self_attn_pos_bias_u_to_fp16)[name = string("op_1151_cast_fp16")]; + tensor encoder_layers_3_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_3_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83883904)))]; + tensor var_1153_cast_fp16 = add(x = q_19_cast_fp16, y = encoder_layers_3_self_attn_pos_bias_v_to_fp16)[name = string("op_1153_cast_fp16")]; + tensor q_with_bias_v_7_perm_0 = const()[name = string("q_with_bias_v_7_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_85_transpose_x_0 = const()[name = string("x_85_transpose_x_0"), val = bool(false)]; + bool x_85_transpose_y_0 = const()[name = string("x_85_transpose_y_0"), val = bool(false)]; + tensor op_1155_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83886016))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83985408))))[name = string("op_1155_to_fp16_quantized")]; + tensor q_with_bias_v_7_cast_fp16 = transpose(perm = q_with_bias_v_7_perm_0, x = var_1153_cast_fp16)[name = string("transpose_335")]; + tensor x_85_cast_fp16 = matmul(transpose_x = x_85_transpose_x_0, transpose_y = x_85_transpose_y_0, x = q_with_bias_v_7_cast_fp16, y = op_1155_to_fp16_quantized)[name = string("x_85_cast_fp16")]; + tensor x_87_pad_0 = const()[name = string("x_87_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_87_mode_0 = const()[name = string("x_87_mode_0"), val = string("constant")]; + fp16 const_118_to_fp16 = const()[name = string("const_118_to_fp16"), val = fp16(0x0p+0)]; + tensor x_87_cast_fp16 = pad(constant_val = const_118_to_fp16, mode = x_87_mode_0, pad = x_87_pad_0, x = x_85_cast_fp16)[name = string("x_87_cast_fp16")]; + tensor var_1163 = const()[name = string("op_1163"), val = tensor([1, 8, -1, 7])]; + tensor x_89_cast_fp16 = reshape(shape = var_1163, x = x_87_cast_fp16)[name = string("x_89_cast_fp16")]; + tensor var_1167_begin_0 = const()[name = string("op_1167_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_1167_end_0 = const()[name = string("op_1167_end_0"), val = tensor([1, 8, 98, 7])]; + tensor var_1167_end_mask_0 = const()[name = string("op_1167_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1167_cast_fp16 = slice_by_index(begin = var_1167_begin_0, end = var_1167_end_0, end_mask = var_1167_end_mask_0, x = x_89_cast_fp16)[name = string("op_1167_cast_fp16")]; + tensor var_1168 = const()[name = string("op_1168"), val = tensor([1, 8, 7, 97])]; + tensor matrix_bd_13_cast_fp16 = reshape(shape = var_1168, x = var_1167_cast_fp16)[name = string("matrix_bd_13_cast_fp16")]; + bool matrix_ac_7_transpose_x_0 = const()[name = string("matrix_ac_7_transpose_x_0"), val = bool(false)]; + bool matrix_ac_7_transpose_y_0 = const()[name = string("matrix_ac_7_transpose_y_0"), val = bool(false)]; + tensor transpose_102_perm_0 = const()[name = string("transpose_102_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_103_perm_0 = const()[name = string("transpose_103_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_103 = transpose(perm = transpose_103_perm_0, x = k_13_cast_fp16)[name = string("transpose_333")]; + tensor transpose_102 = transpose(perm = transpose_102_perm_0, x = var_1151_cast_fp16)[name = string("transpose_334")]; + tensor matrix_ac_7_cast_fp16 = matmul(transpose_x = matrix_ac_7_transpose_x_0, transpose_y = matrix_ac_7_transpose_y_0, x = transpose_102, y = transpose_103)[name = string("matrix_ac_7_cast_fp16")]; + tensor matrix_bd_15_begin_0 = const()[name = string("matrix_bd_15_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_15_end_0 = const()[name = string("matrix_bd_15_end_0"), val = tensor([1, 8, 7, 49])]; + tensor matrix_bd_15_end_mask_0 = const()[name = string("matrix_bd_15_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_15_cast_fp16 = slice_by_index(begin = matrix_bd_15_begin_0, end = matrix_bd_15_end_0, end_mask = matrix_bd_15_end_mask_0, x = matrix_bd_13_cast_fp16)[name = string("matrix_bd_15_cast_fp16")]; + tensor var_1177_cast_fp16 = add(x = matrix_ac_7_cast_fp16, y = matrix_bd_15_cast_fp16)[name = string("op_1177_cast_fp16")]; + fp16 _inversed_scores_13_y_0_to_fp16 = const()[name = string("_inversed_scores_13_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_13_cast_fp16 = mul(x = var_1177_cast_fp16, y = _inversed_scores_13_y_0_to_fp16)[name = string("_inversed_scores_13_cast_fp16")]; + tensor scores_15_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_13_cast_fp16, cond = mask_11)[name = string("scores_15_cast_fp16")]; + tensor var_1183_cast_fp16 = softmax(axis = var_59, x = scores_15_cast_fp16)[name = string("op_1183_cast_fp16")]; + tensor input_197_cast_fp16 = select(a = var_44_to_fp16, b = var_1183_cast_fp16, cond = mask_11)[name = string("input_197_cast_fp16")]; + bool x_91_transpose_x_0 = const()[name = string("x_91_transpose_x_0"), val = bool(false)]; + bool x_91_transpose_y_0 = const()[name = string("x_91_transpose_y_0"), val = bool(false)]; + tensor value_15_cast_fp16 = transpose(perm = value_15_perm_0, x = v_7_cast_fp16)[name = string("transpose_332")]; + tensor x_91_cast_fp16 = matmul(transpose_x = x_91_transpose_x_0, transpose_y = x_91_transpose_y_0, x = input_197_cast_fp16, y = value_15_cast_fp16)[name = string("x_91_cast_fp16")]; + tensor var_1187_perm_0 = const()[name = string("op_1187_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1188 = const()[name = string("op_1188"), val = tensor([1, -1, 1024])]; + tensor var_1187_cast_fp16 = transpose(perm = var_1187_perm_0, x = x_91_cast_fp16)[name = string("transpose_331")]; + tensor input_199_cast_fp16 = reshape(shape = var_1188, x = var_1187_cast_fp16)[name = string("input_199_cast_fp16")]; + tensor encoder_layers_3_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83985728))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84772224))))[name = string("encoder_layers_3_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_3_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_3_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84772416)))]; + tensor linear_34_cast_fp16 = linear(bias = encoder_layers_3_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_3_self_attn_linear_out_weight_to_fp16_palettized, x = input_199_cast_fp16)[name = string("linear_34_cast_fp16")]; + tensor input_203_cast_fp16 = add(x = input_193_cast_fp16, y = linear_34_cast_fp16)[name = string("input_203_cast_fp16")]; + tensor x_95_axes_0 = const()[name = string("x_95_axes_0"), val = tensor([-1])]; + tensor encoder_layers_3_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_3_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84774528)))]; + tensor encoder_layers_3_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_3_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84776640)))]; + tensor x_95_cast_fp16 = layer_norm(axes = x_95_axes_0, beta = encoder_layers_3_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_3_norm_conv_weight_to_fp16, x = input_203_cast_fp16)[name = string("x_95_cast_fp16")]; + tensor input_205_perm_0 = const()[name = string("input_205_perm_0"), val = tensor([0, 2, 1])]; + string input_207_pad_type_0 = const()[name = string("input_207_pad_type_0"), val = string("valid")]; + tensor input_207_strides_0 = const()[name = string("input_207_strides_0"), val = tensor([1])]; + tensor input_207_pad_0 = const()[name = string("input_207_pad_0"), val = tensor([0, 0])]; + tensor input_207_dilations_0 = const()[name = string("input_207_dilations_0"), val = tensor([1])]; + int32 input_207_groups_0 = const()[name = string("input_207_groups_0"), val = int32(1)]; + tensor encoder_layers_3_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84778752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86875968))))[name = string("encoder_layers_3_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_205_cast_fp16 = transpose(perm = input_205_perm_0, x = x_95_cast_fp16)[name = string("transpose_330")]; + tensor input_207_cast_fp16 = conv(dilations = input_207_dilations_0, groups = input_207_groups_0, pad = input_207_pad_0, pad_type = input_207_pad_type_0, strides = input_207_strides_0, weight = encoder_layers_3_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_205_cast_fp16)[name = string("input_207_cast_fp16")]; + int32 x_97_split_num_splits_0 = const()[name = string("x_97_split_num_splits_0"), val = int32(2)]; + int32 x_97_split_axis_0 = const()[name = string("x_97_split_axis_0"), val = int32(1)]; + tensor x_97_split_cast_fp16_0, tensor x_97_split_cast_fp16_1 = split(axis = x_97_split_axis_0, num_splits = x_97_split_num_splits_0, x = input_207_cast_fp16)[name = string("x_97_split_cast_fp16")]; + tensor x_97_split_1_sigmoid_cast_fp16 = sigmoid(x = x_97_split_cast_fp16_1)[name = string("x_97_split_1_sigmoid_cast_fp16")]; + tensor x_97_cast_fp16 = mul(x = x_97_split_cast_fp16_0, y = x_97_split_1_sigmoid_cast_fp16)[name = string("x_97_cast_fp16")]; + tensor input_209_cast_fp16 = select(a = var_44_to_fp16, b = x_97_cast_fp16, cond = var_575)[name = string("input_209_cast_fp16")]; + bool new_x_15_interleave_0 = const()[name = string("new_x_15_interleave_0"), val = bool(false)]; + tensor new_x_15_cast_fp16 = concat(axis = var_59, interleave = new_x_15_interleave_0, values = (cache_15_cast_fp16, input_209_cast_fp16))[name = string("new_x_15_cast_fp16")]; + tensor var_1227_begin_0 = const()[name = string("op_1227_begin_0"), val = tensor([0, 0, 7])]; + tensor var_1227_end_0 = const()[name = string("op_1227_end_0"), val = tensor([1, 1024, 15])]; + tensor var_1227_end_mask_0 = const()[name = string("op_1227_end_mask_0"), val = tensor([true, true, true])]; + tensor var_1227_cast_fp16 = slice_by_index(begin = var_1227_begin_0, end = var_1227_end_0, end_mask = var_1227_end_mask_0, x = new_x_15_cast_fp16)[name = string("op_1227_cast_fp16")]; + string x_99_pad_type_0 = const()[name = string("x_99_pad_type_0"), val = string("valid")]; + int32 x_99_groups_0 = const()[name = string("x_99_groups_0"), val = int32(1024)]; + tensor x_99_strides_0 = const()[name = string("x_99_strides_0"), val = tensor([1])]; + tensor x_99_pad_0 = const()[name = string("x_99_pad_0"), val = tensor([0, 0])]; + tensor x_99_dilations_0 = const()[name = string("x_99_dilations_0"), val = tensor([1])]; + tensor encoder_layers_3_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86880128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86889408))))[name = string("encoder_layers_3_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_99_cast_fp16 = conv(dilations = x_99_dilations_0, groups = x_99_groups_0, pad = x_99_pad_0, pad_type = x_99_pad_type_0, strides = x_99_strides_0, weight = encoder_layers_3_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_15_cast_fp16)[name = string("x_99_cast_fp16")]; + tensor input_211_perm_0 = const()[name = string("input_211_perm_0"), val = tensor([0, 2, 1])]; + tensor x_101_axes_0 = const()[name = string("x_101_axes_0"), val = tensor([-1])]; + tensor encoder_layers_3_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_3_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86891520)))]; + tensor encoder_layers_3_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_3_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86893632)))]; + tensor input_211_cast_fp16 = transpose(perm = input_211_perm_0, x = x_99_cast_fp16)[name = string("transpose_329")]; + tensor x_101_cast_fp16 = layer_norm(axes = x_101_axes_0, beta = encoder_layers_3_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_3_conv_batch_norm_weight_to_fp16, x = input_211_cast_fp16)[name = string("x_101_cast_fp16")]; + tensor input_213_perm_0 = const()[name = string("input_213_perm_0"), val = tensor([0, 2, 1])]; + tensor input_213_cast_fp16 = transpose(perm = input_213_perm_0, x = x_101_cast_fp16)[name = string("transpose_328")]; + tensor input_215_cast_fp16 = silu(x = input_213_cast_fp16)[name = string("input_215_cast_fp16")]; + string x_103_pad_type_0 = const()[name = string("x_103_pad_type_0"), val = string("valid")]; + tensor x_103_strides_0 = const()[name = string("x_103_strides_0"), val = tensor([1])]; + tensor x_103_pad_0 = const()[name = string("x_103_pad_0"), val = tensor([0, 0])]; + tensor x_103_dilations_0 = const()[name = string("x_103_dilations_0"), val = tensor([1])]; + int32 x_103_groups_0 = const()[name = string("x_103_groups_0"), val = int32(1)]; + tensor encoder_layers_3_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86895744))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(87944384))))[name = string("encoder_layers_3_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_103_cast_fp16 = conv(dilations = x_103_dilations_0, groups = x_103_groups_0, pad = x_103_pad_0, pad_type = x_103_pad_type_0, strides = x_103_strides_0, weight = encoder_layers_3_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_215_cast_fp16)[name = string("x_103_cast_fp16")]; + tensor input_217_perm_0 = const()[name = string("input_217_perm_0"), val = tensor([0, 2, 1])]; + tensor input_217_cast_fp16 = transpose(perm = input_217_perm_0, x = x_103_cast_fp16)[name = string("transpose_327")]; + tensor input_219_cast_fp16 = add(x = input_203_cast_fp16, y = input_217_cast_fp16)[name = string("input_219_cast_fp16")]; + tensor input_221_axes_0 = const()[name = string("input_221_axes_0"), val = tensor([-1])]; + tensor encoder_layers_3_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_3_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(87946496)))]; + tensor encoder_layers_3_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_3_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(87948608)))]; + tensor input_221_cast_fp16 = layer_norm(axes = input_221_axes_0, beta = encoder_layers_3_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_3_norm_feed_forward2_weight_to_fp16, x = input_219_cast_fp16)[name = string("input_221_cast_fp16")]; + tensor encoder_layers_3_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(87950720))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91096512))))[name = string("encoder_layers_3_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_3_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_3_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91096704)))]; + tensor linear_35_cast_fp16 = linear(bias = encoder_layers_3_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_3_feed_forward2_linear1_weight_to_fp16_palettized, x = input_221_cast_fp16)[name = string("linear_35_cast_fp16")]; + tensor input_225_cast_fp16 = silu(x = linear_35_cast_fp16)[name = string("input_225_cast_fp16")]; + tensor encoder_layers_3_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91104960))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94250752))))[name = string("encoder_layers_3_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_3_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_3_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94250944)))]; + tensor linear_36_cast_fp16 = linear(bias = encoder_layers_3_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_3_feed_forward2_linear2_weight_to_fp16_palettized, x = input_225_cast_fp16)[name = string("linear_36_cast_fp16")]; + fp16 var_1270_to_fp16 = const()[name = string("op_1270_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1271_cast_fp16 = mul(x = linear_36_cast_fp16, y = var_1270_to_fp16)[name = string("op_1271_cast_fp16")]; + tensor input_231_cast_fp16 = add(x = input_219_cast_fp16, y = var_1271_cast_fp16)[name = string("input_231_cast_fp16")]; + tensor input_233_axes_0 = const()[name = string("input_233_axes_0"), val = tensor([-1])]; + tensor encoder_layers_3_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_3_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94253056)))]; + tensor encoder_layers_3_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_3_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94255168)))]; + tensor input_233_cast_fp16 = layer_norm(axes = input_233_axes_0, beta = encoder_layers_3_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_3_norm_out_weight_to_fp16, x = input_231_cast_fp16)[name = string("input_233_cast_fp16")]; + tensor cache_17_begin_0 = const()[name = string("cache_17_begin_0"), val = tensor([4, 0, 0, 0])]; + tensor cache_17_end_0 = const()[name = string("cache_17_end_0"), val = tensor([5, 1, 42, 1024])]; + tensor cache_17_end_mask_0 = const()[name = string("cache_17_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_17_squeeze_mask_0 = const()[name = string("cache_17_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_17_cast_fp16 = slice_by_index(begin = cache_17_begin_0, end = cache_17_end_0, end_mask = cache_17_end_mask_0, squeeze_mask = cache_17_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_17_cast_fp16")]; + tensor cache_19_begin_0 = const()[name = string("cache_19_begin_0"), val = tensor([4, 0, 0, 0])]; + tensor cache_19_end_0 = const()[name = string("cache_19_end_0"), val = tensor([5, 1, 1024, 8])]; + tensor cache_19_end_mask_0 = const()[name = string("cache_19_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_19_squeeze_mask_0 = const()[name = string("cache_19_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_19_cast_fp16 = slice_by_index(begin = cache_19_begin_0, end = cache_19_end_0, end_mask = cache_19_end_mask_0, squeeze_mask = cache_19_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_19_cast_fp16")]; + tensor input_235_axes_0 = const()[name = string("input_235_axes_0"), val = tensor([-1])]; + tensor encoder_layers_4_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_4_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94257280)))]; + tensor encoder_layers_4_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_4_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94259392)))]; + tensor input_235_cast_fp16 = layer_norm(axes = input_235_axes_0, beta = encoder_layers_4_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_4_norm_feed_forward1_weight_to_fp16, x = input_233_cast_fp16)[name = string("input_235_cast_fp16")]; + tensor encoder_layers_4_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94261504))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97407296))))[name = string("encoder_layers_4_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_4_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_4_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97407488)))]; + tensor linear_37_cast_fp16 = linear(bias = encoder_layers_4_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_4_feed_forward1_linear1_weight_to_fp16_palettized, x = input_235_cast_fp16)[name = string("linear_37_cast_fp16")]; + tensor input_239_cast_fp16 = silu(x = linear_37_cast_fp16)[name = string("input_239_cast_fp16")]; + tensor encoder_layers_4_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97415744))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100561536))))[name = string("encoder_layers_4_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_4_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_4_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100561728)))]; + tensor linear_38_cast_fp16 = linear(bias = encoder_layers_4_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_4_feed_forward1_linear2_weight_to_fp16_palettized, x = input_239_cast_fp16)[name = string("linear_38_cast_fp16")]; + fp16 var_1307_to_fp16 = const()[name = string("op_1307_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1308_cast_fp16 = mul(x = linear_38_cast_fp16, y = var_1307_to_fp16)[name = string("op_1308_cast_fp16")]; + tensor input_245_cast_fp16 = add(x = input_233_cast_fp16, y = var_1308_cast_fp16)[name = string("input_245_cast_fp16")]; + tensor key_9_axes_0 = const()[name = string("key_9_axes_0"), val = tensor([-1])]; + tensor encoder_layers_4_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_4_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100563840)))]; + tensor encoder_layers_4_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_4_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100565952)))]; + tensor key_9_cast_fp16 = layer_norm(axes = key_9_axes_0, beta = encoder_layers_4_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_4_norm_self_att_weight_to_fp16, x = input_245_cast_fp16)[name = string("key_9_cast_fp16")]; + bool input_247_interleave_0 = const()[name = string("input_247_interleave_0"), val = bool(false)]; + tensor input_247_cast_fp16 = concat(axis = var_68, interleave = input_247_interleave_0, values = (cache_17_cast_fp16, key_9_cast_fp16))[name = string("input_247_cast_fp16")]; + tensor var_1330_begin_0 = const()[name = string("op_1330_begin_0"), val = tensor([0, 7, 0])]; + tensor var_1330_end_0 = const()[name = string("op_1330_end_0"), val = tensor([1, 42, 1024])]; + tensor var_1330_end_mask_0 = const()[name = string("op_1330_end_mask_0"), val = tensor([true, true, true])]; + tensor var_1330_cast_fp16 = slice_by_index(begin = var_1330_begin_0, end = var_1330_end_0, end_mask = var_1330_end_mask_0, x = cache_17_cast_fp16)[name = string("op_1330_cast_fp16")]; + bool var_1336_interleave_0 = const()[name = string("op_1336_interleave_0"), val = bool(false)]; + tensor var_1336_cast_fp16 = concat(axis = var_68, interleave = var_1336_interleave_0, values = (var_1330_cast_fp16, key_9_cast_fp16))[name = string("op_1336_cast_fp16")]; + tensor encoder_layers_4_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100568064))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101354560))))[name = string("encoder_layers_4_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_4_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_4_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101354752)))]; + tensor linear_39_cast_fp16 = linear(bias = encoder_layers_4_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_4_self_attn_linear_q_weight_to_fp16_palettized, x = key_9_cast_fp16)[name = string("linear_39_cast_fp16")]; + tensor var_1341 = const()[name = string("op_1341"), val = tensor([1, -1, 8, 128])]; + tensor q_25_cast_fp16 = reshape(shape = var_1341, x = linear_39_cast_fp16)[name = string("q_25_cast_fp16")]; + tensor encoder_layers_4_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101356864))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102143360))))[name = string("encoder_layers_4_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_4_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_4_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102143552)))]; + tensor linear_40_cast_fp16 = linear(bias = encoder_layers_4_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_4_self_attn_linear_k_weight_to_fp16_palettized, x = input_247_cast_fp16)[name = string("linear_40_cast_fp16")]; + tensor var_1346 = const()[name = string("op_1346"), val = tensor([1, -1, 8, 128])]; + tensor k_17_cast_fp16 = reshape(shape = var_1346, x = linear_40_cast_fp16)[name = string("k_17_cast_fp16")]; + tensor encoder_layers_4_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102145664))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102932160))))[name = string("encoder_layers_4_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_4_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_4_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102932352)))]; + tensor linear_41_cast_fp16 = linear(bias = encoder_layers_4_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_4_self_attn_linear_v_weight_to_fp16_palettized, x = input_247_cast_fp16)[name = string("linear_41_cast_fp16")]; + tensor var_1351 = const()[name = string("op_1351"), val = tensor([1, -1, 8, 128])]; + tensor v_9_cast_fp16 = reshape(shape = var_1351, x = linear_41_cast_fp16)[name = string("v_9_cast_fp16")]; + tensor value_17_perm_0 = const()[name = string("value_17_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_4_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_4_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102934464)))]; + tensor var_1364_cast_fp16 = add(x = q_25_cast_fp16, y = encoder_layers_4_self_attn_pos_bias_u_to_fp16)[name = string("op_1364_cast_fp16")]; + tensor encoder_layers_4_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_4_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102936576)))]; + tensor var_1366_cast_fp16 = add(x = q_25_cast_fp16, y = encoder_layers_4_self_attn_pos_bias_v_to_fp16)[name = string("op_1366_cast_fp16")]; + tensor q_with_bias_v_9_perm_0 = const()[name = string("q_with_bias_v_9_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_111_transpose_x_0 = const()[name = string("x_111_transpose_x_0"), val = bool(false)]; + bool x_111_transpose_y_0 = const()[name = string("x_111_transpose_y_0"), val = bool(false)]; + tensor op_1368_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102938688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103038080))))[name = string("op_1368_to_fp16_quantized")]; + tensor q_with_bias_v_9_cast_fp16 = transpose(perm = q_with_bias_v_9_perm_0, x = var_1366_cast_fp16)[name = string("transpose_326")]; + tensor x_111_cast_fp16 = matmul(transpose_x = x_111_transpose_x_0, transpose_y = x_111_transpose_y_0, x = q_with_bias_v_9_cast_fp16, y = op_1368_to_fp16_quantized)[name = string("x_111_cast_fp16")]; + tensor x_113_pad_0 = const()[name = string("x_113_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_113_mode_0 = const()[name = string("x_113_mode_0"), val = string("constant")]; + fp16 const_131_to_fp16 = const()[name = string("const_131_to_fp16"), val = fp16(0x0p+0)]; + tensor x_113_cast_fp16 = pad(constant_val = const_131_to_fp16, mode = x_113_mode_0, pad = x_113_pad_0, x = x_111_cast_fp16)[name = string("x_113_cast_fp16")]; + tensor var_1376 = const()[name = string("op_1376"), val = tensor([1, 8, -1, 7])]; + tensor x_115_cast_fp16 = reshape(shape = var_1376, x = x_113_cast_fp16)[name = string("x_115_cast_fp16")]; + tensor var_1380_begin_0 = const()[name = string("op_1380_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_1380_end_0 = const()[name = string("op_1380_end_0"), val = tensor([1, 8, 98, 7])]; + tensor var_1380_end_mask_0 = const()[name = string("op_1380_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1380_cast_fp16 = slice_by_index(begin = var_1380_begin_0, end = var_1380_end_0, end_mask = var_1380_end_mask_0, x = x_115_cast_fp16)[name = string("op_1380_cast_fp16")]; + tensor var_1381 = const()[name = string("op_1381"), val = tensor([1, 8, 7, 97])]; + tensor matrix_bd_17_cast_fp16 = reshape(shape = var_1381, x = var_1380_cast_fp16)[name = string("matrix_bd_17_cast_fp16")]; + bool matrix_ac_9_transpose_x_0 = const()[name = string("matrix_ac_9_transpose_x_0"), val = bool(false)]; + bool matrix_ac_9_transpose_y_0 = const()[name = string("matrix_ac_9_transpose_y_0"), val = bool(false)]; + tensor transpose_104_perm_0 = const()[name = string("transpose_104_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_105_perm_0 = const()[name = string("transpose_105_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_105 = transpose(perm = transpose_105_perm_0, x = k_17_cast_fp16)[name = string("transpose_324")]; + tensor transpose_104 = transpose(perm = transpose_104_perm_0, x = var_1364_cast_fp16)[name = string("transpose_325")]; + tensor matrix_ac_9_cast_fp16 = matmul(transpose_x = matrix_ac_9_transpose_x_0, transpose_y = matrix_ac_9_transpose_y_0, x = transpose_104, y = transpose_105)[name = string("matrix_ac_9_cast_fp16")]; + tensor matrix_bd_19_begin_0 = const()[name = string("matrix_bd_19_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_19_end_0 = const()[name = string("matrix_bd_19_end_0"), val = tensor([1, 8, 7, 49])]; + tensor matrix_bd_19_end_mask_0 = const()[name = string("matrix_bd_19_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_19_cast_fp16 = slice_by_index(begin = matrix_bd_19_begin_0, end = matrix_bd_19_end_0, end_mask = matrix_bd_19_end_mask_0, x = matrix_bd_17_cast_fp16)[name = string("matrix_bd_19_cast_fp16")]; + tensor var_1390_cast_fp16 = add(x = matrix_ac_9_cast_fp16, y = matrix_bd_19_cast_fp16)[name = string("op_1390_cast_fp16")]; + fp16 _inversed_scores_17_y_0_to_fp16 = const()[name = string("_inversed_scores_17_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_17_cast_fp16 = mul(x = var_1390_cast_fp16, y = _inversed_scores_17_y_0_to_fp16)[name = string("_inversed_scores_17_cast_fp16")]; + tensor scores_19_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_17_cast_fp16, cond = mask_11)[name = string("scores_19_cast_fp16")]; + tensor var_1396_cast_fp16 = softmax(axis = var_59, x = scores_19_cast_fp16)[name = string("op_1396_cast_fp16")]; + tensor input_249_cast_fp16 = select(a = var_44_to_fp16, b = var_1396_cast_fp16, cond = mask_11)[name = string("input_249_cast_fp16")]; + bool x_117_transpose_x_0 = const()[name = string("x_117_transpose_x_0"), val = bool(false)]; + bool x_117_transpose_y_0 = const()[name = string("x_117_transpose_y_0"), val = bool(false)]; + tensor value_17_cast_fp16 = transpose(perm = value_17_perm_0, x = v_9_cast_fp16)[name = string("transpose_323")]; + tensor x_117_cast_fp16 = matmul(transpose_x = x_117_transpose_x_0, transpose_y = x_117_transpose_y_0, x = input_249_cast_fp16, y = value_17_cast_fp16)[name = string("x_117_cast_fp16")]; + tensor var_1400_perm_0 = const()[name = string("op_1400_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1401 = const()[name = string("op_1401"), val = tensor([1, -1, 1024])]; + tensor var_1400_cast_fp16 = transpose(perm = var_1400_perm_0, x = x_117_cast_fp16)[name = string("transpose_322")]; + tensor input_251_cast_fp16 = reshape(shape = var_1401, x = var_1400_cast_fp16)[name = string("input_251_cast_fp16")]; + tensor encoder_layers_4_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103038400))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103824896))))[name = string("encoder_layers_4_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_4_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_4_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103825088)))]; + tensor linear_43_cast_fp16 = linear(bias = encoder_layers_4_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_4_self_attn_linear_out_weight_to_fp16_palettized, x = input_251_cast_fp16)[name = string("linear_43_cast_fp16")]; + tensor input_255_cast_fp16 = add(x = input_245_cast_fp16, y = linear_43_cast_fp16)[name = string("input_255_cast_fp16")]; + tensor x_121_axes_0 = const()[name = string("x_121_axes_0"), val = tensor([-1])]; + tensor encoder_layers_4_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_4_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103827200)))]; + tensor encoder_layers_4_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_4_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103829312)))]; + tensor x_121_cast_fp16 = layer_norm(axes = x_121_axes_0, beta = encoder_layers_4_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_4_norm_conv_weight_to_fp16, x = input_255_cast_fp16)[name = string("x_121_cast_fp16")]; + tensor input_257_perm_0 = const()[name = string("input_257_perm_0"), val = tensor([0, 2, 1])]; + string input_259_pad_type_0 = const()[name = string("input_259_pad_type_0"), val = string("valid")]; + tensor input_259_strides_0 = const()[name = string("input_259_strides_0"), val = tensor([1])]; + tensor input_259_pad_0 = const()[name = string("input_259_pad_0"), val = tensor([0, 0])]; + tensor input_259_dilations_0 = const()[name = string("input_259_dilations_0"), val = tensor([1])]; + int32 input_259_groups_0 = const()[name = string("input_259_groups_0"), val = int32(1)]; + tensor encoder_layers_4_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103831424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105928640))))[name = string("encoder_layers_4_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_257_cast_fp16 = transpose(perm = input_257_perm_0, x = x_121_cast_fp16)[name = string("transpose_321")]; + tensor input_259_cast_fp16 = conv(dilations = input_259_dilations_0, groups = input_259_groups_0, pad = input_259_pad_0, pad_type = input_259_pad_type_0, strides = input_259_strides_0, weight = encoder_layers_4_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_257_cast_fp16)[name = string("input_259_cast_fp16")]; + int32 x_123_split_num_splits_0 = const()[name = string("x_123_split_num_splits_0"), val = int32(2)]; + int32 x_123_split_axis_0 = const()[name = string("x_123_split_axis_0"), val = int32(1)]; + tensor x_123_split_cast_fp16_0, tensor x_123_split_cast_fp16_1 = split(axis = x_123_split_axis_0, num_splits = x_123_split_num_splits_0, x = input_259_cast_fp16)[name = string("x_123_split_cast_fp16")]; + tensor x_123_split_1_sigmoid_cast_fp16 = sigmoid(x = x_123_split_cast_fp16_1)[name = string("x_123_split_1_sigmoid_cast_fp16")]; + tensor x_123_cast_fp16 = mul(x = x_123_split_cast_fp16_0, y = x_123_split_1_sigmoid_cast_fp16)[name = string("x_123_cast_fp16")]; + tensor input_261_cast_fp16 = select(a = var_44_to_fp16, b = x_123_cast_fp16, cond = var_575)[name = string("input_261_cast_fp16")]; + bool new_x_19_interleave_0 = const()[name = string("new_x_19_interleave_0"), val = bool(false)]; + tensor new_x_19_cast_fp16 = concat(axis = var_59, interleave = new_x_19_interleave_0, values = (cache_19_cast_fp16, input_261_cast_fp16))[name = string("new_x_19_cast_fp16")]; + tensor var_1440_begin_0 = const()[name = string("op_1440_begin_0"), val = tensor([0, 0, 7])]; + tensor var_1440_end_0 = const()[name = string("op_1440_end_0"), val = tensor([1, 1024, 15])]; + tensor var_1440_end_mask_0 = const()[name = string("op_1440_end_mask_0"), val = tensor([true, true, true])]; + tensor var_1440_cast_fp16 = slice_by_index(begin = var_1440_begin_0, end = var_1440_end_0, end_mask = var_1440_end_mask_0, x = new_x_19_cast_fp16)[name = string("op_1440_cast_fp16")]; + string x_125_pad_type_0 = const()[name = string("x_125_pad_type_0"), val = string("valid")]; + int32 x_125_groups_0 = const()[name = string("x_125_groups_0"), val = int32(1024)]; + tensor x_125_strides_0 = const()[name = string("x_125_strides_0"), val = tensor([1])]; + tensor x_125_pad_0 = const()[name = string("x_125_pad_0"), val = tensor([0, 0])]; + tensor x_125_dilations_0 = const()[name = string("x_125_dilations_0"), val = tensor([1])]; + tensor encoder_layers_4_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105932800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105942080))))[name = string("encoder_layers_4_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_125_cast_fp16 = conv(dilations = x_125_dilations_0, groups = x_125_groups_0, pad = x_125_pad_0, pad_type = x_125_pad_type_0, strides = x_125_strides_0, weight = encoder_layers_4_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_19_cast_fp16)[name = string("x_125_cast_fp16")]; + tensor input_263_perm_0 = const()[name = string("input_263_perm_0"), val = tensor([0, 2, 1])]; + tensor x_127_axes_0 = const()[name = string("x_127_axes_0"), val = tensor([-1])]; + tensor encoder_layers_4_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_4_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105944192)))]; + tensor encoder_layers_4_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_4_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105946304)))]; + tensor input_263_cast_fp16 = transpose(perm = input_263_perm_0, x = x_125_cast_fp16)[name = string("transpose_320")]; + tensor x_127_cast_fp16 = layer_norm(axes = x_127_axes_0, beta = encoder_layers_4_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_4_conv_batch_norm_weight_to_fp16, x = input_263_cast_fp16)[name = string("x_127_cast_fp16")]; + tensor input_265_perm_0 = const()[name = string("input_265_perm_0"), val = tensor([0, 2, 1])]; + tensor input_265_cast_fp16 = transpose(perm = input_265_perm_0, x = x_127_cast_fp16)[name = string("transpose_319")]; + tensor input_267_cast_fp16 = silu(x = input_265_cast_fp16)[name = string("input_267_cast_fp16")]; + string x_129_pad_type_0 = const()[name = string("x_129_pad_type_0"), val = string("valid")]; + tensor x_129_strides_0 = const()[name = string("x_129_strides_0"), val = tensor([1])]; + tensor x_129_pad_0 = const()[name = string("x_129_pad_0"), val = tensor([0, 0])]; + tensor x_129_dilations_0 = const()[name = string("x_129_dilations_0"), val = tensor([1])]; + int32 x_129_groups_0 = const()[name = string("x_129_groups_0"), val = int32(1)]; + tensor encoder_layers_4_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105948416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106997056))))[name = string("encoder_layers_4_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_129_cast_fp16 = conv(dilations = x_129_dilations_0, groups = x_129_groups_0, pad = x_129_pad_0, pad_type = x_129_pad_type_0, strides = x_129_strides_0, weight = encoder_layers_4_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_267_cast_fp16)[name = string("x_129_cast_fp16")]; + tensor input_269_perm_0 = const()[name = string("input_269_perm_0"), val = tensor([0, 2, 1])]; + tensor input_269_cast_fp16 = transpose(perm = input_269_perm_0, x = x_129_cast_fp16)[name = string("transpose_318")]; + tensor input_271_cast_fp16 = add(x = input_255_cast_fp16, y = input_269_cast_fp16)[name = string("input_271_cast_fp16")]; + tensor input_273_axes_0 = const()[name = string("input_273_axes_0"), val = tensor([-1])]; + tensor encoder_layers_4_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_4_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106999168)))]; + tensor encoder_layers_4_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_4_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(107001280)))]; + tensor input_273_cast_fp16 = layer_norm(axes = input_273_axes_0, beta = encoder_layers_4_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_4_norm_feed_forward2_weight_to_fp16, x = input_271_cast_fp16)[name = string("input_273_cast_fp16")]; + tensor encoder_layers_4_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(107003392))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110149184))))[name = string("encoder_layers_4_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_4_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_4_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110149376)))]; + tensor linear_44_cast_fp16 = linear(bias = encoder_layers_4_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_4_feed_forward2_linear1_weight_to_fp16_palettized, x = input_273_cast_fp16)[name = string("linear_44_cast_fp16")]; + tensor input_277_cast_fp16 = silu(x = linear_44_cast_fp16)[name = string("input_277_cast_fp16")]; + tensor encoder_layers_4_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110157632))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113303424))))[name = string("encoder_layers_4_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_4_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_4_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113303616)))]; + tensor linear_45_cast_fp16 = linear(bias = encoder_layers_4_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_4_feed_forward2_linear2_weight_to_fp16_palettized, x = input_277_cast_fp16)[name = string("linear_45_cast_fp16")]; + fp16 var_1483_to_fp16 = const()[name = string("op_1483_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1484_cast_fp16 = mul(x = linear_45_cast_fp16, y = var_1483_to_fp16)[name = string("op_1484_cast_fp16")]; + tensor input_283_cast_fp16 = add(x = input_271_cast_fp16, y = var_1484_cast_fp16)[name = string("input_283_cast_fp16")]; + tensor input_285_axes_0 = const()[name = string("input_285_axes_0"), val = tensor([-1])]; + tensor encoder_layers_4_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_4_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113305728)))]; + tensor encoder_layers_4_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_4_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113307840)))]; + tensor input_285_cast_fp16 = layer_norm(axes = input_285_axes_0, beta = encoder_layers_4_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_4_norm_out_weight_to_fp16, x = input_283_cast_fp16)[name = string("input_285_cast_fp16")]; + tensor cache_21_begin_0 = const()[name = string("cache_21_begin_0"), val = tensor([5, 0, 0, 0])]; + tensor cache_21_end_0 = const()[name = string("cache_21_end_0"), val = tensor([6, 1, 42, 1024])]; + tensor cache_21_end_mask_0 = const()[name = string("cache_21_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_21_squeeze_mask_0 = const()[name = string("cache_21_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_21_cast_fp16 = slice_by_index(begin = cache_21_begin_0, end = cache_21_end_0, end_mask = cache_21_end_mask_0, squeeze_mask = cache_21_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_21_cast_fp16")]; + tensor cache_23_begin_0 = const()[name = string("cache_23_begin_0"), val = tensor([5, 0, 0, 0])]; + tensor cache_23_end_0 = const()[name = string("cache_23_end_0"), val = tensor([6, 1, 1024, 8])]; + tensor cache_23_end_mask_0 = const()[name = string("cache_23_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_23_squeeze_mask_0 = const()[name = string("cache_23_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_23_cast_fp16 = slice_by_index(begin = cache_23_begin_0, end = cache_23_end_0, end_mask = cache_23_end_mask_0, squeeze_mask = cache_23_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_23_cast_fp16")]; + tensor input_287_axes_0 = const()[name = string("input_287_axes_0"), val = tensor([-1])]; + tensor encoder_layers_5_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_5_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113309952)))]; + tensor encoder_layers_5_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_5_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113312064)))]; + tensor input_287_cast_fp16 = layer_norm(axes = input_287_axes_0, beta = encoder_layers_5_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_5_norm_feed_forward1_weight_to_fp16, x = input_285_cast_fp16)[name = string("input_287_cast_fp16")]; + tensor encoder_layers_5_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113314176))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116459968))))[name = string("encoder_layers_5_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_5_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_5_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116460160)))]; + tensor linear_46_cast_fp16 = linear(bias = encoder_layers_5_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_5_feed_forward1_linear1_weight_to_fp16_palettized, x = input_287_cast_fp16)[name = string("linear_46_cast_fp16")]; + tensor input_291_cast_fp16 = silu(x = linear_46_cast_fp16)[name = string("input_291_cast_fp16")]; + tensor encoder_layers_5_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116468416))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119614208))))[name = string("encoder_layers_5_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_5_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_5_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119614400)))]; + tensor linear_47_cast_fp16 = linear(bias = encoder_layers_5_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_5_feed_forward1_linear2_weight_to_fp16_palettized, x = input_291_cast_fp16)[name = string("linear_47_cast_fp16")]; + fp16 var_1520_to_fp16 = const()[name = string("op_1520_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1521_cast_fp16 = mul(x = linear_47_cast_fp16, y = var_1520_to_fp16)[name = string("op_1521_cast_fp16")]; + tensor input_297_cast_fp16 = add(x = input_285_cast_fp16, y = var_1521_cast_fp16)[name = string("input_297_cast_fp16")]; + tensor key_11_axes_0 = const()[name = string("key_11_axes_0"), val = tensor([-1])]; + tensor encoder_layers_5_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_5_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119616512)))]; + tensor encoder_layers_5_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_5_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119618624)))]; + tensor key_11_cast_fp16 = layer_norm(axes = key_11_axes_0, beta = encoder_layers_5_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_5_norm_self_att_weight_to_fp16, x = input_297_cast_fp16)[name = string("key_11_cast_fp16")]; + bool input_299_interleave_0 = const()[name = string("input_299_interleave_0"), val = bool(false)]; + tensor input_299_cast_fp16 = concat(axis = var_68, interleave = input_299_interleave_0, values = (cache_21_cast_fp16, key_11_cast_fp16))[name = string("input_299_cast_fp16")]; + tensor var_1543_begin_0 = const()[name = string("op_1543_begin_0"), val = tensor([0, 7, 0])]; + tensor var_1543_end_0 = const()[name = string("op_1543_end_0"), val = tensor([1, 42, 1024])]; + tensor var_1543_end_mask_0 = const()[name = string("op_1543_end_mask_0"), val = tensor([true, true, true])]; + tensor var_1543_cast_fp16 = slice_by_index(begin = var_1543_begin_0, end = var_1543_end_0, end_mask = var_1543_end_mask_0, x = cache_21_cast_fp16)[name = string("op_1543_cast_fp16")]; + bool var_1549_interleave_0 = const()[name = string("op_1549_interleave_0"), val = bool(false)]; + tensor var_1549_cast_fp16 = concat(axis = var_68, interleave = var_1549_interleave_0, values = (var_1543_cast_fp16, key_11_cast_fp16))[name = string("op_1549_cast_fp16")]; + tensor encoder_layers_5_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119620736))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120407232))))[name = string("encoder_layers_5_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_5_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_5_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120407424)))]; + tensor linear_48_cast_fp16 = linear(bias = encoder_layers_5_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_5_self_attn_linear_q_weight_to_fp16_palettized, x = key_11_cast_fp16)[name = string("linear_48_cast_fp16")]; + tensor var_1554 = const()[name = string("op_1554"), val = tensor([1, -1, 8, 128])]; + tensor q_31_cast_fp16 = reshape(shape = var_1554, x = linear_48_cast_fp16)[name = string("q_31_cast_fp16")]; + tensor encoder_layers_5_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120409536))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121196032))))[name = string("encoder_layers_5_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_5_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_5_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121196224)))]; + tensor linear_49_cast_fp16 = linear(bias = encoder_layers_5_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_5_self_attn_linear_k_weight_to_fp16_palettized, x = input_299_cast_fp16)[name = string("linear_49_cast_fp16")]; + tensor var_1559 = const()[name = string("op_1559"), val = tensor([1, -1, 8, 128])]; + tensor k_21_cast_fp16 = reshape(shape = var_1559, x = linear_49_cast_fp16)[name = string("k_21_cast_fp16")]; + tensor encoder_layers_5_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121198336))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121984832))))[name = string("encoder_layers_5_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_5_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_5_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121985024)))]; + tensor linear_50_cast_fp16 = linear(bias = encoder_layers_5_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_5_self_attn_linear_v_weight_to_fp16_palettized, x = input_299_cast_fp16)[name = string("linear_50_cast_fp16")]; + tensor var_1564 = const()[name = string("op_1564"), val = tensor([1, -1, 8, 128])]; + tensor v_11_cast_fp16 = reshape(shape = var_1564, x = linear_50_cast_fp16)[name = string("v_11_cast_fp16")]; + tensor value_19_perm_0 = const()[name = string("value_19_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_5_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_5_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121987136)))]; + tensor var_1577_cast_fp16 = add(x = q_31_cast_fp16, y = encoder_layers_5_self_attn_pos_bias_u_to_fp16)[name = string("op_1577_cast_fp16")]; + tensor encoder_layers_5_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_5_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121989248)))]; + tensor var_1579_cast_fp16 = add(x = q_31_cast_fp16, y = encoder_layers_5_self_attn_pos_bias_v_to_fp16)[name = string("op_1579_cast_fp16")]; + tensor q_with_bias_v_11_perm_0 = const()[name = string("q_with_bias_v_11_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_137_transpose_x_0 = const()[name = string("x_137_transpose_x_0"), val = bool(false)]; + bool x_137_transpose_y_0 = const()[name = string("x_137_transpose_y_0"), val = bool(false)]; + tensor op_1581_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121991360))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122090752))))[name = string("op_1581_to_fp16_quantized")]; + tensor q_with_bias_v_11_cast_fp16 = transpose(perm = q_with_bias_v_11_perm_0, x = var_1579_cast_fp16)[name = string("transpose_317")]; + tensor x_137_cast_fp16 = matmul(transpose_x = x_137_transpose_x_0, transpose_y = x_137_transpose_y_0, x = q_with_bias_v_11_cast_fp16, y = op_1581_to_fp16_quantized)[name = string("x_137_cast_fp16")]; + tensor x_139_pad_0 = const()[name = string("x_139_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_139_mode_0 = const()[name = string("x_139_mode_0"), val = string("constant")]; + fp16 const_144_to_fp16 = const()[name = string("const_144_to_fp16"), val = fp16(0x0p+0)]; + tensor x_139_cast_fp16 = pad(constant_val = const_144_to_fp16, mode = x_139_mode_0, pad = x_139_pad_0, x = x_137_cast_fp16)[name = string("x_139_cast_fp16")]; + tensor var_1589 = const()[name = string("op_1589"), val = tensor([1, 8, -1, 7])]; + tensor x_141_cast_fp16 = reshape(shape = var_1589, x = x_139_cast_fp16)[name = string("x_141_cast_fp16")]; + tensor var_1593_begin_0 = const()[name = string("op_1593_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_1593_end_0 = const()[name = string("op_1593_end_0"), val = tensor([1, 8, 98, 7])]; + tensor var_1593_end_mask_0 = const()[name = string("op_1593_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1593_cast_fp16 = slice_by_index(begin = var_1593_begin_0, end = var_1593_end_0, end_mask = var_1593_end_mask_0, x = x_141_cast_fp16)[name = string("op_1593_cast_fp16")]; + tensor var_1594 = const()[name = string("op_1594"), val = tensor([1, 8, 7, 97])]; + tensor matrix_bd_21_cast_fp16 = reshape(shape = var_1594, x = var_1593_cast_fp16)[name = string("matrix_bd_21_cast_fp16")]; + bool matrix_ac_11_transpose_x_0 = const()[name = string("matrix_ac_11_transpose_x_0"), val = bool(false)]; + bool matrix_ac_11_transpose_y_0 = const()[name = string("matrix_ac_11_transpose_y_0"), val = bool(false)]; + tensor transpose_106_perm_0 = const()[name = string("transpose_106_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_107_perm_0 = const()[name = string("transpose_107_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_107 = transpose(perm = transpose_107_perm_0, x = k_21_cast_fp16)[name = string("transpose_315")]; + tensor transpose_106 = transpose(perm = transpose_106_perm_0, x = var_1577_cast_fp16)[name = string("transpose_316")]; + tensor matrix_ac_11_cast_fp16 = matmul(transpose_x = matrix_ac_11_transpose_x_0, transpose_y = matrix_ac_11_transpose_y_0, x = transpose_106, y = transpose_107)[name = string("matrix_ac_11_cast_fp16")]; + tensor matrix_bd_23_begin_0 = const()[name = string("matrix_bd_23_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_23_end_0 = const()[name = string("matrix_bd_23_end_0"), val = tensor([1, 8, 7, 49])]; + tensor matrix_bd_23_end_mask_0 = const()[name = string("matrix_bd_23_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_23_cast_fp16 = slice_by_index(begin = matrix_bd_23_begin_0, end = matrix_bd_23_end_0, end_mask = matrix_bd_23_end_mask_0, x = matrix_bd_21_cast_fp16)[name = string("matrix_bd_23_cast_fp16")]; + tensor var_1603_cast_fp16 = add(x = matrix_ac_11_cast_fp16, y = matrix_bd_23_cast_fp16)[name = string("op_1603_cast_fp16")]; + fp16 _inversed_scores_21_y_0_to_fp16 = const()[name = string("_inversed_scores_21_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_21_cast_fp16 = mul(x = var_1603_cast_fp16, y = _inversed_scores_21_y_0_to_fp16)[name = string("_inversed_scores_21_cast_fp16")]; + tensor scores_23_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_21_cast_fp16, cond = mask_11)[name = string("scores_23_cast_fp16")]; + tensor var_1609_cast_fp16 = softmax(axis = var_59, x = scores_23_cast_fp16)[name = string("op_1609_cast_fp16")]; + tensor input_301_cast_fp16 = select(a = var_44_to_fp16, b = var_1609_cast_fp16, cond = mask_11)[name = string("input_301_cast_fp16")]; + bool x_143_transpose_x_0 = const()[name = string("x_143_transpose_x_0"), val = bool(false)]; + bool x_143_transpose_y_0 = const()[name = string("x_143_transpose_y_0"), val = bool(false)]; + tensor value_19_cast_fp16 = transpose(perm = value_19_perm_0, x = v_11_cast_fp16)[name = string("transpose_314")]; + tensor x_143_cast_fp16 = matmul(transpose_x = x_143_transpose_x_0, transpose_y = x_143_transpose_y_0, x = input_301_cast_fp16, y = value_19_cast_fp16)[name = string("x_143_cast_fp16")]; + tensor var_1613_perm_0 = const()[name = string("op_1613_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1614 = const()[name = string("op_1614"), val = tensor([1, -1, 1024])]; + tensor var_1613_cast_fp16 = transpose(perm = var_1613_perm_0, x = x_143_cast_fp16)[name = string("transpose_313")]; + tensor input_303_cast_fp16 = reshape(shape = var_1614, x = var_1613_cast_fp16)[name = string("input_303_cast_fp16")]; + tensor encoder_layers_5_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122091072))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122877568))))[name = string("encoder_layers_5_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_5_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_5_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122877760)))]; + tensor linear_52_cast_fp16 = linear(bias = encoder_layers_5_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_5_self_attn_linear_out_weight_to_fp16_palettized, x = input_303_cast_fp16)[name = string("linear_52_cast_fp16")]; + tensor input_307_cast_fp16 = add(x = input_297_cast_fp16, y = linear_52_cast_fp16)[name = string("input_307_cast_fp16")]; + tensor x_147_axes_0 = const()[name = string("x_147_axes_0"), val = tensor([-1])]; + tensor encoder_layers_5_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_5_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122879872)))]; + tensor encoder_layers_5_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_5_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122881984)))]; + tensor x_147_cast_fp16 = layer_norm(axes = x_147_axes_0, beta = encoder_layers_5_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_5_norm_conv_weight_to_fp16, x = input_307_cast_fp16)[name = string("x_147_cast_fp16")]; + tensor input_309_perm_0 = const()[name = string("input_309_perm_0"), val = tensor([0, 2, 1])]; + string input_311_pad_type_0 = const()[name = string("input_311_pad_type_0"), val = string("valid")]; + tensor input_311_strides_0 = const()[name = string("input_311_strides_0"), val = tensor([1])]; + tensor input_311_pad_0 = const()[name = string("input_311_pad_0"), val = tensor([0, 0])]; + tensor input_311_dilations_0 = const()[name = string("input_311_dilations_0"), val = tensor([1])]; + int32 input_311_groups_0 = const()[name = string("input_311_groups_0"), val = int32(1)]; + tensor encoder_layers_5_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122884096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(124981312))))[name = string("encoder_layers_5_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_309_cast_fp16 = transpose(perm = input_309_perm_0, x = x_147_cast_fp16)[name = string("transpose_312")]; + tensor input_311_cast_fp16 = conv(dilations = input_311_dilations_0, groups = input_311_groups_0, pad = input_311_pad_0, pad_type = input_311_pad_type_0, strides = input_311_strides_0, weight = encoder_layers_5_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_309_cast_fp16)[name = string("input_311_cast_fp16")]; + int32 x_149_split_num_splits_0 = const()[name = string("x_149_split_num_splits_0"), val = int32(2)]; + int32 x_149_split_axis_0 = const()[name = string("x_149_split_axis_0"), val = int32(1)]; + tensor x_149_split_cast_fp16_0, tensor x_149_split_cast_fp16_1 = split(axis = x_149_split_axis_0, num_splits = x_149_split_num_splits_0, x = input_311_cast_fp16)[name = string("x_149_split_cast_fp16")]; + tensor x_149_split_1_sigmoid_cast_fp16 = sigmoid(x = x_149_split_cast_fp16_1)[name = string("x_149_split_1_sigmoid_cast_fp16")]; + tensor x_149_cast_fp16 = mul(x = x_149_split_cast_fp16_0, y = x_149_split_1_sigmoid_cast_fp16)[name = string("x_149_cast_fp16")]; + tensor input_313_cast_fp16 = select(a = var_44_to_fp16, b = x_149_cast_fp16, cond = var_575)[name = string("input_313_cast_fp16")]; + bool new_x_23_interleave_0 = const()[name = string("new_x_23_interleave_0"), val = bool(false)]; + tensor new_x_23_cast_fp16 = concat(axis = var_59, interleave = new_x_23_interleave_0, values = (cache_23_cast_fp16, input_313_cast_fp16))[name = string("new_x_23_cast_fp16")]; + tensor var_1653_begin_0 = const()[name = string("op_1653_begin_0"), val = tensor([0, 0, 7])]; + tensor var_1653_end_0 = const()[name = string("op_1653_end_0"), val = tensor([1, 1024, 15])]; + tensor var_1653_end_mask_0 = const()[name = string("op_1653_end_mask_0"), val = tensor([true, true, true])]; + tensor var_1653_cast_fp16 = slice_by_index(begin = var_1653_begin_0, end = var_1653_end_0, end_mask = var_1653_end_mask_0, x = new_x_23_cast_fp16)[name = string("op_1653_cast_fp16")]; + string x_151_pad_type_0 = const()[name = string("x_151_pad_type_0"), val = string("valid")]; + int32 x_151_groups_0 = const()[name = string("x_151_groups_0"), val = int32(1024)]; + tensor x_151_strides_0 = const()[name = string("x_151_strides_0"), val = tensor([1])]; + tensor x_151_pad_0 = const()[name = string("x_151_pad_0"), val = tensor([0, 0])]; + tensor x_151_dilations_0 = const()[name = string("x_151_dilations_0"), val = tensor([1])]; + tensor encoder_layers_5_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(124985472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(124994752))))[name = string("encoder_layers_5_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_151_cast_fp16 = conv(dilations = x_151_dilations_0, groups = x_151_groups_0, pad = x_151_pad_0, pad_type = x_151_pad_type_0, strides = x_151_strides_0, weight = encoder_layers_5_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_23_cast_fp16)[name = string("x_151_cast_fp16")]; + tensor input_315_perm_0 = const()[name = string("input_315_perm_0"), val = tensor([0, 2, 1])]; + tensor x_153_axes_0 = const()[name = string("x_153_axes_0"), val = tensor([-1])]; + tensor encoder_layers_5_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_5_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(124996864)))]; + tensor encoder_layers_5_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_5_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(124998976)))]; + tensor input_315_cast_fp16 = transpose(perm = input_315_perm_0, x = x_151_cast_fp16)[name = string("transpose_311")]; + tensor x_153_cast_fp16 = layer_norm(axes = x_153_axes_0, beta = encoder_layers_5_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_5_conv_batch_norm_weight_to_fp16, x = input_315_cast_fp16)[name = string("x_153_cast_fp16")]; + tensor input_317_perm_0 = const()[name = string("input_317_perm_0"), val = tensor([0, 2, 1])]; + tensor input_317_cast_fp16 = transpose(perm = input_317_perm_0, x = x_153_cast_fp16)[name = string("transpose_310")]; + tensor input_319_cast_fp16 = silu(x = input_317_cast_fp16)[name = string("input_319_cast_fp16")]; + string x_155_pad_type_0 = const()[name = string("x_155_pad_type_0"), val = string("valid")]; + tensor x_155_strides_0 = const()[name = string("x_155_strides_0"), val = tensor([1])]; + tensor x_155_pad_0 = const()[name = string("x_155_pad_0"), val = tensor([0, 0])]; + tensor x_155_dilations_0 = const()[name = string("x_155_dilations_0"), val = tensor([1])]; + int32 x_155_groups_0 = const()[name = string("x_155_groups_0"), val = int32(1)]; + tensor encoder_layers_5_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125001088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(126049728))))[name = string("encoder_layers_5_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_155_cast_fp16 = conv(dilations = x_155_dilations_0, groups = x_155_groups_0, pad = x_155_pad_0, pad_type = x_155_pad_type_0, strides = x_155_strides_0, weight = encoder_layers_5_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_319_cast_fp16)[name = string("x_155_cast_fp16")]; + tensor input_321_perm_0 = const()[name = string("input_321_perm_0"), val = tensor([0, 2, 1])]; + tensor input_321_cast_fp16 = transpose(perm = input_321_perm_0, x = x_155_cast_fp16)[name = string("transpose_309")]; + tensor input_323_cast_fp16 = add(x = input_307_cast_fp16, y = input_321_cast_fp16)[name = string("input_323_cast_fp16")]; + tensor input_325_axes_0 = const()[name = string("input_325_axes_0"), val = tensor([-1])]; + tensor encoder_layers_5_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_5_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(126051840)))]; + tensor encoder_layers_5_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_5_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(126053952)))]; + tensor input_325_cast_fp16 = layer_norm(axes = input_325_axes_0, beta = encoder_layers_5_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_5_norm_feed_forward2_weight_to_fp16, x = input_323_cast_fp16)[name = string("input_325_cast_fp16")]; + tensor encoder_layers_5_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(126056064))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129201856))))[name = string("encoder_layers_5_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_5_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_5_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129202048)))]; + tensor linear_53_cast_fp16 = linear(bias = encoder_layers_5_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_5_feed_forward2_linear1_weight_to_fp16_palettized, x = input_325_cast_fp16)[name = string("linear_53_cast_fp16")]; + tensor input_329_cast_fp16 = silu(x = linear_53_cast_fp16)[name = string("input_329_cast_fp16")]; + tensor encoder_layers_5_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129210304))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132356096))))[name = string("encoder_layers_5_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_5_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_5_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132356288)))]; + tensor linear_54_cast_fp16 = linear(bias = encoder_layers_5_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_5_feed_forward2_linear2_weight_to_fp16_palettized, x = input_329_cast_fp16)[name = string("linear_54_cast_fp16")]; + fp16 var_1696_to_fp16 = const()[name = string("op_1696_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1697_cast_fp16 = mul(x = linear_54_cast_fp16, y = var_1696_to_fp16)[name = string("op_1697_cast_fp16")]; + tensor input_335_cast_fp16 = add(x = input_323_cast_fp16, y = var_1697_cast_fp16)[name = string("input_335_cast_fp16")]; + tensor input_337_axes_0 = const()[name = string("input_337_axes_0"), val = tensor([-1])]; + tensor encoder_layers_5_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_5_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132358400)))]; + tensor encoder_layers_5_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_5_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132360512)))]; + tensor input_337_cast_fp16 = layer_norm(axes = input_337_axes_0, beta = encoder_layers_5_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_5_norm_out_weight_to_fp16, x = input_335_cast_fp16)[name = string("input_337_cast_fp16")]; + tensor cache_25_begin_0 = const()[name = string("cache_25_begin_0"), val = tensor([6, 0, 0, 0])]; + tensor cache_25_end_0 = const()[name = string("cache_25_end_0"), val = tensor([7, 1, 42, 1024])]; + tensor cache_25_end_mask_0 = const()[name = string("cache_25_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_25_squeeze_mask_0 = const()[name = string("cache_25_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_25_cast_fp16 = slice_by_index(begin = cache_25_begin_0, end = cache_25_end_0, end_mask = cache_25_end_mask_0, squeeze_mask = cache_25_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_25_cast_fp16")]; + tensor cache_27_begin_0 = const()[name = string("cache_27_begin_0"), val = tensor([6, 0, 0, 0])]; + tensor cache_27_end_0 = const()[name = string("cache_27_end_0"), val = tensor([7, 1, 1024, 8])]; + tensor cache_27_end_mask_0 = const()[name = string("cache_27_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_27_squeeze_mask_0 = const()[name = string("cache_27_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_27_cast_fp16 = slice_by_index(begin = cache_27_begin_0, end = cache_27_end_0, end_mask = cache_27_end_mask_0, squeeze_mask = cache_27_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_27_cast_fp16")]; + tensor input_339_axes_0 = const()[name = string("input_339_axes_0"), val = tensor([-1])]; + tensor encoder_layers_6_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_6_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132362624)))]; + tensor encoder_layers_6_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_6_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132364736)))]; + tensor input_339_cast_fp16 = layer_norm(axes = input_339_axes_0, beta = encoder_layers_6_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_6_norm_feed_forward1_weight_to_fp16, x = input_337_cast_fp16)[name = string("input_339_cast_fp16")]; + tensor encoder_layers_6_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132366848))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135512640))))[name = string("encoder_layers_6_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_6_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_6_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135512832)))]; + tensor linear_55_cast_fp16 = linear(bias = encoder_layers_6_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_6_feed_forward1_linear1_weight_to_fp16_palettized, x = input_339_cast_fp16)[name = string("linear_55_cast_fp16")]; + tensor input_343_cast_fp16 = silu(x = linear_55_cast_fp16)[name = string("input_343_cast_fp16")]; + tensor encoder_layers_6_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135521088))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138666880))))[name = string("encoder_layers_6_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_6_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_6_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138667072)))]; + tensor linear_56_cast_fp16 = linear(bias = encoder_layers_6_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_6_feed_forward1_linear2_weight_to_fp16_palettized, x = input_343_cast_fp16)[name = string("linear_56_cast_fp16")]; + fp16 var_1733_to_fp16 = const()[name = string("op_1733_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1734_cast_fp16 = mul(x = linear_56_cast_fp16, y = var_1733_to_fp16)[name = string("op_1734_cast_fp16")]; + tensor input_349_cast_fp16 = add(x = input_337_cast_fp16, y = var_1734_cast_fp16)[name = string("input_349_cast_fp16")]; + tensor key_13_axes_0 = const()[name = string("key_13_axes_0"), val = tensor([-1])]; + tensor encoder_layers_6_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_6_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138669184)))]; + tensor encoder_layers_6_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_6_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138671296)))]; + tensor key_13_cast_fp16 = layer_norm(axes = key_13_axes_0, beta = encoder_layers_6_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_6_norm_self_att_weight_to_fp16, x = input_349_cast_fp16)[name = string("key_13_cast_fp16")]; + bool input_351_interleave_0 = const()[name = string("input_351_interleave_0"), val = bool(false)]; + tensor input_351_cast_fp16 = concat(axis = var_68, interleave = input_351_interleave_0, values = (cache_25_cast_fp16, key_13_cast_fp16))[name = string("input_351_cast_fp16")]; + tensor var_1756_begin_0 = const()[name = string("op_1756_begin_0"), val = tensor([0, 7, 0])]; + tensor var_1756_end_0 = const()[name = string("op_1756_end_0"), val = tensor([1, 42, 1024])]; + tensor var_1756_end_mask_0 = const()[name = string("op_1756_end_mask_0"), val = tensor([true, true, true])]; + tensor var_1756_cast_fp16 = slice_by_index(begin = var_1756_begin_0, end = var_1756_end_0, end_mask = var_1756_end_mask_0, x = cache_25_cast_fp16)[name = string("op_1756_cast_fp16")]; + bool var_1762_interleave_0 = const()[name = string("op_1762_interleave_0"), val = bool(false)]; + tensor var_1762_cast_fp16 = concat(axis = var_68, interleave = var_1762_interleave_0, values = (var_1756_cast_fp16, key_13_cast_fp16))[name = string("op_1762_cast_fp16")]; + tensor encoder_layers_6_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138673408))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(139459904))))[name = string("encoder_layers_6_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_6_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_6_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(139460096)))]; + tensor linear_57_cast_fp16 = linear(bias = encoder_layers_6_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_6_self_attn_linear_q_weight_to_fp16_palettized, x = key_13_cast_fp16)[name = string("linear_57_cast_fp16")]; + tensor var_1767 = const()[name = string("op_1767"), val = tensor([1, -1, 8, 128])]; + tensor q_37_cast_fp16 = reshape(shape = var_1767, x = linear_57_cast_fp16)[name = string("q_37_cast_fp16")]; + tensor encoder_layers_6_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(139462208))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140248704))))[name = string("encoder_layers_6_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_6_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_6_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140248896)))]; + tensor linear_58_cast_fp16 = linear(bias = encoder_layers_6_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_6_self_attn_linear_k_weight_to_fp16_palettized, x = input_351_cast_fp16)[name = string("linear_58_cast_fp16")]; + tensor var_1772 = const()[name = string("op_1772"), val = tensor([1, -1, 8, 128])]; + tensor k_25_cast_fp16 = reshape(shape = var_1772, x = linear_58_cast_fp16)[name = string("k_25_cast_fp16")]; + tensor encoder_layers_6_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140251008))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141037504))))[name = string("encoder_layers_6_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_6_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_6_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141037696)))]; + tensor linear_59_cast_fp16 = linear(bias = encoder_layers_6_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_6_self_attn_linear_v_weight_to_fp16_palettized, x = input_351_cast_fp16)[name = string("linear_59_cast_fp16")]; + tensor var_1777 = const()[name = string("op_1777"), val = tensor([1, -1, 8, 128])]; + tensor v_13_cast_fp16 = reshape(shape = var_1777, x = linear_59_cast_fp16)[name = string("v_13_cast_fp16")]; + tensor value_21_perm_0 = const()[name = string("value_21_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_6_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_6_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141039808)))]; + tensor var_1790_cast_fp16 = add(x = q_37_cast_fp16, y = encoder_layers_6_self_attn_pos_bias_u_to_fp16)[name = string("op_1790_cast_fp16")]; + tensor encoder_layers_6_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_6_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141041920)))]; + tensor var_1792_cast_fp16 = add(x = q_37_cast_fp16, y = encoder_layers_6_self_attn_pos_bias_v_to_fp16)[name = string("op_1792_cast_fp16")]; + tensor q_with_bias_v_13_perm_0 = const()[name = string("q_with_bias_v_13_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_163_transpose_x_0 = const()[name = string("x_163_transpose_x_0"), val = bool(false)]; + bool x_163_transpose_y_0 = const()[name = string("x_163_transpose_y_0"), val = bool(false)]; + tensor op_1794_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141044032))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141143424))))[name = string("op_1794_to_fp16_quantized")]; + tensor q_with_bias_v_13_cast_fp16 = transpose(perm = q_with_bias_v_13_perm_0, x = var_1792_cast_fp16)[name = string("transpose_308")]; + tensor x_163_cast_fp16 = matmul(transpose_x = x_163_transpose_x_0, transpose_y = x_163_transpose_y_0, x = q_with_bias_v_13_cast_fp16, y = op_1794_to_fp16_quantized)[name = string("x_163_cast_fp16")]; + tensor x_165_pad_0 = const()[name = string("x_165_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_165_mode_0 = const()[name = string("x_165_mode_0"), val = string("constant")]; + fp16 const_157_to_fp16 = const()[name = string("const_157_to_fp16"), val = fp16(0x0p+0)]; + tensor x_165_cast_fp16 = pad(constant_val = const_157_to_fp16, mode = x_165_mode_0, pad = x_165_pad_0, x = x_163_cast_fp16)[name = string("x_165_cast_fp16")]; + tensor var_1802 = const()[name = string("op_1802"), val = tensor([1, 8, -1, 7])]; + tensor x_167_cast_fp16 = reshape(shape = var_1802, x = x_165_cast_fp16)[name = string("x_167_cast_fp16")]; + tensor var_1806_begin_0 = const()[name = string("op_1806_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_1806_end_0 = const()[name = string("op_1806_end_0"), val = tensor([1, 8, 98, 7])]; + tensor var_1806_end_mask_0 = const()[name = string("op_1806_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1806_cast_fp16 = slice_by_index(begin = var_1806_begin_0, end = var_1806_end_0, end_mask = var_1806_end_mask_0, x = x_167_cast_fp16)[name = string("op_1806_cast_fp16")]; + tensor var_1807 = const()[name = string("op_1807"), val = tensor([1, 8, 7, 97])]; + tensor matrix_bd_25_cast_fp16 = reshape(shape = var_1807, x = var_1806_cast_fp16)[name = string("matrix_bd_25_cast_fp16")]; + bool matrix_ac_13_transpose_x_0 = const()[name = string("matrix_ac_13_transpose_x_0"), val = bool(false)]; + bool matrix_ac_13_transpose_y_0 = const()[name = string("matrix_ac_13_transpose_y_0"), val = bool(false)]; + tensor transpose_108_perm_0 = const()[name = string("transpose_108_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_109_perm_0 = const()[name = string("transpose_109_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_109 = transpose(perm = transpose_109_perm_0, x = k_25_cast_fp16)[name = string("transpose_306")]; + tensor transpose_108 = transpose(perm = transpose_108_perm_0, x = var_1790_cast_fp16)[name = string("transpose_307")]; + tensor matrix_ac_13_cast_fp16 = matmul(transpose_x = matrix_ac_13_transpose_x_0, transpose_y = matrix_ac_13_transpose_y_0, x = transpose_108, y = transpose_109)[name = string("matrix_ac_13_cast_fp16")]; + tensor matrix_bd_27_begin_0 = const()[name = string("matrix_bd_27_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_27_end_0 = const()[name = string("matrix_bd_27_end_0"), val = tensor([1, 8, 7, 49])]; + tensor matrix_bd_27_end_mask_0 = const()[name = string("matrix_bd_27_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_27_cast_fp16 = slice_by_index(begin = matrix_bd_27_begin_0, end = matrix_bd_27_end_0, end_mask = matrix_bd_27_end_mask_0, x = matrix_bd_25_cast_fp16)[name = string("matrix_bd_27_cast_fp16")]; + tensor var_1816_cast_fp16 = add(x = matrix_ac_13_cast_fp16, y = matrix_bd_27_cast_fp16)[name = string("op_1816_cast_fp16")]; + fp16 _inversed_scores_25_y_0_to_fp16 = const()[name = string("_inversed_scores_25_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_25_cast_fp16 = mul(x = var_1816_cast_fp16, y = _inversed_scores_25_y_0_to_fp16)[name = string("_inversed_scores_25_cast_fp16")]; + tensor scores_27_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_25_cast_fp16, cond = mask_11)[name = string("scores_27_cast_fp16")]; + tensor var_1822_cast_fp16 = softmax(axis = var_59, x = scores_27_cast_fp16)[name = string("op_1822_cast_fp16")]; + tensor input_353_cast_fp16 = select(a = var_44_to_fp16, b = var_1822_cast_fp16, cond = mask_11)[name = string("input_353_cast_fp16")]; + bool x_169_transpose_x_0 = const()[name = string("x_169_transpose_x_0"), val = bool(false)]; + bool x_169_transpose_y_0 = const()[name = string("x_169_transpose_y_0"), val = bool(false)]; + tensor value_21_cast_fp16 = transpose(perm = value_21_perm_0, x = v_13_cast_fp16)[name = string("transpose_305")]; + tensor x_169_cast_fp16 = matmul(transpose_x = x_169_transpose_x_0, transpose_y = x_169_transpose_y_0, x = input_353_cast_fp16, y = value_21_cast_fp16)[name = string("x_169_cast_fp16")]; + tensor var_1826_perm_0 = const()[name = string("op_1826_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1827 = const()[name = string("op_1827"), val = tensor([1, -1, 1024])]; + tensor var_1826_cast_fp16 = transpose(perm = var_1826_perm_0, x = x_169_cast_fp16)[name = string("transpose_304")]; + tensor input_355_cast_fp16 = reshape(shape = var_1827, x = var_1826_cast_fp16)[name = string("input_355_cast_fp16")]; + tensor encoder_layers_6_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141143744))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141930240))))[name = string("encoder_layers_6_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_6_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_6_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141930432)))]; + tensor linear_61_cast_fp16 = linear(bias = encoder_layers_6_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_6_self_attn_linear_out_weight_to_fp16_palettized, x = input_355_cast_fp16)[name = string("linear_61_cast_fp16")]; + tensor input_359_cast_fp16 = add(x = input_349_cast_fp16, y = linear_61_cast_fp16)[name = string("input_359_cast_fp16")]; + tensor x_173_axes_0 = const()[name = string("x_173_axes_0"), val = tensor([-1])]; + tensor encoder_layers_6_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_6_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141932544)))]; + tensor encoder_layers_6_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_6_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141934656)))]; + tensor x_173_cast_fp16 = layer_norm(axes = x_173_axes_0, beta = encoder_layers_6_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_6_norm_conv_weight_to_fp16, x = input_359_cast_fp16)[name = string("x_173_cast_fp16")]; + tensor input_361_perm_0 = const()[name = string("input_361_perm_0"), val = tensor([0, 2, 1])]; + string input_363_pad_type_0 = const()[name = string("input_363_pad_type_0"), val = string("valid")]; + tensor input_363_strides_0 = const()[name = string("input_363_strides_0"), val = tensor([1])]; + tensor input_363_pad_0 = const()[name = string("input_363_pad_0"), val = tensor([0, 0])]; + tensor input_363_dilations_0 = const()[name = string("input_363_dilations_0"), val = tensor([1])]; + int32 input_363_groups_0 = const()[name = string("input_363_groups_0"), val = int32(1)]; + tensor encoder_layers_6_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141936768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144033984))))[name = string("encoder_layers_6_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_361_cast_fp16 = transpose(perm = input_361_perm_0, x = x_173_cast_fp16)[name = string("transpose_303")]; + tensor input_363_cast_fp16 = conv(dilations = input_363_dilations_0, groups = input_363_groups_0, pad = input_363_pad_0, pad_type = input_363_pad_type_0, strides = input_363_strides_0, weight = encoder_layers_6_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_361_cast_fp16)[name = string("input_363_cast_fp16")]; + int32 x_175_split_num_splits_0 = const()[name = string("x_175_split_num_splits_0"), val = int32(2)]; + int32 x_175_split_axis_0 = const()[name = string("x_175_split_axis_0"), val = int32(1)]; + tensor x_175_split_cast_fp16_0, tensor x_175_split_cast_fp16_1 = split(axis = x_175_split_axis_0, num_splits = x_175_split_num_splits_0, x = input_363_cast_fp16)[name = string("x_175_split_cast_fp16")]; + tensor x_175_split_1_sigmoid_cast_fp16 = sigmoid(x = x_175_split_cast_fp16_1)[name = string("x_175_split_1_sigmoid_cast_fp16")]; + tensor x_175_cast_fp16 = mul(x = x_175_split_cast_fp16_0, y = x_175_split_1_sigmoid_cast_fp16)[name = string("x_175_cast_fp16")]; + tensor input_365_cast_fp16 = select(a = var_44_to_fp16, b = x_175_cast_fp16, cond = var_575)[name = string("input_365_cast_fp16")]; + bool new_x_27_interleave_0 = const()[name = string("new_x_27_interleave_0"), val = bool(false)]; + tensor new_x_27_cast_fp16 = concat(axis = var_59, interleave = new_x_27_interleave_0, values = (cache_27_cast_fp16, input_365_cast_fp16))[name = string("new_x_27_cast_fp16")]; + tensor var_1866_begin_0 = const()[name = string("op_1866_begin_0"), val = tensor([0, 0, 7])]; + tensor var_1866_end_0 = const()[name = string("op_1866_end_0"), val = tensor([1, 1024, 15])]; + tensor var_1866_end_mask_0 = const()[name = string("op_1866_end_mask_0"), val = tensor([true, true, true])]; + tensor var_1866_cast_fp16 = slice_by_index(begin = var_1866_begin_0, end = var_1866_end_0, end_mask = var_1866_end_mask_0, x = new_x_27_cast_fp16)[name = string("op_1866_cast_fp16")]; + string x_177_pad_type_0 = const()[name = string("x_177_pad_type_0"), val = string("valid")]; + int32 x_177_groups_0 = const()[name = string("x_177_groups_0"), val = int32(1024)]; + tensor x_177_strides_0 = const()[name = string("x_177_strides_0"), val = tensor([1])]; + tensor x_177_pad_0 = const()[name = string("x_177_pad_0"), val = tensor([0, 0])]; + tensor x_177_dilations_0 = const()[name = string("x_177_dilations_0"), val = tensor([1])]; + tensor encoder_layers_6_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144038144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144047424))))[name = string("encoder_layers_6_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_177_cast_fp16 = conv(dilations = x_177_dilations_0, groups = x_177_groups_0, pad = x_177_pad_0, pad_type = x_177_pad_type_0, strides = x_177_strides_0, weight = encoder_layers_6_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_27_cast_fp16)[name = string("x_177_cast_fp16")]; + tensor input_367_perm_0 = const()[name = string("input_367_perm_0"), val = tensor([0, 2, 1])]; + tensor x_179_axes_0 = const()[name = string("x_179_axes_0"), val = tensor([-1])]; + tensor encoder_layers_6_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_6_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144049536)))]; + tensor encoder_layers_6_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_6_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144051648)))]; + tensor input_367_cast_fp16 = transpose(perm = input_367_perm_0, x = x_177_cast_fp16)[name = string("transpose_302")]; + tensor x_179_cast_fp16 = layer_norm(axes = x_179_axes_0, beta = encoder_layers_6_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_6_conv_batch_norm_weight_to_fp16, x = input_367_cast_fp16)[name = string("x_179_cast_fp16")]; + tensor input_369_perm_0 = const()[name = string("input_369_perm_0"), val = tensor([0, 2, 1])]; + tensor input_369_cast_fp16 = transpose(perm = input_369_perm_0, x = x_179_cast_fp16)[name = string("transpose_301")]; + tensor input_371_cast_fp16 = silu(x = input_369_cast_fp16)[name = string("input_371_cast_fp16")]; + string x_181_pad_type_0 = const()[name = string("x_181_pad_type_0"), val = string("valid")]; + tensor x_181_strides_0 = const()[name = string("x_181_strides_0"), val = tensor([1])]; + tensor x_181_pad_0 = const()[name = string("x_181_pad_0"), val = tensor([0, 0])]; + tensor x_181_dilations_0 = const()[name = string("x_181_dilations_0"), val = tensor([1])]; + int32 x_181_groups_0 = const()[name = string("x_181_groups_0"), val = int32(1)]; + tensor encoder_layers_6_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144053760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145102400))))[name = string("encoder_layers_6_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_181_cast_fp16 = conv(dilations = x_181_dilations_0, groups = x_181_groups_0, pad = x_181_pad_0, pad_type = x_181_pad_type_0, strides = x_181_strides_0, weight = encoder_layers_6_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_371_cast_fp16)[name = string("x_181_cast_fp16")]; + tensor input_373_perm_0 = const()[name = string("input_373_perm_0"), val = tensor([0, 2, 1])]; + tensor input_373_cast_fp16 = transpose(perm = input_373_perm_0, x = x_181_cast_fp16)[name = string("transpose_300")]; + tensor input_375_cast_fp16 = add(x = input_359_cast_fp16, y = input_373_cast_fp16)[name = string("input_375_cast_fp16")]; + tensor input_377_axes_0 = const()[name = string("input_377_axes_0"), val = tensor([-1])]; + tensor encoder_layers_6_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_6_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145104512)))]; + tensor encoder_layers_6_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_6_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145106624)))]; + tensor input_377_cast_fp16 = layer_norm(axes = input_377_axes_0, beta = encoder_layers_6_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_6_norm_feed_forward2_weight_to_fp16, x = input_375_cast_fp16)[name = string("input_377_cast_fp16")]; + tensor encoder_layers_6_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145108736))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(148254528))))[name = string("encoder_layers_6_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_6_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_6_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(148254720)))]; + tensor linear_62_cast_fp16 = linear(bias = encoder_layers_6_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_6_feed_forward2_linear1_weight_to_fp16_palettized, x = input_377_cast_fp16)[name = string("linear_62_cast_fp16")]; + tensor input_381_cast_fp16 = silu(x = linear_62_cast_fp16)[name = string("input_381_cast_fp16")]; + tensor encoder_layers_6_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(148262976))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(151408768))))[name = string("encoder_layers_6_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_6_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_6_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(151408960)))]; + tensor linear_63_cast_fp16 = linear(bias = encoder_layers_6_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_6_feed_forward2_linear2_weight_to_fp16_palettized, x = input_381_cast_fp16)[name = string("linear_63_cast_fp16")]; + fp16 var_1909_to_fp16 = const()[name = string("op_1909_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1910_cast_fp16 = mul(x = linear_63_cast_fp16, y = var_1909_to_fp16)[name = string("op_1910_cast_fp16")]; + tensor input_387_cast_fp16 = add(x = input_375_cast_fp16, y = var_1910_cast_fp16)[name = string("input_387_cast_fp16")]; + tensor input_389_axes_0 = const()[name = string("input_389_axes_0"), val = tensor([-1])]; + tensor encoder_layers_6_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_6_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(151411072)))]; + tensor encoder_layers_6_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_6_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(151413184)))]; + tensor input_389_cast_fp16 = layer_norm(axes = input_389_axes_0, beta = encoder_layers_6_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_6_norm_out_weight_to_fp16, x = input_387_cast_fp16)[name = string("input_389_cast_fp16")]; + tensor cache_29_begin_0 = const()[name = string("cache_29_begin_0"), val = tensor([7, 0, 0, 0])]; + tensor cache_29_end_0 = const()[name = string("cache_29_end_0"), val = tensor([8, 1, 42, 1024])]; + tensor cache_29_end_mask_0 = const()[name = string("cache_29_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_29_squeeze_mask_0 = const()[name = string("cache_29_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_29_cast_fp16 = slice_by_index(begin = cache_29_begin_0, end = cache_29_end_0, end_mask = cache_29_end_mask_0, squeeze_mask = cache_29_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_29_cast_fp16")]; + tensor cache_31_begin_0 = const()[name = string("cache_31_begin_0"), val = tensor([7, 0, 0, 0])]; + tensor cache_31_end_0 = const()[name = string("cache_31_end_0"), val = tensor([8, 1, 1024, 8])]; + tensor cache_31_end_mask_0 = const()[name = string("cache_31_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_31_squeeze_mask_0 = const()[name = string("cache_31_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_31_cast_fp16 = slice_by_index(begin = cache_31_begin_0, end = cache_31_end_0, end_mask = cache_31_end_mask_0, squeeze_mask = cache_31_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_31_cast_fp16")]; + tensor input_391_axes_0 = const()[name = string("input_391_axes_0"), val = tensor([-1])]; + tensor encoder_layers_7_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_7_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(151415296)))]; + tensor encoder_layers_7_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_7_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(151417408)))]; + tensor input_391_cast_fp16 = layer_norm(axes = input_391_axes_0, beta = encoder_layers_7_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_7_norm_feed_forward1_weight_to_fp16, x = input_389_cast_fp16)[name = string("input_391_cast_fp16")]; + tensor encoder_layers_7_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(151419520))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154565312))))[name = string("encoder_layers_7_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_7_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_7_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154565504)))]; + tensor linear_64_cast_fp16 = linear(bias = encoder_layers_7_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_7_feed_forward1_linear1_weight_to_fp16_palettized, x = input_391_cast_fp16)[name = string("linear_64_cast_fp16")]; + tensor input_395_cast_fp16 = silu(x = linear_64_cast_fp16)[name = string("input_395_cast_fp16")]; + tensor encoder_layers_7_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154573760))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157719552))))[name = string("encoder_layers_7_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_7_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_7_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157719744)))]; + tensor linear_65_cast_fp16 = linear(bias = encoder_layers_7_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_7_feed_forward1_linear2_weight_to_fp16_palettized, x = input_395_cast_fp16)[name = string("linear_65_cast_fp16")]; + fp16 var_1946_to_fp16 = const()[name = string("op_1946_to_fp16"), val = fp16(0x1p-1)]; + tensor var_1947_cast_fp16 = mul(x = linear_65_cast_fp16, y = var_1946_to_fp16)[name = string("op_1947_cast_fp16")]; + tensor input_401_cast_fp16 = add(x = input_389_cast_fp16, y = var_1947_cast_fp16)[name = string("input_401_cast_fp16")]; + tensor key_15_axes_0 = const()[name = string("key_15_axes_0"), val = tensor([-1])]; + tensor encoder_layers_7_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_7_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157721856)))]; + tensor encoder_layers_7_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_7_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157723968)))]; + tensor key_15_cast_fp16 = layer_norm(axes = key_15_axes_0, beta = encoder_layers_7_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_7_norm_self_att_weight_to_fp16, x = input_401_cast_fp16)[name = string("key_15_cast_fp16")]; + bool input_403_interleave_0 = const()[name = string("input_403_interleave_0"), val = bool(false)]; + tensor input_403_cast_fp16 = concat(axis = var_68, interleave = input_403_interleave_0, values = (cache_29_cast_fp16, key_15_cast_fp16))[name = string("input_403_cast_fp16")]; + tensor var_1969_begin_0 = const()[name = string("op_1969_begin_0"), val = tensor([0, 7, 0])]; + tensor var_1969_end_0 = const()[name = string("op_1969_end_0"), val = tensor([1, 42, 1024])]; + tensor var_1969_end_mask_0 = const()[name = string("op_1969_end_mask_0"), val = tensor([true, true, true])]; + tensor var_1969_cast_fp16 = slice_by_index(begin = var_1969_begin_0, end = var_1969_end_0, end_mask = var_1969_end_mask_0, x = cache_29_cast_fp16)[name = string("op_1969_cast_fp16")]; + bool var_1975_interleave_0 = const()[name = string("op_1975_interleave_0"), val = bool(false)]; + tensor var_1975_cast_fp16 = concat(axis = var_68, interleave = var_1975_interleave_0, values = (var_1969_cast_fp16, key_15_cast_fp16))[name = string("op_1975_cast_fp16")]; + tensor encoder_layers_7_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157726080))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158512576))))[name = string("encoder_layers_7_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_7_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_7_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158512768)))]; + tensor linear_66_cast_fp16 = linear(bias = encoder_layers_7_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_7_self_attn_linear_q_weight_to_fp16_palettized, x = key_15_cast_fp16)[name = string("linear_66_cast_fp16")]; + tensor var_1980 = const()[name = string("op_1980"), val = tensor([1, -1, 8, 128])]; + tensor q_43_cast_fp16 = reshape(shape = var_1980, x = linear_66_cast_fp16)[name = string("q_43_cast_fp16")]; + tensor encoder_layers_7_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158514880))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(159301376))))[name = string("encoder_layers_7_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_7_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_7_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(159301568)))]; + tensor linear_67_cast_fp16 = linear(bias = encoder_layers_7_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_7_self_attn_linear_k_weight_to_fp16_palettized, x = input_403_cast_fp16)[name = string("linear_67_cast_fp16")]; + tensor var_1985 = const()[name = string("op_1985"), val = tensor([1, -1, 8, 128])]; + tensor k_29_cast_fp16 = reshape(shape = var_1985, x = linear_67_cast_fp16)[name = string("k_29_cast_fp16")]; + tensor encoder_layers_7_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(159303680))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160090176))))[name = string("encoder_layers_7_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_7_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_7_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160090368)))]; + tensor linear_68_cast_fp16 = linear(bias = encoder_layers_7_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_7_self_attn_linear_v_weight_to_fp16_palettized, x = input_403_cast_fp16)[name = string("linear_68_cast_fp16")]; + tensor var_1990 = const()[name = string("op_1990"), val = tensor([1, -1, 8, 128])]; + tensor v_15_cast_fp16 = reshape(shape = var_1990, x = linear_68_cast_fp16)[name = string("v_15_cast_fp16")]; + tensor value_23_perm_0 = const()[name = string("value_23_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_7_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_7_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160092480)))]; + tensor var_2003_cast_fp16 = add(x = q_43_cast_fp16, y = encoder_layers_7_self_attn_pos_bias_u_to_fp16)[name = string("op_2003_cast_fp16")]; + tensor encoder_layers_7_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_7_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160094592)))]; + tensor var_2005_cast_fp16 = add(x = q_43_cast_fp16, y = encoder_layers_7_self_attn_pos_bias_v_to_fp16)[name = string("op_2005_cast_fp16")]; + tensor q_with_bias_v_15_perm_0 = const()[name = string("q_with_bias_v_15_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_189_transpose_x_0 = const()[name = string("x_189_transpose_x_0"), val = bool(false)]; + bool x_189_transpose_y_0 = const()[name = string("x_189_transpose_y_0"), val = bool(false)]; + tensor op_2007_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160096704))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160196096))))[name = string("op_2007_to_fp16_quantized")]; + tensor q_with_bias_v_15_cast_fp16 = transpose(perm = q_with_bias_v_15_perm_0, x = var_2005_cast_fp16)[name = string("transpose_299")]; + tensor x_189_cast_fp16 = matmul(transpose_x = x_189_transpose_x_0, transpose_y = x_189_transpose_y_0, x = q_with_bias_v_15_cast_fp16, y = op_2007_to_fp16_quantized)[name = string("x_189_cast_fp16")]; + tensor x_191_pad_0 = const()[name = string("x_191_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_191_mode_0 = const()[name = string("x_191_mode_0"), val = string("constant")]; + fp16 const_170_to_fp16 = const()[name = string("const_170_to_fp16"), val = fp16(0x0p+0)]; + tensor x_191_cast_fp16 = pad(constant_val = const_170_to_fp16, mode = x_191_mode_0, pad = x_191_pad_0, x = x_189_cast_fp16)[name = string("x_191_cast_fp16")]; + tensor var_2015 = const()[name = string("op_2015"), val = tensor([1, 8, -1, 7])]; + tensor x_193_cast_fp16 = reshape(shape = var_2015, x = x_191_cast_fp16)[name = string("x_193_cast_fp16")]; + tensor var_2019_begin_0 = const()[name = string("op_2019_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_2019_end_0 = const()[name = string("op_2019_end_0"), val = tensor([1, 8, 98, 7])]; + tensor var_2019_end_mask_0 = const()[name = string("op_2019_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_2019_cast_fp16 = slice_by_index(begin = var_2019_begin_0, end = var_2019_end_0, end_mask = var_2019_end_mask_0, x = x_193_cast_fp16)[name = string("op_2019_cast_fp16")]; + tensor var_2020 = const()[name = string("op_2020"), val = tensor([1, 8, 7, 97])]; + tensor matrix_bd_29_cast_fp16 = reshape(shape = var_2020, x = var_2019_cast_fp16)[name = string("matrix_bd_29_cast_fp16")]; + bool matrix_ac_15_transpose_x_0 = const()[name = string("matrix_ac_15_transpose_x_0"), val = bool(false)]; + bool matrix_ac_15_transpose_y_0 = const()[name = string("matrix_ac_15_transpose_y_0"), val = bool(false)]; + tensor transpose_110_perm_0 = const()[name = string("transpose_110_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_111_perm_0 = const()[name = string("transpose_111_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_111 = transpose(perm = transpose_111_perm_0, x = k_29_cast_fp16)[name = string("transpose_297")]; + tensor transpose_110 = transpose(perm = transpose_110_perm_0, x = var_2003_cast_fp16)[name = string("transpose_298")]; + tensor matrix_ac_15_cast_fp16 = matmul(transpose_x = matrix_ac_15_transpose_x_0, transpose_y = matrix_ac_15_transpose_y_0, x = transpose_110, y = transpose_111)[name = string("matrix_ac_15_cast_fp16")]; + tensor matrix_bd_31_begin_0 = const()[name = string("matrix_bd_31_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_31_end_0 = const()[name = string("matrix_bd_31_end_0"), val = tensor([1, 8, 7, 49])]; + tensor matrix_bd_31_end_mask_0 = const()[name = string("matrix_bd_31_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_31_cast_fp16 = slice_by_index(begin = matrix_bd_31_begin_0, end = matrix_bd_31_end_0, end_mask = matrix_bd_31_end_mask_0, x = matrix_bd_29_cast_fp16)[name = string("matrix_bd_31_cast_fp16")]; + tensor var_2029_cast_fp16 = add(x = matrix_ac_15_cast_fp16, y = matrix_bd_31_cast_fp16)[name = string("op_2029_cast_fp16")]; + fp16 _inversed_scores_29_y_0_to_fp16 = const()[name = string("_inversed_scores_29_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_29_cast_fp16 = mul(x = var_2029_cast_fp16, y = _inversed_scores_29_y_0_to_fp16)[name = string("_inversed_scores_29_cast_fp16")]; + tensor scores_31_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_29_cast_fp16, cond = mask_11)[name = string("scores_31_cast_fp16")]; + tensor var_2035_cast_fp16 = softmax(axis = var_59, x = scores_31_cast_fp16)[name = string("op_2035_cast_fp16")]; + tensor input_405_cast_fp16 = select(a = var_44_to_fp16, b = var_2035_cast_fp16, cond = mask_11)[name = string("input_405_cast_fp16")]; + bool x_195_transpose_x_0 = const()[name = string("x_195_transpose_x_0"), val = bool(false)]; + bool x_195_transpose_y_0 = const()[name = string("x_195_transpose_y_0"), val = bool(false)]; + tensor value_23_cast_fp16 = transpose(perm = value_23_perm_0, x = v_15_cast_fp16)[name = string("transpose_296")]; + tensor x_195_cast_fp16 = matmul(transpose_x = x_195_transpose_x_0, transpose_y = x_195_transpose_y_0, x = input_405_cast_fp16, y = value_23_cast_fp16)[name = string("x_195_cast_fp16")]; + tensor var_2039_perm_0 = const()[name = string("op_2039_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2040 = const()[name = string("op_2040"), val = tensor([1, -1, 1024])]; + tensor var_2039_cast_fp16 = transpose(perm = var_2039_perm_0, x = x_195_cast_fp16)[name = string("transpose_295")]; + tensor input_407_cast_fp16 = reshape(shape = var_2040, x = var_2039_cast_fp16)[name = string("input_407_cast_fp16")]; + tensor encoder_layers_7_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160196416))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160982912))))[name = string("encoder_layers_7_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_7_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_7_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160983104)))]; + tensor linear_70_cast_fp16 = linear(bias = encoder_layers_7_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_7_self_attn_linear_out_weight_to_fp16_palettized, x = input_407_cast_fp16)[name = string("linear_70_cast_fp16")]; + tensor input_411_cast_fp16 = add(x = input_401_cast_fp16, y = linear_70_cast_fp16)[name = string("input_411_cast_fp16")]; + tensor x_199_axes_0 = const()[name = string("x_199_axes_0"), val = tensor([-1])]; + tensor encoder_layers_7_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_7_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160985216)))]; + tensor encoder_layers_7_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_7_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160987328)))]; + tensor x_199_cast_fp16 = layer_norm(axes = x_199_axes_0, beta = encoder_layers_7_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_7_norm_conv_weight_to_fp16, x = input_411_cast_fp16)[name = string("x_199_cast_fp16")]; + tensor input_413_perm_0 = const()[name = string("input_413_perm_0"), val = tensor([0, 2, 1])]; + string input_415_pad_type_0 = const()[name = string("input_415_pad_type_0"), val = string("valid")]; + tensor input_415_strides_0 = const()[name = string("input_415_strides_0"), val = tensor([1])]; + tensor input_415_pad_0 = const()[name = string("input_415_pad_0"), val = tensor([0, 0])]; + tensor input_415_dilations_0 = const()[name = string("input_415_dilations_0"), val = tensor([1])]; + int32 input_415_groups_0 = const()[name = string("input_415_groups_0"), val = int32(1)]; + tensor encoder_layers_7_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160989440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163086656))))[name = string("encoder_layers_7_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_413_cast_fp16 = transpose(perm = input_413_perm_0, x = x_199_cast_fp16)[name = string("transpose_294")]; + tensor input_415_cast_fp16 = conv(dilations = input_415_dilations_0, groups = input_415_groups_0, pad = input_415_pad_0, pad_type = input_415_pad_type_0, strides = input_415_strides_0, weight = encoder_layers_7_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_413_cast_fp16)[name = string("input_415_cast_fp16")]; + int32 x_201_split_num_splits_0 = const()[name = string("x_201_split_num_splits_0"), val = int32(2)]; + int32 x_201_split_axis_0 = const()[name = string("x_201_split_axis_0"), val = int32(1)]; + tensor x_201_split_cast_fp16_0, tensor x_201_split_cast_fp16_1 = split(axis = x_201_split_axis_0, num_splits = x_201_split_num_splits_0, x = input_415_cast_fp16)[name = string("x_201_split_cast_fp16")]; + tensor x_201_split_1_sigmoid_cast_fp16 = sigmoid(x = x_201_split_cast_fp16_1)[name = string("x_201_split_1_sigmoid_cast_fp16")]; + tensor x_201_cast_fp16 = mul(x = x_201_split_cast_fp16_0, y = x_201_split_1_sigmoid_cast_fp16)[name = string("x_201_cast_fp16")]; + tensor input_417_cast_fp16 = select(a = var_44_to_fp16, b = x_201_cast_fp16, cond = var_575)[name = string("input_417_cast_fp16")]; + bool new_x_31_interleave_0 = const()[name = string("new_x_31_interleave_0"), val = bool(false)]; + tensor new_x_31_cast_fp16 = concat(axis = var_59, interleave = new_x_31_interleave_0, values = (cache_31_cast_fp16, input_417_cast_fp16))[name = string("new_x_31_cast_fp16")]; + tensor var_2079_begin_0 = const()[name = string("op_2079_begin_0"), val = tensor([0, 0, 7])]; + tensor var_2079_end_0 = const()[name = string("op_2079_end_0"), val = tensor([1, 1024, 15])]; + tensor var_2079_end_mask_0 = const()[name = string("op_2079_end_mask_0"), val = tensor([true, true, true])]; + tensor var_2079_cast_fp16 = slice_by_index(begin = var_2079_begin_0, end = var_2079_end_0, end_mask = var_2079_end_mask_0, x = new_x_31_cast_fp16)[name = string("op_2079_cast_fp16")]; + string x_203_pad_type_0 = const()[name = string("x_203_pad_type_0"), val = string("valid")]; + int32 x_203_groups_0 = const()[name = string("x_203_groups_0"), val = int32(1024)]; + tensor x_203_strides_0 = const()[name = string("x_203_strides_0"), val = tensor([1])]; + tensor x_203_pad_0 = const()[name = string("x_203_pad_0"), val = tensor([0, 0])]; + tensor x_203_dilations_0 = const()[name = string("x_203_dilations_0"), val = tensor([1])]; + tensor encoder_layers_7_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163090816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163100096))))[name = string("encoder_layers_7_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_203_cast_fp16 = conv(dilations = x_203_dilations_0, groups = x_203_groups_0, pad = x_203_pad_0, pad_type = x_203_pad_type_0, strides = x_203_strides_0, weight = encoder_layers_7_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_31_cast_fp16)[name = string("x_203_cast_fp16")]; + tensor input_419_perm_0 = const()[name = string("input_419_perm_0"), val = tensor([0, 2, 1])]; + tensor x_205_axes_0 = const()[name = string("x_205_axes_0"), val = tensor([-1])]; + tensor encoder_layers_7_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_7_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163102208)))]; + tensor encoder_layers_7_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_7_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163104320)))]; + tensor input_419_cast_fp16 = transpose(perm = input_419_perm_0, x = x_203_cast_fp16)[name = string("transpose_293")]; + tensor x_205_cast_fp16 = layer_norm(axes = x_205_axes_0, beta = encoder_layers_7_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_7_conv_batch_norm_weight_to_fp16, x = input_419_cast_fp16)[name = string("x_205_cast_fp16")]; + tensor input_421_perm_0 = const()[name = string("input_421_perm_0"), val = tensor([0, 2, 1])]; + tensor input_421_cast_fp16 = transpose(perm = input_421_perm_0, x = x_205_cast_fp16)[name = string("transpose_292")]; + tensor input_423_cast_fp16 = silu(x = input_421_cast_fp16)[name = string("input_423_cast_fp16")]; + string x_207_pad_type_0 = const()[name = string("x_207_pad_type_0"), val = string("valid")]; + tensor x_207_strides_0 = const()[name = string("x_207_strides_0"), val = tensor([1])]; + tensor x_207_pad_0 = const()[name = string("x_207_pad_0"), val = tensor([0, 0])]; + tensor x_207_dilations_0 = const()[name = string("x_207_dilations_0"), val = tensor([1])]; + int32 x_207_groups_0 = const()[name = string("x_207_groups_0"), val = int32(1)]; + tensor encoder_layers_7_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163106432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164155072))))[name = string("encoder_layers_7_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_207_cast_fp16 = conv(dilations = x_207_dilations_0, groups = x_207_groups_0, pad = x_207_pad_0, pad_type = x_207_pad_type_0, strides = x_207_strides_0, weight = encoder_layers_7_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_423_cast_fp16)[name = string("x_207_cast_fp16")]; + tensor input_425_perm_0 = const()[name = string("input_425_perm_0"), val = tensor([0, 2, 1])]; + tensor input_425_cast_fp16 = transpose(perm = input_425_perm_0, x = x_207_cast_fp16)[name = string("transpose_291")]; + tensor input_427_cast_fp16 = add(x = input_411_cast_fp16, y = input_425_cast_fp16)[name = string("input_427_cast_fp16")]; + tensor input_429_axes_0 = const()[name = string("input_429_axes_0"), val = tensor([-1])]; + tensor encoder_layers_7_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_7_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164157184)))]; + tensor encoder_layers_7_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_7_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164159296)))]; + tensor input_429_cast_fp16 = layer_norm(axes = input_429_axes_0, beta = encoder_layers_7_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_7_norm_feed_forward2_weight_to_fp16, x = input_427_cast_fp16)[name = string("input_429_cast_fp16")]; + tensor encoder_layers_7_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164161408))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167307200))))[name = string("encoder_layers_7_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_7_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_7_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167307392)))]; + tensor linear_71_cast_fp16 = linear(bias = encoder_layers_7_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_7_feed_forward2_linear1_weight_to_fp16_palettized, x = input_429_cast_fp16)[name = string("linear_71_cast_fp16")]; + tensor input_433_cast_fp16 = silu(x = linear_71_cast_fp16)[name = string("input_433_cast_fp16")]; + tensor encoder_layers_7_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167315648))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170461440))))[name = string("encoder_layers_7_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_7_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_7_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170461632)))]; + tensor linear_72_cast_fp16 = linear(bias = encoder_layers_7_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_7_feed_forward2_linear2_weight_to_fp16_palettized, x = input_433_cast_fp16)[name = string("linear_72_cast_fp16")]; + fp16 var_2122_to_fp16 = const()[name = string("op_2122_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2123_cast_fp16 = mul(x = linear_72_cast_fp16, y = var_2122_to_fp16)[name = string("op_2123_cast_fp16")]; + tensor input_439_cast_fp16 = add(x = input_427_cast_fp16, y = var_2123_cast_fp16)[name = string("input_439_cast_fp16")]; + tensor input_441_axes_0 = const()[name = string("input_441_axes_0"), val = tensor([-1])]; + tensor encoder_layers_7_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_7_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170463744)))]; + tensor encoder_layers_7_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_7_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170465856)))]; + tensor input_441_cast_fp16 = layer_norm(axes = input_441_axes_0, beta = encoder_layers_7_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_7_norm_out_weight_to_fp16, x = input_439_cast_fp16)[name = string("input_441_cast_fp16")]; + tensor cache_33_begin_0 = const()[name = string("cache_33_begin_0"), val = tensor([8, 0, 0, 0])]; + tensor cache_33_end_0 = const()[name = string("cache_33_end_0"), val = tensor([9, 1, 42, 1024])]; + tensor cache_33_end_mask_0 = const()[name = string("cache_33_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_33_squeeze_mask_0 = const()[name = string("cache_33_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_33_cast_fp16 = slice_by_index(begin = cache_33_begin_0, end = cache_33_end_0, end_mask = cache_33_end_mask_0, squeeze_mask = cache_33_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_33_cast_fp16")]; + tensor cache_35_begin_0 = const()[name = string("cache_35_begin_0"), val = tensor([8, 0, 0, 0])]; + tensor cache_35_end_0 = const()[name = string("cache_35_end_0"), val = tensor([9, 1, 1024, 8])]; + tensor cache_35_end_mask_0 = const()[name = string("cache_35_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_35_squeeze_mask_0 = const()[name = string("cache_35_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_35_cast_fp16 = slice_by_index(begin = cache_35_begin_0, end = cache_35_end_0, end_mask = cache_35_end_mask_0, squeeze_mask = cache_35_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_35_cast_fp16")]; + tensor input_443_axes_0 = const()[name = string("input_443_axes_0"), val = tensor([-1])]; + tensor encoder_layers_8_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_8_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170467968)))]; + tensor encoder_layers_8_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_8_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170470080)))]; + tensor input_443_cast_fp16 = layer_norm(axes = input_443_axes_0, beta = encoder_layers_8_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_8_norm_feed_forward1_weight_to_fp16, x = input_441_cast_fp16)[name = string("input_443_cast_fp16")]; + tensor encoder_layers_8_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170472192))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(173617984))))[name = string("encoder_layers_8_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_8_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_8_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(173618176)))]; + tensor linear_73_cast_fp16 = linear(bias = encoder_layers_8_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_8_feed_forward1_linear1_weight_to_fp16_palettized, x = input_443_cast_fp16)[name = string("linear_73_cast_fp16")]; + tensor input_447_cast_fp16 = silu(x = linear_73_cast_fp16)[name = string("input_447_cast_fp16")]; + tensor encoder_layers_8_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(173626432))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176772224))))[name = string("encoder_layers_8_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_8_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_8_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176772416)))]; + tensor linear_74_cast_fp16 = linear(bias = encoder_layers_8_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_8_feed_forward1_linear2_weight_to_fp16_palettized, x = input_447_cast_fp16)[name = string("linear_74_cast_fp16")]; + fp16 var_2159_to_fp16 = const()[name = string("op_2159_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2160_cast_fp16 = mul(x = linear_74_cast_fp16, y = var_2159_to_fp16)[name = string("op_2160_cast_fp16")]; + tensor input_453_cast_fp16 = add(x = input_441_cast_fp16, y = var_2160_cast_fp16)[name = string("input_453_cast_fp16")]; + tensor key_17_axes_0 = const()[name = string("key_17_axes_0"), val = tensor([-1])]; + tensor encoder_layers_8_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_8_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176774528)))]; + tensor encoder_layers_8_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_8_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176776640)))]; + tensor key_17_cast_fp16 = layer_norm(axes = key_17_axes_0, beta = encoder_layers_8_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_8_norm_self_att_weight_to_fp16, x = input_453_cast_fp16)[name = string("key_17_cast_fp16")]; + bool input_455_interleave_0 = const()[name = string("input_455_interleave_0"), val = bool(false)]; + tensor input_455_cast_fp16 = concat(axis = var_68, interleave = input_455_interleave_0, values = (cache_33_cast_fp16, key_17_cast_fp16))[name = string("input_455_cast_fp16")]; + tensor var_2182_begin_0 = const()[name = string("op_2182_begin_0"), val = tensor([0, 7, 0])]; + tensor var_2182_end_0 = const()[name = string("op_2182_end_0"), val = tensor([1, 42, 1024])]; + tensor var_2182_end_mask_0 = const()[name = string("op_2182_end_mask_0"), val = tensor([true, true, true])]; + tensor var_2182_cast_fp16 = slice_by_index(begin = var_2182_begin_0, end = var_2182_end_0, end_mask = var_2182_end_mask_0, x = cache_33_cast_fp16)[name = string("op_2182_cast_fp16")]; + bool var_2188_interleave_0 = const()[name = string("op_2188_interleave_0"), val = bool(false)]; + tensor var_2188_cast_fp16 = concat(axis = var_68, interleave = var_2188_interleave_0, values = (var_2182_cast_fp16, key_17_cast_fp16))[name = string("op_2188_cast_fp16")]; + tensor encoder_layers_8_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176778752))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177565248))))[name = string("encoder_layers_8_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_8_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_8_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177565440)))]; + tensor linear_75_cast_fp16 = linear(bias = encoder_layers_8_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_8_self_attn_linear_q_weight_to_fp16_palettized, x = key_17_cast_fp16)[name = string("linear_75_cast_fp16")]; + tensor var_2193 = const()[name = string("op_2193"), val = tensor([1, -1, 8, 128])]; + tensor q_49_cast_fp16 = reshape(shape = var_2193, x = linear_75_cast_fp16)[name = string("q_49_cast_fp16")]; + tensor encoder_layers_8_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177567552))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(178354048))))[name = string("encoder_layers_8_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_8_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_8_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(178354240)))]; + tensor linear_76_cast_fp16 = linear(bias = encoder_layers_8_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_8_self_attn_linear_k_weight_to_fp16_palettized, x = input_455_cast_fp16)[name = string("linear_76_cast_fp16")]; + tensor var_2198 = const()[name = string("op_2198"), val = tensor([1, -1, 8, 128])]; + tensor k_33_cast_fp16 = reshape(shape = var_2198, x = linear_76_cast_fp16)[name = string("k_33_cast_fp16")]; + tensor encoder_layers_8_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(178356352))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179142848))))[name = string("encoder_layers_8_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_8_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_8_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179143040)))]; + tensor linear_77_cast_fp16 = linear(bias = encoder_layers_8_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_8_self_attn_linear_v_weight_to_fp16_palettized, x = input_455_cast_fp16)[name = string("linear_77_cast_fp16")]; + tensor var_2203 = const()[name = string("op_2203"), val = tensor([1, -1, 8, 128])]; + tensor v_17_cast_fp16 = reshape(shape = var_2203, x = linear_77_cast_fp16)[name = string("v_17_cast_fp16")]; + tensor value_25_perm_0 = const()[name = string("value_25_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_8_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_8_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179145152)))]; + tensor var_2216_cast_fp16 = add(x = q_49_cast_fp16, y = encoder_layers_8_self_attn_pos_bias_u_to_fp16)[name = string("op_2216_cast_fp16")]; + tensor encoder_layers_8_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_8_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179147264)))]; + tensor var_2218_cast_fp16 = add(x = q_49_cast_fp16, y = encoder_layers_8_self_attn_pos_bias_v_to_fp16)[name = string("op_2218_cast_fp16")]; + tensor q_with_bias_v_17_perm_0 = const()[name = string("q_with_bias_v_17_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_215_transpose_x_0 = const()[name = string("x_215_transpose_x_0"), val = bool(false)]; + bool x_215_transpose_y_0 = const()[name = string("x_215_transpose_y_0"), val = bool(false)]; + tensor op_2220_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179149376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179248768))))[name = string("op_2220_to_fp16_quantized")]; + tensor q_with_bias_v_17_cast_fp16 = transpose(perm = q_with_bias_v_17_perm_0, x = var_2218_cast_fp16)[name = string("transpose_290")]; + tensor x_215_cast_fp16 = matmul(transpose_x = x_215_transpose_x_0, transpose_y = x_215_transpose_y_0, x = q_with_bias_v_17_cast_fp16, y = op_2220_to_fp16_quantized)[name = string("x_215_cast_fp16")]; + tensor x_217_pad_0 = const()[name = string("x_217_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_217_mode_0 = const()[name = string("x_217_mode_0"), val = string("constant")]; + fp16 const_183_to_fp16 = const()[name = string("const_183_to_fp16"), val = fp16(0x0p+0)]; + tensor x_217_cast_fp16 = pad(constant_val = const_183_to_fp16, mode = x_217_mode_0, pad = x_217_pad_0, x = x_215_cast_fp16)[name = string("x_217_cast_fp16")]; + tensor var_2228 = const()[name = string("op_2228"), val = tensor([1, 8, -1, 7])]; + tensor x_219_cast_fp16 = reshape(shape = var_2228, x = x_217_cast_fp16)[name = string("x_219_cast_fp16")]; + tensor var_2232_begin_0 = const()[name = string("op_2232_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_2232_end_0 = const()[name = string("op_2232_end_0"), val = tensor([1, 8, 98, 7])]; + tensor var_2232_end_mask_0 = const()[name = string("op_2232_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_2232_cast_fp16 = slice_by_index(begin = var_2232_begin_0, end = var_2232_end_0, end_mask = var_2232_end_mask_0, x = x_219_cast_fp16)[name = string("op_2232_cast_fp16")]; + tensor var_2233 = const()[name = string("op_2233"), val = tensor([1, 8, 7, 97])]; + tensor matrix_bd_33_cast_fp16 = reshape(shape = var_2233, x = var_2232_cast_fp16)[name = string("matrix_bd_33_cast_fp16")]; + bool matrix_ac_17_transpose_x_0 = const()[name = string("matrix_ac_17_transpose_x_0"), val = bool(false)]; + bool matrix_ac_17_transpose_y_0 = const()[name = string("matrix_ac_17_transpose_y_0"), val = bool(false)]; + tensor transpose_112_perm_0 = const()[name = string("transpose_112_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_113_perm_0 = const()[name = string("transpose_113_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_113 = transpose(perm = transpose_113_perm_0, x = k_33_cast_fp16)[name = string("transpose_288")]; + tensor transpose_112 = transpose(perm = transpose_112_perm_0, x = var_2216_cast_fp16)[name = string("transpose_289")]; + tensor matrix_ac_17_cast_fp16 = matmul(transpose_x = matrix_ac_17_transpose_x_0, transpose_y = matrix_ac_17_transpose_y_0, x = transpose_112, y = transpose_113)[name = string("matrix_ac_17_cast_fp16")]; + tensor matrix_bd_35_begin_0 = const()[name = string("matrix_bd_35_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_35_end_0 = const()[name = string("matrix_bd_35_end_0"), val = tensor([1, 8, 7, 49])]; + tensor matrix_bd_35_end_mask_0 = const()[name = string("matrix_bd_35_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_35_cast_fp16 = slice_by_index(begin = matrix_bd_35_begin_0, end = matrix_bd_35_end_0, end_mask = matrix_bd_35_end_mask_0, x = matrix_bd_33_cast_fp16)[name = string("matrix_bd_35_cast_fp16")]; + tensor var_2242_cast_fp16 = add(x = matrix_ac_17_cast_fp16, y = matrix_bd_35_cast_fp16)[name = string("op_2242_cast_fp16")]; + fp16 _inversed_scores_33_y_0_to_fp16 = const()[name = string("_inversed_scores_33_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_33_cast_fp16 = mul(x = var_2242_cast_fp16, y = _inversed_scores_33_y_0_to_fp16)[name = string("_inversed_scores_33_cast_fp16")]; + tensor scores_35_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_33_cast_fp16, cond = mask_11)[name = string("scores_35_cast_fp16")]; + tensor var_2248_cast_fp16 = softmax(axis = var_59, x = scores_35_cast_fp16)[name = string("op_2248_cast_fp16")]; + tensor input_457_cast_fp16 = select(a = var_44_to_fp16, b = var_2248_cast_fp16, cond = mask_11)[name = string("input_457_cast_fp16")]; + bool x_221_transpose_x_0 = const()[name = string("x_221_transpose_x_0"), val = bool(false)]; + bool x_221_transpose_y_0 = const()[name = string("x_221_transpose_y_0"), val = bool(false)]; + tensor value_25_cast_fp16 = transpose(perm = value_25_perm_0, x = v_17_cast_fp16)[name = string("transpose_287")]; + tensor x_221_cast_fp16 = matmul(transpose_x = x_221_transpose_x_0, transpose_y = x_221_transpose_y_0, x = input_457_cast_fp16, y = value_25_cast_fp16)[name = string("x_221_cast_fp16")]; + tensor var_2252_perm_0 = const()[name = string("op_2252_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2253 = const()[name = string("op_2253"), val = tensor([1, -1, 1024])]; + tensor var_2252_cast_fp16 = transpose(perm = var_2252_perm_0, x = x_221_cast_fp16)[name = string("transpose_286")]; + tensor input_459_cast_fp16 = reshape(shape = var_2253, x = var_2252_cast_fp16)[name = string("input_459_cast_fp16")]; + tensor encoder_layers_8_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179249088))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180035584))))[name = string("encoder_layers_8_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_8_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_8_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180035776)))]; + tensor linear_79_cast_fp16 = linear(bias = encoder_layers_8_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_8_self_attn_linear_out_weight_to_fp16_palettized, x = input_459_cast_fp16)[name = string("linear_79_cast_fp16")]; + tensor input_463_cast_fp16 = add(x = input_453_cast_fp16, y = linear_79_cast_fp16)[name = string("input_463_cast_fp16")]; + tensor x_225_axes_0 = const()[name = string("x_225_axes_0"), val = tensor([-1])]; + tensor encoder_layers_8_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_8_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180037888)))]; + tensor encoder_layers_8_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_8_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180040000)))]; + tensor x_225_cast_fp16 = layer_norm(axes = x_225_axes_0, beta = encoder_layers_8_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_8_norm_conv_weight_to_fp16, x = input_463_cast_fp16)[name = string("x_225_cast_fp16")]; + tensor input_465_perm_0 = const()[name = string("input_465_perm_0"), val = tensor([0, 2, 1])]; + string input_467_pad_type_0 = const()[name = string("input_467_pad_type_0"), val = string("valid")]; + tensor input_467_strides_0 = const()[name = string("input_467_strides_0"), val = tensor([1])]; + tensor input_467_pad_0 = const()[name = string("input_467_pad_0"), val = tensor([0, 0])]; + tensor input_467_dilations_0 = const()[name = string("input_467_dilations_0"), val = tensor([1])]; + int32 input_467_groups_0 = const()[name = string("input_467_groups_0"), val = int32(1)]; + tensor encoder_layers_8_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180042112))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(182139328))))[name = string("encoder_layers_8_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_465_cast_fp16 = transpose(perm = input_465_perm_0, x = x_225_cast_fp16)[name = string("transpose_285")]; + tensor input_467_cast_fp16 = conv(dilations = input_467_dilations_0, groups = input_467_groups_0, pad = input_467_pad_0, pad_type = input_467_pad_type_0, strides = input_467_strides_0, weight = encoder_layers_8_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_465_cast_fp16)[name = string("input_467_cast_fp16")]; + int32 x_227_split_num_splits_0 = const()[name = string("x_227_split_num_splits_0"), val = int32(2)]; + int32 x_227_split_axis_0 = const()[name = string("x_227_split_axis_0"), val = int32(1)]; + tensor x_227_split_cast_fp16_0, tensor x_227_split_cast_fp16_1 = split(axis = x_227_split_axis_0, num_splits = x_227_split_num_splits_0, x = input_467_cast_fp16)[name = string("x_227_split_cast_fp16")]; + tensor x_227_split_1_sigmoid_cast_fp16 = sigmoid(x = x_227_split_cast_fp16_1)[name = string("x_227_split_1_sigmoid_cast_fp16")]; + tensor x_227_cast_fp16 = mul(x = x_227_split_cast_fp16_0, y = x_227_split_1_sigmoid_cast_fp16)[name = string("x_227_cast_fp16")]; + tensor input_469_cast_fp16 = select(a = var_44_to_fp16, b = x_227_cast_fp16, cond = var_575)[name = string("input_469_cast_fp16")]; + bool new_x_35_interleave_0 = const()[name = string("new_x_35_interleave_0"), val = bool(false)]; + tensor new_x_35_cast_fp16 = concat(axis = var_59, interleave = new_x_35_interleave_0, values = (cache_35_cast_fp16, input_469_cast_fp16))[name = string("new_x_35_cast_fp16")]; + tensor var_2292_begin_0 = const()[name = string("op_2292_begin_0"), val = tensor([0, 0, 7])]; + tensor var_2292_end_0 = const()[name = string("op_2292_end_0"), val = tensor([1, 1024, 15])]; + tensor var_2292_end_mask_0 = const()[name = string("op_2292_end_mask_0"), val = tensor([true, true, true])]; + tensor var_2292_cast_fp16 = slice_by_index(begin = var_2292_begin_0, end = var_2292_end_0, end_mask = var_2292_end_mask_0, x = new_x_35_cast_fp16)[name = string("op_2292_cast_fp16")]; + string x_229_pad_type_0 = const()[name = string("x_229_pad_type_0"), val = string("valid")]; + int32 x_229_groups_0 = const()[name = string("x_229_groups_0"), val = int32(1024)]; + tensor x_229_strides_0 = const()[name = string("x_229_strides_0"), val = tensor([1])]; + tensor x_229_pad_0 = const()[name = string("x_229_pad_0"), val = tensor([0, 0])]; + tensor x_229_dilations_0 = const()[name = string("x_229_dilations_0"), val = tensor([1])]; + tensor encoder_layers_8_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(182143488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(182152768))))[name = string("encoder_layers_8_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_229_cast_fp16 = conv(dilations = x_229_dilations_0, groups = x_229_groups_0, pad = x_229_pad_0, pad_type = x_229_pad_type_0, strides = x_229_strides_0, weight = encoder_layers_8_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_35_cast_fp16)[name = string("x_229_cast_fp16")]; + tensor input_471_perm_0 = const()[name = string("input_471_perm_0"), val = tensor([0, 2, 1])]; + tensor x_231_axes_0 = const()[name = string("x_231_axes_0"), val = tensor([-1])]; + tensor encoder_layers_8_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_8_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(182154880)))]; + tensor encoder_layers_8_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_8_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(182156992)))]; + tensor input_471_cast_fp16 = transpose(perm = input_471_perm_0, x = x_229_cast_fp16)[name = string("transpose_284")]; + tensor x_231_cast_fp16 = layer_norm(axes = x_231_axes_0, beta = encoder_layers_8_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_8_conv_batch_norm_weight_to_fp16, x = input_471_cast_fp16)[name = string("x_231_cast_fp16")]; + tensor input_473_perm_0 = const()[name = string("input_473_perm_0"), val = tensor([0, 2, 1])]; + tensor input_473_cast_fp16 = transpose(perm = input_473_perm_0, x = x_231_cast_fp16)[name = string("transpose_283")]; + tensor input_475_cast_fp16 = silu(x = input_473_cast_fp16)[name = string("input_475_cast_fp16")]; + string x_233_pad_type_0 = const()[name = string("x_233_pad_type_0"), val = string("valid")]; + tensor x_233_strides_0 = const()[name = string("x_233_strides_0"), val = tensor([1])]; + tensor x_233_pad_0 = const()[name = string("x_233_pad_0"), val = tensor([0, 0])]; + tensor x_233_dilations_0 = const()[name = string("x_233_dilations_0"), val = tensor([1])]; + int32 x_233_groups_0 = const()[name = string("x_233_groups_0"), val = int32(1)]; + tensor encoder_layers_8_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(182159104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(183207744))))[name = string("encoder_layers_8_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_233_cast_fp16 = conv(dilations = x_233_dilations_0, groups = x_233_groups_0, pad = x_233_pad_0, pad_type = x_233_pad_type_0, strides = x_233_strides_0, weight = encoder_layers_8_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_475_cast_fp16)[name = string("x_233_cast_fp16")]; + tensor input_477_perm_0 = const()[name = string("input_477_perm_0"), val = tensor([0, 2, 1])]; + tensor input_477_cast_fp16 = transpose(perm = input_477_perm_0, x = x_233_cast_fp16)[name = string("transpose_282")]; + tensor input_479_cast_fp16 = add(x = input_463_cast_fp16, y = input_477_cast_fp16)[name = string("input_479_cast_fp16")]; + tensor input_481_axes_0 = const()[name = string("input_481_axes_0"), val = tensor([-1])]; + tensor encoder_layers_8_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_8_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(183209856)))]; + tensor encoder_layers_8_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_8_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(183211968)))]; + tensor input_481_cast_fp16 = layer_norm(axes = input_481_axes_0, beta = encoder_layers_8_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_8_norm_feed_forward2_weight_to_fp16, x = input_479_cast_fp16)[name = string("input_481_cast_fp16")]; + tensor encoder_layers_8_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(183214080))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(186359872))))[name = string("encoder_layers_8_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_8_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_8_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(186360064)))]; + tensor linear_80_cast_fp16 = linear(bias = encoder_layers_8_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_8_feed_forward2_linear1_weight_to_fp16_palettized, x = input_481_cast_fp16)[name = string("linear_80_cast_fp16")]; + tensor input_485_cast_fp16 = silu(x = linear_80_cast_fp16)[name = string("input_485_cast_fp16")]; + tensor encoder_layers_8_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(186368320))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189514112))))[name = string("encoder_layers_8_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_8_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_8_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189514304)))]; + tensor linear_81_cast_fp16 = linear(bias = encoder_layers_8_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_8_feed_forward2_linear2_weight_to_fp16_palettized, x = input_485_cast_fp16)[name = string("linear_81_cast_fp16")]; + fp16 var_2335_to_fp16 = const()[name = string("op_2335_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2336_cast_fp16 = mul(x = linear_81_cast_fp16, y = var_2335_to_fp16)[name = string("op_2336_cast_fp16")]; + tensor input_491_cast_fp16 = add(x = input_479_cast_fp16, y = var_2336_cast_fp16)[name = string("input_491_cast_fp16")]; + tensor input_493_axes_0 = const()[name = string("input_493_axes_0"), val = tensor([-1])]; + tensor encoder_layers_8_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_8_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189516416)))]; + tensor encoder_layers_8_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_8_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189518528)))]; + tensor input_493_cast_fp16 = layer_norm(axes = input_493_axes_0, beta = encoder_layers_8_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_8_norm_out_weight_to_fp16, x = input_491_cast_fp16)[name = string("input_493_cast_fp16")]; + tensor cache_37_begin_0 = const()[name = string("cache_37_begin_0"), val = tensor([9, 0, 0, 0])]; + tensor cache_37_end_0 = const()[name = string("cache_37_end_0"), val = tensor([10, 1, 42, 1024])]; + tensor cache_37_end_mask_0 = const()[name = string("cache_37_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_37_squeeze_mask_0 = const()[name = string("cache_37_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_37_cast_fp16 = slice_by_index(begin = cache_37_begin_0, end = cache_37_end_0, end_mask = cache_37_end_mask_0, squeeze_mask = cache_37_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_37_cast_fp16")]; + tensor cache_39_begin_0 = const()[name = string("cache_39_begin_0"), val = tensor([9, 0, 0, 0])]; + tensor cache_39_end_0 = const()[name = string("cache_39_end_0"), val = tensor([10, 1, 1024, 8])]; + tensor cache_39_end_mask_0 = const()[name = string("cache_39_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_39_squeeze_mask_0 = const()[name = string("cache_39_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_39_cast_fp16 = slice_by_index(begin = cache_39_begin_0, end = cache_39_end_0, end_mask = cache_39_end_mask_0, squeeze_mask = cache_39_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_39_cast_fp16")]; + tensor input_495_axes_0 = const()[name = string("input_495_axes_0"), val = tensor([-1])]; + tensor encoder_layers_9_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_9_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189520640)))]; + tensor encoder_layers_9_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_9_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189522752)))]; + tensor input_495_cast_fp16 = layer_norm(axes = input_495_axes_0, beta = encoder_layers_9_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_9_norm_feed_forward1_weight_to_fp16, x = input_493_cast_fp16)[name = string("input_495_cast_fp16")]; + tensor encoder_layers_9_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189524864))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(192670656))))[name = string("encoder_layers_9_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_9_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_9_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(192670848)))]; + tensor linear_82_cast_fp16 = linear(bias = encoder_layers_9_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_9_feed_forward1_linear1_weight_to_fp16_palettized, x = input_495_cast_fp16)[name = string("linear_82_cast_fp16")]; + tensor input_499_cast_fp16 = silu(x = linear_82_cast_fp16)[name = string("input_499_cast_fp16")]; + tensor encoder_layers_9_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(192679104))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(195824896))))[name = string("encoder_layers_9_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_9_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_9_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(195825088)))]; + tensor linear_83_cast_fp16 = linear(bias = encoder_layers_9_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_9_feed_forward1_linear2_weight_to_fp16_palettized, x = input_499_cast_fp16)[name = string("linear_83_cast_fp16")]; + fp16 var_2372_to_fp16 = const()[name = string("op_2372_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2373_cast_fp16 = mul(x = linear_83_cast_fp16, y = var_2372_to_fp16)[name = string("op_2373_cast_fp16")]; + tensor input_505_cast_fp16 = add(x = input_493_cast_fp16, y = var_2373_cast_fp16)[name = string("input_505_cast_fp16")]; + tensor key_19_axes_0 = const()[name = string("key_19_axes_0"), val = tensor([-1])]; + tensor encoder_layers_9_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_9_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(195827200)))]; + tensor encoder_layers_9_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_9_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(195829312)))]; + tensor key_19_cast_fp16 = layer_norm(axes = key_19_axes_0, beta = encoder_layers_9_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_9_norm_self_att_weight_to_fp16, x = input_505_cast_fp16)[name = string("key_19_cast_fp16")]; + bool input_507_interleave_0 = const()[name = string("input_507_interleave_0"), val = bool(false)]; + tensor input_507_cast_fp16 = concat(axis = var_68, interleave = input_507_interleave_0, values = (cache_37_cast_fp16, key_19_cast_fp16))[name = string("input_507_cast_fp16")]; + tensor var_2395_begin_0 = const()[name = string("op_2395_begin_0"), val = tensor([0, 7, 0])]; + tensor var_2395_end_0 = const()[name = string("op_2395_end_0"), val = tensor([1, 42, 1024])]; + tensor var_2395_end_mask_0 = const()[name = string("op_2395_end_mask_0"), val = tensor([true, true, true])]; + tensor var_2395_cast_fp16 = slice_by_index(begin = var_2395_begin_0, end = var_2395_end_0, end_mask = var_2395_end_mask_0, x = cache_37_cast_fp16)[name = string("op_2395_cast_fp16")]; + bool var_2401_interleave_0 = const()[name = string("op_2401_interleave_0"), val = bool(false)]; + tensor var_2401_cast_fp16 = concat(axis = var_68, interleave = var_2401_interleave_0, values = (var_2395_cast_fp16, key_19_cast_fp16))[name = string("op_2401_cast_fp16")]; + tensor encoder_layers_9_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(195831424))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196617920))))[name = string("encoder_layers_9_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_9_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_9_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196618112)))]; + tensor linear_84_cast_fp16 = linear(bias = encoder_layers_9_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_9_self_attn_linear_q_weight_to_fp16_palettized, x = key_19_cast_fp16)[name = string("linear_84_cast_fp16")]; + tensor var_2406 = const()[name = string("op_2406"), val = tensor([1, -1, 8, 128])]; + tensor q_55_cast_fp16 = reshape(shape = var_2406, x = linear_84_cast_fp16)[name = string("q_55_cast_fp16")]; + tensor encoder_layers_9_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196620224))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197406720))))[name = string("encoder_layers_9_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_9_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_9_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197406912)))]; + tensor linear_85_cast_fp16 = linear(bias = encoder_layers_9_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_9_self_attn_linear_k_weight_to_fp16_palettized, x = input_507_cast_fp16)[name = string("linear_85_cast_fp16")]; + tensor var_2411 = const()[name = string("op_2411"), val = tensor([1, -1, 8, 128])]; + tensor k_37_cast_fp16 = reshape(shape = var_2411, x = linear_85_cast_fp16)[name = string("k_37_cast_fp16")]; + tensor encoder_layers_9_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197409024))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198195520))))[name = string("encoder_layers_9_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_9_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_9_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198195712)))]; + tensor linear_86_cast_fp16 = linear(bias = encoder_layers_9_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_9_self_attn_linear_v_weight_to_fp16_palettized, x = input_507_cast_fp16)[name = string("linear_86_cast_fp16")]; + tensor var_2416 = const()[name = string("op_2416"), val = tensor([1, -1, 8, 128])]; + tensor v_19_cast_fp16 = reshape(shape = var_2416, x = linear_86_cast_fp16)[name = string("v_19_cast_fp16")]; + tensor value_27_perm_0 = const()[name = string("value_27_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_9_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_9_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198197824)))]; + tensor var_2429_cast_fp16 = add(x = q_55_cast_fp16, y = encoder_layers_9_self_attn_pos_bias_u_to_fp16)[name = string("op_2429_cast_fp16")]; + tensor encoder_layers_9_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_9_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198199936)))]; + tensor var_2431_cast_fp16 = add(x = q_55_cast_fp16, y = encoder_layers_9_self_attn_pos_bias_v_to_fp16)[name = string("op_2431_cast_fp16")]; + tensor q_with_bias_v_19_perm_0 = const()[name = string("q_with_bias_v_19_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_241_transpose_x_0 = const()[name = string("x_241_transpose_x_0"), val = bool(false)]; + bool x_241_transpose_y_0 = const()[name = string("x_241_transpose_y_0"), val = bool(false)]; + tensor op_2433_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198202048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198301440))))[name = string("op_2433_to_fp16_quantized")]; + tensor q_with_bias_v_19_cast_fp16 = transpose(perm = q_with_bias_v_19_perm_0, x = var_2431_cast_fp16)[name = string("transpose_281")]; + tensor x_241_cast_fp16 = matmul(transpose_x = x_241_transpose_x_0, transpose_y = x_241_transpose_y_0, x = q_with_bias_v_19_cast_fp16, y = op_2433_to_fp16_quantized)[name = string("x_241_cast_fp16")]; + tensor x_243_pad_0 = const()[name = string("x_243_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_243_mode_0 = const()[name = string("x_243_mode_0"), val = string("constant")]; + fp16 const_196_to_fp16 = const()[name = string("const_196_to_fp16"), val = fp16(0x0p+0)]; + tensor x_243_cast_fp16 = pad(constant_val = const_196_to_fp16, mode = x_243_mode_0, pad = x_243_pad_0, x = x_241_cast_fp16)[name = string("x_243_cast_fp16")]; + tensor var_2441 = const()[name = string("op_2441"), val = tensor([1, 8, -1, 7])]; + tensor x_245_cast_fp16 = reshape(shape = var_2441, x = x_243_cast_fp16)[name = string("x_245_cast_fp16")]; + tensor var_2445_begin_0 = const()[name = string("op_2445_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_2445_end_0 = const()[name = string("op_2445_end_0"), val = tensor([1, 8, 98, 7])]; + tensor var_2445_end_mask_0 = const()[name = string("op_2445_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_2445_cast_fp16 = slice_by_index(begin = var_2445_begin_0, end = var_2445_end_0, end_mask = var_2445_end_mask_0, x = x_245_cast_fp16)[name = string("op_2445_cast_fp16")]; + tensor var_2446 = const()[name = string("op_2446"), val = tensor([1, 8, 7, 97])]; + tensor matrix_bd_37_cast_fp16 = reshape(shape = var_2446, x = var_2445_cast_fp16)[name = string("matrix_bd_37_cast_fp16")]; + bool matrix_ac_19_transpose_x_0 = const()[name = string("matrix_ac_19_transpose_x_0"), val = bool(false)]; + bool matrix_ac_19_transpose_y_0 = const()[name = string("matrix_ac_19_transpose_y_0"), val = bool(false)]; + tensor transpose_114_perm_0 = const()[name = string("transpose_114_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_115_perm_0 = const()[name = string("transpose_115_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_115 = transpose(perm = transpose_115_perm_0, x = k_37_cast_fp16)[name = string("transpose_279")]; + tensor transpose_114 = transpose(perm = transpose_114_perm_0, x = var_2429_cast_fp16)[name = string("transpose_280")]; + tensor matrix_ac_19_cast_fp16 = matmul(transpose_x = matrix_ac_19_transpose_x_0, transpose_y = matrix_ac_19_transpose_y_0, x = transpose_114, y = transpose_115)[name = string("matrix_ac_19_cast_fp16")]; + tensor matrix_bd_39_begin_0 = const()[name = string("matrix_bd_39_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_39_end_0 = const()[name = string("matrix_bd_39_end_0"), val = tensor([1, 8, 7, 49])]; + tensor matrix_bd_39_end_mask_0 = const()[name = string("matrix_bd_39_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_39_cast_fp16 = slice_by_index(begin = matrix_bd_39_begin_0, end = matrix_bd_39_end_0, end_mask = matrix_bd_39_end_mask_0, x = matrix_bd_37_cast_fp16)[name = string("matrix_bd_39_cast_fp16")]; + tensor var_2455_cast_fp16 = add(x = matrix_ac_19_cast_fp16, y = matrix_bd_39_cast_fp16)[name = string("op_2455_cast_fp16")]; + fp16 _inversed_scores_37_y_0_to_fp16 = const()[name = string("_inversed_scores_37_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_37_cast_fp16 = mul(x = var_2455_cast_fp16, y = _inversed_scores_37_y_0_to_fp16)[name = string("_inversed_scores_37_cast_fp16")]; + tensor scores_39_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_37_cast_fp16, cond = mask_11)[name = string("scores_39_cast_fp16")]; + tensor var_2461_cast_fp16 = softmax(axis = var_59, x = scores_39_cast_fp16)[name = string("op_2461_cast_fp16")]; + tensor input_509_cast_fp16 = select(a = var_44_to_fp16, b = var_2461_cast_fp16, cond = mask_11)[name = string("input_509_cast_fp16")]; + bool x_247_transpose_x_0 = const()[name = string("x_247_transpose_x_0"), val = bool(false)]; + bool x_247_transpose_y_0 = const()[name = string("x_247_transpose_y_0"), val = bool(false)]; + tensor value_27_cast_fp16 = transpose(perm = value_27_perm_0, x = v_19_cast_fp16)[name = string("transpose_278")]; + tensor x_247_cast_fp16 = matmul(transpose_x = x_247_transpose_x_0, transpose_y = x_247_transpose_y_0, x = input_509_cast_fp16, y = value_27_cast_fp16)[name = string("x_247_cast_fp16")]; + tensor var_2465_perm_0 = const()[name = string("op_2465_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2466 = const()[name = string("op_2466"), val = tensor([1, -1, 1024])]; + tensor var_2465_cast_fp16 = transpose(perm = var_2465_perm_0, x = x_247_cast_fp16)[name = string("transpose_277")]; + tensor input_511_cast_fp16 = reshape(shape = var_2466, x = var_2465_cast_fp16)[name = string("input_511_cast_fp16")]; + tensor encoder_layers_9_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198301760))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199088256))))[name = string("encoder_layers_9_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_9_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_9_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199088448)))]; + tensor linear_88_cast_fp16 = linear(bias = encoder_layers_9_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_9_self_attn_linear_out_weight_to_fp16_palettized, x = input_511_cast_fp16)[name = string("linear_88_cast_fp16")]; + tensor input_515_cast_fp16 = add(x = input_505_cast_fp16, y = linear_88_cast_fp16)[name = string("input_515_cast_fp16")]; + tensor x_251_axes_0 = const()[name = string("x_251_axes_0"), val = tensor([-1])]; + tensor encoder_layers_9_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_9_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199090560)))]; + tensor encoder_layers_9_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_9_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199092672)))]; + tensor x_251_cast_fp16 = layer_norm(axes = x_251_axes_0, beta = encoder_layers_9_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_9_norm_conv_weight_to_fp16, x = input_515_cast_fp16)[name = string("x_251_cast_fp16")]; + tensor input_517_perm_0 = const()[name = string("input_517_perm_0"), val = tensor([0, 2, 1])]; + string input_519_pad_type_0 = const()[name = string("input_519_pad_type_0"), val = string("valid")]; + tensor input_519_strides_0 = const()[name = string("input_519_strides_0"), val = tensor([1])]; + tensor input_519_pad_0 = const()[name = string("input_519_pad_0"), val = tensor([0, 0])]; + tensor input_519_dilations_0 = const()[name = string("input_519_dilations_0"), val = tensor([1])]; + int32 input_519_groups_0 = const()[name = string("input_519_groups_0"), val = int32(1)]; + tensor encoder_layers_9_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199094784))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201192000))))[name = string("encoder_layers_9_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_517_cast_fp16 = transpose(perm = input_517_perm_0, x = x_251_cast_fp16)[name = string("transpose_276")]; + tensor input_519_cast_fp16 = conv(dilations = input_519_dilations_0, groups = input_519_groups_0, pad = input_519_pad_0, pad_type = input_519_pad_type_0, strides = input_519_strides_0, weight = encoder_layers_9_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_517_cast_fp16)[name = string("input_519_cast_fp16")]; + int32 x_253_split_num_splits_0 = const()[name = string("x_253_split_num_splits_0"), val = int32(2)]; + int32 x_253_split_axis_0 = const()[name = string("x_253_split_axis_0"), val = int32(1)]; + tensor x_253_split_cast_fp16_0, tensor x_253_split_cast_fp16_1 = split(axis = x_253_split_axis_0, num_splits = x_253_split_num_splits_0, x = input_519_cast_fp16)[name = string("x_253_split_cast_fp16")]; + tensor x_253_split_1_sigmoid_cast_fp16 = sigmoid(x = x_253_split_cast_fp16_1)[name = string("x_253_split_1_sigmoid_cast_fp16")]; + tensor x_253_cast_fp16 = mul(x = x_253_split_cast_fp16_0, y = x_253_split_1_sigmoid_cast_fp16)[name = string("x_253_cast_fp16")]; + tensor input_521_cast_fp16 = select(a = var_44_to_fp16, b = x_253_cast_fp16, cond = var_575)[name = string("input_521_cast_fp16")]; + bool new_x_39_interleave_0 = const()[name = string("new_x_39_interleave_0"), val = bool(false)]; + tensor new_x_39_cast_fp16 = concat(axis = var_59, interleave = new_x_39_interleave_0, values = (cache_39_cast_fp16, input_521_cast_fp16))[name = string("new_x_39_cast_fp16")]; + tensor var_2505_begin_0 = const()[name = string("op_2505_begin_0"), val = tensor([0, 0, 7])]; + tensor var_2505_end_0 = const()[name = string("op_2505_end_0"), val = tensor([1, 1024, 15])]; + tensor var_2505_end_mask_0 = const()[name = string("op_2505_end_mask_0"), val = tensor([true, true, true])]; + tensor var_2505_cast_fp16 = slice_by_index(begin = var_2505_begin_0, end = var_2505_end_0, end_mask = var_2505_end_mask_0, x = new_x_39_cast_fp16)[name = string("op_2505_cast_fp16")]; + string x_255_pad_type_0 = const()[name = string("x_255_pad_type_0"), val = string("valid")]; + int32 x_255_groups_0 = const()[name = string("x_255_groups_0"), val = int32(1024)]; + tensor x_255_strides_0 = const()[name = string("x_255_strides_0"), val = tensor([1])]; + tensor x_255_pad_0 = const()[name = string("x_255_pad_0"), val = tensor([0, 0])]; + tensor x_255_dilations_0 = const()[name = string("x_255_dilations_0"), val = tensor([1])]; + tensor encoder_layers_9_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201196160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201205440))))[name = string("encoder_layers_9_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_255_cast_fp16 = conv(dilations = x_255_dilations_0, groups = x_255_groups_0, pad = x_255_pad_0, pad_type = x_255_pad_type_0, strides = x_255_strides_0, weight = encoder_layers_9_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_39_cast_fp16)[name = string("x_255_cast_fp16")]; + tensor input_523_perm_0 = const()[name = string("input_523_perm_0"), val = tensor([0, 2, 1])]; + tensor x_257_axes_0 = const()[name = string("x_257_axes_0"), val = tensor([-1])]; + tensor encoder_layers_9_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_9_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201207552)))]; + tensor encoder_layers_9_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_9_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201209664)))]; + tensor input_523_cast_fp16 = transpose(perm = input_523_perm_0, x = x_255_cast_fp16)[name = string("transpose_275")]; + tensor x_257_cast_fp16 = layer_norm(axes = x_257_axes_0, beta = encoder_layers_9_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_9_conv_batch_norm_weight_to_fp16, x = input_523_cast_fp16)[name = string("x_257_cast_fp16")]; + tensor input_525_perm_0 = const()[name = string("input_525_perm_0"), val = tensor([0, 2, 1])]; + tensor input_525_cast_fp16 = transpose(perm = input_525_perm_0, x = x_257_cast_fp16)[name = string("transpose_274")]; + tensor input_527_cast_fp16 = silu(x = input_525_cast_fp16)[name = string("input_527_cast_fp16")]; + string x_259_pad_type_0 = const()[name = string("x_259_pad_type_0"), val = string("valid")]; + tensor x_259_strides_0 = const()[name = string("x_259_strides_0"), val = tensor([1])]; + tensor x_259_pad_0 = const()[name = string("x_259_pad_0"), val = tensor([0, 0])]; + tensor x_259_dilations_0 = const()[name = string("x_259_dilations_0"), val = tensor([1])]; + int32 x_259_groups_0 = const()[name = string("x_259_groups_0"), val = int32(1)]; + tensor encoder_layers_9_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201211776))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(202260416))))[name = string("encoder_layers_9_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_259_cast_fp16 = conv(dilations = x_259_dilations_0, groups = x_259_groups_0, pad = x_259_pad_0, pad_type = x_259_pad_type_0, strides = x_259_strides_0, weight = encoder_layers_9_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_527_cast_fp16)[name = string("x_259_cast_fp16")]; + tensor input_529_perm_0 = const()[name = string("input_529_perm_0"), val = tensor([0, 2, 1])]; + tensor input_529_cast_fp16 = transpose(perm = input_529_perm_0, x = x_259_cast_fp16)[name = string("transpose_273")]; + tensor input_531_cast_fp16 = add(x = input_515_cast_fp16, y = input_529_cast_fp16)[name = string("input_531_cast_fp16")]; + tensor input_533_axes_0 = const()[name = string("input_533_axes_0"), val = tensor([-1])]; + tensor encoder_layers_9_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_9_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(202262528)))]; + tensor encoder_layers_9_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_9_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(202264640)))]; + tensor input_533_cast_fp16 = layer_norm(axes = input_533_axes_0, beta = encoder_layers_9_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_9_norm_feed_forward2_weight_to_fp16, x = input_531_cast_fp16)[name = string("input_533_cast_fp16")]; + tensor encoder_layers_9_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(202266752))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205412544))))[name = string("encoder_layers_9_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_9_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_9_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205412736)))]; + tensor linear_89_cast_fp16 = linear(bias = encoder_layers_9_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_9_feed_forward2_linear1_weight_to_fp16_palettized, x = input_533_cast_fp16)[name = string("linear_89_cast_fp16")]; + tensor input_537_cast_fp16 = silu(x = linear_89_cast_fp16)[name = string("input_537_cast_fp16")]; + tensor encoder_layers_9_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205420992))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(208566784))))[name = string("encoder_layers_9_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_9_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_9_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(208566976)))]; + tensor linear_90_cast_fp16 = linear(bias = encoder_layers_9_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_9_feed_forward2_linear2_weight_to_fp16_palettized, x = input_537_cast_fp16)[name = string("linear_90_cast_fp16")]; + fp16 var_2548_to_fp16 = const()[name = string("op_2548_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2549_cast_fp16 = mul(x = linear_90_cast_fp16, y = var_2548_to_fp16)[name = string("op_2549_cast_fp16")]; + tensor input_543_cast_fp16 = add(x = input_531_cast_fp16, y = var_2549_cast_fp16)[name = string("input_543_cast_fp16")]; + tensor input_545_axes_0 = const()[name = string("input_545_axes_0"), val = tensor([-1])]; + tensor encoder_layers_9_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_9_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(208569088)))]; + tensor encoder_layers_9_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_9_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(208571200)))]; + tensor input_545_cast_fp16 = layer_norm(axes = input_545_axes_0, beta = encoder_layers_9_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_9_norm_out_weight_to_fp16, x = input_543_cast_fp16)[name = string("input_545_cast_fp16")]; + tensor cache_41_begin_0 = const()[name = string("cache_41_begin_0"), val = tensor([10, 0, 0, 0])]; + tensor cache_41_end_0 = const()[name = string("cache_41_end_0"), val = tensor([11, 1, 42, 1024])]; + tensor cache_41_end_mask_0 = const()[name = string("cache_41_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_41_squeeze_mask_0 = const()[name = string("cache_41_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_41_cast_fp16 = slice_by_index(begin = cache_41_begin_0, end = cache_41_end_0, end_mask = cache_41_end_mask_0, squeeze_mask = cache_41_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_41_cast_fp16")]; + tensor cache_43_begin_0 = const()[name = string("cache_43_begin_0"), val = tensor([10, 0, 0, 0])]; + tensor cache_43_end_0 = const()[name = string("cache_43_end_0"), val = tensor([11, 1, 1024, 8])]; + tensor cache_43_end_mask_0 = const()[name = string("cache_43_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_43_squeeze_mask_0 = const()[name = string("cache_43_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_43_cast_fp16 = slice_by_index(begin = cache_43_begin_0, end = cache_43_end_0, end_mask = cache_43_end_mask_0, squeeze_mask = cache_43_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_43_cast_fp16")]; + tensor input_547_axes_0 = const()[name = string("input_547_axes_0"), val = tensor([-1])]; + tensor encoder_layers_10_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_10_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(208573312)))]; + tensor encoder_layers_10_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_10_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(208575424)))]; + tensor input_547_cast_fp16 = layer_norm(axes = input_547_axes_0, beta = encoder_layers_10_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_10_norm_feed_forward1_weight_to_fp16, x = input_545_cast_fp16)[name = string("input_547_cast_fp16")]; + tensor encoder_layers_10_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(208577536))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(211723328))))[name = string("encoder_layers_10_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_10_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_10_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(211723520)))]; + tensor linear_91_cast_fp16 = linear(bias = encoder_layers_10_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_10_feed_forward1_linear1_weight_to_fp16_palettized, x = input_547_cast_fp16)[name = string("linear_91_cast_fp16")]; + tensor input_551_cast_fp16 = silu(x = linear_91_cast_fp16)[name = string("input_551_cast_fp16")]; + tensor encoder_layers_10_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(211731776))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214877568))))[name = string("encoder_layers_10_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_10_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_10_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214877760)))]; + tensor linear_92_cast_fp16 = linear(bias = encoder_layers_10_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_10_feed_forward1_linear2_weight_to_fp16_palettized, x = input_551_cast_fp16)[name = string("linear_92_cast_fp16")]; + fp16 var_2585_to_fp16 = const()[name = string("op_2585_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2586_cast_fp16 = mul(x = linear_92_cast_fp16, y = var_2585_to_fp16)[name = string("op_2586_cast_fp16")]; + tensor input_557_cast_fp16 = add(x = input_545_cast_fp16, y = var_2586_cast_fp16)[name = string("input_557_cast_fp16")]; + tensor key_21_axes_0 = const()[name = string("key_21_axes_0"), val = tensor([-1])]; + tensor encoder_layers_10_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_10_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214879872)))]; + tensor encoder_layers_10_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_10_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214881984)))]; + tensor key_21_cast_fp16 = layer_norm(axes = key_21_axes_0, beta = encoder_layers_10_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_10_norm_self_att_weight_to_fp16, x = input_557_cast_fp16)[name = string("key_21_cast_fp16")]; + bool input_559_interleave_0 = const()[name = string("input_559_interleave_0"), val = bool(false)]; + tensor input_559_cast_fp16 = concat(axis = var_68, interleave = input_559_interleave_0, values = (cache_41_cast_fp16, key_21_cast_fp16))[name = string("input_559_cast_fp16")]; + tensor var_2608_begin_0 = const()[name = string("op_2608_begin_0"), val = tensor([0, 7, 0])]; + tensor var_2608_end_0 = const()[name = string("op_2608_end_0"), val = tensor([1, 42, 1024])]; + tensor var_2608_end_mask_0 = const()[name = string("op_2608_end_mask_0"), val = tensor([true, true, true])]; + tensor var_2608_cast_fp16 = slice_by_index(begin = var_2608_begin_0, end = var_2608_end_0, end_mask = var_2608_end_mask_0, x = cache_41_cast_fp16)[name = string("op_2608_cast_fp16")]; + bool var_2614_interleave_0 = const()[name = string("op_2614_interleave_0"), val = bool(false)]; + tensor var_2614_cast_fp16 = concat(axis = var_68, interleave = var_2614_interleave_0, values = (var_2608_cast_fp16, key_21_cast_fp16))[name = string("op_2614_cast_fp16")]; + tensor encoder_layers_10_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214884096))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215670592))))[name = string("encoder_layers_10_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_10_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_10_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215670784)))]; + tensor linear_93_cast_fp16 = linear(bias = encoder_layers_10_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_10_self_attn_linear_q_weight_to_fp16_palettized, x = key_21_cast_fp16)[name = string("linear_93_cast_fp16")]; + tensor var_2619 = const()[name = string("op_2619"), val = tensor([1, -1, 8, 128])]; + tensor q_61_cast_fp16 = reshape(shape = var_2619, x = linear_93_cast_fp16)[name = string("q_61_cast_fp16")]; + tensor encoder_layers_10_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215672896))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216459392))))[name = string("encoder_layers_10_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_10_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_10_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216459584)))]; + tensor linear_94_cast_fp16 = linear(bias = encoder_layers_10_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_10_self_attn_linear_k_weight_to_fp16_palettized, x = input_559_cast_fp16)[name = string("linear_94_cast_fp16")]; + tensor var_2624 = const()[name = string("op_2624"), val = tensor([1, -1, 8, 128])]; + tensor k_41_cast_fp16 = reshape(shape = var_2624, x = linear_94_cast_fp16)[name = string("k_41_cast_fp16")]; + tensor encoder_layers_10_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216461696))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217248192))))[name = string("encoder_layers_10_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_10_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_10_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217248384)))]; + tensor linear_95_cast_fp16 = linear(bias = encoder_layers_10_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_10_self_attn_linear_v_weight_to_fp16_palettized, x = input_559_cast_fp16)[name = string("linear_95_cast_fp16")]; + tensor var_2629 = const()[name = string("op_2629"), val = tensor([1, -1, 8, 128])]; + tensor v_21_cast_fp16 = reshape(shape = var_2629, x = linear_95_cast_fp16)[name = string("v_21_cast_fp16")]; + tensor value_29_perm_0 = const()[name = string("value_29_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_10_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_10_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217250496)))]; + tensor var_2642_cast_fp16 = add(x = q_61_cast_fp16, y = encoder_layers_10_self_attn_pos_bias_u_to_fp16)[name = string("op_2642_cast_fp16")]; + tensor encoder_layers_10_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_10_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217252608)))]; + tensor var_2644_cast_fp16 = add(x = q_61_cast_fp16, y = encoder_layers_10_self_attn_pos_bias_v_to_fp16)[name = string("op_2644_cast_fp16")]; + tensor q_with_bias_v_21_perm_0 = const()[name = string("q_with_bias_v_21_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_267_transpose_x_0 = const()[name = string("x_267_transpose_x_0"), val = bool(false)]; + bool x_267_transpose_y_0 = const()[name = string("x_267_transpose_y_0"), val = bool(false)]; + tensor op_2646_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217254720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217354112))))[name = string("op_2646_to_fp16_quantized")]; + tensor q_with_bias_v_21_cast_fp16 = transpose(perm = q_with_bias_v_21_perm_0, x = var_2644_cast_fp16)[name = string("transpose_272")]; + tensor x_267_cast_fp16 = matmul(transpose_x = x_267_transpose_x_0, transpose_y = x_267_transpose_y_0, x = q_with_bias_v_21_cast_fp16, y = op_2646_to_fp16_quantized)[name = string("x_267_cast_fp16")]; + tensor x_269_pad_0 = const()[name = string("x_269_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_269_mode_0 = const()[name = string("x_269_mode_0"), val = string("constant")]; + fp16 const_209_to_fp16 = const()[name = string("const_209_to_fp16"), val = fp16(0x0p+0)]; + tensor x_269_cast_fp16 = pad(constant_val = const_209_to_fp16, mode = x_269_mode_0, pad = x_269_pad_0, x = x_267_cast_fp16)[name = string("x_269_cast_fp16")]; + tensor var_2654 = const()[name = string("op_2654"), val = tensor([1, 8, -1, 7])]; + tensor x_271_cast_fp16 = reshape(shape = var_2654, x = x_269_cast_fp16)[name = string("x_271_cast_fp16")]; + tensor var_2658_begin_0 = const()[name = string("op_2658_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_2658_end_0 = const()[name = string("op_2658_end_0"), val = tensor([1, 8, 98, 7])]; + tensor var_2658_end_mask_0 = const()[name = string("op_2658_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_2658_cast_fp16 = slice_by_index(begin = var_2658_begin_0, end = var_2658_end_0, end_mask = var_2658_end_mask_0, x = x_271_cast_fp16)[name = string("op_2658_cast_fp16")]; + tensor var_2659 = const()[name = string("op_2659"), val = tensor([1, 8, 7, 97])]; + tensor matrix_bd_41_cast_fp16 = reshape(shape = var_2659, x = var_2658_cast_fp16)[name = string("matrix_bd_41_cast_fp16")]; + bool matrix_ac_21_transpose_x_0 = const()[name = string("matrix_ac_21_transpose_x_0"), val = bool(false)]; + bool matrix_ac_21_transpose_y_0 = const()[name = string("matrix_ac_21_transpose_y_0"), val = bool(false)]; + tensor transpose_116_perm_0 = const()[name = string("transpose_116_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_117_perm_0 = const()[name = string("transpose_117_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_117 = transpose(perm = transpose_117_perm_0, x = k_41_cast_fp16)[name = string("transpose_270")]; + tensor transpose_116 = transpose(perm = transpose_116_perm_0, x = var_2642_cast_fp16)[name = string("transpose_271")]; + tensor matrix_ac_21_cast_fp16 = matmul(transpose_x = matrix_ac_21_transpose_x_0, transpose_y = matrix_ac_21_transpose_y_0, x = transpose_116, y = transpose_117)[name = string("matrix_ac_21_cast_fp16")]; + tensor matrix_bd_43_begin_0 = const()[name = string("matrix_bd_43_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_43_end_0 = const()[name = string("matrix_bd_43_end_0"), val = tensor([1, 8, 7, 49])]; + tensor matrix_bd_43_end_mask_0 = const()[name = string("matrix_bd_43_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_43_cast_fp16 = slice_by_index(begin = matrix_bd_43_begin_0, end = matrix_bd_43_end_0, end_mask = matrix_bd_43_end_mask_0, x = matrix_bd_41_cast_fp16)[name = string("matrix_bd_43_cast_fp16")]; + tensor var_2668_cast_fp16 = add(x = matrix_ac_21_cast_fp16, y = matrix_bd_43_cast_fp16)[name = string("op_2668_cast_fp16")]; + fp16 _inversed_scores_41_y_0_to_fp16 = const()[name = string("_inversed_scores_41_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_41_cast_fp16 = mul(x = var_2668_cast_fp16, y = _inversed_scores_41_y_0_to_fp16)[name = string("_inversed_scores_41_cast_fp16")]; + tensor scores_43_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_41_cast_fp16, cond = mask_11)[name = string("scores_43_cast_fp16")]; + tensor var_2674_cast_fp16 = softmax(axis = var_59, x = scores_43_cast_fp16)[name = string("op_2674_cast_fp16")]; + tensor input_561_cast_fp16 = select(a = var_44_to_fp16, b = var_2674_cast_fp16, cond = mask_11)[name = string("input_561_cast_fp16")]; + bool x_273_transpose_x_0 = const()[name = string("x_273_transpose_x_0"), val = bool(false)]; + bool x_273_transpose_y_0 = const()[name = string("x_273_transpose_y_0"), val = bool(false)]; + tensor value_29_cast_fp16 = transpose(perm = value_29_perm_0, x = v_21_cast_fp16)[name = string("transpose_269")]; + tensor x_273_cast_fp16 = matmul(transpose_x = x_273_transpose_x_0, transpose_y = x_273_transpose_y_0, x = input_561_cast_fp16, y = value_29_cast_fp16)[name = string("x_273_cast_fp16")]; + tensor var_2678_perm_0 = const()[name = string("op_2678_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2679 = const()[name = string("op_2679"), val = tensor([1, -1, 1024])]; + tensor var_2678_cast_fp16 = transpose(perm = var_2678_perm_0, x = x_273_cast_fp16)[name = string("transpose_268")]; + tensor input_563_cast_fp16 = reshape(shape = var_2679, x = var_2678_cast_fp16)[name = string("input_563_cast_fp16")]; + tensor encoder_layers_10_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217354432))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218140928))))[name = string("encoder_layers_10_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_10_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_10_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218141120)))]; + tensor linear_97_cast_fp16 = linear(bias = encoder_layers_10_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_10_self_attn_linear_out_weight_to_fp16_palettized, x = input_563_cast_fp16)[name = string("linear_97_cast_fp16")]; + tensor input_567_cast_fp16 = add(x = input_557_cast_fp16, y = linear_97_cast_fp16)[name = string("input_567_cast_fp16")]; + tensor x_277_axes_0 = const()[name = string("x_277_axes_0"), val = tensor([-1])]; + tensor encoder_layers_10_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_10_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218143232)))]; + tensor encoder_layers_10_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_10_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218145344)))]; + tensor x_277_cast_fp16 = layer_norm(axes = x_277_axes_0, beta = encoder_layers_10_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_10_norm_conv_weight_to_fp16, x = input_567_cast_fp16)[name = string("x_277_cast_fp16")]; + tensor input_569_perm_0 = const()[name = string("input_569_perm_0"), val = tensor([0, 2, 1])]; + string input_571_pad_type_0 = const()[name = string("input_571_pad_type_0"), val = string("valid")]; + tensor input_571_strides_0 = const()[name = string("input_571_strides_0"), val = tensor([1])]; + tensor input_571_pad_0 = const()[name = string("input_571_pad_0"), val = tensor([0, 0])]; + tensor input_571_dilations_0 = const()[name = string("input_571_dilations_0"), val = tensor([1])]; + int32 input_571_groups_0 = const()[name = string("input_571_groups_0"), val = int32(1)]; + tensor encoder_layers_10_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218147456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220244672))))[name = string("encoder_layers_10_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_569_cast_fp16 = transpose(perm = input_569_perm_0, x = x_277_cast_fp16)[name = string("transpose_267")]; + tensor input_571_cast_fp16 = conv(dilations = input_571_dilations_0, groups = input_571_groups_0, pad = input_571_pad_0, pad_type = input_571_pad_type_0, strides = input_571_strides_0, weight = encoder_layers_10_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_569_cast_fp16)[name = string("input_571_cast_fp16")]; + int32 x_279_split_num_splits_0 = const()[name = string("x_279_split_num_splits_0"), val = int32(2)]; + int32 x_279_split_axis_0 = const()[name = string("x_279_split_axis_0"), val = int32(1)]; + tensor x_279_split_cast_fp16_0, tensor x_279_split_cast_fp16_1 = split(axis = x_279_split_axis_0, num_splits = x_279_split_num_splits_0, x = input_571_cast_fp16)[name = string("x_279_split_cast_fp16")]; + tensor x_279_split_1_sigmoid_cast_fp16 = sigmoid(x = x_279_split_cast_fp16_1)[name = string("x_279_split_1_sigmoid_cast_fp16")]; + tensor x_279_cast_fp16 = mul(x = x_279_split_cast_fp16_0, y = x_279_split_1_sigmoid_cast_fp16)[name = string("x_279_cast_fp16")]; + tensor input_573_cast_fp16 = select(a = var_44_to_fp16, b = x_279_cast_fp16, cond = var_575)[name = string("input_573_cast_fp16")]; + bool new_x_43_interleave_0 = const()[name = string("new_x_43_interleave_0"), val = bool(false)]; + tensor new_x_43_cast_fp16 = concat(axis = var_59, interleave = new_x_43_interleave_0, values = (cache_43_cast_fp16, input_573_cast_fp16))[name = string("new_x_43_cast_fp16")]; + tensor var_2718_begin_0 = const()[name = string("op_2718_begin_0"), val = tensor([0, 0, 7])]; + tensor var_2718_end_0 = const()[name = string("op_2718_end_0"), val = tensor([1, 1024, 15])]; + tensor var_2718_end_mask_0 = const()[name = string("op_2718_end_mask_0"), val = tensor([true, true, true])]; + tensor var_2718_cast_fp16 = slice_by_index(begin = var_2718_begin_0, end = var_2718_end_0, end_mask = var_2718_end_mask_0, x = new_x_43_cast_fp16)[name = string("op_2718_cast_fp16")]; + string x_281_pad_type_0 = const()[name = string("x_281_pad_type_0"), val = string("valid")]; + int32 x_281_groups_0 = const()[name = string("x_281_groups_0"), val = int32(1024)]; + tensor x_281_strides_0 = const()[name = string("x_281_strides_0"), val = tensor([1])]; + tensor x_281_pad_0 = const()[name = string("x_281_pad_0"), val = tensor([0, 0])]; + tensor x_281_dilations_0 = const()[name = string("x_281_dilations_0"), val = tensor([1])]; + tensor encoder_layers_10_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220248832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220258112))))[name = string("encoder_layers_10_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_281_cast_fp16 = conv(dilations = x_281_dilations_0, groups = x_281_groups_0, pad = x_281_pad_0, pad_type = x_281_pad_type_0, strides = x_281_strides_0, weight = encoder_layers_10_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_43_cast_fp16)[name = string("x_281_cast_fp16")]; + tensor input_575_perm_0 = const()[name = string("input_575_perm_0"), val = tensor([0, 2, 1])]; + tensor x_283_axes_0 = const()[name = string("x_283_axes_0"), val = tensor([-1])]; + tensor encoder_layers_10_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_10_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220260224)))]; + tensor encoder_layers_10_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_10_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220262336)))]; + tensor input_575_cast_fp16 = transpose(perm = input_575_perm_0, x = x_281_cast_fp16)[name = string("transpose_266")]; + tensor x_283_cast_fp16 = layer_norm(axes = x_283_axes_0, beta = encoder_layers_10_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_10_conv_batch_norm_weight_to_fp16, x = input_575_cast_fp16)[name = string("x_283_cast_fp16")]; + tensor input_577_perm_0 = const()[name = string("input_577_perm_0"), val = tensor([0, 2, 1])]; + tensor input_577_cast_fp16 = transpose(perm = input_577_perm_0, x = x_283_cast_fp16)[name = string("transpose_265")]; + tensor input_579_cast_fp16 = silu(x = input_577_cast_fp16)[name = string("input_579_cast_fp16")]; + string x_285_pad_type_0 = const()[name = string("x_285_pad_type_0"), val = string("valid")]; + tensor x_285_strides_0 = const()[name = string("x_285_strides_0"), val = tensor([1])]; + tensor x_285_pad_0 = const()[name = string("x_285_pad_0"), val = tensor([0, 0])]; + tensor x_285_dilations_0 = const()[name = string("x_285_dilations_0"), val = tensor([1])]; + int32 x_285_groups_0 = const()[name = string("x_285_groups_0"), val = int32(1)]; + tensor encoder_layers_10_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220264448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(221313088))))[name = string("encoder_layers_10_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_285_cast_fp16 = conv(dilations = x_285_dilations_0, groups = x_285_groups_0, pad = x_285_pad_0, pad_type = x_285_pad_type_0, strides = x_285_strides_0, weight = encoder_layers_10_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_579_cast_fp16)[name = string("x_285_cast_fp16")]; + tensor input_581_perm_0 = const()[name = string("input_581_perm_0"), val = tensor([0, 2, 1])]; + tensor input_581_cast_fp16 = transpose(perm = input_581_perm_0, x = x_285_cast_fp16)[name = string("transpose_264")]; + tensor input_583_cast_fp16 = add(x = input_567_cast_fp16, y = input_581_cast_fp16)[name = string("input_583_cast_fp16")]; + tensor input_585_axes_0 = const()[name = string("input_585_axes_0"), val = tensor([-1])]; + tensor encoder_layers_10_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_10_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(221315200)))]; + tensor encoder_layers_10_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_10_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(221317312)))]; + tensor input_585_cast_fp16 = layer_norm(axes = input_585_axes_0, beta = encoder_layers_10_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_10_norm_feed_forward2_weight_to_fp16, x = input_583_cast_fp16)[name = string("input_585_cast_fp16")]; + tensor encoder_layers_10_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(221319424))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224465216))))[name = string("encoder_layers_10_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_10_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_10_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224465408)))]; + tensor linear_98_cast_fp16 = linear(bias = encoder_layers_10_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_10_feed_forward2_linear1_weight_to_fp16_palettized, x = input_585_cast_fp16)[name = string("linear_98_cast_fp16")]; + tensor input_589_cast_fp16 = silu(x = linear_98_cast_fp16)[name = string("input_589_cast_fp16")]; + tensor encoder_layers_10_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224473664))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(227619456))))[name = string("encoder_layers_10_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_10_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_10_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(227619648)))]; + tensor linear_99_cast_fp16 = linear(bias = encoder_layers_10_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_10_feed_forward2_linear2_weight_to_fp16_palettized, x = input_589_cast_fp16)[name = string("linear_99_cast_fp16")]; + fp16 var_2761_to_fp16 = const()[name = string("op_2761_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2762_cast_fp16 = mul(x = linear_99_cast_fp16, y = var_2761_to_fp16)[name = string("op_2762_cast_fp16")]; + tensor input_595_cast_fp16 = add(x = input_583_cast_fp16, y = var_2762_cast_fp16)[name = string("input_595_cast_fp16")]; + tensor input_597_axes_0 = const()[name = string("input_597_axes_0"), val = tensor([-1])]; + tensor encoder_layers_10_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_10_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(227621760)))]; + tensor encoder_layers_10_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_10_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(227623872)))]; + tensor input_597_cast_fp16 = layer_norm(axes = input_597_axes_0, beta = encoder_layers_10_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_10_norm_out_weight_to_fp16, x = input_595_cast_fp16)[name = string("input_597_cast_fp16")]; + tensor cache_45_begin_0 = const()[name = string("cache_45_begin_0"), val = tensor([11, 0, 0, 0])]; + tensor cache_45_end_0 = const()[name = string("cache_45_end_0"), val = tensor([12, 1, 42, 1024])]; + tensor cache_45_end_mask_0 = const()[name = string("cache_45_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_45_squeeze_mask_0 = const()[name = string("cache_45_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_45_cast_fp16 = slice_by_index(begin = cache_45_begin_0, end = cache_45_end_0, end_mask = cache_45_end_mask_0, squeeze_mask = cache_45_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_45_cast_fp16")]; + tensor cache_47_begin_0 = const()[name = string("cache_47_begin_0"), val = tensor([11, 0, 0, 0])]; + tensor cache_47_end_0 = const()[name = string("cache_47_end_0"), val = tensor([12, 1, 1024, 8])]; + tensor cache_47_end_mask_0 = const()[name = string("cache_47_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_47_squeeze_mask_0 = const()[name = string("cache_47_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_47_cast_fp16 = slice_by_index(begin = cache_47_begin_0, end = cache_47_end_0, end_mask = cache_47_end_mask_0, squeeze_mask = cache_47_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_47_cast_fp16")]; + tensor input_599_axes_0 = const()[name = string("input_599_axes_0"), val = tensor([-1])]; + tensor encoder_layers_11_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_11_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(227625984)))]; + tensor encoder_layers_11_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_11_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(227628096)))]; + tensor input_599_cast_fp16 = layer_norm(axes = input_599_axes_0, beta = encoder_layers_11_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_11_norm_feed_forward1_weight_to_fp16, x = input_597_cast_fp16)[name = string("input_599_cast_fp16")]; + tensor encoder_layers_11_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(227630208))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230776000))))[name = string("encoder_layers_11_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_11_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_11_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230776192)))]; + tensor linear_100_cast_fp16 = linear(bias = encoder_layers_11_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_11_feed_forward1_linear1_weight_to_fp16_palettized, x = input_599_cast_fp16)[name = string("linear_100_cast_fp16")]; + tensor input_603_cast_fp16 = silu(x = linear_100_cast_fp16)[name = string("input_603_cast_fp16")]; + tensor encoder_layers_11_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230784448))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(233930240))))[name = string("encoder_layers_11_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_11_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_11_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(233930432)))]; + tensor linear_101_cast_fp16 = linear(bias = encoder_layers_11_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_11_feed_forward1_linear2_weight_to_fp16_palettized, x = input_603_cast_fp16)[name = string("linear_101_cast_fp16")]; + fp16 var_2798_to_fp16 = const()[name = string("op_2798_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2799_cast_fp16 = mul(x = linear_101_cast_fp16, y = var_2798_to_fp16)[name = string("op_2799_cast_fp16")]; + tensor input_609_cast_fp16 = add(x = input_597_cast_fp16, y = var_2799_cast_fp16)[name = string("input_609_cast_fp16")]; + tensor key_23_axes_0 = const()[name = string("key_23_axes_0"), val = tensor([-1])]; + tensor encoder_layers_11_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_11_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(233932544)))]; + tensor encoder_layers_11_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_11_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(233934656)))]; + tensor key_23_cast_fp16 = layer_norm(axes = key_23_axes_0, beta = encoder_layers_11_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_11_norm_self_att_weight_to_fp16, x = input_609_cast_fp16)[name = string("key_23_cast_fp16")]; + bool input_611_interleave_0 = const()[name = string("input_611_interleave_0"), val = bool(false)]; + tensor input_611_cast_fp16 = concat(axis = var_68, interleave = input_611_interleave_0, values = (cache_45_cast_fp16, key_23_cast_fp16))[name = string("input_611_cast_fp16")]; + tensor var_2821_begin_0 = const()[name = string("op_2821_begin_0"), val = tensor([0, 7, 0])]; + tensor var_2821_end_0 = const()[name = string("op_2821_end_0"), val = tensor([1, 42, 1024])]; + tensor var_2821_end_mask_0 = const()[name = string("op_2821_end_mask_0"), val = tensor([true, true, true])]; + tensor var_2821_cast_fp16 = slice_by_index(begin = var_2821_begin_0, end = var_2821_end_0, end_mask = var_2821_end_mask_0, x = cache_45_cast_fp16)[name = string("op_2821_cast_fp16")]; + bool var_2827_interleave_0 = const()[name = string("op_2827_interleave_0"), val = bool(false)]; + tensor var_2827_cast_fp16 = concat(axis = var_68, interleave = var_2827_interleave_0, values = (var_2821_cast_fp16, key_23_cast_fp16))[name = string("op_2827_cast_fp16")]; + tensor encoder_layers_11_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(233936768))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(234723264))))[name = string("encoder_layers_11_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_11_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_11_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(234723456)))]; + tensor linear_102_cast_fp16 = linear(bias = encoder_layers_11_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_11_self_attn_linear_q_weight_to_fp16_palettized, x = key_23_cast_fp16)[name = string("linear_102_cast_fp16")]; + tensor var_2832 = const()[name = string("op_2832"), val = tensor([1, -1, 8, 128])]; + tensor q_67_cast_fp16 = reshape(shape = var_2832, x = linear_102_cast_fp16)[name = string("q_67_cast_fp16")]; + tensor encoder_layers_11_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(234725568))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235512064))))[name = string("encoder_layers_11_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_11_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_11_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235512256)))]; + tensor linear_103_cast_fp16 = linear(bias = encoder_layers_11_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_11_self_attn_linear_k_weight_to_fp16_palettized, x = input_611_cast_fp16)[name = string("linear_103_cast_fp16")]; + tensor var_2837 = const()[name = string("op_2837"), val = tensor([1, -1, 8, 128])]; + tensor k_45_cast_fp16 = reshape(shape = var_2837, x = linear_103_cast_fp16)[name = string("k_45_cast_fp16")]; + tensor encoder_layers_11_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235514368))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236300864))))[name = string("encoder_layers_11_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_11_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_11_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236301056)))]; + tensor linear_104_cast_fp16 = linear(bias = encoder_layers_11_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_11_self_attn_linear_v_weight_to_fp16_palettized, x = input_611_cast_fp16)[name = string("linear_104_cast_fp16")]; + tensor var_2842 = const()[name = string("op_2842"), val = tensor([1, -1, 8, 128])]; + tensor v_23_cast_fp16 = reshape(shape = var_2842, x = linear_104_cast_fp16)[name = string("v_23_cast_fp16")]; + tensor value_31_perm_0 = const()[name = string("value_31_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_11_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_11_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236303168)))]; + tensor var_2855_cast_fp16 = add(x = q_67_cast_fp16, y = encoder_layers_11_self_attn_pos_bias_u_to_fp16)[name = string("op_2855_cast_fp16")]; + tensor encoder_layers_11_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_11_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236305280)))]; + tensor var_2857_cast_fp16 = add(x = q_67_cast_fp16, y = encoder_layers_11_self_attn_pos_bias_v_to_fp16)[name = string("op_2857_cast_fp16")]; + tensor q_with_bias_v_23_perm_0 = const()[name = string("q_with_bias_v_23_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_293_transpose_x_0 = const()[name = string("x_293_transpose_x_0"), val = bool(false)]; + bool x_293_transpose_y_0 = const()[name = string("x_293_transpose_y_0"), val = bool(false)]; + tensor op_2859_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236307392))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236406784))))[name = string("op_2859_to_fp16_quantized")]; + tensor q_with_bias_v_23_cast_fp16 = transpose(perm = q_with_bias_v_23_perm_0, x = var_2857_cast_fp16)[name = string("transpose_263")]; + tensor x_293_cast_fp16 = matmul(transpose_x = x_293_transpose_x_0, transpose_y = x_293_transpose_y_0, x = q_with_bias_v_23_cast_fp16, y = op_2859_to_fp16_quantized)[name = string("x_293_cast_fp16")]; + tensor x_295_pad_0 = const()[name = string("x_295_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_295_mode_0 = const()[name = string("x_295_mode_0"), val = string("constant")]; + fp16 const_222_to_fp16 = const()[name = string("const_222_to_fp16"), val = fp16(0x0p+0)]; + tensor x_295_cast_fp16 = pad(constant_val = const_222_to_fp16, mode = x_295_mode_0, pad = x_295_pad_0, x = x_293_cast_fp16)[name = string("x_295_cast_fp16")]; + tensor var_2867 = const()[name = string("op_2867"), val = tensor([1, 8, -1, 7])]; + tensor x_297_cast_fp16 = reshape(shape = var_2867, x = x_295_cast_fp16)[name = string("x_297_cast_fp16")]; + tensor var_2871_begin_0 = const()[name = string("op_2871_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_2871_end_0 = const()[name = string("op_2871_end_0"), val = tensor([1, 8, 98, 7])]; + tensor var_2871_end_mask_0 = const()[name = string("op_2871_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_2871_cast_fp16 = slice_by_index(begin = var_2871_begin_0, end = var_2871_end_0, end_mask = var_2871_end_mask_0, x = x_297_cast_fp16)[name = string("op_2871_cast_fp16")]; + tensor var_2872 = const()[name = string("op_2872"), val = tensor([1, 8, 7, 97])]; + tensor matrix_bd_45_cast_fp16 = reshape(shape = var_2872, x = var_2871_cast_fp16)[name = string("matrix_bd_45_cast_fp16")]; + bool matrix_ac_23_transpose_x_0 = const()[name = string("matrix_ac_23_transpose_x_0"), val = bool(false)]; + bool matrix_ac_23_transpose_y_0 = const()[name = string("matrix_ac_23_transpose_y_0"), val = bool(false)]; + tensor transpose_118_perm_0 = const()[name = string("transpose_118_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_119_perm_0 = const()[name = string("transpose_119_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_119 = transpose(perm = transpose_119_perm_0, x = k_45_cast_fp16)[name = string("transpose_261")]; + tensor transpose_118 = transpose(perm = transpose_118_perm_0, x = var_2855_cast_fp16)[name = string("transpose_262")]; + tensor matrix_ac_23_cast_fp16 = matmul(transpose_x = matrix_ac_23_transpose_x_0, transpose_y = matrix_ac_23_transpose_y_0, x = transpose_118, y = transpose_119)[name = string("matrix_ac_23_cast_fp16")]; + tensor matrix_bd_47_begin_0 = const()[name = string("matrix_bd_47_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_47_end_0 = const()[name = string("matrix_bd_47_end_0"), val = tensor([1, 8, 7, 49])]; + tensor matrix_bd_47_end_mask_0 = const()[name = string("matrix_bd_47_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_47_cast_fp16 = slice_by_index(begin = matrix_bd_47_begin_0, end = matrix_bd_47_end_0, end_mask = matrix_bd_47_end_mask_0, x = matrix_bd_45_cast_fp16)[name = string("matrix_bd_47_cast_fp16")]; + tensor var_2881_cast_fp16 = add(x = matrix_ac_23_cast_fp16, y = matrix_bd_47_cast_fp16)[name = string("op_2881_cast_fp16")]; + fp16 _inversed_scores_45_y_0_to_fp16 = const()[name = string("_inversed_scores_45_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_45_cast_fp16 = mul(x = var_2881_cast_fp16, y = _inversed_scores_45_y_0_to_fp16)[name = string("_inversed_scores_45_cast_fp16")]; + tensor scores_47_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_45_cast_fp16, cond = mask_11)[name = string("scores_47_cast_fp16")]; + tensor var_2887_cast_fp16 = softmax(axis = var_59, x = scores_47_cast_fp16)[name = string("op_2887_cast_fp16")]; + tensor input_613_cast_fp16 = select(a = var_44_to_fp16, b = var_2887_cast_fp16, cond = mask_11)[name = string("input_613_cast_fp16")]; + bool x_299_transpose_x_0 = const()[name = string("x_299_transpose_x_0"), val = bool(false)]; + bool x_299_transpose_y_0 = const()[name = string("x_299_transpose_y_0"), val = bool(false)]; + tensor value_31_cast_fp16 = transpose(perm = value_31_perm_0, x = v_23_cast_fp16)[name = string("transpose_260")]; + tensor x_299_cast_fp16 = matmul(transpose_x = x_299_transpose_x_0, transpose_y = x_299_transpose_y_0, x = input_613_cast_fp16, y = value_31_cast_fp16)[name = string("x_299_cast_fp16")]; + tensor var_2891_perm_0 = const()[name = string("op_2891_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2892 = const()[name = string("op_2892"), val = tensor([1, -1, 1024])]; + tensor var_2891_cast_fp16 = transpose(perm = var_2891_perm_0, x = x_299_cast_fp16)[name = string("transpose_259")]; + tensor input_615_cast_fp16 = reshape(shape = var_2892, x = var_2891_cast_fp16)[name = string("input_615_cast_fp16")]; + tensor encoder_layers_11_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236407104))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(237193600))))[name = string("encoder_layers_11_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_11_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_11_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(237193792)))]; + tensor linear_106_cast_fp16 = linear(bias = encoder_layers_11_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_11_self_attn_linear_out_weight_to_fp16_palettized, x = input_615_cast_fp16)[name = string("linear_106_cast_fp16")]; + tensor input_619_cast_fp16 = add(x = input_609_cast_fp16, y = linear_106_cast_fp16)[name = string("input_619_cast_fp16")]; + tensor x_303_axes_0 = const()[name = string("x_303_axes_0"), val = tensor([-1])]; + tensor encoder_layers_11_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_11_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(237195904)))]; + tensor encoder_layers_11_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_11_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(237198016)))]; + tensor x_303_cast_fp16 = layer_norm(axes = x_303_axes_0, beta = encoder_layers_11_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_11_norm_conv_weight_to_fp16, x = input_619_cast_fp16)[name = string("x_303_cast_fp16")]; + tensor input_621_perm_0 = const()[name = string("input_621_perm_0"), val = tensor([0, 2, 1])]; + string input_623_pad_type_0 = const()[name = string("input_623_pad_type_0"), val = string("valid")]; + tensor input_623_strides_0 = const()[name = string("input_623_strides_0"), val = tensor([1])]; + tensor input_623_pad_0 = const()[name = string("input_623_pad_0"), val = tensor([0, 0])]; + tensor input_623_dilations_0 = const()[name = string("input_623_dilations_0"), val = tensor([1])]; + int32 input_623_groups_0 = const()[name = string("input_623_groups_0"), val = int32(1)]; + tensor encoder_layers_11_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(237200128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(239297344))))[name = string("encoder_layers_11_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_621_cast_fp16 = transpose(perm = input_621_perm_0, x = x_303_cast_fp16)[name = string("transpose_258")]; + tensor input_623_cast_fp16 = conv(dilations = input_623_dilations_0, groups = input_623_groups_0, pad = input_623_pad_0, pad_type = input_623_pad_type_0, strides = input_623_strides_0, weight = encoder_layers_11_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_621_cast_fp16)[name = string("input_623_cast_fp16")]; + int32 x_305_split_num_splits_0 = const()[name = string("x_305_split_num_splits_0"), val = int32(2)]; + int32 x_305_split_axis_0 = const()[name = string("x_305_split_axis_0"), val = int32(1)]; + tensor x_305_split_cast_fp16_0, tensor x_305_split_cast_fp16_1 = split(axis = x_305_split_axis_0, num_splits = x_305_split_num_splits_0, x = input_623_cast_fp16)[name = string("x_305_split_cast_fp16")]; + tensor x_305_split_1_sigmoid_cast_fp16 = sigmoid(x = x_305_split_cast_fp16_1)[name = string("x_305_split_1_sigmoid_cast_fp16")]; + tensor x_305_cast_fp16 = mul(x = x_305_split_cast_fp16_0, y = x_305_split_1_sigmoid_cast_fp16)[name = string("x_305_cast_fp16")]; + tensor input_625_cast_fp16 = select(a = var_44_to_fp16, b = x_305_cast_fp16, cond = var_575)[name = string("input_625_cast_fp16")]; + bool new_x_47_interleave_0 = const()[name = string("new_x_47_interleave_0"), val = bool(false)]; + tensor new_x_47_cast_fp16 = concat(axis = var_59, interleave = new_x_47_interleave_0, values = (cache_47_cast_fp16, input_625_cast_fp16))[name = string("new_x_47_cast_fp16")]; + tensor var_2931_begin_0 = const()[name = string("op_2931_begin_0"), val = tensor([0, 0, 7])]; + tensor var_2931_end_0 = const()[name = string("op_2931_end_0"), val = tensor([1, 1024, 15])]; + tensor var_2931_end_mask_0 = const()[name = string("op_2931_end_mask_0"), val = tensor([true, true, true])]; + tensor var_2931_cast_fp16 = slice_by_index(begin = var_2931_begin_0, end = var_2931_end_0, end_mask = var_2931_end_mask_0, x = new_x_47_cast_fp16)[name = string("op_2931_cast_fp16")]; + string x_307_pad_type_0 = const()[name = string("x_307_pad_type_0"), val = string("valid")]; + int32 x_307_groups_0 = const()[name = string("x_307_groups_0"), val = int32(1024)]; + tensor x_307_strides_0 = const()[name = string("x_307_strides_0"), val = tensor([1])]; + tensor x_307_pad_0 = const()[name = string("x_307_pad_0"), val = tensor([0, 0])]; + tensor x_307_dilations_0 = const()[name = string("x_307_dilations_0"), val = tensor([1])]; + tensor encoder_layers_11_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(239301504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(239310784))))[name = string("encoder_layers_11_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_307_cast_fp16 = conv(dilations = x_307_dilations_0, groups = x_307_groups_0, pad = x_307_pad_0, pad_type = x_307_pad_type_0, strides = x_307_strides_0, weight = encoder_layers_11_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_47_cast_fp16)[name = string("x_307_cast_fp16")]; + tensor input_627_perm_0 = const()[name = string("input_627_perm_0"), val = tensor([0, 2, 1])]; + tensor x_309_axes_0 = const()[name = string("x_309_axes_0"), val = tensor([-1])]; + tensor encoder_layers_11_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_11_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(239312896)))]; + tensor encoder_layers_11_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_11_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(239315008)))]; + tensor input_627_cast_fp16 = transpose(perm = input_627_perm_0, x = x_307_cast_fp16)[name = string("transpose_257")]; + tensor x_309_cast_fp16 = layer_norm(axes = x_309_axes_0, beta = encoder_layers_11_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_11_conv_batch_norm_weight_to_fp16, x = input_627_cast_fp16)[name = string("x_309_cast_fp16")]; + tensor input_629_perm_0 = const()[name = string("input_629_perm_0"), val = tensor([0, 2, 1])]; + tensor input_629_cast_fp16 = transpose(perm = input_629_perm_0, x = x_309_cast_fp16)[name = string("transpose_256")]; + tensor input_631_cast_fp16 = silu(x = input_629_cast_fp16)[name = string("input_631_cast_fp16")]; + string x_311_pad_type_0 = const()[name = string("x_311_pad_type_0"), val = string("valid")]; + tensor x_311_strides_0 = const()[name = string("x_311_strides_0"), val = tensor([1])]; + tensor x_311_pad_0 = const()[name = string("x_311_pad_0"), val = tensor([0, 0])]; + tensor x_311_dilations_0 = const()[name = string("x_311_dilations_0"), val = tensor([1])]; + int32 x_311_groups_0 = const()[name = string("x_311_groups_0"), val = int32(1)]; + tensor encoder_layers_11_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(239317120))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240365760))))[name = string("encoder_layers_11_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_311_cast_fp16 = conv(dilations = x_311_dilations_0, groups = x_311_groups_0, pad = x_311_pad_0, pad_type = x_311_pad_type_0, strides = x_311_strides_0, weight = encoder_layers_11_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_631_cast_fp16)[name = string("x_311_cast_fp16")]; + tensor input_633_perm_0 = const()[name = string("input_633_perm_0"), val = tensor([0, 2, 1])]; + tensor input_633_cast_fp16 = transpose(perm = input_633_perm_0, x = x_311_cast_fp16)[name = string("transpose_255")]; + tensor input_635_cast_fp16 = add(x = input_619_cast_fp16, y = input_633_cast_fp16)[name = string("input_635_cast_fp16")]; + tensor input_637_axes_0 = const()[name = string("input_637_axes_0"), val = tensor([-1])]; + tensor encoder_layers_11_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_11_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240367872)))]; + tensor encoder_layers_11_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_11_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240369984)))]; + tensor input_637_cast_fp16 = layer_norm(axes = input_637_axes_0, beta = encoder_layers_11_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_11_norm_feed_forward2_weight_to_fp16, x = input_635_cast_fp16)[name = string("input_637_cast_fp16")]; + tensor encoder_layers_11_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240372096))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(243517888))))[name = string("encoder_layers_11_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_11_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_11_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(243518080)))]; + tensor linear_107_cast_fp16 = linear(bias = encoder_layers_11_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_11_feed_forward2_linear1_weight_to_fp16_palettized, x = input_637_cast_fp16)[name = string("linear_107_cast_fp16")]; + tensor input_641_cast_fp16 = silu(x = linear_107_cast_fp16)[name = string("input_641_cast_fp16")]; + tensor encoder_layers_11_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(243526336))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(246672128))))[name = string("encoder_layers_11_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_11_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_11_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(246672320)))]; + tensor linear_108_cast_fp16 = linear(bias = encoder_layers_11_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_11_feed_forward2_linear2_weight_to_fp16_palettized, x = input_641_cast_fp16)[name = string("linear_108_cast_fp16")]; + fp16 var_2974_to_fp16 = const()[name = string("op_2974_to_fp16"), val = fp16(0x1p-1)]; + tensor var_2975_cast_fp16 = mul(x = linear_108_cast_fp16, y = var_2974_to_fp16)[name = string("op_2975_cast_fp16")]; + tensor input_647_cast_fp16 = add(x = input_635_cast_fp16, y = var_2975_cast_fp16)[name = string("input_647_cast_fp16")]; + tensor input_649_axes_0 = const()[name = string("input_649_axes_0"), val = tensor([-1])]; + tensor encoder_layers_11_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_11_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(246674432)))]; + tensor encoder_layers_11_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_11_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(246676544)))]; + tensor input_649_cast_fp16 = layer_norm(axes = input_649_axes_0, beta = encoder_layers_11_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_11_norm_out_weight_to_fp16, x = input_647_cast_fp16)[name = string("input_649_cast_fp16")]; + tensor cache_49_begin_0 = const()[name = string("cache_49_begin_0"), val = tensor([12, 0, 0, 0])]; + tensor cache_49_end_0 = const()[name = string("cache_49_end_0"), val = tensor([13, 1, 42, 1024])]; + tensor cache_49_end_mask_0 = const()[name = string("cache_49_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_49_squeeze_mask_0 = const()[name = string("cache_49_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_49_cast_fp16 = slice_by_index(begin = cache_49_begin_0, end = cache_49_end_0, end_mask = cache_49_end_mask_0, squeeze_mask = cache_49_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_49_cast_fp16")]; + tensor cache_51_begin_0 = const()[name = string("cache_51_begin_0"), val = tensor([12, 0, 0, 0])]; + tensor cache_51_end_0 = const()[name = string("cache_51_end_0"), val = tensor([13, 1, 1024, 8])]; + tensor cache_51_end_mask_0 = const()[name = string("cache_51_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_51_squeeze_mask_0 = const()[name = string("cache_51_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_51_cast_fp16 = slice_by_index(begin = cache_51_begin_0, end = cache_51_end_0, end_mask = cache_51_end_mask_0, squeeze_mask = cache_51_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_51_cast_fp16")]; + tensor input_651_axes_0 = const()[name = string("input_651_axes_0"), val = tensor([-1])]; + tensor encoder_layers_12_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_12_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(246678656)))]; + tensor encoder_layers_12_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_12_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(246680768)))]; + tensor input_651_cast_fp16 = layer_norm(axes = input_651_axes_0, beta = encoder_layers_12_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_12_norm_feed_forward1_weight_to_fp16, x = input_649_cast_fp16)[name = string("input_651_cast_fp16")]; + tensor encoder_layers_12_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(246682880))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(249828672))))[name = string("encoder_layers_12_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_12_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_12_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(249828864)))]; + tensor linear_109_cast_fp16 = linear(bias = encoder_layers_12_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_12_feed_forward1_linear1_weight_to_fp16_palettized, x = input_651_cast_fp16)[name = string("linear_109_cast_fp16")]; + tensor input_655_cast_fp16 = silu(x = linear_109_cast_fp16)[name = string("input_655_cast_fp16")]; + tensor encoder_layers_12_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(249837120))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(252982912))))[name = string("encoder_layers_12_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_12_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_12_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(252983104)))]; + tensor linear_110_cast_fp16 = linear(bias = encoder_layers_12_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_12_feed_forward1_linear2_weight_to_fp16_palettized, x = input_655_cast_fp16)[name = string("linear_110_cast_fp16")]; + fp16 var_3011_to_fp16 = const()[name = string("op_3011_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3012_cast_fp16 = mul(x = linear_110_cast_fp16, y = var_3011_to_fp16)[name = string("op_3012_cast_fp16")]; + tensor input_661_cast_fp16 = add(x = input_649_cast_fp16, y = var_3012_cast_fp16)[name = string("input_661_cast_fp16")]; + tensor key_25_axes_0 = const()[name = string("key_25_axes_0"), val = tensor([-1])]; + tensor encoder_layers_12_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_12_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(252985216)))]; + tensor encoder_layers_12_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_12_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(252987328)))]; + tensor key_25_cast_fp16 = layer_norm(axes = key_25_axes_0, beta = encoder_layers_12_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_12_norm_self_att_weight_to_fp16, x = input_661_cast_fp16)[name = string("key_25_cast_fp16")]; + bool input_663_interleave_0 = const()[name = string("input_663_interleave_0"), val = bool(false)]; + tensor input_663_cast_fp16 = concat(axis = var_68, interleave = input_663_interleave_0, values = (cache_49_cast_fp16, key_25_cast_fp16))[name = string("input_663_cast_fp16")]; + tensor var_3034_begin_0 = const()[name = string("op_3034_begin_0"), val = tensor([0, 7, 0])]; + tensor var_3034_end_0 = const()[name = string("op_3034_end_0"), val = tensor([1, 42, 1024])]; + tensor var_3034_end_mask_0 = const()[name = string("op_3034_end_mask_0"), val = tensor([true, true, true])]; + tensor var_3034_cast_fp16 = slice_by_index(begin = var_3034_begin_0, end = var_3034_end_0, end_mask = var_3034_end_mask_0, x = cache_49_cast_fp16)[name = string("op_3034_cast_fp16")]; + bool var_3040_interleave_0 = const()[name = string("op_3040_interleave_0"), val = bool(false)]; + tensor var_3040_cast_fp16 = concat(axis = var_68, interleave = var_3040_interleave_0, values = (var_3034_cast_fp16, key_25_cast_fp16))[name = string("op_3040_cast_fp16")]; + tensor encoder_layers_12_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(252989440))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(253775936))))[name = string("encoder_layers_12_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_12_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_12_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(253776128)))]; + tensor linear_111_cast_fp16 = linear(bias = encoder_layers_12_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_12_self_attn_linear_q_weight_to_fp16_palettized, x = key_25_cast_fp16)[name = string("linear_111_cast_fp16")]; + tensor var_3045 = const()[name = string("op_3045"), val = tensor([1, -1, 8, 128])]; + tensor q_73_cast_fp16 = reshape(shape = var_3045, x = linear_111_cast_fp16)[name = string("q_73_cast_fp16")]; + tensor encoder_layers_12_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(253778240))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(254564736))))[name = string("encoder_layers_12_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_12_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_12_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(254564928)))]; + tensor linear_112_cast_fp16 = linear(bias = encoder_layers_12_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_12_self_attn_linear_k_weight_to_fp16_palettized, x = input_663_cast_fp16)[name = string("linear_112_cast_fp16")]; + tensor var_3050 = const()[name = string("op_3050"), val = tensor([1, -1, 8, 128])]; + tensor k_49_cast_fp16 = reshape(shape = var_3050, x = linear_112_cast_fp16)[name = string("k_49_cast_fp16")]; + tensor encoder_layers_12_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(254567040))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(255353536))))[name = string("encoder_layers_12_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_12_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_12_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(255353728)))]; + tensor linear_113_cast_fp16 = linear(bias = encoder_layers_12_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_12_self_attn_linear_v_weight_to_fp16_palettized, x = input_663_cast_fp16)[name = string("linear_113_cast_fp16")]; + tensor var_3055 = const()[name = string("op_3055"), val = tensor([1, -1, 8, 128])]; + tensor v_25_cast_fp16 = reshape(shape = var_3055, x = linear_113_cast_fp16)[name = string("v_25_cast_fp16")]; + tensor value_33_perm_0 = const()[name = string("value_33_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_12_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_12_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(255355840)))]; + tensor var_3068_cast_fp16 = add(x = q_73_cast_fp16, y = encoder_layers_12_self_attn_pos_bias_u_to_fp16)[name = string("op_3068_cast_fp16")]; + tensor encoder_layers_12_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_12_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(255357952)))]; + tensor var_3070_cast_fp16 = add(x = q_73_cast_fp16, y = encoder_layers_12_self_attn_pos_bias_v_to_fp16)[name = string("op_3070_cast_fp16")]; + tensor q_with_bias_v_25_perm_0 = const()[name = string("q_with_bias_v_25_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_319_transpose_x_0 = const()[name = string("x_319_transpose_x_0"), val = bool(false)]; + bool x_319_transpose_y_0 = const()[name = string("x_319_transpose_y_0"), val = bool(false)]; + tensor op_3072_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(255360064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(255459456))))[name = string("op_3072_to_fp16_quantized")]; + tensor q_with_bias_v_25_cast_fp16 = transpose(perm = q_with_bias_v_25_perm_0, x = var_3070_cast_fp16)[name = string("transpose_254")]; + tensor x_319_cast_fp16 = matmul(transpose_x = x_319_transpose_x_0, transpose_y = x_319_transpose_y_0, x = q_with_bias_v_25_cast_fp16, y = op_3072_to_fp16_quantized)[name = string("x_319_cast_fp16")]; + tensor x_321_pad_0 = const()[name = string("x_321_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_321_mode_0 = const()[name = string("x_321_mode_0"), val = string("constant")]; + fp16 const_235_to_fp16 = const()[name = string("const_235_to_fp16"), val = fp16(0x0p+0)]; + tensor x_321_cast_fp16 = pad(constant_val = const_235_to_fp16, mode = x_321_mode_0, pad = x_321_pad_0, x = x_319_cast_fp16)[name = string("x_321_cast_fp16")]; + tensor var_3080 = const()[name = string("op_3080"), val = tensor([1, 8, -1, 7])]; + tensor x_323_cast_fp16 = reshape(shape = var_3080, x = x_321_cast_fp16)[name = string("x_323_cast_fp16")]; + tensor var_3084_begin_0 = const()[name = string("op_3084_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_3084_end_0 = const()[name = string("op_3084_end_0"), val = tensor([1, 8, 98, 7])]; + tensor var_3084_end_mask_0 = const()[name = string("op_3084_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_3084_cast_fp16 = slice_by_index(begin = var_3084_begin_0, end = var_3084_end_0, end_mask = var_3084_end_mask_0, x = x_323_cast_fp16)[name = string("op_3084_cast_fp16")]; + tensor var_3085 = const()[name = string("op_3085"), val = tensor([1, 8, 7, 97])]; + tensor matrix_bd_49_cast_fp16 = reshape(shape = var_3085, x = var_3084_cast_fp16)[name = string("matrix_bd_49_cast_fp16")]; + bool matrix_ac_25_transpose_x_0 = const()[name = string("matrix_ac_25_transpose_x_0"), val = bool(false)]; + bool matrix_ac_25_transpose_y_0 = const()[name = string("matrix_ac_25_transpose_y_0"), val = bool(false)]; + tensor transpose_120_perm_0 = const()[name = string("transpose_120_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_121_perm_0 = const()[name = string("transpose_121_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_121 = transpose(perm = transpose_121_perm_0, x = k_49_cast_fp16)[name = string("transpose_252")]; + tensor transpose_120 = transpose(perm = transpose_120_perm_0, x = var_3068_cast_fp16)[name = string("transpose_253")]; + tensor matrix_ac_25_cast_fp16 = matmul(transpose_x = matrix_ac_25_transpose_x_0, transpose_y = matrix_ac_25_transpose_y_0, x = transpose_120, y = transpose_121)[name = string("matrix_ac_25_cast_fp16")]; + tensor matrix_bd_51_begin_0 = const()[name = string("matrix_bd_51_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_51_end_0 = const()[name = string("matrix_bd_51_end_0"), val = tensor([1, 8, 7, 49])]; + tensor matrix_bd_51_end_mask_0 = const()[name = string("matrix_bd_51_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_51_cast_fp16 = slice_by_index(begin = matrix_bd_51_begin_0, end = matrix_bd_51_end_0, end_mask = matrix_bd_51_end_mask_0, x = matrix_bd_49_cast_fp16)[name = string("matrix_bd_51_cast_fp16")]; + tensor var_3094_cast_fp16 = add(x = matrix_ac_25_cast_fp16, y = matrix_bd_51_cast_fp16)[name = string("op_3094_cast_fp16")]; + fp16 _inversed_scores_49_y_0_to_fp16 = const()[name = string("_inversed_scores_49_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_49_cast_fp16 = mul(x = var_3094_cast_fp16, y = _inversed_scores_49_y_0_to_fp16)[name = string("_inversed_scores_49_cast_fp16")]; + tensor scores_51_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_49_cast_fp16, cond = mask_11)[name = string("scores_51_cast_fp16")]; + tensor var_3100_cast_fp16 = softmax(axis = var_59, x = scores_51_cast_fp16)[name = string("op_3100_cast_fp16")]; + tensor input_665_cast_fp16 = select(a = var_44_to_fp16, b = var_3100_cast_fp16, cond = mask_11)[name = string("input_665_cast_fp16")]; + bool x_325_transpose_x_0 = const()[name = string("x_325_transpose_x_0"), val = bool(false)]; + bool x_325_transpose_y_0 = const()[name = string("x_325_transpose_y_0"), val = bool(false)]; + tensor value_33_cast_fp16 = transpose(perm = value_33_perm_0, x = v_25_cast_fp16)[name = string("transpose_251")]; + tensor x_325_cast_fp16 = matmul(transpose_x = x_325_transpose_x_0, transpose_y = x_325_transpose_y_0, x = input_665_cast_fp16, y = value_33_cast_fp16)[name = string("x_325_cast_fp16")]; + tensor var_3104_perm_0 = const()[name = string("op_3104_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3105 = const()[name = string("op_3105"), val = tensor([1, -1, 1024])]; + tensor var_3104_cast_fp16 = transpose(perm = var_3104_perm_0, x = x_325_cast_fp16)[name = string("transpose_250")]; + tensor input_667_cast_fp16 = reshape(shape = var_3105, x = var_3104_cast_fp16)[name = string("input_667_cast_fp16")]; + tensor encoder_layers_12_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(255459776))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(256246272))))[name = string("encoder_layers_12_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_12_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_12_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(256246464)))]; + tensor linear_115_cast_fp16 = linear(bias = encoder_layers_12_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_12_self_attn_linear_out_weight_to_fp16_palettized, x = input_667_cast_fp16)[name = string("linear_115_cast_fp16")]; + tensor input_671_cast_fp16 = add(x = input_661_cast_fp16, y = linear_115_cast_fp16)[name = string("input_671_cast_fp16")]; + tensor x_329_axes_0 = const()[name = string("x_329_axes_0"), val = tensor([-1])]; + tensor encoder_layers_12_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_12_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(256248576)))]; + tensor encoder_layers_12_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_12_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(256250688)))]; + tensor x_329_cast_fp16 = layer_norm(axes = x_329_axes_0, beta = encoder_layers_12_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_12_norm_conv_weight_to_fp16, x = input_671_cast_fp16)[name = string("x_329_cast_fp16")]; + tensor input_673_perm_0 = const()[name = string("input_673_perm_0"), val = tensor([0, 2, 1])]; + string input_675_pad_type_0 = const()[name = string("input_675_pad_type_0"), val = string("valid")]; + tensor input_675_strides_0 = const()[name = string("input_675_strides_0"), val = tensor([1])]; + tensor input_675_pad_0 = const()[name = string("input_675_pad_0"), val = tensor([0, 0])]; + tensor input_675_dilations_0 = const()[name = string("input_675_dilations_0"), val = tensor([1])]; + int32 input_675_groups_0 = const()[name = string("input_675_groups_0"), val = int32(1)]; + tensor encoder_layers_12_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(256252800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(258350016))))[name = string("encoder_layers_12_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_673_cast_fp16 = transpose(perm = input_673_perm_0, x = x_329_cast_fp16)[name = string("transpose_249")]; + tensor input_675_cast_fp16 = conv(dilations = input_675_dilations_0, groups = input_675_groups_0, pad = input_675_pad_0, pad_type = input_675_pad_type_0, strides = input_675_strides_0, weight = encoder_layers_12_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_673_cast_fp16)[name = string("input_675_cast_fp16")]; + int32 x_331_split_num_splits_0 = const()[name = string("x_331_split_num_splits_0"), val = int32(2)]; + int32 x_331_split_axis_0 = const()[name = string("x_331_split_axis_0"), val = int32(1)]; + tensor x_331_split_cast_fp16_0, tensor x_331_split_cast_fp16_1 = split(axis = x_331_split_axis_0, num_splits = x_331_split_num_splits_0, x = input_675_cast_fp16)[name = string("x_331_split_cast_fp16")]; + tensor x_331_split_1_sigmoid_cast_fp16 = sigmoid(x = x_331_split_cast_fp16_1)[name = string("x_331_split_1_sigmoid_cast_fp16")]; + tensor x_331_cast_fp16 = mul(x = x_331_split_cast_fp16_0, y = x_331_split_1_sigmoid_cast_fp16)[name = string("x_331_cast_fp16")]; + tensor input_677_cast_fp16 = select(a = var_44_to_fp16, b = x_331_cast_fp16, cond = var_575)[name = string("input_677_cast_fp16")]; + bool new_x_51_interleave_0 = const()[name = string("new_x_51_interleave_0"), val = bool(false)]; + tensor new_x_51_cast_fp16 = concat(axis = var_59, interleave = new_x_51_interleave_0, values = (cache_51_cast_fp16, input_677_cast_fp16))[name = string("new_x_51_cast_fp16")]; + tensor var_3144_begin_0 = const()[name = string("op_3144_begin_0"), val = tensor([0, 0, 7])]; + tensor var_3144_end_0 = const()[name = string("op_3144_end_0"), val = tensor([1, 1024, 15])]; + tensor var_3144_end_mask_0 = const()[name = string("op_3144_end_mask_0"), val = tensor([true, true, true])]; + tensor var_3144_cast_fp16 = slice_by_index(begin = var_3144_begin_0, end = var_3144_end_0, end_mask = var_3144_end_mask_0, x = new_x_51_cast_fp16)[name = string("op_3144_cast_fp16")]; + string x_333_pad_type_0 = const()[name = string("x_333_pad_type_0"), val = string("valid")]; + int32 x_333_groups_0 = const()[name = string("x_333_groups_0"), val = int32(1024)]; + tensor x_333_strides_0 = const()[name = string("x_333_strides_0"), val = tensor([1])]; + tensor x_333_pad_0 = const()[name = string("x_333_pad_0"), val = tensor([0, 0])]; + tensor x_333_dilations_0 = const()[name = string("x_333_dilations_0"), val = tensor([1])]; + tensor encoder_layers_12_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(258354176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(258363456))))[name = string("encoder_layers_12_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_333_cast_fp16 = conv(dilations = x_333_dilations_0, groups = x_333_groups_0, pad = x_333_pad_0, pad_type = x_333_pad_type_0, strides = x_333_strides_0, weight = encoder_layers_12_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_51_cast_fp16)[name = string("x_333_cast_fp16")]; + tensor input_679_perm_0 = const()[name = string("input_679_perm_0"), val = tensor([0, 2, 1])]; + tensor x_335_axes_0 = const()[name = string("x_335_axes_0"), val = tensor([-1])]; + tensor encoder_layers_12_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_12_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(258365568)))]; + tensor encoder_layers_12_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_12_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(258367680)))]; + tensor input_679_cast_fp16 = transpose(perm = input_679_perm_0, x = x_333_cast_fp16)[name = string("transpose_248")]; + tensor x_335_cast_fp16 = layer_norm(axes = x_335_axes_0, beta = encoder_layers_12_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_12_conv_batch_norm_weight_to_fp16, x = input_679_cast_fp16)[name = string("x_335_cast_fp16")]; + tensor input_681_perm_0 = const()[name = string("input_681_perm_0"), val = tensor([0, 2, 1])]; + tensor input_681_cast_fp16 = transpose(perm = input_681_perm_0, x = x_335_cast_fp16)[name = string("transpose_247")]; + tensor input_683_cast_fp16 = silu(x = input_681_cast_fp16)[name = string("input_683_cast_fp16")]; + string x_337_pad_type_0 = const()[name = string("x_337_pad_type_0"), val = string("valid")]; + tensor x_337_strides_0 = const()[name = string("x_337_strides_0"), val = tensor([1])]; + tensor x_337_pad_0 = const()[name = string("x_337_pad_0"), val = tensor([0, 0])]; + tensor x_337_dilations_0 = const()[name = string("x_337_dilations_0"), val = tensor([1])]; + int32 x_337_groups_0 = const()[name = string("x_337_groups_0"), val = int32(1)]; + tensor encoder_layers_12_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(258369792))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259418432))))[name = string("encoder_layers_12_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_337_cast_fp16 = conv(dilations = x_337_dilations_0, groups = x_337_groups_0, pad = x_337_pad_0, pad_type = x_337_pad_type_0, strides = x_337_strides_0, weight = encoder_layers_12_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_683_cast_fp16)[name = string("x_337_cast_fp16")]; + tensor input_685_perm_0 = const()[name = string("input_685_perm_0"), val = tensor([0, 2, 1])]; + tensor input_685_cast_fp16 = transpose(perm = input_685_perm_0, x = x_337_cast_fp16)[name = string("transpose_246")]; + tensor input_687_cast_fp16 = add(x = input_671_cast_fp16, y = input_685_cast_fp16)[name = string("input_687_cast_fp16")]; + tensor input_689_axes_0 = const()[name = string("input_689_axes_0"), val = tensor([-1])]; + tensor encoder_layers_12_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_12_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259420544)))]; + tensor encoder_layers_12_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_12_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259422656)))]; + tensor input_689_cast_fp16 = layer_norm(axes = input_689_axes_0, beta = encoder_layers_12_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_12_norm_feed_forward2_weight_to_fp16, x = input_687_cast_fp16)[name = string("input_689_cast_fp16")]; + tensor encoder_layers_12_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259424768))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(262570560))))[name = string("encoder_layers_12_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_12_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_12_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(262570752)))]; + tensor linear_116_cast_fp16 = linear(bias = encoder_layers_12_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_12_feed_forward2_linear1_weight_to_fp16_palettized, x = input_689_cast_fp16)[name = string("linear_116_cast_fp16")]; + tensor input_693_cast_fp16 = silu(x = linear_116_cast_fp16)[name = string("input_693_cast_fp16")]; + tensor encoder_layers_12_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(262579008))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265724800))))[name = string("encoder_layers_12_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_12_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_12_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265724992)))]; + tensor linear_117_cast_fp16 = linear(bias = encoder_layers_12_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_12_feed_forward2_linear2_weight_to_fp16_palettized, x = input_693_cast_fp16)[name = string("linear_117_cast_fp16")]; + fp16 var_3187_to_fp16 = const()[name = string("op_3187_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3188_cast_fp16 = mul(x = linear_117_cast_fp16, y = var_3187_to_fp16)[name = string("op_3188_cast_fp16")]; + tensor input_699_cast_fp16 = add(x = input_687_cast_fp16, y = var_3188_cast_fp16)[name = string("input_699_cast_fp16")]; + tensor input_701_axes_0 = const()[name = string("input_701_axes_0"), val = tensor([-1])]; + tensor encoder_layers_12_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_12_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265727104)))]; + tensor encoder_layers_12_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_12_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265729216)))]; + tensor input_701_cast_fp16 = layer_norm(axes = input_701_axes_0, beta = encoder_layers_12_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_12_norm_out_weight_to_fp16, x = input_699_cast_fp16)[name = string("input_701_cast_fp16")]; + tensor cache_53_begin_0 = const()[name = string("cache_53_begin_0"), val = tensor([13, 0, 0, 0])]; + tensor cache_53_end_0 = const()[name = string("cache_53_end_0"), val = tensor([14, 1, 42, 1024])]; + tensor cache_53_end_mask_0 = const()[name = string("cache_53_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_53_squeeze_mask_0 = const()[name = string("cache_53_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_53_cast_fp16 = slice_by_index(begin = cache_53_begin_0, end = cache_53_end_0, end_mask = cache_53_end_mask_0, squeeze_mask = cache_53_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_53_cast_fp16")]; + tensor cache_55_begin_0 = const()[name = string("cache_55_begin_0"), val = tensor([13, 0, 0, 0])]; + tensor cache_55_end_0 = const()[name = string("cache_55_end_0"), val = tensor([14, 1, 1024, 8])]; + tensor cache_55_end_mask_0 = const()[name = string("cache_55_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_55_squeeze_mask_0 = const()[name = string("cache_55_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_55_cast_fp16 = slice_by_index(begin = cache_55_begin_0, end = cache_55_end_0, end_mask = cache_55_end_mask_0, squeeze_mask = cache_55_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_55_cast_fp16")]; + tensor input_703_axes_0 = const()[name = string("input_703_axes_0"), val = tensor([-1])]; + tensor encoder_layers_13_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_13_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265731328)))]; + tensor encoder_layers_13_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_13_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265733440)))]; + tensor input_703_cast_fp16 = layer_norm(axes = input_703_axes_0, beta = encoder_layers_13_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_13_norm_feed_forward1_weight_to_fp16, x = input_701_cast_fp16)[name = string("input_703_cast_fp16")]; + tensor encoder_layers_13_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265735552))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(268881344))))[name = string("encoder_layers_13_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_13_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_13_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(268881536)))]; + tensor linear_118_cast_fp16 = linear(bias = encoder_layers_13_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_13_feed_forward1_linear1_weight_to_fp16_palettized, x = input_703_cast_fp16)[name = string("linear_118_cast_fp16")]; + tensor input_707_cast_fp16 = silu(x = linear_118_cast_fp16)[name = string("input_707_cast_fp16")]; + tensor encoder_layers_13_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(268889792))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(272035584))))[name = string("encoder_layers_13_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_13_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_13_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(272035776)))]; + tensor linear_119_cast_fp16 = linear(bias = encoder_layers_13_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_13_feed_forward1_linear2_weight_to_fp16_palettized, x = input_707_cast_fp16)[name = string("linear_119_cast_fp16")]; + fp16 var_3224_to_fp16 = const()[name = string("op_3224_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3225_cast_fp16 = mul(x = linear_119_cast_fp16, y = var_3224_to_fp16)[name = string("op_3225_cast_fp16")]; + tensor input_713_cast_fp16 = add(x = input_701_cast_fp16, y = var_3225_cast_fp16)[name = string("input_713_cast_fp16")]; + tensor key_27_axes_0 = const()[name = string("key_27_axes_0"), val = tensor([-1])]; + tensor encoder_layers_13_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_13_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(272037888)))]; + tensor encoder_layers_13_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_13_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(272040000)))]; + tensor key_27_cast_fp16 = layer_norm(axes = key_27_axes_0, beta = encoder_layers_13_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_13_norm_self_att_weight_to_fp16, x = input_713_cast_fp16)[name = string("key_27_cast_fp16")]; + bool input_715_interleave_0 = const()[name = string("input_715_interleave_0"), val = bool(false)]; + tensor input_715_cast_fp16 = concat(axis = var_68, interleave = input_715_interleave_0, values = (cache_53_cast_fp16, key_27_cast_fp16))[name = string("input_715_cast_fp16")]; + tensor var_3247_begin_0 = const()[name = string("op_3247_begin_0"), val = tensor([0, 7, 0])]; + tensor var_3247_end_0 = const()[name = string("op_3247_end_0"), val = tensor([1, 42, 1024])]; + tensor var_3247_end_mask_0 = const()[name = string("op_3247_end_mask_0"), val = tensor([true, true, true])]; + tensor var_3247_cast_fp16 = slice_by_index(begin = var_3247_begin_0, end = var_3247_end_0, end_mask = var_3247_end_mask_0, x = cache_53_cast_fp16)[name = string("op_3247_cast_fp16")]; + bool var_3253_interleave_0 = const()[name = string("op_3253_interleave_0"), val = bool(false)]; + tensor var_3253_cast_fp16 = concat(axis = var_68, interleave = var_3253_interleave_0, values = (var_3247_cast_fp16, key_27_cast_fp16))[name = string("op_3253_cast_fp16")]; + tensor encoder_layers_13_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(272042112))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(272828608))))[name = string("encoder_layers_13_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_13_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_13_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(272828800)))]; + tensor linear_120_cast_fp16 = linear(bias = encoder_layers_13_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_13_self_attn_linear_q_weight_to_fp16_palettized, x = key_27_cast_fp16)[name = string("linear_120_cast_fp16")]; + tensor var_3258 = const()[name = string("op_3258"), val = tensor([1, -1, 8, 128])]; + tensor q_79_cast_fp16 = reshape(shape = var_3258, x = linear_120_cast_fp16)[name = string("q_79_cast_fp16")]; + tensor encoder_layers_13_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(272830912))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(273617408))))[name = string("encoder_layers_13_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_13_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_13_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(273617600)))]; + tensor linear_121_cast_fp16 = linear(bias = encoder_layers_13_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_13_self_attn_linear_k_weight_to_fp16_palettized, x = input_715_cast_fp16)[name = string("linear_121_cast_fp16")]; + tensor var_3263 = const()[name = string("op_3263"), val = tensor([1, -1, 8, 128])]; + tensor k_53_cast_fp16 = reshape(shape = var_3263, x = linear_121_cast_fp16)[name = string("k_53_cast_fp16")]; + tensor encoder_layers_13_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(273619712))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(274406208))))[name = string("encoder_layers_13_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_13_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_13_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(274406400)))]; + tensor linear_122_cast_fp16 = linear(bias = encoder_layers_13_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_13_self_attn_linear_v_weight_to_fp16_palettized, x = input_715_cast_fp16)[name = string("linear_122_cast_fp16")]; + tensor var_3268 = const()[name = string("op_3268"), val = tensor([1, -1, 8, 128])]; + tensor v_27_cast_fp16 = reshape(shape = var_3268, x = linear_122_cast_fp16)[name = string("v_27_cast_fp16")]; + tensor value_35_perm_0 = const()[name = string("value_35_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_13_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_13_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(274408512)))]; + tensor var_3281_cast_fp16 = add(x = q_79_cast_fp16, y = encoder_layers_13_self_attn_pos_bias_u_to_fp16)[name = string("op_3281_cast_fp16")]; + tensor encoder_layers_13_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_13_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(274410624)))]; + tensor var_3283_cast_fp16 = add(x = q_79_cast_fp16, y = encoder_layers_13_self_attn_pos_bias_v_to_fp16)[name = string("op_3283_cast_fp16")]; + tensor q_with_bias_v_27_perm_0 = const()[name = string("q_with_bias_v_27_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_345_transpose_x_0 = const()[name = string("x_345_transpose_x_0"), val = bool(false)]; + bool x_345_transpose_y_0 = const()[name = string("x_345_transpose_y_0"), val = bool(false)]; + tensor op_3285_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(274412736))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(274512128))))[name = string("op_3285_to_fp16_quantized")]; + tensor q_with_bias_v_27_cast_fp16 = transpose(perm = q_with_bias_v_27_perm_0, x = var_3283_cast_fp16)[name = string("transpose_245")]; + tensor x_345_cast_fp16 = matmul(transpose_x = x_345_transpose_x_0, transpose_y = x_345_transpose_y_0, x = q_with_bias_v_27_cast_fp16, y = op_3285_to_fp16_quantized)[name = string("x_345_cast_fp16")]; + tensor x_347_pad_0 = const()[name = string("x_347_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_347_mode_0 = const()[name = string("x_347_mode_0"), val = string("constant")]; + fp16 const_248_to_fp16 = const()[name = string("const_248_to_fp16"), val = fp16(0x0p+0)]; + tensor x_347_cast_fp16 = pad(constant_val = const_248_to_fp16, mode = x_347_mode_0, pad = x_347_pad_0, x = x_345_cast_fp16)[name = string("x_347_cast_fp16")]; + tensor var_3293 = const()[name = string("op_3293"), val = tensor([1, 8, -1, 7])]; + tensor x_349_cast_fp16 = reshape(shape = var_3293, x = x_347_cast_fp16)[name = string("x_349_cast_fp16")]; + tensor var_3297_begin_0 = const()[name = string("op_3297_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_3297_end_0 = const()[name = string("op_3297_end_0"), val = tensor([1, 8, 98, 7])]; + tensor var_3297_end_mask_0 = const()[name = string("op_3297_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_3297_cast_fp16 = slice_by_index(begin = var_3297_begin_0, end = var_3297_end_0, end_mask = var_3297_end_mask_0, x = x_349_cast_fp16)[name = string("op_3297_cast_fp16")]; + tensor var_3298 = const()[name = string("op_3298"), val = tensor([1, 8, 7, 97])]; + tensor matrix_bd_53_cast_fp16 = reshape(shape = var_3298, x = var_3297_cast_fp16)[name = string("matrix_bd_53_cast_fp16")]; + bool matrix_ac_27_transpose_x_0 = const()[name = string("matrix_ac_27_transpose_x_0"), val = bool(false)]; + bool matrix_ac_27_transpose_y_0 = const()[name = string("matrix_ac_27_transpose_y_0"), val = bool(false)]; + tensor transpose_122_perm_0 = const()[name = string("transpose_122_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_123_perm_0 = const()[name = string("transpose_123_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_123 = transpose(perm = transpose_123_perm_0, x = k_53_cast_fp16)[name = string("transpose_243")]; + tensor transpose_122 = transpose(perm = transpose_122_perm_0, x = var_3281_cast_fp16)[name = string("transpose_244")]; + tensor matrix_ac_27_cast_fp16 = matmul(transpose_x = matrix_ac_27_transpose_x_0, transpose_y = matrix_ac_27_transpose_y_0, x = transpose_122, y = transpose_123)[name = string("matrix_ac_27_cast_fp16")]; + tensor matrix_bd_55_begin_0 = const()[name = string("matrix_bd_55_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_55_end_0 = const()[name = string("matrix_bd_55_end_0"), val = tensor([1, 8, 7, 49])]; + tensor matrix_bd_55_end_mask_0 = const()[name = string("matrix_bd_55_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_55_cast_fp16 = slice_by_index(begin = matrix_bd_55_begin_0, end = matrix_bd_55_end_0, end_mask = matrix_bd_55_end_mask_0, x = matrix_bd_53_cast_fp16)[name = string("matrix_bd_55_cast_fp16")]; + tensor var_3307_cast_fp16 = add(x = matrix_ac_27_cast_fp16, y = matrix_bd_55_cast_fp16)[name = string("op_3307_cast_fp16")]; + fp16 _inversed_scores_53_y_0_to_fp16 = const()[name = string("_inversed_scores_53_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_53_cast_fp16 = mul(x = var_3307_cast_fp16, y = _inversed_scores_53_y_0_to_fp16)[name = string("_inversed_scores_53_cast_fp16")]; + tensor scores_55_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_53_cast_fp16, cond = mask_11)[name = string("scores_55_cast_fp16")]; + tensor var_3313_cast_fp16 = softmax(axis = var_59, x = scores_55_cast_fp16)[name = string("op_3313_cast_fp16")]; + tensor input_717_cast_fp16 = select(a = var_44_to_fp16, b = var_3313_cast_fp16, cond = mask_11)[name = string("input_717_cast_fp16")]; + bool x_351_transpose_x_0 = const()[name = string("x_351_transpose_x_0"), val = bool(false)]; + bool x_351_transpose_y_0 = const()[name = string("x_351_transpose_y_0"), val = bool(false)]; + tensor value_35_cast_fp16 = transpose(perm = value_35_perm_0, x = v_27_cast_fp16)[name = string("transpose_242")]; + tensor x_351_cast_fp16 = matmul(transpose_x = x_351_transpose_x_0, transpose_y = x_351_transpose_y_0, x = input_717_cast_fp16, y = value_35_cast_fp16)[name = string("x_351_cast_fp16")]; + tensor var_3317_perm_0 = const()[name = string("op_3317_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3318 = const()[name = string("op_3318"), val = tensor([1, -1, 1024])]; + tensor var_3317_cast_fp16 = transpose(perm = var_3317_perm_0, x = x_351_cast_fp16)[name = string("transpose_241")]; + tensor input_719_cast_fp16 = reshape(shape = var_3318, x = var_3317_cast_fp16)[name = string("input_719_cast_fp16")]; + tensor encoder_layers_13_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(274512448))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275298944))))[name = string("encoder_layers_13_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_13_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_13_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275299136)))]; + tensor linear_124_cast_fp16 = linear(bias = encoder_layers_13_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_13_self_attn_linear_out_weight_to_fp16_palettized, x = input_719_cast_fp16)[name = string("linear_124_cast_fp16")]; + tensor input_723_cast_fp16 = add(x = input_713_cast_fp16, y = linear_124_cast_fp16)[name = string("input_723_cast_fp16")]; + tensor x_355_axes_0 = const()[name = string("x_355_axes_0"), val = tensor([-1])]; + tensor encoder_layers_13_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_13_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275301248)))]; + tensor encoder_layers_13_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_13_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275303360)))]; + tensor x_355_cast_fp16 = layer_norm(axes = x_355_axes_0, beta = encoder_layers_13_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_13_norm_conv_weight_to_fp16, x = input_723_cast_fp16)[name = string("x_355_cast_fp16")]; + tensor input_725_perm_0 = const()[name = string("input_725_perm_0"), val = tensor([0, 2, 1])]; + string input_727_pad_type_0 = const()[name = string("input_727_pad_type_0"), val = string("valid")]; + tensor input_727_strides_0 = const()[name = string("input_727_strides_0"), val = tensor([1])]; + tensor input_727_pad_0 = const()[name = string("input_727_pad_0"), val = tensor([0, 0])]; + tensor input_727_dilations_0 = const()[name = string("input_727_dilations_0"), val = tensor([1])]; + int32 input_727_groups_0 = const()[name = string("input_727_groups_0"), val = int32(1)]; + tensor encoder_layers_13_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275305472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(277402688))))[name = string("encoder_layers_13_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_725_cast_fp16 = transpose(perm = input_725_perm_0, x = x_355_cast_fp16)[name = string("transpose_240")]; + tensor input_727_cast_fp16 = conv(dilations = input_727_dilations_0, groups = input_727_groups_0, pad = input_727_pad_0, pad_type = input_727_pad_type_0, strides = input_727_strides_0, weight = encoder_layers_13_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_725_cast_fp16)[name = string("input_727_cast_fp16")]; + int32 x_357_split_num_splits_0 = const()[name = string("x_357_split_num_splits_0"), val = int32(2)]; + int32 x_357_split_axis_0 = const()[name = string("x_357_split_axis_0"), val = int32(1)]; + tensor x_357_split_cast_fp16_0, tensor x_357_split_cast_fp16_1 = split(axis = x_357_split_axis_0, num_splits = x_357_split_num_splits_0, x = input_727_cast_fp16)[name = string("x_357_split_cast_fp16")]; + tensor x_357_split_1_sigmoid_cast_fp16 = sigmoid(x = x_357_split_cast_fp16_1)[name = string("x_357_split_1_sigmoid_cast_fp16")]; + tensor x_357_cast_fp16 = mul(x = x_357_split_cast_fp16_0, y = x_357_split_1_sigmoid_cast_fp16)[name = string("x_357_cast_fp16")]; + tensor input_729_cast_fp16 = select(a = var_44_to_fp16, b = x_357_cast_fp16, cond = var_575)[name = string("input_729_cast_fp16")]; + bool new_x_55_interleave_0 = const()[name = string("new_x_55_interleave_0"), val = bool(false)]; + tensor new_x_55_cast_fp16 = concat(axis = var_59, interleave = new_x_55_interleave_0, values = (cache_55_cast_fp16, input_729_cast_fp16))[name = string("new_x_55_cast_fp16")]; + tensor var_3357_begin_0 = const()[name = string("op_3357_begin_0"), val = tensor([0, 0, 7])]; + tensor var_3357_end_0 = const()[name = string("op_3357_end_0"), val = tensor([1, 1024, 15])]; + tensor var_3357_end_mask_0 = const()[name = string("op_3357_end_mask_0"), val = tensor([true, true, true])]; + tensor var_3357_cast_fp16 = slice_by_index(begin = var_3357_begin_0, end = var_3357_end_0, end_mask = var_3357_end_mask_0, x = new_x_55_cast_fp16)[name = string("op_3357_cast_fp16")]; + string x_359_pad_type_0 = const()[name = string("x_359_pad_type_0"), val = string("valid")]; + int32 x_359_groups_0 = const()[name = string("x_359_groups_0"), val = int32(1024)]; + tensor x_359_strides_0 = const()[name = string("x_359_strides_0"), val = tensor([1])]; + tensor x_359_pad_0 = const()[name = string("x_359_pad_0"), val = tensor([0, 0])]; + tensor x_359_dilations_0 = const()[name = string("x_359_dilations_0"), val = tensor([1])]; + tensor encoder_layers_13_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(277406848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(277416128))))[name = string("encoder_layers_13_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_359_cast_fp16 = conv(dilations = x_359_dilations_0, groups = x_359_groups_0, pad = x_359_pad_0, pad_type = x_359_pad_type_0, strides = x_359_strides_0, weight = encoder_layers_13_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_55_cast_fp16)[name = string("x_359_cast_fp16")]; + tensor input_731_perm_0 = const()[name = string("input_731_perm_0"), val = tensor([0, 2, 1])]; + tensor x_361_axes_0 = const()[name = string("x_361_axes_0"), val = tensor([-1])]; + tensor encoder_layers_13_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_13_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(277418240)))]; + tensor encoder_layers_13_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_13_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(277420352)))]; + tensor input_731_cast_fp16 = transpose(perm = input_731_perm_0, x = x_359_cast_fp16)[name = string("transpose_239")]; + tensor x_361_cast_fp16 = layer_norm(axes = x_361_axes_0, beta = encoder_layers_13_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_13_conv_batch_norm_weight_to_fp16, x = input_731_cast_fp16)[name = string("x_361_cast_fp16")]; + tensor input_733_perm_0 = const()[name = string("input_733_perm_0"), val = tensor([0, 2, 1])]; + tensor input_733_cast_fp16 = transpose(perm = input_733_perm_0, x = x_361_cast_fp16)[name = string("transpose_238")]; + tensor input_735_cast_fp16 = silu(x = input_733_cast_fp16)[name = string("input_735_cast_fp16")]; + string x_363_pad_type_0 = const()[name = string("x_363_pad_type_0"), val = string("valid")]; + tensor x_363_strides_0 = const()[name = string("x_363_strides_0"), val = tensor([1])]; + tensor x_363_pad_0 = const()[name = string("x_363_pad_0"), val = tensor([0, 0])]; + tensor x_363_dilations_0 = const()[name = string("x_363_dilations_0"), val = tensor([1])]; + int32 x_363_groups_0 = const()[name = string("x_363_groups_0"), val = int32(1)]; + tensor encoder_layers_13_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(277422464))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278471104))))[name = string("encoder_layers_13_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_363_cast_fp16 = conv(dilations = x_363_dilations_0, groups = x_363_groups_0, pad = x_363_pad_0, pad_type = x_363_pad_type_0, strides = x_363_strides_0, weight = encoder_layers_13_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_735_cast_fp16)[name = string("x_363_cast_fp16")]; + tensor input_737_perm_0 = const()[name = string("input_737_perm_0"), val = tensor([0, 2, 1])]; + tensor input_737_cast_fp16 = transpose(perm = input_737_perm_0, x = x_363_cast_fp16)[name = string("transpose_237")]; + tensor input_739_cast_fp16 = add(x = input_723_cast_fp16, y = input_737_cast_fp16)[name = string("input_739_cast_fp16")]; + tensor input_741_axes_0 = const()[name = string("input_741_axes_0"), val = tensor([-1])]; + tensor encoder_layers_13_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_13_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278473216)))]; + tensor encoder_layers_13_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_13_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278475328)))]; + tensor input_741_cast_fp16 = layer_norm(axes = input_741_axes_0, beta = encoder_layers_13_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_13_norm_feed_forward2_weight_to_fp16, x = input_739_cast_fp16)[name = string("input_741_cast_fp16")]; + tensor encoder_layers_13_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278477440))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(281623232))))[name = string("encoder_layers_13_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_13_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_13_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(281623424)))]; + tensor linear_125_cast_fp16 = linear(bias = encoder_layers_13_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_13_feed_forward2_linear1_weight_to_fp16_palettized, x = input_741_cast_fp16)[name = string("linear_125_cast_fp16")]; + tensor input_745_cast_fp16 = silu(x = linear_125_cast_fp16)[name = string("input_745_cast_fp16")]; + tensor encoder_layers_13_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(281631680))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284777472))))[name = string("encoder_layers_13_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_13_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_13_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284777664)))]; + tensor linear_126_cast_fp16 = linear(bias = encoder_layers_13_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_13_feed_forward2_linear2_weight_to_fp16_palettized, x = input_745_cast_fp16)[name = string("linear_126_cast_fp16")]; + fp16 var_3400_to_fp16 = const()[name = string("op_3400_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3401_cast_fp16 = mul(x = linear_126_cast_fp16, y = var_3400_to_fp16)[name = string("op_3401_cast_fp16")]; + tensor input_751_cast_fp16 = add(x = input_739_cast_fp16, y = var_3401_cast_fp16)[name = string("input_751_cast_fp16")]; + tensor input_753_axes_0 = const()[name = string("input_753_axes_0"), val = tensor([-1])]; + tensor encoder_layers_13_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_13_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284779776)))]; + tensor encoder_layers_13_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_13_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284781888)))]; + tensor input_753_cast_fp16 = layer_norm(axes = input_753_axes_0, beta = encoder_layers_13_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_13_norm_out_weight_to_fp16, x = input_751_cast_fp16)[name = string("input_753_cast_fp16")]; + tensor cache_57_begin_0 = const()[name = string("cache_57_begin_0"), val = tensor([14, 0, 0, 0])]; + tensor cache_57_end_0 = const()[name = string("cache_57_end_0"), val = tensor([15, 1, 42, 1024])]; + tensor cache_57_end_mask_0 = const()[name = string("cache_57_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_57_squeeze_mask_0 = const()[name = string("cache_57_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_57_cast_fp16 = slice_by_index(begin = cache_57_begin_0, end = cache_57_end_0, end_mask = cache_57_end_mask_0, squeeze_mask = cache_57_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_57_cast_fp16")]; + tensor cache_59_begin_0 = const()[name = string("cache_59_begin_0"), val = tensor([14, 0, 0, 0])]; + tensor cache_59_end_0 = const()[name = string("cache_59_end_0"), val = tensor([15, 1, 1024, 8])]; + tensor cache_59_end_mask_0 = const()[name = string("cache_59_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_59_squeeze_mask_0 = const()[name = string("cache_59_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_59_cast_fp16 = slice_by_index(begin = cache_59_begin_0, end = cache_59_end_0, end_mask = cache_59_end_mask_0, squeeze_mask = cache_59_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_59_cast_fp16")]; + tensor input_755_axes_0 = const()[name = string("input_755_axes_0"), val = tensor([-1])]; + tensor encoder_layers_14_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_14_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284784000)))]; + tensor encoder_layers_14_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_14_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284786112)))]; + tensor input_755_cast_fp16 = layer_norm(axes = input_755_axes_0, beta = encoder_layers_14_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_14_norm_feed_forward1_weight_to_fp16, x = input_753_cast_fp16)[name = string("input_755_cast_fp16")]; + tensor encoder_layers_14_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284788224))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(287934016))))[name = string("encoder_layers_14_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_14_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_14_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(287934208)))]; + tensor linear_127_cast_fp16 = linear(bias = encoder_layers_14_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_14_feed_forward1_linear1_weight_to_fp16_palettized, x = input_755_cast_fp16)[name = string("linear_127_cast_fp16")]; + tensor input_759_cast_fp16 = silu(x = linear_127_cast_fp16)[name = string("input_759_cast_fp16")]; + tensor encoder_layers_14_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(287942464))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291088256))))[name = string("encoder_layers_14_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_14_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_14_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291088448)))]; + tensor linear_128_cast_fp16 = linear(bias = encoder_layers_14_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_14_feed_forward1_linear2_weight_to_fp16_palettized, x = input_759_cast_fp16)[name = string("linear_128_cast_fp16")]; + fp16 var_3437_to_fp16 = const()[name = string("op_3437_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3438_cast_fp16 = mul(x = linear_128_cast_fp16, y = var_3437_to_fp16)[name = string("op_3438_cast_fp16")]; + tensor input_765_cast_fp16 = add(x = input_753_cast_fp16, y = var_3438_cast_fp16)[name = string("input_765_cast_fp16")]; + tensor key_29_axes_0 = const()[name = string("key_29_axes_0"), val = tensor([-1])]; + tensor encoder_layers_14_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_14_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291090560)))]; + tensor encoder_layers_14_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_14_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291092672)))]; + tensor key_29_cast_fp16 = layer_norm(axes = key_29_axes_0, beta = encoder_layers_14_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_14_norm_self_att_weight_to_fp16, x = input_765_cast_fp16)[name = string("key_29_cast_fp16")]; + bool input_767_interleave_0 = const()[name = string("input_767_interleave_0"), val = bool(false)]; + tensor input_767_cast_fp16 = concat(axis = var_68, interleave = input_767_interleave_0, values = (cache_57_cast_fp16, key_29_cast_fp16))[name = string("input_767_cast_fp16")]; + tensor var_3460_begin_0 = const()[name = string("op_3460_begin_0"), val = tensor([0, 7, 0])]; + tensor var_3460_end_0 = const()[name = string("op_3460_end_0"), val = tensor([1, 42, 1024])]; + tensor var_3460_end_mask_0 = const()[name = string("op_3460_end_mask_0"), val = tensor([true, true, true])]; + tensor var_3460_cast_fp16 = slice_by_index(begin = var_3460_begin_0, end = var_3460_end_0, end_mask = var_3460_end_mask_0, x = cache_57_cast_fp16)[name = string("op_3460_cast_fp16")]; + bool var_3466_interleave_0 = const()[name = string("op_3466_interleave_0"), val = bool(false)]; + tensor var_3466_cast_fp16 = concat(axis = var_68, interleave = var_3466_interleave_0, values = (var_3460_cast_fp16, key_29_cast_fp16))[name = string("op_3466_cast_fp16")]; + tensor encoder_layers_14_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291094784))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291881280))))[name = string("encoder_layers_14_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_14_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_14_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291881472)))]; + tensor linear_129_cast_fp16 = linear(bias = encoder_layers_14_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_14_self_attn_linear_q_weight_to_fp16_palettized, x = key_29_cast_fp16)[name = string("linear_129_cast_fp16")]; + tensor var_3471 = const()[name = string("op_3471"), val = tensor([1, -1, 8, 128])]; + tensor q_85_cast_fp16 = reshape(shape = var_3471, x = linear_129_cast_fp16)[name = string("q_85_cast_fp16")]; + tensor encoder_layers_14_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291883584))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292670080))))[name = string("encoder_layers_14_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_14_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_14_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292670272)))]; + tensor linear_130_cast_fp16 = linear(bias = encoder_layers_14_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_14_self_attn_linear_k_weight_to_fp16_palettized, x = input_767_cast_fp16)[name = string("linear_130_cast_fp16")]; + tensor var_3476 = const()[name = string("op_3476"), val = tensor([1, -1, 8, 128])]; + tensor k_57_cast_fp16 = reshape(shape = var_3476, x = linear_130_cast_fp16)[name = string("k_57_cast_fp16")]; + tensor encoder_layers_14_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292672384))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(293458880))))[name = string("encoder_layers_14_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_14_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_14_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(293459072)))]; + tensor linear_131_cast_fp16 = linear(bias = encoder_layers_14_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_14_self_attn_linear_v_weight_to_fp16_palettized, x = input_767_cast_fp16)[name = string("linear_131_cast_fp16")]; + tensor var_3481 = const()[name = string("op_3481"), val = tensor([1, -1, 8, 128])]; + tensor v_29_cast_fp16 = reshape(shape = var_3481, x = linear_131_cast_fp16)[name = string("v_29_cast_fp16")]; + tensor value_37_perm_0 = const()[name = string("value_37_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_14_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_14_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(293461184)))]; + tensor var_3494_cast_fp16 = add(x = q_85_cast_fp16, y = encoder_layers_14_self_attn_pos_bias_u_to_fp16)[name = string("op_3494_cast_fp16")]; + tensor encoder_layers_14_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_14_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(293463296)))]; + tensor var_3496_cast_fp16 = add(x = q_85_cast_fp16, y = encoder_layers_14_self_attn_pos_bias_v_to_fp16)[name = string("op_3496_cast_fp16")]; + tensor q_with_bias_v_29_perm_0 = const()[name = string("q_with_bias_v_29_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_371_transpose_x_0 = const()[name = string("x_371_transpose_x_0"), val = bool(false)]; + bool x_371_transpose_y_0 = const()[name = string("x_371_transpose_y_0"), val = bool(false)]; + tensor op_3498_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(293465408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(293564800))))[name = string("op_3498_to_fp16_quantized")]; + tensor q_with_bias_v_29_cast_fp16 = transpose(perm = q_with_bias_v_29_perm_0, x = var_3496_cast_fp16)[name = string("transpose_236")]; + tensor x_371_cast_fp16 = matmul(transpose_x = x_371_transpose_x_0, transpose_y = x_371_transpose_y_0, x = q_with_bias_v_29_cast_fp16, y = op_3498_to_fp16_quantized)[name = string("x_371_cast_fp16")]; + tensor x_373_pad_0 = const()[name = string("x_373_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_373_mode_0 = const()[name = string("x_373_mode_0"), val = string("constant")]; + fp16 const_261_to_fp16 = const()[name = string("const_261_to_fp16"), val = fp16(0x0p+0)]; + tensor x_373_cast_fp16 = pad(constant_val = const_261_to_fp16, mode = x_373_mode_0, pad = x_373_pad_0, x = x_371_cast_fp16)[name = string("x_373_cast_fp16")]; + tensor var_3506 = const()[name = string("op_3506"), val = tensor([1, 8, -1, 7])]; + tensor x_375_cast_fp16 = reshape(shape = var_3506, x = x_373_cast_fp16)[name = string("x_375_cast_fp16")]; + tensor var_3510_begin_0 = const()[name = string("op_3510_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_3510_end_0 = const()[name = string("op_3510_end_0"), val = tensor([1, 8, 98, 7])]; + tensor var_3510_end_mask_0 = const()[name = string("op_3510_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_3510_cast_fp16 = slice_by_index(begin = var_3510_begin_0, end = var_3510_end_0, end_mask = var_3510_end_mask_0, x = x_375_cast_fp16)[name = string("op_3510_cast_fp16")]; + tensor var_3511 = const()[name = string("op_3511"), val = tensor([1, 8, 7, 97])]; + tensor matrix_bd_57_cast_fp16 = reshape(shape = var_3511, x = var_3510_cast_fp16)[name = string("matrix_bd_57_cast_fp16")]; + bool matrix_ac_29_transpose_x_0 = const()[name = string("matrix_ac_29_transpose_x_0"), val = bool(false)]; + bool matrix_ac_29_transpose_y_0 = const()[name = string("matrix_ac_29_transpose_y_0"), val = bool(false)]; + tensor transpose_124_perm_0 = const()[name = string("transpose_124_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_125_perm_0 = const()[name = string("transpose_125_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_125 = transpose(perm = transpose_125_perm_0, x = k_57_cast_fp16)[name = string("transpose_234")]; + tensor transpose_124 = transpose(perm = transpose_124_perm_0, x = var_3494_cast_fp16)[name = string("transpose_235")]; + tensor matrix_ac_29_cast_fp16 = matmul(transpose_x = matrix_ac_29_transpose_x_0, transpose_y = matrix_ac_29_transpose_y_0, x = transpose_124, y = transpose_125)[name = string("matrix_ac_29_cast_fp16")]; + tensor matrix_bd_59_begin_0 = const()[name = string("matrix_bd_59_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_59_end_0 = const()[name = string("matrix_bd_59_end_0"), val = tensor([1, 8, 7, 49])]; + tensor matrix_bd_59_end_mask_0 = const()[name = string("matrix_bd_59_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_59_cast_fp16 = slice_by_index(begin = matrix_bd_59_begin_0, end = matrix_bd_59_end_0, end_mask = matrix_bd_59_end_mask_0, x = matrix_bd_57_cast_fp16)[name = string("matrix_bd_59_cast_fp16")]; + tensor var_3520_cast_fp16 = add(x = matrix_ac_29_cast_fp16, y = matrix_bd_59_cast_fp16)[name = string("op_3520_cast_fp16")]; + fp16 _inversed_scores_57_y_0_to_fp16 = const()[name = string("_inversed_scores_57_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_57_cast_fp16 = mul(x = var_3520_cast_fp16, y = _inversed_scores_57_y_0_to_fp16)[name = string("_inversed_scores_57_cast_fp16")]; + tensor scores_59_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_57_cast_fp16, cond = mask_11)[name = string("scores_59_cast_fp16")]; + tensor var_3526_cast_fp16 = softmax(axis = var_59, x = scores_59_cast_fp16)[name = string("op_3526_cast_fp16")]; + tensor input_769_cast_fp16 = select(a = var_44_to_fp16, b = var_3526_cast_fp16, cond = mask_11)[name = string("input_769_cast_fp16")]; + bool x_377_transpose_x_0 = const()[name = string("x_377_transpose_x_0"), val = bool(false)]; + bool x_377_transpose_y_0 = const()[name = string("x_377_transpose_y_0"), val = bool(false)]; + tensor value_37_cast_fp16 = transpose(perm = value_37_perm_0, x = v_29_cast_fp16)[name = string("transpose_233")]; + tensor x_377_cast_fp16 = matmul(transpose_x = x_377_transpose_x_0, transpose_y = x_377_transpose_y_0, x = input_769_cast_fp16, y = value_37_cast_fp16)[name = string("x_377_cast_fp16")]; + tensor var_3530_perm_0 = const()[name = string("op_3530_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3531 = const()[name = string("op_3531"), val = tensor([1, -1, 1024])]; + tensor var_3530_cast_fp16 = transpose(perm = var_3530_perm_0, x = x_377_cast_fp16)[name = string("transpose_232")]; + tensor input_771_cast_fp16 = reshape(shape = var_3531, x = var_3530_cast_fp16)[name = string("input_771_cast_fp16")]; + tensor encoder_layers_14_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(293565120))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(294351616))))[name = string("encoder_layers_14_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_14_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_14_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(294351808)))]; + tensor linear_133_cast_fp16 = linear(bias = encoder_layers_14_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_14_self_attn_linear_out_weight_to_fp16_palettized, x = input_771_cast_fp16)[name = string("linear_133_cast_fp16")]; + tensor input_775_cast_fp16 = add(x = input_765_cast_fp16, y = linear_133_cast_fp16)[name = string("input_775_cast_fp16")]; + tensor x_381_axes_0 = const()[name = string("x_381_axes_0"), val = tensor([-1])]; + tensor encoder_layers_14_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_14_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(294353920)))]; + tensor encoder_layers_14_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_14_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(294356032)))]; + tensor x_381_cast_fp16 = layer_norm(axes = x_381_axes_0, beta = encoder_layers_14_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_14_norm_conv_weight_to_fp16, x = input_775_cast_fp16)[name = string("x_381_cast_fp16")]; + tensor input_777_perm_0 = const()[name = string("input_777_perm_0"), val = tensor([0, 2, 1])]; + string input_779_pad_type_0 = const()[name = string("input_779_pad_type_0"), val = string("valid")]; + tensor input_779_strides_0 = const()[name = string("input_779_strides_0"), val = tensor([1])]; + tensor input_779_pad_0 = const()[name = string("input_779_pad_0"), val = tensor([0, 0])]; + tensor input_779_dilations_0 = const()[name = string("input_779_dilations_0"), val = tensor([1])]; + int32 input_779_groups_0 = const()[name = string("input_779_groups_0"), val = int32(1)]; + tensor encoder_layers_14_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(294358144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(296455360))))[name = string("encoder_layers_14_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_777_cast_fp16 = transpose(perm = input_777_perm_0, x = x_381_cast_fp16)[name = string("transpose_231")]; + tensor input_779_cast_fp16 = conv(dilations = input_779_dilations_0, groups = input_779_groups_0, pad = input_779_pad_0, pad_type = input_779_pad_type_0, strides = input_779_strides_0, weight = encoder_layers_14_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_777_cast_fp16)[name = string("input_779_cast_fp16")]; + int32 x_383_split_num_splits_0 = const()[name = string("x_383_split_num_splits_0"), val = int32(2)]; + int32 x_383_split_axis_0 = const()[name = string("x_383_split_axis_0"), val = int32(1)]; + tensor x_383_split_cast_fp16_0, tensor x_383_split_cast_fp16_1 = split(axis = x_383_split_axis_0, num_splits = x_383_split_num_splits_0, x = input_779_cast_fp16)[name = string("x_383_split_cast_fp16")]; + tensor x_383_split_1_sigmoid_cast_fp16 = sigmoid(x = x_383_split_cast_fp16_1)[name = string("x_383_split_1_sigmoid_cast_fp16")]; + tensor x_383_cast_fp16 = mul(x = x_383_split_cast_fp16_0, y = x_383_split_1_sigmoid_cast_fp16)[name = string("x_383_cast_fp16")]; + tensor input_781_cast_fp16 = select(a = var_44_to_fp16, b = x_383_cast_fp16, cond = var_575)[name = string("input_781_cast_fp16")]; + bool new_x_59_interleave_0 = const()[name = string("new_x_59_interleave_0"), val = bool(false)]; + tensor new_x_59_cast_fp16 = concat(axis = var_59, interleave = new_x_59_interleave_0, values = (cache_59_cast_fp16, input_781_cast_fp16))[name = string("new_x_59_cast_fp16")]; + tensor var_3570_begin_0 = const()[name = string("op_3570_begin_0"), val = tensor([0, 0, 7])]; + tensor var_3570_end_0 = const()[name = string("op_3570_end_0"), val = tensor([1, 1024, 15])]; + tensor var_3570_end_mask_0 = const()[name = string("op_3570_end_mask_0"), val = tensor([true, true, true])]; + tensor var_3570_cast_fp16 = slice_by_index(begin = var_3570_begin_0, end = var_3570_end_0, end_mask = var_3570_end_mask_0, x = new_x_59_cast_fp16)[name = string("op_3570_cast_fp16")]; + string x_385_pad_type_0 = const()[name = string("x_385_pad_type_0"), val = string("valid")]; + int32 x_385_groups_0 = const()[name = string("x_385_groups_0"), val = int32(1024)]; + tensor x_385_strides_0 = const()[name = string("x_385_strides_0"), val = tensor([1])]; + tensor x_385_pad_0 = const()[name = string("x_385_pad_0"), val = tensor([0, 0])]; + tensor x_385_dilations_0 = const()[name = string("x_385_dilations_0"), val = tensor([1])]; + tensor encoder_layers_14_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(296459520))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(296468800))))[name = string("encoder_layers_14_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_385_cast_fp16 = conv(dilations = x_385_dilations_0, groups = x_385_groups_0, pad = x_385_pad_0, pad_type = x_385_pad_type_0, strides = x_385_strides_0, weight = encoder_layers_14_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_59_cast_fp16)[name = string("x_385_cast_fp16")]; + tensor input_783_perm_0 = const()[name = string("input_783_perm_0"), val = tensor([0, 2, 1])]; + tensor x_387_axes_0 = const()[name = string("x_387_axes_0"), val = tensor([-1])]; + tensor encoder_layers_14_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_14_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(296470912)))]; + tensor encoder_layers_14_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_14_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(296473024)))]; + tensor input_783_cast_fp16 = transpose(perm = input_783_perm_0, x = x_385_cast_fp16)[name = string("transpose_230")]; + tensor x_387_cast_fp16 = layer_norm(axes = x_387_axes_0, beta = encoder_layers_14_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_14_conv_batch_norm_weight_to_fp16, x = input_783_cast_fp16)[name = string("x_387_cast_fp16")]; + tensor input_785_perm_0 = const()[name = string("input_785_perm_0"), val = tensor([0, 2, 1])]; + tensor input_785_cast_fp16 = transpose(perm = input_785_perm_0, x = x_387_cast_fp16)[name = string("transpose_229")]; + tensor input_787_cast_fp16 = silu(x = input_785_cast_fp16)[name = string("input_787_cast_fp16")]; + string x_389_pad_type_0 = const()[name = string("x_389_pad_type_0"), val = string("valid")]; + tensor x_389_strides_0 = const()[name = string("x_389_strides_0"), val = tensor([1])]; + tensor x_389_pad_0 = const()[name = string("x_389_pad_0"), val = tensor([0, 0])]; + tensor x_389_dilations_0 = const()[name = string("x_389_dilations_0"), val = tensor([1])]; + int32 x_389_groups_0 = const()[name = string("x_389_groups_0"), val = int32(1)]; + tensor encoder_layers_14_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(296475136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(297523776))))[name = string("encoder_layers_14_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_389_cast_fp16 = conv(dilations = x_389_dilations_0, groups = x_389_groups_0, pad = x_389_pad_0, pad_type = x_389_pad_type_0, strides = x_389_strides_0, weight = encoder_layers_14_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_787_cast_fp16)[name = string("x_389_cast_fp16")]; + tensor input_789_perm_0 = const()[name = string("input_789_perm_0"), val = tensor([0, 2, 1])]; + tensor input_789_cast_fp16 = transpose(perm = input_789_perm_0, x = x_389_cast_fp16)[name = string("transpose_228")]; + tensor input_791_cast_fp16 = add(x = input_775_cast_fp16, y = input_789_cast_fp16)[name = string("input_791_cast_fp16")]; + tensor input_793_axes_0 = const()[name = string("input_793_axes_0"), val = tensor([-1])]; + tensor encoder_layers_14_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_14_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(297525888)))]; + tensor encoder_layers_14_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_14_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(297528000)))]; + tensor input_793_cast_fp16 = layer_norm(axes = input_793_axes_0, beta = encoder_layers_14_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_14_norm_feed_forward2_weight_to_fp16, x = input_791_cast_fp16)[name = string("input_793_cast_fp16")]; + tensor encoder_layers_14_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(297530112))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(300675904))))[name = string("encoder_layers_14_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_14_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_14_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(300676096)))]; + tensor linear_134_cast_fp16 = linear(bias = encoder_layers_14_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_14_feed_forward2_linear1_weight_to_fp16_palettized, x = input_793_cast_fp16)[name = string("linear_134_cast_fp16")]; + tensor input_797_cast_fp16 = silu(x = linear_134_cast_fp16)[name = string("input_797_cast_fp16")]; + tensor encoder_layers_14_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(300684352))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303830144))))[name = string("encoder_layers_14_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_14_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_14_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303830336)))]; + tensor linear_135_cast_fp16 = linear(bias = encoder_layers_14_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_14_feed_forward2_linear2_weight_to_fp16_palettized, x = input_797_cast_fp16)[name = string("linear_135_cast_fp16")]; + fp16 var_3613_to_fp16 = const()[name = string("op_3613_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3614_cast_fp16 = mul(x = linear_135_cast_fp16, y = var_3613_to_fp16)[name = string("op_3614_cast_fp16")]; + tensor input_803_cast_fp16 = add(x = input_791_cast_fp16, y = var_3614_cast_fp16)[name = string("input_803_cast_fp16")]; + tensor input_805_axes_0 = const()[name = string("input_805_axes_0"), val = tensor([-1])]; + tensor encoder_layers_14_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_14_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303832448)))]; + tensor encoder_layers_14_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_14_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303834560)))]; + tensor input_805_cast_fp16 = layer_norm(axes = input_805_axes_0, beta = encoder_layers_14_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_14_norm_out_weight_to_fp16, x = input_803_cast_fp16)[name = string("input_805_cast_fp16")]; + tensor cache_61_begin_0 = const()[name = string("cache_61_begin_0"), val = tensor([15, 0, 0, 0])]; + tensor cache_61_end_0 = const()[name = string("cache_61_end_0"), val = tensor([16, 1, 42, 1024])]; + tensor cache_61_end_mask_0 = const()[name = string("cache_61_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_61_squeeze_mask_0 = const()[name = string("cache_61_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_61_cast_fp16 = slice_by_index(begin = cache_61_begin_0, end = cache_61_end_0, end_mask = cache_61_end_mask_0, squeeze_mask = cache_61_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_61_cast_fp16")]; + tensor cache_63_begin_0 = const()[name = string("cache_63_begin_0"), val = tensor([15, 0, 0, 0])]; + tensor cache_63_end_0 = const()[name = string("cache_63_end_0"), val = tensor([16, 1, 1024, 8])]; + tensor cache_63_end_mask_0 = const()[name = string("cache_63_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_63_squeeze_mask_0 = const()[name = string("cache_63_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_63_cast_fp16 = slice_by_index(begin = cache_63_begin_0, end = cache_63_end_0, end_mask = cache_63_end_mask_0, squeeze_mask = cache_63_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_63_cast_fp16")]; + tensor input_807_axes_0 = const()[name = string("input_807_axes_0"), val = tensor([-1])]; + tensor encoder_layers_15_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_15_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303836672)))]; + tensor encoder_layers_15_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_15_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303838784)))]; + tensor input_807_cast_fp16 = layer_norm(axes = input_807_axes_0, beta = encoder_layers_15_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_15_norm_feed_forward1_weight_to_fp16, x = input_805_cast_fp16)[name = string("input_807_cast_fp16")]; + tensor encoder_layers_15_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303840896))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(306986688))))[name = string("encoder_layers_15_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_15_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_15_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(306986880)))]; + tensor linear_136_cast_fp16 = linear(bias = encoder_layers_15_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_15_feed_forward1_linear1_weight_to_fp16_palettized, x = input_807_cast_fp16)[name = string("linear_136_cast_fp16")]; + tensor input_811_cast_fp16 = silu(x = linear_136_cast_fp16)[name = string("input_811_cast_fp16")]; + tensor encoder_layers_15_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(306995136))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(310140928))))[name = string("encoder_layers_15_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_15_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_15_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(310141120)))]; + tensor linear_137_cast_fp16 = linear(bias = encoder_layers_15_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_15_feed_forward1_linear2_weight_to_fp16_palettized, x = input_811_cast_fp16)[name = string("linear_137_cast_fp16")]; + fp16 var_3650_to_fp16 = const()[name = string("op_3650_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3651_cast_fp16 = mul(x = linear_137_cast_fp16, y = var_3650_to_fp16)[name = string("op_3651_cast_fp16")]; + tensor input_817_cast_fp16 = add(x = input_805_cast_fp16, y = var_3651_cast_fp16)[name = string("input_817_cast_fp16")]; + tensor key_31_axes_0 = const()[name = string("key_31_axes_0"), val = tensor([-1])]; + tensor encoder_layers_15_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_15_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(310143232)))]; + tensor encoder_layers_15_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_15_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(310145344)))]; + tensor key_31_cast_fp16 = layer_norm(axes = key_31_axes_0, beta = encoder_layers_15_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_15_norm_self_att_weight_to_fp16, x = input_817_cast_fp16)[name = string("key_31_cast_fp16")]; + bool input_819_interleave_0 = const()[name = string("input_819_interleave_0"), val = bool(false)]; + tensor input_819_cast_fp16 = concat(axis = var_68, interleave = input_819_interleave_0, values = (cache_61_cast_fp16, key_31_cast_fp16))[name = string("input_819_cast_fp16")]; + tensor var_3673_begin_0 = const()[name = string("op_3673_begin_0"), val = tensor([0, 7, 0])]; + tensor var_3673_end_0 = const()[name = string("op_3673_end_0"), val = tensor([1, 42, 1024])]; + tensor var_3673_end_mask_0 = const()[name = string("op_3673_end_mask_0"), val = tensor([true, true, true])]; + tensor var_3673_cast_fp16 = slice_by_index(begin = var_3673_begin_0, end = var_3673_end_0, end_mask = var_3673_end_mask_0, x = cache_61_cast_fp16)[name = string("op_3673_cast_fp16")]; + bool var_3679_interleave_0 = const()[name = string("op_3679_interleave_0"), val = bool(false)]; + tensor var_3679_cast_fp16 = concat(axis = var_68, interleave = var_3679_interleave_0, values = (var_3673_cast_fp16, key_31_cast_fp16))[name = string("op_3679_cast_fp16")]; + tensor encoder_layers_15_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(310147456))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(310933952))))[name = string("encoder_layers_15_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_15_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_15_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(310934144)))]; + tensor linear_138_cast_fp16 = linear(bias = encoder_layers_15_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_15_self_attn_linear_q_weight_to_fp16_palettized, x = key_31_cast_fp16)[name = string("linear_138_cast_fp16")]; + tensor var_3684 = const()[name = string("op_3684"), val = tensor([1, -1, 8, 128])]; + tensor q_91_cast_fp16 = reshape(shape = var_3684, x = linear_138_cast_fp16)[name = string("q_91_cast_fp16")]; + tensor encoder_layers_15_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(310936256))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(311722752))))[name = string("encoder_layers_15_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_15_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_15_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(311722944)))]; + tensor linear_139_cast_fp16 = linear(bias = encoder_layers_15_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_15_self_attn_linear_k_weight_to_fp16_palettized, x = input_819_cast_fp16)[name = string("linear_139_cast_fp16")]; + tensor var_3689 = const()[name = string("op_3689"), val = tensor([1, -1, 8, 128])]; + tensor k_61_cast_fp16 = reshape(shape = var_3689, x = linear_139_cast_fp16)[name = string("k_61_cast_fp16")]; + tensor encoder_layers_15_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(311725056))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312511552))))[name = string("encoder_layers_15_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_15_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_15_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312511744)))]; + tensor linear_140_cast_fp16 = linear(bias = encoder_layers_15_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_15_self_attn_linear_v_weight_to_fp16_palettized, x = input_819_cast_fp16)[name = string("linear_140_cast_fp16")]; + tensor var_3694 = const()[name = string("op_3694"), val = tensor([1, -1, 8, 128])]; + tensor v_31_cast_fp16 = reshape(shape = var_3694, x = linear_140_cast_fp16)[name = string("v_31_cast_fp16")]; + tensor value_39_perm_0 = const()[name = string("value_39_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_15_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_15_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312513856)))]; + tensor var_3707_cast_fp16 = add(x = q_91_cast_fp16, y = encoder_layers_15_self_attn_pos_bias_u_to_fp16)[name = string("op_3707_cast_fp16")]; + tensor encoder_layers_15_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_15_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312515968)))]; + tensor var_3709_cast_fp16 = add(x = q_91_cast_fp16, y = encoder_layers_15_self_attn_pos_bias_v_to_fp16)[name = string("op_3709_cast_fp16")]; + tensor q_with_bias_v_31_perm_0 = const()[name = string("q_with_bias_v_31_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_397_transpose_x_0 = const()[name = string("x_397_transpose_x_0"), val = bool(false)]; + bool x_397_transpose_y_0 = const()[name = string("x_397_transpose_y_0"), val = bool(false)]; + tensor op_3711_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312518080))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312617472))))[name = string("op_3711_to_fp16_quantized")]; + tensor q_with_bias_v_31_cast_fp16 = transpose(perm = q_with_bias_v_31_perm_0, x = var_3709_cast_fp16)[name = string("transpose_227")]; + tensor x_397_cast_fp16 = matmul(transpose_x = x_397_transpose_x_0, transpose_y = x_397_transpose_y_0, x = q_with_bias_v_31_cast_fp16, y = op_3711_to_fp16_quantized)[name = string("x_397_cast_fp16")]; + tensor x_399_pad_0 = const()[name = string("x_399_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_399_mode_0 = const()[name = string("x_399_mode_0"), val = string("constant")]; + fp16 const_274_to_fp16 = const()[name = string("const_274_to_fp16"), val = fp16(0x0p+0)]; + tensor x_399_cast_fp16 = pad(constant_val = const_274_to_fp16, mode = x_399_mode_0, pad = x_399_pad_0, x = x_397_cast_fp16)[name = string("x_399_cast_fp16")]; + tensor var_3719 = const()[name = string("op_3719"), val = tensor([1, 8, -1, 7])]; + tensor x_401_cast_fp16 = reshape(shape = var_3719, x = x_399_cast_fp16)[name = string("x_401_cast_fp16")]; + tensor var_3723_begin_0 = const()[name = string("op_3723_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_3723_end_0 = const()[name = string("op_3723_end_0"), val = tensor([1, 8, 98, 7])]; + tensor var_3723_end_mask_0 = const()[name = string("op_3723_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_3723_cast_fp16 = slice_by_index(begin = var_3723_begin_0, end = var_3723_end_0, end_mask = var_3723_end_mask_0, x = x_401_cast_fp16)[name = string("op_3723_cast_fp16")]; + tensor var_3724 = const()[name = string("op_3724"), val = tensor([1, 8, 7, 97])]; + tensor matrix_bd_61_cast_fp16 = reshape(shape = var_3724, x = var_3723_cast_fp16)[name = string("matrix_bd_61_cast_fp16")]; + bool matrix_ac_31_transpose_x_0 = const()[name = string("matrix_ac_31_transpose_x_0"), val = bool(false)]; + bool matrix_ac_31_transpose_y_0 = const()[name = string("matrix_ac_31_transpose_y_0"), val = bool(false)]; + tensor transpose_126_perm_0 = const()[name = string("transpose_126_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_127_perm_0 = const()[name = string("transpose_127_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_127 = transpose(perm = transpose_127_perm_0, x = k_61_cast_fp16)[name = string("transpose_225")]; + tensor transpose_126 = transpose(perm = transpose_126_perm_0, x = var_3707_cast_fp16)[name = string("transpose_226")]; + tensor matrix_ac_31_cast_fp16 = matmul(transpose_x = matrix_ac_31_transpose_x_0, transpose_y = matrix_ac_31_transpose_y_0, x = transpose_126, y = transpose_127)[name = string("matrix_ac_31_cast_fp16")]; + tensor matrix_bd_63_begin_0 = const()[name = string("matrix_bd_63_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_63_end_0 = const()[name = string("matrix_bd_63_end_0"), val = tensor([1, 8, 7, 49])]; + tensor matrix_bd_63_end_mask_0 = const()[name = string("matrix_bd_63_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_63_cast_fp16 = slice_by_index(begin = matrix_bd_63_begin_0, end = matrix_bd_63_end_0, end_mask = matrix_bd_63_end_mask_0, x = matrix_bd_61_cast_fp16)[name = string("matrix_bd_63_cast_fp16")]; + tensor var_3733_cast_fp16 = add(x = matrix_ac_31_cast_fp16, y = matrix_bd_63_cast_fp16)[name = string("op_3733_cast_fp16")]; + fp16 _inversed_scores_61_y_0_to_fp16 = const()[name = string("_inversed_scores_61_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_61_cast_fp16 = mul(x = var_3733_cast_fp16, y = _inversed_scores_61_y_0_to_fp16)[name = string("_inversed_scores_61_cast_fp16")]; + tensor scores_63_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_61_cast_fp16, cond = mask_11)[name = string("scores_63_cast_fp16")]; + tensor var_3739_cast_fp16 = softmax(axis = var_59, x = scores_63_cast_fp16)[name = string("op_3739_cast_fp16")]; + tensor input_821_cast_fp16 = select(a = var_44_to_fp16, b = var_3739_cast_fp16, cond = mask_11)[name = string("input_821_cast_fp16")]; + bool x_403_transpose_x_0 = const()[name = string("x_403_transpose_x_0"), val = bool(false)]; + bool x_403_transpose_y_0 = const()[name = string("x_403_transpose_y_0"), val = bool(false)]; + tensor value_39_cast_fp16 = transpose(perm = value_39_perm_0, x = v_31_cast_fp16)[name = string("transpose_224")]; + tensor x_403_cast_fp16 = matmul(transpose_x = x_403_transpose_x_0, transpose_y = x_403_transpose_y_0, x = input_821_cast_fp16, y = value_39_cast_fp16)[name = string("x_403_cast_fp16")]; + tensor var_3743_perm_0 = const()[name = string("op_3743_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3744 = const()[name = string("op_3744"), val = tensor([1, -1, 1024])]; + tensor var_3743_cast_fp16 = transpose(perm = var_3743_perm_0, x = x_403_cast_fp16)[name = string("transpose_223")]; + tensor input_823_cast_fp16 = reshape(shape = var_3744, x = var_3743_cast_fp16)[name = string("input_823_cast_fp16")]; + tensor encoder_layers_15_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312617792))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(313404288))))[name = string("encoder_layers_15_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_15_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_15_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(313404480)))]; + tensor linear_142_cast_fp16 = linear(bias = encoder_layers_15_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_15_self_attn_linear_out_weight_to_fp16_palettized, x = input_823_cast_fp16)[name = string("linear_142_cast_fp16")]; + tensor input_827_cast_fp16 = add(x = input_817_cast_fp16, y = linear_142_cast_fp16)[name = string("input_827_cast_fp16")]; + tensor x_407_axes_0 = const()[name = string("x_407_axes_0"), val = tensor([-1])]; + tensor encoder_layers_15_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_15_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(313406592)))]; + tensor encoder_layers_15_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_15_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(313408704)))]; + tensor x_407_cast_fp16 = layer_norm(axes = x_407_axes_0, beta = encoder_layers_15_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_15_norm_conv_weight_to_fp16, x = input_827_cast_fp16)[name = string("x_407_cast_fp16")]; + tensor input_829_perm_0 = const()[name = string("input_829_perm_0"), val = tensor([0, 2, 1])]; + string input_831_pad_type_0 = const()[name = string("input_831_pad_type_0"), val = string("valid")]; + tensor input_831_strides_0 = const()[name = string("input_831_strides_0"), val = tensor([1])]; + tensor input_831_pad_0 = const()[name = string("input_831_pad_0"), val = tensor([0, 0])]; + tensor input_831_dilations_0 = const()[name = string("input_831_dilations_0"), val = tensor([1])]; + int32 input_831_groups_0 = const()[name = string("input_831_groups_0"), val = int32(1)]; + tensor encoder_layers_15_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(313410816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(315508032))))[name = string("encoder_layers_15_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_829_cast_fp16 = transpose(perm = input_829_perm_0, x = x_407_cast_fp16)[name = string("transpose_222")]; + tensor input_831_cast_fp16 = conv(dilations = input_831_dilations_0, groups = input_831_groups_0, pad = input_831_pad_0, pad_type = input_831_pad_type_0, strides = input_831_strides_0, weight = encoder_layers_15_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_829_cast_fp16)[name = string("input_831_cast_fp16")]; + int32 x_409_split_num_splits_0 = const()[name = string("x_409_split_num_splits_0"), val = int32(2)]; + int32 x_409_split_axis_0 = const()[name = string("x_409_split_axis_0"), val = int32(1)]; + tensor x_409_split_cast_fp16_0, tensor x_409_split_cast_fp16_1 = split(axis = x_409_split_axis_0, num_splits = x_409_split_num_splits_0, x = input_831_cast_fp16)[name = string("x_409_split_cast_fp16")]; + tensor x_409_split_1_sigmoid_cast_fp16 = sigmoid(x = x_409_split_cast_fp16_1)[name = string("x_409_split_1_sigmoid_cast_fp16")]; + tensor x_409_cast_fp16 = mul(x = x_409_split_cast_fp16_0, y = x_409_split_1_sigmoid_cast_fp16)[name = string("x_409_cast_fp16")]; + tensor input_833_cast_fp16 = select(a = var_44_to_fp16, b = x_409_cast_fp16, cond = var_575)[name = string("input_833_cast_fp16")]; + bool new_x_63_interleave_0 = const()[name = string("new_x_63_interleave_0"), val = bool(false)]; + tensor new_x_63_cast_fp16 = concat(axis = var_59, interleave = new_x_63_interleave_0, values = (cache_63_cast_fp16, input_833_cast_fp16))[name = string("new_x_63_cast_fp16")]; + tensor var_3783_begin_0 = const()[name = string("op_3783_begin_0"), val = tensor([0, 0, 7])]; + tensor var_3783_end_0 = const()[name = string("op_3783_end_0"), val = tensor([1, 1024, 15])]; + tensor var_3783_end_mask_0 = const()[name = string("op_3783_end_mask_0"), val = tensor([true, true, true])]; + tensor var_3783_cast_fp16 = slice_by_index(begin = var_3783_begin_0, end = var_3783_end_0, end_mask = var_3783_end_mask_0, x = new_x_63_cast_fp16)[name = string("op_3783_cast_fp16")]; + string x_411_pad_type_0 = const()[name = string("x_411_pad_type_0"), val = string("valid")]; + int32 x_411_groups_0 = const()[name = string("x_411_groups_0"), val = int32(1024)]; + tensor x_411_strides_0 = const()[name = string("x_411_strides_0"), val = tensor([1])]; + tensor x_411_pad_0 = const()[name = string("x_411_pad_0"), val = tensor([0, 0])]; + tensor x_411_dilations_0 = const()[name = string("x_411_dilations_0"), val = tensor([1])]; + tensor encoder_layers_15_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(315512192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(315521472))))[name = string("encoder_layers_15_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_411_cast_fp16 = conv(dilations = x_411_dilations_0, groups = x_411_groups_0, pad = x_411_pad_0, pad_type = x_411_pad_type_0, strides = x_411_strides_0, weight = encoder_layers_15_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_63_cast_fp16)[name = string("x_411_cast_fp16")]; + tensor input_835_perm_0 = const()[name = string("input_835_perm_0"), val = tensor([0, 2, 1])]; + tensor x_413_axes_0 = const()[name = string("x_413_axes_0"), val = tensor([-1])]; + tensor encoder_layers_15_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_15_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(315523584)))]; + tensor encoder_layers_15_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_15_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(315525696)))]; + tensor input_835_cast_fp16 = transpose(perm = input_835_perm_0, x = x_411_cast_fp16)[name = string("transpose_221")]; + tensor x_413_cast_fp16 = layer_norm(axes = x_413_axes_0, beta = encoder_layers_15_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_15_conv_batch_norm_weight_to_fp16, x = input_835_cast_fp16)[name = string("x_413_cast_fp16")]; + tensor input_837_perm_0 = const()[name = string("input_837_perm_0"), val = tensor([0, 2, 1])]; + tensor input_837_cast_fp16 = transpose(perm = input_837_perm_0, x = x_413_cast_fp16)[name = string("transpose_220")]; + tensor input_839_cast_fp16 = silu(x = input_837_cast_fp16)[name = string("input_839_cast_fp16")]; + string x_415_pad_type_0 = const()[name = string("x_415_pad_type_0"), val = string("valid")]; + tensor x_415_strides_0 = const()[name = string("x_415_strides_0"), val = tensor([1])]; + tensor x_415_pad_0 = const()[name = string("x_415_pad_0"), val = tensor([0, 0])]; + tensor x_415_dilations_0 = const()[name = string("x_415_dilations_0"), val = tensor([1])]; + int32 x_415_groups_0 = const()[name = string("x_415_groups_0"), val = int32(1)]; + tensor encoder_layers_15_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(315527808))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(316576448))))[name = string("encoder_layers_15_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_415_cast_fp16 = conv(dilations = x_415_dilations_0, groups = x_415_groups_0, pad = x_415_pad_0, pad_type = x_415_pad_type_0, strides = x_415_strides_0, weight = encoder_layers_15_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_839_cast_fp16)[name = string("x_415_cast_fp16")]; + tensor input_841_perm_0 = const()[name = string("input_841_perm_0"), val = tensor([0, 2, 1])]; + tensor input_841_cast_fp16 = transpose(perm = input_841_perm_0, x = x_415_cast_fp16)[name = string("transpose_219")]; + tensor input_843_cast_fp16 = add(x = input_827_cast_fp16, y = input_841_cast_fp16)[name = string("input_843_cast_fp16")]; + tensor input_845_axes_0 = const()[name = string("input_845_axes_0"), val = tensor([-1])]; + tensor encoder_layers_15_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_15_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(316578560)))]; + tensor encoder_layers_15_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_15_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(316580672)))]; + tensor input_845_cast_fp16 = layer_norm(axes = input_845_axes_0, beta = encoder_layers_15_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_15_norm_feed_forward2_weight_to_fp16, x = input_843_cast_fp16)[name = string("input_845_cast_fp16")]; + tensor encoder_layers_15_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(316582784))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(319728576))))[name = string("encoder_layers_15_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_15_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_15_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(319728768)))]; + tensor linear_143_cast_fp16 = linear(bias = encoder_layers_15_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_15_feed_forward2_linear1_weight_to_fp16_palettized, x = input_845_cast_fp16)[name = string("linear_143_cast_fp16")]; + tensor input_849_cast_fp16 = silu(x = linear_143_cast_fp16)[name = string("input_849_cast_fp16")]; + tensor encoder_layers_15_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(319737024))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(322882816))))[name = string("encoder_layers_15_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_15_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_15_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(322883008)))]; + tensor linear_144_cast_fp16 = linear(bias = encoder_layers_15_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_15_feed_forward2_linear2_weight_to_fp16_palettized, x = input_849_cast_fp16)[name = string("linear_144_cast_fp16")]; + fp16 var_3826_to_fp16 = const()[name = string("op_3826_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3827_cast_fp16 = mul(x = linear_144_cast_fp16, y = var_3826_to_fp16)[name = string("op_3827_cast_fp16")]; + tensor input_855_cast_fp16 = add(x = input_843_cast_fp16, y = var_3827_cast_fp16)[name = string("input_855_cast_fp16")]; + tensor input_857_axes_0 = const()[name = string("input_857_axes_0"), val = tensor([-1])]; + tensor encoder_layers_15_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_15_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(322885120)))]; + tensor encoder_layers_15_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_15_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(322887232)))]; + tensor input_857_cast_fp16 = layer_norm(axes = input_857_axes_0, beta = encoder_layers_15_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_15_norm_out_weight_to_fp16, x = input_855_cast_fp16)[name = string("input_857_cast_fp16")]; + tensor cache_65_begin_0 = const()[name = string("cache_65_begin_0"), val = tensor([16, 0, 0, 0])]; + tensor cache_65_end_0 = const()[name = string("cache_65_end_0"), val = tensor([17, 1, 42, 1024])]; + tensor cache_65_end_mask_0 = const()[name = string("cache_65_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_65_squeeze_mask_0 = const()[name = string("cache_65_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_65_cast_fp16 = slice_by_index(begin = cache_65_begin_0, end = cache_65_end_0, end_mask = cache_65_end_mask_0, squeeze_mask = cache_65_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_65_cast_fp16")]; + tensor cache_67_begin_0 = const()[name = string("cache_67_begin_0"), val = tensor([16, 0, 0, 0])]; + tensor cache_67_end_0 = const()[name = string("cache_67_end_0"), val = tensor([17, 1, 1024, 8])]; + tensor cache_67_end_mask_0 = const()[name = string("cache_67_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_67_squeeze_mask_0 = const()[name = string("cache_67_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_67_cast_fp16 = slice_by_index(begin = cache_67_begin_0, end = cache_67_end_0, end_mask = cache_67_end_mask_0, squeeze_mask = cache_67_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_67_cast_fp16")]; + tensor input_859_axes_0 = const()[name = string("input_859_axes_0"), val = tensor([-1])]; + tensor encoder_layers_16_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_16_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(322889344)))]; + tensor encoder_layers_16_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_16_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(322891456)))]; + tensor input_859_cast_fp16 = layer_norm(axes = input_859_axes_0, beta = encoder_layers_16_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_16_norm_feed_forward1_weight_to_fp16, x = input_857_cast_fp16)[name = string("input_859_cast_fp16")]; + tensor encoder_layers_16_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(322893568))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(326039360))))[name = string("encoder_layers_16_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_16_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_16_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(326039552)))]; + tensor linear_145_cast_fp16 = linear(bias = encoder_layers_16_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_16_feed_forward1_linear1_weight_to_fp16_palettized, x = input_859_cast_fp16)[name = string("linear_145_cast_fp16")]; + tensor input_863_cast_fp16 = silu(x = linear_145_cast_fp16)[name = string("input_863_cast_fp16")]; + tensor encoder_layers_16_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(326047808))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(329193600))))[name = string("encoder_layers_16_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_16_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_16_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(329193792)))]; + tensor linear_146_cast_fp16 = linear(bias = encoder_layers_16_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_16_feed_forward1_linear2_weight_to_fp16_palettized, x = input_863_cast_fp16)[name = string("linear_146_cast_fp16")]; + fp16 var_3863_to_fp16 = const()[name = string("op_3863_to_fp16"), val = fp16(0x1p-1)]; + tensor var_3864_cast_fp16 = mul(x = linear_146_cast_fp16, y = var_3863_to_fp16)[name = string("op_3864_cast_fp16")]; + tensor input_869_cast_fp16 = add(x = input_857_cast_fp16, y = var_3864_cast_fp16)[name = string("input_869_cast_fp16")]; + tensor key_33_axes_0 = const()[name = string("key_33_axes_0"), val = tensor([-1])]; + tensor encoder_layers_16_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_16_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(329195904)))]; + tensor encoder_layers_16_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_16_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(329198016)))]; + tensor key_33_cast_fp16 = layer_norm(axes = key_33_axes_0, beta = encoder_layers_16_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_16_norm_self_att_weight_to_fp16, x = input_869_cast_fp16)[name = string("key_33_cast_fp16")]; + bool input_871_interleave_0 = const()[name = string("input_871_interleave_0"), val = bool(false)]; + tensor input_871_cast_fp16 = concat(axis = var_68, interleave = input_871_interleave_0, values = (cache_65_cast_fp16, key_33_cast_fp16))[name = string("input_871_cast_fp16")]; + tensor var_3886_begin_0 = const()[name = string("op_3886_begin_0"), val = tensor([0, 7, 0])]; + tensor var_3886_end_0 = const()[name = string("op_3886_end_0"), val = tensor([1, 42, 1024])]; + tensor var_3886_end_mask_0 = const()[name = string("op_3886_end_mask_0"), val = tensor([true, true, true])]; + tensor var_3886_cast_fp16 = slice_by_index(begin = var_3886_begin_0, end = var_3886_end_0, end_mask = var_3886_end_mask_0, x = cache_65_cast_fp16)[name = string("op_3886_cast_fp16")]; + bool var_3892_interleave_0 = const()[name = string("op_3892_interleave_0"), val = bool(false)]; + tensor var_3892_cast_fp16 = concat(axis = var_68, interleave = var_3892_interleave_0, values = (var_3886_cast_fp16, key_33_cast_fp16))[name = string("op_3892_cast_fp16")]; + tensor encoder_layers_16_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(329200128))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(329986624))))[name = string("encoder_layers_16_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_16_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_16_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(329986816)))]; + tensor linear_147_cast_fp16 = linear(bias = encoder_layers_16_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_16_self_attn_linear_q_weight_to_fp16_palettized, x = key_33_cast_fp16)[name = string("linear_147_cast_fp16")]; + tensor var_3897 = const()[name = string("op_3897"), val = tensor([1, -1, 8, 128])]; + tensor q_97_cast_fp16 = reshape(shape = var_3897, x = linear_147_cast_fp16)[name = string("q_97_cast_fp16")]; + tensor encoder_layers_16_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(329988928))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(330775424))))[name = string("encoder_layers_16_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_16_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_16_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(330775616)))]; + tensor linear_148_cast_fp16 = linear(bias = encoder_layers_16_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_16_self_attn_linear_k_weight_to_fp16_palettized, x = input_871_cast_fp16)[name = string("linear_148_cast_fp16")]; + tensor var_3902 = const()[name = string("op_3902"), val = tensor([1, -1, 8, 128])]; + tensor k_65_cast_fp16 = reshape(shape = var_3902, x = linear_148_cast_fp16)[name = string("k_65_cast_fp16")]; + tensor encoder_layers_16_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(330777728))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(331564224))))[name = string("encoder_layers_16_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_16_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_16_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(331564416)))]; + tensor linear_149_cast_fp16 = linear(bias = encoder_layers_16_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_16_self_attn_linear_v_weight_to_fp16_palettized, x = input_871_cast_fp16)[name = string("linear_149_cast_fp16")]; + tensor var_3907 = const()[name = string("op_3907"), val = tensor([1, -1, 8, 128])]; + tensor v_33_cast_fp16 = reshape(shape = var_3907, x = linear_149_cast_fp16)[name = string("v_33_cast_fp16")]; + tensor value_41_perm_0 = const()[name = string("value_41_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_16_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_16_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(331566528)))]; + tensor var_3920_cast_fp16 = add(x = q_97_cast_fp16, y = encoder_layers_16_self_attn_pos_bias_u_to_fp16)[name = string("op_3920_cast_fp16")]; + tensor encoder_layers_16_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_16_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(331568640)))]; + tensor var_3922_cast_fp16 = add(x = q_97_cast_fp16, y = encoder_layers_16_self_attn_pos_bias_v_to_fp16)[name = string("op_3922_cast_fp16")]; + tensor q_with_bias_v_33_perm_0 = const()[name = string("q_with_bias_v_33_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_423_transpose_x_0 = const()[name = string("x_423_transpose_x_0"), val = bool(false)]; + bool x_423_transpose_y_0 = const()[name = string("x_423_transpose_y_0"), val = bool(false)]; + tensor op_3924_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(331570752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(331670144))))[name = string("op_3924_to_fp16_quantized")]; + tensor q_with_bias_v_33_cast_fp16 = transpose(perm = q_with_bias_v_33_perm_0, x = var_3922_cast_fp16)[name = string("transpose_218")]; + tensor x_423_cast_fp16 = matmul(transpose_x = x_423_transpose_x_0, transpose_y = x_423_transpose_y_0, x = q_with_bias_v_33_cast_fp16, y = op_3924_to_fp16_quantized)[name = string("x_423_cast_fp16")]; + tensor x_425_pad_0 = const()[name = string("x_425_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_425_mode_0 = const()[name = string("x_425_mode_0"), val = string("constant")]; + fp16 const_287_to_fp16 = const()[name = string("const_287_to_fp16"), val = fp16(0x0p+0)]; + tensor x_425_cast_fp16 = pad(constant_val = const_287_to_fp16, mode = x_425_mode_0, pad = x_425_pad_0, x = x_423_cast_fp16)[name = string("x_425_cast_fp16")]; + tensor var_3932 = const()[name = string("op_3932"), val = tensor([1, 8, -1, 7])]; + tensor x_427_cast_fp16 = reshape(shape = var_3932, x = x_425_cast_fp16)[name = string("x_427_cast_fp16")]; + tensor var_3936_begin_0 = const()[name = string("op_3936_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_3936_end_0 = const()[name = string("op_3936_end_0"), val = tensor([1, 8, 98, 7])]; + tensor var_3936_end_mask_0 = const()[name = string("op_3936_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_3936_cast_fp16 = slice_by_index(begin = var_3936_begin_0, end = var_3936_end_0, end_mask = var_3936_end_mask_0, x = x_427_cast_fp16)[name = string("op_3936_cast_fp16")]; + tensor var_3937 = const()[name = string("op_3937"), val = tensor([1, 8, 7, 97])]; + tensor matrix_bd_65_cast_fp16 = reshape(shape = var_3937, x = var_3936_cast_fp16)[name = string("matrix_bd_65_cast_fp16")]; + bool matrix_ac_33_transpose_x_0 = const()[name = string("matrix_ac_33_transpose_x_0"), val = bool(false)]; + bool matrix_ac_33_transpose_y_0 = const()[name = string("matrix_ac_33_transpose_y_0"), val = bool(false)]; + tensor transpose_128_perm_0 = const()[name = string("transpose_128_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_129_perm_0 = const()[name = string("transpose_129_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_129 = transpose(perm = transpose_129_perm_0, x = k_65_cast_fp16)[name = string("transpose_216")]; + tensor transpose_128 = transpose(perm = transpose_128_perm_0, x = var_3920_cast_fp16)[name = string("transpose_217")]; + tensor matrix_ac_33_cast_fp16 = matmul(transpose_x = matrix_ac_33_transpose_x_0, transpose_y = matrix_ac_33_transpose_y_0, x = transpose_128, y = transpose_129)[name = string("matrix_ac_33_cast_fp16")]; + tensor matrix_bd_67_begin_0 = const()[name = string("matrix_bd_67_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_67_end_0 = const()[name = string("matrix_bd_67_end_0"), val = tensor([1, 8, 7, 49])]; + tensor matrix_bd_67_end_mask_0 = const()[name = string("matrix_bd_67_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_67_cast_fp16 = slice_by_index(begin = matrix_bd_67_begin_0, end = matrix_bd_67_end_0, end_mask = matrix_bd_67_end_mask_0, x = matrix_bd_65_cast_fp16)[name = string("matrix_bd_67_cast_fp16")]; + tensor var_3946_cast_fp16 = add(x = matrix_ac_33_cast_fp16, y = matrix_bd_67_cast_fp16)[name = string("op_3946_cast_fp16")]; + fp16 _inversed_scores_65_y_0_to_fp16 = const()[name = string("_inversed_scores_65_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_65_cast_fp16 = mul(x = var_3946_cast_fp16, y = _inversed_scores_65_y_0_to_fp16)[name = string("_inversed_scores_65_cast_fp16")]; + tensor scores_67_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_65_cast_fp16, cond = mask_11)[name = string("scores_67_cast_fp16")]; + tensor var_3952_cast_fp16 = softmax(axis = var_59, x = scores_67_cast_fp16)[name = string("op_3952_cast_fp16")]; + tensor input_873_cast_fp16 = select(a = var_44_to_fp16, b = var_3952_cast_fp16, cond = mask_11)[name = string("input_873_cast_fp16")]; + bool x_429_transpose_x_0 = const()[name = string("x_429_transpose_x_0"), val = bool(false)]; + bool x_429_transpose_y_0 = const()[name = string("x_429_transpose_y_0"), val = bool(false)]; + tensor value_41_cast_fp16 = transpose(perm = value_41_perm_0, x = v_33_cast_fp16)[name = string("transpose_215")]; + tensor x_429_cast_fp16 = matmul(transpose_x = x_429_transpose_x_0, transpose_y = x_429_transpose_y_0, x = input_873_cast_fp16, y = value_41_cast_fp16)[name = string("x_429_cast_fp16")]; + tensor var_3956_perm_0 = const()[name = string("op_3956_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_3957 = const()[name = string("op_3957"), val = tensor([1, -1, 1024])]; + tensor var_3956_cast_fp16 = transpose(perm = var_3956_perm_0, x = x_429_cast_fp16)[name = string("transpose_214")]; + tensor input_875_cast_fp16 = reshape(shape = var_3957, x = var_3956_cast_fp16)[name = string("input_875_cast_fp16")]; + tensor encoder_layers_16_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(331670464))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(332456960))))[name = string("encoder_layers_16_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_16_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_16_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(332457152)))]; + tensor linear_151_cast_fp16 = linear(bias = encoder_layers_16_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_16_self_attn_linear_out_weight_to_fp16_palettized, x = input_875_cast_fp16)[name = string("linear_151_cast_fp16")]; + tensor input_879_cast_fp16 = add(x = input_869_cast_fp16, y = linear_151_cast_fp16)[name = string("input_879_cast_fp16")]; + tensor x_433_axes_0 = const()[name = string("x_433_axes_0"), val = tensor([-1])]; + tensor encoder_layers_16_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_16_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(332459264)))]; + tensor encoder_layers_16_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_16_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(332461376)))]; + tensor x_433_cast_fp16 = layer_norm(axes = x_433_axes_0, beta = encoder_layers_16_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_16_norm_conv_weight_to_fp16, x = input_879_cast_fp16)[name = string("x_433_cast_fp16")]; + tensor input_881_perm_0 = const()[name = string("input_881_perm_0"), val = tensor([0, 2, 1])]; + string input_883_pad_type_0 = const()[name = string("input_883_pad_type_0"), val = string("valid")]; + tensor input_883_strides_0 = const()[name = string("input_883_strides_0"), val = tensor([1])]; + tensor input_883_pad_0 = const()[name = string("input_883_pad_0"), val = tensor([0, 0])]; + tensor input_883_dilations_0 = const()[name = string("input_883_dilations_0"), val = tensor([1])]; + int32 input_883_groups_0 = const()[name = string("input_883_groups_0"), val = int32(1)]; + tensor encoder_layers_16_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(332463488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(334560704))))[name = string("encoder_layers_16_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_881_cast_fp16 = transpose(perm = input_881_perm_0, x = x_433_cast_fp16)[name = string("transpose_213")]; + tensor input_883_cast_fp16 = conv(dilations = input_883_dilations_0, groups = input_883_groups_0, pad = input_883_pad_0, pad_type = input_883_pad_type_0, strides = input_883_strides_0, weight = encoder_layers_16_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_881_cast_fp16)[name = string("input_883_cast_fp16")]; + int32 x_435_split_num_splits_0 = const()[name = string("x_435_split_num_splits_0"), val = int32(2)]; + int32 x_435_split_axis_0 = const()[name = string("x_435_split_axis_0"), val = int32(1)]; + tensor x_435_split_cast_fp16_0, tensor x_435_split_cast_fp16_1 = split(axis = x_435_split_axis_0, num_splits = x_435_split_num_splits_0, x = input_883_cast_fp16)[name = string("x_435_split_cast_fp16")]; + tensor x_435_split_1_sigmoid_cast_fp16 = sigmoid(x = x_435_split_cast_fp16_1)[name = string("x_435_split_1_sigmoid_cast_fp16")]; + tensor x_435_cast_fp16 = mul(x = x_435_split_cast_fp16_0, y = x_435_split_1_sigmoid_cast_fp16)[name = string("x_435_cast_fp16")]; + tensor input_885_cast_fp16 = select(a = var_44_to_fp16, b = x_435_cast_fp16, cond = var_575)[name = string("input_885_cast_fp16")]; + bool new_x_67_interleave_0 = const()[name = string("new_x_67_interleave_0"), val = bool(false)]; + tensor new_x_67_cast_fp16 = concat(axis = var_59, interleave = new_x_67_interleave_0, values = (cache_67_cast_fp16, input_885_cast_fp16))[name = string("new_x_67_cast_fp16")]; + tensor var_3996_begin_0 = const()[name = string("op_3996_begin_0"), val = tensor([0, 0, 7])]; + tensor var_3996_end_0 = const()[name = string("op_3996_end_0"), val = tensor([1, 1024, 15])]; + tensor var_3996_end_mask_0 = const()[name = string("op_3996_end_mask_0"), val = tensor([true, true, true])]; + tensor var_3996_cast_fp16 = slice_by_index(begin = var_3996_begin_0, end = var_3996_end_0, end_mask = var_3996_end_mask_0, x = new_x_67_cast_fp16)[name = string("op_3996_cast_fp16")]; + string x_437_pad_type_0 = const()[name = string("x_437_pad_type_0"), val = string("valid")]; + int32 x_437_groups_0 = const()[name = string("x_437_groups_0"), val = int32(1024)]; + tensor x_437_strides_0 = const()[name = string("x_437_strides_0"), val = tensor([1])]; + tensor x_437_pad_0 = const()[name = string("x_437_pad_0"), val = tensor([0, 0])]; + tensor x_437_dilations_0 = const()[name = string("x_437_dilations_0"), val = tensor([1])]; + tensor encoder_layers_16_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(334564864))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(334574144))))[name = string("encoder_layers_16_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_437_cast_fp16 = conv(dilations = x_437_dilations_0, groups = x_437_groups_0, pad = x_437_pad_0, pad_type = x_437_pad_type_0, strides = x_437_strides_0, weight = encoder_layers_16_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_67_cast_fp16)[name = string("x_437_cast_fp16")]; + tensor input_887_perm_0 = const()[name = string("input_887_perm_0"), val = tensor([0, 2, 1])]; + tensor x_439_axes_0 = const()[name = string("x_439_axes_0"), val = tensor([-1])]; + tensor encoder_layers_16_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_16_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(334576256)))]; + tensor encoder_layers_16_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_16_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(334578368)))]; + tensor input_887_cast_fp16 = transpose(perm = input_887_perm_0, x = x_437_cast_fp16)[name = string("transpose_212")]; + tensor x_439_cast_fp16 = layer_norm(axes = x_439_axes_0, beta = encoder_layers_16_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_16_conv_batch_norm_weight_to_fp16, x = input_887_cast_fp16)[name = string("x_439_cast_fp16")]; + tensor input_889_perm_0 = const()[name = string("input_889_perm_0"), val = tensor([0, 2, 1])]; + tensor input_889_cast_fp16 = transpose(perm = input_889_perm_0, x = x_439_cast_fp16)[name = string("transpose_211")]; + tensor input_891_cast_fp16 = silu(x = input_889_cast_fp16)[name = string("input_891_cast_fp16")]; + string x_441_pad_type_0 = const()[name = string("x_441_pad_type_0"), val = string("valid")]; + tensor x_441_strides_0 = const()[name = string("x_441_strides_0"), val = tensor([1])]; + tensor x_441_pad_0 = const()[name = string("x_441_pad_0"), val = tensor([0, 0])]; + tensor x_441_dilations_0 = const()[name = string("x_441_dilations_0"), val = tensor([1])]; + int32 x_441_groups_0 = const()[name = string("x_441_groups_0"), val = int32(1)]; + tensor encoder_layers_16_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(334580480))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335629120))))[name = string("encoder_layers_16_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_441_cast_fp16 = conv(dilations = x_441_dilations_0, groups = x_441_groups_0, pad = x_441_pad_0, pad_type = x_441_pad_type_0, strides = x_441_strides_0, weight = encoder_layers_16_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_891_cast_fp16)[name = string("x_441_cast_fp16")]; + tensor input_893_perm_0 = const()[name = string("input_893_perm_0"), val = tensor([0, 2, 1])]; + tensor input_893_cast_fp16 = transpose(perm = input_893_perm_0, x = x_441_cast_fp16)[name = string("transpose_210")]; + tensor input_895_cast_fp16 = add(x = input_879_cast_fp16, y = input_893_cast_fp16)[name = string("input_895_cast_fp16")]; + tensor input_897_axes_0 = const()[name = string("input_897_axes_0"), val = tensor([-1])]; + tensor encoder_layers_16_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_16_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335631232)))]; + tensor encoder_layers_16_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_16_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335633344)))]; + tensor input_897_cast_fp16 = layer_norm(axes = input_897_axes_0, beta = encoder_layers_16_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_16_norm_feed_forward2_weight_to_fp16, x = input_895_cast_fp16)[name = string("input_897_cast_fp16")]; + tensor encoder_layers_16_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335635456))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(338781248))))[name = string("encoder_layers_16_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_16_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_16_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(338781440)))]; + tensor linear_152_cast_fp16 = linear(bias = encoder_layers_16_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_16_feed_forward2_linear1_weight_to_fp16_palettized, x = input_897_cast_fp16)[name = string("linear_152_cast_fp16")]; + tensor input_901_cast_fp16 = silu(x = linear_152_cast_fp16)[name = string("input_901_cast_fp16")]; + tensor encoder_layers_16_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(338789696))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(341935488))))[name = string("encoder_layers_16_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_16_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_16_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(341935680)))]; + tensor linear_153_cast_fp16 = linear(bias = encoder_layers_16_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_16_feed_forward2_linear2_weight_to_fp16_palettized, x = input_901_cast_fp16)[name = string("linear_153_cast_fp16")]; + fp16 var_4039_to_fp16 = const()[name = string("op_4039_to_fp16"), val = fp16(0x1p-1)]; + tensor var_4040_cast_fp16 = mul(x = linear_153_cast_fp16, y = var_4039_to_fp16)[name = string("op_4040_cast_fp16")]; + tensor input_907_cast_fp16 = add(x = input_895_cast_fp16, y = var_4040_cast_fp16)[name = string("input_907_cast_fp16")]; + tensor input_909_axes_0 = const()[name = string("input_909_axes_0"), val = tensor([-1])]; + tensor encoder_layers_16_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_16_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(341937792)))]; + tensor encoder_layers_16_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_16_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(341939904)))]; + tensor input_909_cast_fp16 = layer_norm(axes = input_909_axes_0, beta = encoder_layers_16_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_16_norm_out_weight_to_fp16, x = input_907_cast_fp16)[name = string("input_909_cast_fp16")]; + tensor cache_69_begin_0 = const()[name = string("cache_69_begin_0"), val = tensor([17, 0, 0, 0])]; + tensor cache_69_end_0 = const()[name = string("cache_69_end_0"), val = tensor([18, 1, 42, 1024])]; + tensor cache_69_end_mask_0 = const()[name = string("cache_69_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_69_squeeze_mask_0 = const()[name = string("cache_69_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_69_cast_fp16 = slice_by_index(begin = cache_69_begin_0, end = cache_69_end_0, end_mask = cache_69_end_mask_0, squeeze_mask = cache_69_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_69_cast_fp16")]; + tensor cache_71_begin_0 = const()[name = string("cache_71_begin_0"), val = tensor([17, 0, 0, 0])]; + tensor cache_71_end_0 = const()[name = string("cache_71_end_0"), val = tensor([18, 1, 1024, 8])]; + tensor cache_71_end_mask_0 = const()[name = string("cache_71_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_71_squeeze_mask_0 = const()[name = string("cache_71_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_71_cast_fp16 = slice_by_index(begin = cache_71_begin_0, end = cache_71_end_0, end_mask = cache_71_end_mask_0, squeeze_mask = cache_71_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_71_cast_fp16")]; + tensor input_911_axes_0 = const()[name = string("input_911_axes_0"), val = tensor([-1])]; + tensor encoder_layers_17_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_17_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(341942016)))]; + tensor encoder_layers_17_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_17_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(341944128)))]; + tensor input_911_cast_fp16 = layer_norm(axes = input_911_axes_0, beta = encoder_layers_17_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_17_norm_feed_forward1_weight_to_fp16, x = input_909_cast_fp16)[name = string("input_911_cast_fp16")]; + tensor encoder_layers_17_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(341946240))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(345092032))))[name = string("encoder_layers_17_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_17_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_17_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(345092224)))]; + tensor linear_154_cast_fp16 = linear(bias = encoder_layers_17_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_17_feed_forward1_linear1_weight_to_fp16_palettized, x = input_911_cast_fp16)[name = string("linear_154_cast_fp16")]; + tensor input_915_cast_fp16 = silu(x = linear_154_cast_fp16)[name = string("input_915_cast_fp16")]; + tensor encoder_layers_17_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(345100480))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(348246272))))[name = string("encoder_layers_17_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_17_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_17_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(348246464)))]; + tensor linear_155_cast_fp16 = linear(bias = encoder_layers_17_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_17_feed_forward1_linear2_weight_to_fp16_palettized, x = input_915_cast_fp16)[name = string("linear_155_cast_fp16")]; + fp16 var_4076_to_fp16 = const()[name = string("op_4076_to_fp16"), val = fp16(0x1p-1)]; + tensor var_4077_cast_fp16 = mul(x = linear_155_cast_fp16, y = var_4076_to_fp16)[name = string("op_4077_cast_fp16")]; + tensor input_921_cast_fp16 = add(x = input_909_cast_fp16, y = var_4077_cast_fp16)[name = string("input_921_cast_fp16")]; + tensor key_35_axes_0 = const()[name = string("key_35_axes_0"), val = tensor([-1])]; + tensor encoder_layers_17_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_17_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(348248576)))]; + tensor encoder_layers_17_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_17_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(348250688)))]; + tensor key_35_cast_fp16 = layer_norm(axes = key_35_axes_0, beta = encoder_layers_17_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_17_norm_self_att_weight_to_fp16, x = input_921_cast_fp16)[name = string("key_35_cast_fp16")]; + bool input_923_interleave_0 = const()[name = string("input_923_interleave_0"), val = bool(false)]; + tensor input_923_cast_fp16 = concat(axis = var_68, interleave = input_923_interleave_0, values = (cache_69_cast_fp16, key_35_cast_fp16))[name = string("input_923_cast_fp16")]; + tensor var_4099_begin_0 = const()[name = string("op_4099_begin_0"), val = tensor([0, 7, 0])]; + tensor var_4099_end_0 = const()[name = string("op_4099_end_0"), val = tensor([1, 42, 1024])]; + tensor var_4099_end_mask_0 = const()[name = string("op_4099_end_mask_0"), val = tensor([true, true, true])]; + tensor var_4099_cast_fp16 = slice_by_index(begin = var_4099_begin_0, end = var_4099_end_0, end_mask = var_4099_end_mask_0, x = cache_69_cast_fp16)[name = string("op_4099_cast_fp16")]; + bool var_4105_interleave_0 = const()[name = string("op_4105_interleave_0"), val = bool(false)]; + tensor var_4105_cast_fp16 = concat(axis = var_68, interleave = var_4105_interleave_0, values = (var_4099_cast_fp16, key_35_cast_fp16))[name = string("op_4105_cast_fp16")]; + tensor encoder_layers_17_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(348252800))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(349039296))))[name = string("encoder_layers_17_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_17_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_17_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(349039488)))]; + tensor linear_156_cast_fp16 = linear(bias = encoder_layers_17_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_17_self_attn_linear_q_weight_to_fp16_palettized, x = key_35_cast_fp16)[name = string("linear_156_cast_fp16")]; + tensor var_4110 = const()[name = string("op_4110"), val = tensor([1, -1, 8, 128])]; + tensor q_103_cast_fp16 = reshape(shape = var_4110, x = linear_156_cast_fp16)[name = string("q_103_cast_fp16")]; + tensor encoder_layers_17_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(349041600))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(349828096))))[name = string("encoder_layers_17_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_17_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_17_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(349828288)))]; + tensor linear_157_cast_fp16 = linear(bias = encoder_layers_17_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_17_self_attn_linear_k_weight_to_fp16_palettized, x = input_923_cast_fp16)[name = string("linear_157_cast_fp16")]; + tensor var_4115 = const()[name = string("op_4115"), val = tensor([1, -1, 8, 128])]; + tensor k_69_cast_fp16 = reshape(shape = var_4115, x = linear_157_cast_fp16)[name = string("k_69_cast_fp16")]; + tensor encoder_layers_17_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(349830400))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350616896))))[name = string("encoder_layers_17_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_17_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_17_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350617088)))]; + tensor linear_158_cast_fp16 = linear(bias = encoder_layers_17_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_17_self_attn_linear_v_weight_to_fp16_palettized, x = input_923_cast_fp16)[name = string("linear_158_cast_fp16")]; + tensor var_4120 = const()[name = string("op_4120"), val = tensor([1, -1, 8, 128])]; + tensor v_35_cast_fp16 = reshape(shape = var_4120, x = linear_158_cast_fp16)[name = string("v_35_cast_fp16")]; + tensor value_43_perm_0 = const()[name = string("value_43_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_17_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_17_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350619200)))]; + tensor var_4133_cast_fp16 = add(x = q_103_cast_fp16, y = encoder_layers_17_self_attn_pos_bias_u_to_fp16)[name = string("op_4133_cast_fp16")]; + tensor encoder_layers_17_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_17_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350621312)))]; + tensor var_4135_cast_fp16 = add(x = q_103_cast_fp16, y = encoder_layers_17_self_attn_pos_bias_v_to_fp16)[name = string("op_4135_cast_fp16")]; + tensor q_with_bias_v_35_perm_0 = const()[name = string("q_with_bias_v_35_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_449_transpose_x_0 = const()[name = string("x_449_transpose_x_0"), val = bool(false)]; + bool x_449_transpose_y_0 = const()[name = string("x_449_transpose_y_0"), val = bool(false)]; + tensor op_4137_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350623424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350722816))))[name = string("op_4137_to_fp16_quantized")]; + tensor q_with_bias_v_35_cast_fp16 = transpose(perm = q_with_bias_v_35_perm_0, x = var_4135_cast_fp16)[name = string("transpose_209")]; + tensor x_449_cast_fp16 = matmul(transpose_x = x_449_transpose_x_0, transpose_y = x_449_transpose_y_0, x = q_with_bias_v_35_cast_fp16, y = op_4137_to_fp16_quantized)[name = string("x_449_cast_fp16")]; + tensor x_451_pad_0 = const()[name = string("x_451_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_451_mode_0 = const()[name = string("x_451_mode_0"), val = string("constant")]; + fp16 const_300_to_fp16 = const()[name = string("const_300_to_fp16"), val = fp16(0x0p+0)]; + tensor x_451_cast_fp16 = pad(constant_val = const_300_to_fp16, mode = x_451_mode_0, pad = x_451_pad_0, x = x_449_cast_fp16)[name = string("x_451_cast_fp16")]; + tensor var_4145 = const()[name = string("op_4145"), val = tensor([1, 8, -1, 7])]; + tensor x_453_cast_fp16 = reshape(shape = var_4145, x = x_451_cast_fp16)[name = string("x_453_cast_fp16")]; + tensor var_4149_begin_0 = const()[name = string("op_4149_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_4149_end_0 = const()[name = string("op_4149_end_0"), val = tensor([1, 8, 98, 7])]; + tensor var_4149_end_mask_0 = const()[name = string("op_4149_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_4149_cast_fp16 = slice_by_index(begin = var_4149_begin_0, end = var_4149_end_0, end_mask = var_4149_end_mask_0, x = x_453_cast_fp16)[name = string("op_4149_cast_fp16")]; + tensor var_4150 = const()[name = string("op_4150"), val = tensor([1, 8, 7, 97])]; + tensor matrix_bd_69_cast_fp16 = reshape(shape = var_4150, x = var_4149_cast_fp16)[name = string("matrix_bd_69_cast_fp16")]; + bool matrix_ac_35_transpose_x_0 = const()[name = string("matrix_ac_35_transpose_x_0"), val = bool(false)]; + bool matrix_ac_35_transpose_y_0 = const()[name = string("matrix_ac_35_transpose_y_0"), val = bool(false)]; + tensor transpose_130_perm_0 = const()[name = string("transpose_130_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_131_perm_0 = const()[name = string("transpose_131_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_131 = transpose(perm = transpose_131_perm_0, x = k_69_cast_fp16)[name = string("transpose_207")]; + tensor transpose_130 = transpose(perm = transpose_130_perm_0, x = var_4133_cast_fp16)[name = string("transpose_208")]; + tensor matrix_ac_35_cast_fp16 = matmul(transpose_x = matrix_ac_35_transpose_x_0, transpose_y = matrix_ac_35_transpose_y_0, x = transpose_130, y = transpose_131)[name = string("matrix_ac_35_cast_fp16")]; + tensor matrix_bd_71_begin_0 = const()[name = string("matrix_bd_71_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_71_end_0 = const()[name = string("matrix_bd_71_end_0"), val = tensor([1, 8, 7, 49])]; + tensor matrix_bd_71_end_mask_0 = const()[name = string("matrix_bd_71_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_71_cast_fp16 = slice_by_index(begin = matrix_bd_71_begin_0, end = matrix_bd_71_end_0, end_mask = matrix_bd_71_end_mask_0, x = matrix_bd_69_cast_fp16)[name = string("matrix_bd_71_cast_fp16")]; + tensor var_4159_cast_fp16 = add(x = matrix_ac_35_cast_fp16, y = matrix_bd_71_cast_fp16)[name = string("op_4159_cast_fp16")]; + fp16 _inversed_scores_69_y_0_to_fp16 = const()[name = string("_inversed_scores_69_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_69_cast_fp16 = mul(x = var_4159_cast_fp16, y = _inversed_scores_69_y_0_to_fp16)[name = string("_inversed_scores_69_cast_fp16")]; + tensor scores_71_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_69_cast_fp16, cond = mask_11)[name = string("scores_71_cast_fp16")]; + tensor var_4165_cast_fp16 = softmax(axis = var_59, x = scores_71_cast_fp16)[name = string("op_4165_cast_fp16")]; + tensor input_925_cast_fp16 = select(a = var_44_to_fp16, b = var_4165_cast_fp16, cond = mask_11)[name = string("input_925_cast_fp16")]; + bool x_455_transpose_x_0 = const()[name = string("x_455_transpose_x_0"), val = bool(false)]; + bool x_455_transpose_y_0 = const()[name = string("x_455_transpose_y_0"), val = bool(false)]; + tensor value_43_cast_fp16 = transpose(perm = value_43_perm_0, x = v_35_cast_fp16)[name = string("transpose_206")]; + tensor x_455_cast_fp16 = matmul(transpose_x = x_455_transpose_x_0, transpose_y = x_455_transpose_y_0, x = input_925_cast_fp16, y = value_43_cast_fp16)[name = string("x_455_cast_fp16")]; + tensor var_4169_perm_0 = const()[name = string("op_4169_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_4170 = const()[name = string("op_4170"), val = tensor([1, -1, 1024])]; + tensor var_4169_cast_fp16 = transpose(perm = var_4169_perm_0, x = x_455_cast_fp16)[name = string("transpose_205")]; + tensor input_927_cast_fp16 = reshape(shape = var_4170, x = var_4169_cast_fp16)[name = string("input_927_cast_fp16")]; + tensor encoder_layers_17_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350723136))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(351509632))))[name = string("encoder_layers_17_self_attn_linear_out_weight_to_fp16_palettized")]; + tensor encoder_layers_17_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_17_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(351509824)))]; + tensor linear_160_cast_fp16 = linear(bias = encoder_layers_17_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_17_self_attn_linear_out_weight_to_fp16_palettized, x = input_927_cast_fp16)[name = string("linear_160_cast_fp16")]; + tensor input_931_cast_fp16 = add(x = input_921_cast_fp16, y = linear_160_cast_fp16)[name = string("input_931_cast_fp16")]; + tensor x_459_axes_0 = const()[name = string("x_459_axes_0"), val = tensor([-1])]; + tensor encoder_layers_17_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_17_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(351511936)))]; + tensor encoder_layers_17_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_17_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(351514048)))]; + tensor x_459_cast_fp16 = layer_norm(axes = x_459_axes_0, beta = encoder_layers_17_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_17_norm_conv_weight_to_fp16, x = input_931_cast_fp16)[name = string("x_459_cast_fp16")]; + tensor input_933_perm_0 = const()[name = string("input_933_perm_0"), val = tensor([0, 2, 1])]; + string input_935_pad_type_0 = const()[name = string("input_935_pad_type_0"), val = string("valid")]; + tensor input_935_strides_0 = const()[name = string("input_935_strides_0"), val = tensor([1])]; + tensor input_935_pad_0 = const()[name = string("input_935_pad_0"), val = tensor([0, 0])]; + tensor input_935_dilations_0 = const()[name = string("input_935_dilations_0"), val = tensor([1])]; + int32 input_935_groups_0 = const()[name = string("input_935_groups_0"), val = int32(1)]; + tensor encoder_layers_17_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(351516160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(353613376))))[name = string("encoder_layers_17_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_933_cast_fp16 = transpose(perm = input_933_perm_0, x = x_459_cast_fp16)[name = string("transpose_204")]; + tensor input_935_cast_fp16 = conv(dilations = input_935_dilations_0, groups = input_935_groups_0, pad = input_935_pad_0, pad_type = input_935_pad_type_0, strides = input_935_strides_0, weight = encoder_layers_17_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_933_cast_fp16)[name = string("input_935_cast_fp16")]; + int32 x_461_split_num_splits_0 = const()[name = string("x_461_split_num_splits_0"), val = int32(2)]; + int32 x_461_split_axis_0 = const()[name = string("x_461_split_axis_0"), val = int32(1)]; + tensor x_461_split_cast_fp16_0, tensor x_461_split_cast_fp16_1 = split(axis = x_461_split_axis_0, num_splits = x_461_split_num_splits_0, x = input_935_cast_fp16)[name = string("x_461_split_cast_fp16")]; + tensor x_461_split_1_sigmoid_cast_fp16 = sigmoid(x = x_461_split_cast_fp16_1)[name = string("x_461_split_1_sigmoid_cast_fp16")]; + tensor x_461_cast_fp16 = mul(x = x_461_split_cast_fp16_0, y = x_461_split_1_sigmoid_cast_fp16)[name = string("x_461_cast_fp16")]; + tensor input_937_cast_fp16 = select(a = var_44_to_fp16, b = x_461_cast_fp16, cond = var_575)[name = string("input_937_cast_fp16")]; + bool new_x_71_interleave_0 = const()[name = string("new_x_71_interleave_0"), val = bool(false)]; + tensor new_x_71_cast_fp16 = concat(axis = var_59, interleave = new_x_71_interleave_0, values = (cache_71_cast_fp16, input_937_cast_fp16))[name = string("new_x_71_cast_fp16")]; + tensor var_4209_begin_0 = const()[name = string("op_4209_begin_0"), val = tensor([0, 0, 7])]; + tensor var_4209_end_0 = const()[name = string("op_4209_end_0"), val = tensor([1, 1024, 15])]; + tensor var_4209_end_mask_0 = const()[name = string("op_4209_end_mask_0"), val = tensor([true, true, true])]; + tensor var_4209_cast_fp16 = slice_by_index(begin = var_4209_begin_0, end = var_4209_end_0, end_mask = var_4209_end_mask_0, x = new_x_71_cast_fp16)[name = string("op_4209_cast_fp16")]; + string x_463_pad_type_0 = const()[name = string("x_463_pad_type_0"), val = string("valid")]; + int32 x_463_groups_0 = const()[name = string("x_463_groups_0"), val = int32(1024)]; + tensor x_463_strides_0 = const()[name = string("x_463_strides_0"), val = tensor([1])]; + tensor x_463_pad_0 = const()[name = string("x_463_pad_0"), val = tensor([0, 0])]; + tensor x_463_dilations_0 = const()[name = string("x_463_dilations_0"), val = tensor([1])]; + tensor encoder_layers_17_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(353617536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(353626816))))[name = string("encoder_layers_17_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_463_cast_fp16 = conv(dilations = x_463_dilations_0, groups = x_463_groups_0, pad = x_463_pad_0, pad_type = x_463_pad_type_0, strides = x_463_strides_0, weight = encoder_layers_17_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_71_cast_fp16)[name = string("x_463_cast_fp16")]; + tensor input_939_perm_0 = const()[name = string("input_939_perm_0"), val = tensor([0, 2, 1])]; + tensor x_465_axes_0 = const()[name = string("x_465_axes_0"), val = tensor([-1])]; + tensor encoder_layers_17_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_17_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(353628928)))]; + tensor encoder_layers_17_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_17_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(353631040)))]; + tensor input_939_cast_fp16 = transpose(perm = input_939_perm_0, x = x_463_cast_fp16)[name = string("transpose_203")]; + tensor x_465_cast_fp16 = layer_norm(axes = x_465_axes_0, beta = encoder_layers_17_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_17_conv_batch_norm_weight_to_fp16, x = input_939_cast_fp16)[name = string("x_465_cast_fp16")]; + tensor input_941_perm_0 = const()[name = string("input_941_perm_0"), val = tensor([0, 2, 1])]; + tensor input_941_cast_fp16 = transpose(perm = input_941_perm_0, x = x_465_cast_fp16)[name = string("transpose_202")]; + tensor input_943_cast_fp16 = silu(x = input_941_cast_fp16)[name = string("input_943_cast_fp16")]; + string x_467_pad_type_0 = const()[name = string("x_467_pad_type_0"), val = string("valid")]; + tensor x_467_strides_0 = const()[name = string("x_467_strides_0"), val = tensor([1])]; + tensor x_467_pad_0 = const()[name = string("x_467_pad_0"), val = tensor([0, 0])]; + tensor x_467_dilations_0 = const()[name = string("x_467_dilations_0"), val = tensor([1])]; + int32 x_467_groups_0 = const()[name = string("x_467_groups_0"), val = int32(1)]; + tensor encoder_layers_17_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(353633152))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(354681792))))[name = string("encoder_layers_17_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_467_cast_fp16 = conv(dilations = x_467_dilations_0, groups = x_467_groups_0, pad = x_467_pad_0, pad_type = x_467_pad_type_0, strides = x_467_strides_0, weight = encoder_layers_17_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_943_cast_fp16)[name = string("x_467_cast_fp16")]; + tensor input_945_perm_0 = const()[name = string("input_945_perm_0"), val = tensor([0, 2, 1])]; + tensor input_945_cast_fp16 = transpose(perm = input_945_perm_0, x = x_467_cast_fp16)[name = string("transpose_201")]; + tensor input_947_cast_fp16 = add(x = input_931_cast_fp16, y = input_945_cast_fp16)[name = string("input_947_cast_fp16")]; + tensor input_949_axes_0 = const()[name = string("input_949_axes_0"), val = tensor([-1])]; + tensor encoder_layers_17_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_17_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(354683904)))]; + tensor encoder_layers_17_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_17_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(354686016)))]; + tensor input_949_cast_fp16 = layer_norm(axes = input_949_axes_0, beta = encoder_layers_17_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_17_norm_feed_forward2_weight_to_fp16, x = input_947_cast_fp16)[name = string("input_949_cast_fp16")]; + tensor encoder_layers_17_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(354688128))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(357833920))))[name = string("encoder_layers_17_feed_forward2_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_17_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_17_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(357834112)))]; + tensor linear_161_cast_fp16 = linear(bias = encoder_layers_17_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_17_feed_forward2_linear1_weight_to_fp16_palettized, x = input_949_cast_fp16)[name = string("linear_161_cast_fp16")]; + tensor input_953_cast_fp16 = silu(x = linear_161_cast_fp16)[name = string("input_953_cast_fp16")]; + tensor encoder_layers_17_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(357842368))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360988160))))[name = string("encoder_layers_17_feed_forward2_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_17_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_17_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360988352)))]; + tensor linear_162_cast_fp16 = linear(bias = encoder_layers_17_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_17_feed_forward2_linear2_weight_to_fp16_palettized, x = input_953_cast_fp16)[name = string("linear_162_cast_fp16")]; + fp16 var_4252_to_fp16 = const()[name = string("op_4252_to_fp16"), val = fp16(0x1p-1)]; + tensor var_4253_cast_fp16 = mul(x = linear_162_cast_fp16, y = var_4252_to_fp16)[name = string("op_4253_cast_fp16")]; + tensor input_959_cast_fp16 = add(x = input_947_cast_fp16, y = var_4253_cast_fp16)[name = string("input_959_cast_fp16")]; + tensor input_961_axes_0 = const()[name = string("input_961_axes_0"), val = tensor([-1])]; + tensor encoder_layers_17_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_17_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360990464)))]; + tensor encoder_layers_17_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_17_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360992576)))]; + tensor input_961_cast_fp16 = layer_norm(axes = input_961_axes_0, beta = encoder_layers_17_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_17_norm_out_weight_to_fp16, x = input_959_cast_fp16)[name = string("input_961_cast_fp16")]; + tensor cache_73_begin_0 = const()[name = string("cache_73_begin_0"), val = tensor([18, 0, 0, 0])]; + tensor cache_73_end_0 = const()[name = string("cache_73_end_0"), val = tensor([19, 1, 42, 1024])]; + tensor cache_73_end_mask_0 = const()[name = string("cache_73_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_73_squeeze_mask_0 = const()[name = string("cache_73_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_73_cast_fp16 = slice_by_index(begin = cache_73_begin_0, end = cache_73_end_0, end_mask = cache_73_end_mask_0, squeeze_mask = cache_73_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_73_cast_fp16")]; + tensor cache_75_begin_0 = const()[name = string("cache_75_begin_0"), val = tensor([18, 0, 0, 0])]; + tensor cache_75_end_0 = const()[name = string("cache_75_end_0"), val = tensor([19, 1, 1024, 8])]; + tensor cache_75_end_mask_0 = const()[name = string("cache_75_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_75_squeeze_mask_0 = const()[name = string("cache_75_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_75_cast_fp16 = slice_by_index(begin = cache_75_begin_0, end = cache_75_end_0, end_mask = cache_75_end_mask_0, squeeze_mask = cache_75_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_75_cast_fp16")]; + tensor input_963_axes_0 = const()[name = string("input_963_axes_0"), val = tensor([-1])]; + tensor encoder_layers_18_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_18_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360994688)))]; + tensor encoder_layers_18_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_18_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360996800)))]; + tensor input_963_cast_fp16 = layer_norm(axes = input_963_axes_0, beta = encoder_layers_18_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_18_norm_feed_forward1_weight_to_fp16, x = input_961_cast_fp16)[name = string("input_963_cast_fp16")]; + tensor encoder_layers_18_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360998912))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(364144704))))[name = string("encoder_layers_18_feed_forward1_linear1_weight_to_fp16_palettized")]; + tensor encoder_layers_18_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_18_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(364144896)))]; + tensor linear_163_cast_fp16 = linear(bias = encoder_layers_18_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_18_feed_forward1_linear1_weight_to_fp16_palettized, x = input_963_cast_fp16)[name = string("linear_163_cast_fp16")]; + tensor input_967_cast_fp16 = silu(x = linear_163_cast_fp16)[name = string("input_967_cast_fp16")]; + tensor encoder_layers_18_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(364153152))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(367298944))))[name = string("encoder_layers_18_feed_forward1_linear2_weight_to_fp16_palettized")]; + tensor encoder_layers_18_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_18_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(367299136)))]; + tensor linear_164_cast_fp16 = linear(bias = encoder_layers_18_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_18_feed_forward1_linear2_weight_to_fp16_palettized, x = input_967_cast_fp16)[name = string("linear_164_cast_fp16")]; + fp16 var_4289_to_fp16 = const()[name = string("op_4289_to_fp16"), val = fp16(0x1p-1)]; + tensor var_4290_cast_fp16 = mul(x = linear_164_cast_fp16, y = var_4289_to_fp16)[name = string("op_4290_cast_fp16")]; + tensor input_973_cast_fp16 = add(x = input_961_cast_fp16, y = var_4290_cast_fp16)[name = string("input_973_cast_fp16")]; + tensor key_37_axes_0 = const()[name = string("key_37_axes_0"), val = tensor([-1])]; + tensor encoder_layers_18_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_18_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(367301248)))]; + tensor encoder_layers_18_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_18_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(367303360)))]; + tensor key_37_cast_fp16 = layer_norm(axes = key_37_axes_0, beta = encoder_layers_18_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_18_norm_self_att_weight_to_fp16, x = input_973_cast_fp16)[name = string("key_37_cast_fp16")]; + bool input_975_interleave_0 = const()[name = string("input_975_interleave_0"), val = bool(false)]; + tensor input_975_cast_fp16 = concat(axis = var_68, interleave = input_975_interleave_0, values = (cache_73_cast_fp16, key_37_cast_fp16))[name = string("input_975_cast_fp16")]; + tensor var_4312_begin_0 = const()[name = string("op_4312_begin_0"), val = tensor([0, 7, 0])]; + tensor var_4312_end_0 = const()[name = string("op_4312_end_0"), val = tensor([1, 42, 1024])]; + tensor var_4312_end_mask_0 = const()[name = string("op_4312_end_mask_0"), val = tensor([true, true, true])]; + tensor var_4312_cast_fp16 = slice_by_index(begin = var_4312_begin_0, end = var_4312_end_0, end_mask = var_4312_end_mask_0, x = cache_73_cast_fp16)[name = string("op_4312_cast_fp16")]; + bool var_4318_interleave_0 = const()[name = string("op_4318_interleave_0"), val = bool(false)]; + tensor var_4318_cast_fp16 = concat(axis = var_68, interleave = var_4318_interleave_0, values = (var_4312_cast_fp16, key_37_cast_fp16))[name = string("op_4318_cast_fp16")]; + tensor encoder_layers_18_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(367305472))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(368091968))))[name = string("encoder_layers_18_self_attn_linear_q_weight_to_fp16_palettized")]; + tensor encoder_layers_18_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_18_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(368092160)))]; + tensor linear_165_cast_fp16 = linear(bias = encoder_layers_18_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_18_self_attn_linear_q_weight_to_fp16_palettized, x = key_37_cast_fp16)[name = string("linear_165_cast_fp16")]; + tensor var_4323 = const()[name = string("op_4323"), val = tensor([1, -1, 8, 128])]; + tensor q_109_cast_fp16 = reshape(shape = var_4323, x = linear_165_cast_fp16)[name = string("q_109_cast_fp16")]; + tensor encoder_layers_18_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(368094272))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(368880768))))[name = string("encoder_layers_18_self_attn_linear_k_weight_to_fp16_palettized")]; + tensor encoder_layers_18_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_18_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(368880960)))]; + tensor linear_166_cast_fp16 = linear(bias = encoder_layers_18_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_18_self_attn_linear_k_weight_to_fp16_palettized, x = input_975_cast_fp16)[name = string("linear_166_cast_fp16")]; + tensor var_4328 = const()[name = string("op_4328"), val = tensor([1, -1, 8, 128])]; + tensor k_73_cast_fp16 = reshape(shape = var_4328, x = linear_166_cast_fp16)[name = string("k_73_cast_fp16")]; + tensor encoder_layers_18_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(368883072))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(369669568))))[name = string("encoder_layers_18_self_attn_linear_v_weight_to_fp16_palettized")]; + tensor encoder_layers_18_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_18_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(369669760)))]; + tensor linear_167_cast_fp16 = linear(bias = encoder_layers_18_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_18_self_attn_linear_v_weight_to_fp16_palettized, x = input_975_cast_fp16)[name = string("linear_167_cast_fp16")]; + tensor var_4333 = const()[name = string("op_4333"), val = tensor([1, -1, 8, 128])]; + tensor v_37_cast_fp16 = reshape(shape = var_4333, x = linear_167_cast_fp16)[name = string("v_37_cast_fp16")]; + tensor value_45_perm_0 = const()[name = string("value_45_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_18_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_18_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(369671872)))]; + tensor var_4346_cast_fp16 = add(x = q_109_cast_fp16, y = encoder_layers_18_self_attn_pos_bias_u_to_fp16)[name = string("op_4346_cast_fp16")]; + tensor encoder_layers_18_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_18_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(369673984)))]; + tensor var_4348_cast_fp16 = add(x = q_109_cast_fp16, y = encoder_layers_18_self_attn_pos_bias_v_to_fp16)[name = string("op_4348_cast_fp16")]; + tensor q_with_bias_v_37_perm_0 = const()[name = string("q_with_bias_v_37_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_475_transpose_x_0 = const()[name = string("x_475_transpose_x_0"), val = bool(false)]; + bool x_475_transpose_y_0 = const()[name = string("x_475_transpose_y_0"), val = bool(false)]; + tensor op_4350_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(369676096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(369775488))))[name = string("op_4350_to_fp16_quantized")]; + tensor q_with_bias_v_37_cast_fp16 = transpose(perm = q_with_bias_v_37_perm_0, x = var_4348_cast_fp16)[name = string("transpose_200")]; + tensor x_475_cast_fp16 = matmul(transpose_x = x_475_transpose_x_0, transpose_y = x_475_transpose_y_0, x = q_with_bias_v_37_cast_fp16, y = op_4350_to_fp16_quantized)[name = string("x_475_cast_fp16")]; + tensor x_477_pad_0 = const()[name = string("x_477_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_477_mode_0 = const()[name = string("x_477_mode_0"), val = string("constant")]; + fp16 const_313_to_fp16 = const()[name = string("const_313_to_fp16"), val = fp16(0x0p+0)]; + tensor x_477_cast_fp16 = pad(constant_val = const_313_to_fp16, mode = x_477_mode_0, pad = x_477_pad_0, x = x_475_cast_fp16)[name = string("x_477_cast_fp16")]; + tensor var_4358 = const()[name = string("op_4358"), val = tensor([1, 8, -1, 7])]; + tensor x_479_cast_fp16 = reshape(shape = var_4358, x = x_477_cast_fp16)[name = string("x_479_cast_fp16")]; + tensor var_4362_begin_0 = const()[name = string("op_4362_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_4362_end_0 = const()[name = string("op_4362_end_0"), val = tensor([1, 8, 98, 7])]; + tensor var_4362_end_mask_0 = const()[name = string("op_4362_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_4362_cast_fp16 = slice_by_index(begin = var_4362_begin_0, end = var_4362_end_0, end_mask = var_4362_end_mask_0, x = x_479_cast_fp16)[name = string("op_4362_cast_fp16")]; + tensor var_4363 = const()[name = string("op_4363"), val = tensor([1, 8, 7, 97])]; + tensor matrix_bd_73_cast_fp16 = reshape(shape = var_4363, x = var_4362_cast_fp16)[name = string("matrix_bd_73_cast_fp16")]; + bool matrix_ac_37_transpose_x_0 = const()[name = string("matrix_ac_37_transpose_x_0"), val = bool(false)]; + bool matrix_ac_37_transpose_y_0 = const()[name = string("matrix_ac_37_transpose_y_0"), val = bool(false)]; + tensor transpose_132_perm_0 = const()[name = string("transpose_132_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_133_perm_0 = const()[name = string("transpose_133_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_133 = transpose(perm = transpose_133_perm_0, x = k_73_cast_fp16)[name = string("transpose_198")]; + tensor transpose_132 = transpose(perm = transpose_132_perm_0, x = var_4346_cast_fp16)[name = string("transpose_199")]; + tensor matrix_ac_37_cast_fp16 = matmul(transpose_x = matrix_ac_37_transpose_x_0, transpose_y = matrix_ac_37_transpose_y_0, x = transpose_132, y = transpose_133)[name = string("matrix_ac_37_cast_fp16")]; + tensor matrix_bd_75_begin_0 = const()[name = string("matrix_bd_75_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_75_end_0 = const()[name = string("matrix_bd_75_end_0"), val = tensor([1, 8, 7, 49])]; + tensor matrix_bd_75_end_mask_0 = const()[name = string("matrix_bd_75_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_75_cast_fp16 = slice_by_index(begin = matrix_bd_75_begin_0, end = matrix_bd_75_end_0, end_mask = matrix_bd_75_end_mask_0, x = matrix_bd_73_cast_fp16)[name = string("matrix_bd_75_cast_fp16")]; + tensor var_4372_cast_fp16 = add(x = matrix_ac_37_cast_fp16, y = matrix_bd_75_cast_fp16)[name = string("op_4372_cast_fp16")]; + fp16 _inversed_scores_73_y_0_to_fp16 = const()[name = string("_inversed_scores_73_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_73_cast_fp16 = mul(x = var_4372_cast_fp16, y = _inversed_scores_73_y_0_to_fp16)[name = string("_inversed_scores_73_cast_fp16")]; + tensor scores_75_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_73_cast_fp16, cond = mask_11)[name = string("scores_75_cast_fp16")]; + tensor var_4378_cast_fp16 = softmax(axis = var_59, x = scores_75_cast_fp16)[name = string("op_4378_cast_fp16")]; + tensor input_977_cast_fp16 = select(a = var_44_to_fp16, b = var_4378_cast_fp16, cond = mask_11)[name = string("input_977_cast_fp16")]; + bool x_481_transpose_x_0 = const()[name = string("x_481_transpose_x_0"), val = bool(false)]; + bool x_481_transpose_y_0 = const()[name = string("x_481_transpose_y_0"), val = bool(false)]; + tensor value_45_cast_fp16 = transpose(perm = value_45_perm_0, x = v_37_cast_fp16)[name = string("transpose_197")]; + tensor x_481_cast_fp16 = matmul(transpose_x = x_481_transpose_x_0, transpose_y = x_481_transpose_y_0, x = input_977_cast_fp16, y = value_45_cast_fp16)[name = string("x_481_cast_fp16")]; + tensor var_4382_perm_0 = const()[name = string("op_4382_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_4383 = const()[name = string("op_4383"), val = tensor([1, -1, 1024])]; + tensor var_4382_cast_fp16 = transpose(perm = var_4382_perm_0, x = x_481_cast_fp16)[name = string("transpose_196")]; + tensor input_979_cast_fp16 = reshape(shape = var_4383, x = var_4382_cast_fp16)[name = string("input_979_cast_fp16")]; + tensor encoder_layers_18_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(369775808))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(370824448))))[name = string("encoder_layers_18_self_attn_linear_out_weight_to_fp16_quantized")]; + tensor encoder_layers_18_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_18_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(370826560)))]; + tensor linear_169_cast_fp16 = linear(bias = encoder_layers_18_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_18_self_attn_linear_out_weight_to_fp16_quantized, x = input_979_cast_fp16)[name = string("linear_169_cast_fp16")]; + tensor input_983_cast_fp16 = add(x = input_973_cast_fp16, y = linear_169_cast_fp16)[name = string("input_983_cast_fp16")]; + tensor x_485_axes_0 = const()[name = string("x_485_axes_0"), val = tensor([-1])]; + tensor encoder_layers_18_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_18_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(370828672)))]; + tensor encoder_layers_18_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_18_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(370830784)))]; + tensor x_485_cast_fp16 = layer_norm(axes = x_485_axes_0, beta = encoder_layers_18_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_18_norm_conv_weight_to_fp16, x = input_983_cast_fp16)[name = string("x_485_cast_fp16")]; + tensor input_985_perm_0 = const()[name = string("input_985_perm_0"), val = tensor([0, 2, 1])]; + string input_987_pad_type_0 = const()[name = string("input_987_pad_type_0"), val = string("valid")]; + tensor input_987_strides_0 = const()[name = string("input_987_strides_0"), val = tensor([1])]; + tensor input_987_pad_0 = const()[name = string("input_987_pad_0"), val = tensor([0, 0])]; + tensor input_987_dilations_0 = const()[name = string("input_987_dilations_0"), val = tensor([1])]; + int32 input_987_groups_0 = const()[name = string("input_987_groups_0"), val = int32(1)]; + tensor encoder_layers_18_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(370832896))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372930112))))[name = string("encoder_layers_18_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_985_cast_fp16 = transpose(perm = input_985_perm_0, x = x_485_cast_fp16)[name = string("transpose_195")]; + tensor input_987_cast_fp16 = conv(dilations = input_987_dilations_0, groups = input_987_groups_0, pad = input_987_pad_0, pad_type = input_987_pad_type_0, strides = input_987_strides_0, weight = encoder_layers_18_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_985_cast_fp16)[name = string("input_987_cast_fp16")]; + int32 x_487_split_num_splits_0 = const()[name = string("x_487_split_num_splits_0"), val = int32(2)]; + int32 x_487_split_axis_0 = const()[name = string("x_487_split_axis_0"), val = int32(1)]; + tensor x_487_split_cast_fp16_0, tensor x_487_split_cast_fp16_1 = split(axis = x_487_split_axis_0, num_splits = x_487_split_num_splits_0, x = input_987_cast_fp16)[name = string("x_487_split_cast_fp16")]; + tensor x_487_split_1_sigmoid_cast_fp16 = sigmoid(x = x_487_split_cast_fp16_1)[name = string("x_487_split_1_sigmoid_cast_fp16")]; + tensor x_487_cast_fp16 = mul(x = x_487_split_cast_fp16_0, y = x_487_split_1_sigmoid_cast_fp16)[name = string("x_487_cast_fp16")]; + tensor input_989_cast_fp16 = select(a = var_44_to_fp16, b = x_487_cast_fp16, cond = var_575)[name = string("input_989_cast_fp16")]; + bool new_x_75_interleave_0 = const()[name = string("new_x_75_interleave_0"), val = bool(false)]; + tensor new_x_75_cast_fp16 = concat(axis = var_59, interleave = new_x_75_interleave_0, values = (cache_75_cast_fp16, input_989_cast_fp16))[name = string("new_x_75_cast_fp16")]; + tensor var_4422_begin_0 = const()[name = string("op_4422_begin_0"), val = tensor([0, 0, 7])]; + tensor var_4422_end_0 = const()[name = string("op_4422_end_0"), val = tensor([1, 1024, 15])]; + tensor var_4422_end_mask_0 = const()[name = string("op_4422_end_mask_0"), val = tensor([true, true, true])]; + tensor var_4422_cast_fp16 = slice_by_index(begin = var_4422_begin_0, end = var_4422_end_0, end_mask = var_4422_end_mask_0, x = new_x_75_cast_fp16)[name = string("op_4422_cast_fp16")]; + string x_489_pad_type_0 = const()[name = string("x_489_pad_type_0"), val = string("valid")]; + int32 x_489_groups_0 = const()[name = string("x_489_groups_0"), val = int32(1024)]; + tensor x_489_strides_0 = const()[name = string("x_489_strides_0"), val = tensor([1])]; + tensor x_489_pad_0 = const()[name = string("x_489_pad_0"), val = tensor([0, 0])]; + tensor x_489_dilations_0 = const()[name = string("x_489_dilations_0"), val = tensor([1])]; + tensor encoder_layers_18_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372934272))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372943552))))[name = string("encoder_layers_18_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_489_cast_fp16 = conv(dilations = x_489_dilations_0, groups = x_489_groups_0, pad = x_489_pad_0, pad_type = x_489_pad_type_0, strides = x_489_strides_0, weight = encoder_layers_18_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_75_cast_fp16)[name = string("x_489_cast_fp16")]; + tensor input_991_perm_0 = const()[name = string("input_991_perm_0"), val = tensor([0, 2, 1])]; + tensor x_491_axes_0 = const()[name = string("x_491_axes_0"), val = tensor([-1])]; + tensor encoder_layers_18_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_18_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372945664)))]; + tensor encoder_layers_18_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_18_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372947776)))]; + tensor input_991_cast_fp16 = transpose(perm = input_991_perm_0, x = x_489_cast_fp16)[name = string("transpose_194")]; + tensor x_491_cast_fp16 = layer_norm(axes = x_491_axes_0, beta = encoder_layers_18_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_18_conv_batch_norm_weight_to_fp16, x = input_991_cast_fp16)[name = string("x_491_cast_fp16")]; + tensor input_993_perm_0 = const()[name = string("input_993_perm_0"), val = tensor([0, 2, 1])]; + tensor input_993_cast_fp16 = transpose(perm = input_993_perm_0, x = x_491_cast_fp16)[name = string("transpose_193")]; + tensor input_995_cast_fp16 = silu(x = input_993_cast_fp16)[name = string("input_995_cast_fp16")]; + string x_493_pad_type_0 = const()[name = string("x_493_pad_type_0"), val = string("valid")]; + tensor x_493_strides_0 = const()[name = string("x_493_strides_0"), val = tensor([1])]; + tensor x_493_pad_0 = const()[name = string("x_493_pad_0"), val = tensor([0, 0])]; + tensor x_493_dilations_0 = const()[name = string("x_493_dilations_0"), val = tensor([1])]; + int32 x_493_groups_0 = const()[name = string("x_493_groups_0"), val = int32(1)]; + tensor encoder_layers_18_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372949888))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(373998528))))[name = string("encoder_layers_18_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_493_cast_fp16 = conv(dilations = x_493_dilations_0, groups = x_493_groups_0, pad = x_493_pad_0, pad_type = x_493_pad_type_0, strides = x_493_strides_0, weight = encoder_layers_18_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_995_cast_fp16)[name = string("x_493_cast_fp16")]; + tensor input_997_perm_0 = const()[name = string("input_997_perm_0"), val = tensor([0, 2, 1])]; + tensor input_997_cast_fp16 = transpose(perm = input_997_perm_0, x = x_493_cast_fp16)[name = string("transpose_192")]; + tensor input_999_cast_fp16 = add(x = input_983_cast_fp16, y = input_997_cast_fp16)[name = string("input_999_cast_fp16")]; + tensor input_1001_axes_0 = const()[name = string("input_1001_axes_0"), val = tensor([-1])]; + tensor encoder_layers_18_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_18_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(374000640)))]; + tensor encoder_layers_18_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_18_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(374002752)))]; + tensor input_1001_cast_fp16 = layer_norm(axes = input_1001_axes_0, beta = encoder_layers_18_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_18_norm_feed_forward2_weight_to_fp16, x = input_999_cast_fp16)[name = string("input_1001_cast_fp16")]; + tensor encoder_layers_18_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(374004864))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(378199232))))[name = string("encoder_layers_18_feed_forward2_linear1_weight_to_fp16_quantized")]; + tensor encoder_layers_18_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_18_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(378207488)))]; + tensor linear_170_cast_fp16 = linear(bias = encoder_layers_18_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_18_feed_forward2_linear1_weight_to_fp16_quantized, x = input_1001_cast_fp16)[name = string("linear_170_cast_fp16")]; + tensor input_1005_cast_fp16 = silu(x = linear_170_cast_fp16)[name = string("input_1005_cast_fp16")]; + tensor encoder_layers_18_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(378215744))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(382410112))))[name = string("encoder_layers_18_feed_forward2_linear2_weight_to_fp16_quantized")]; + tensor encoder_layers_18_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_18_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(382412224)))]; + tensor linear_171_cast_fp16 = linear(bias = encoder_layers_18_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_18_feed_forward2_linear2_weight_to_fp16_quantized, x = input_1005_cast_fp16)[name = string("linear_171_cast_fp16")]; + fp16 var_4465_to_fp16 = const()[name = string("op_4465_to_fp16"), val = fp16(0x1p-1)]; + tensor var_4466_cast_fp16 = mul(x = linear_171_cast_fp16, y = var_4465_to_fp16)[name = string("op_4466_cast_fp16")]; + tensor input_1011_cast_fp16 = add(x = input_999_cast_fp16, y = var_4466_cast_fp16)[name = string("input_1011_cast_fp16")]; + tensor input_1013_axes_0 = const()[name = string("input_1013_axes_0"), val = tensor([-1])]; + tensor encoder_layers_18_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_18_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(382414336)))]; + tensor encoder_layers_18_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_18_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(382416448)))]; + tensor input_1013_cast_fp16 = layer_norm(axes = input_1013_axes_0, beta = encoder_layers_18_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_18_norm_out_weight_to_fp16, x = input_1011_cast_fp16)[name = string("input_1013_cast_fp16")]; + tensor cache_77_begin_0 = const()[name = string("cache_77_begin_0"), val = tensor([19, 0, 0, 0])]; + tensor cache_77_end_0 = const()[name = string("cache_77_end_0"), val = tensor([20, 1, 42, 1024])]; + tensor cache_77_end_mask_0 = const()[name = string("cache_77_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_77_squeeze_mask_0 = const()[name = string("cache_77_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_77_cast_fp16 = slice_by_index(begin = cache_77_begin_0, end = cache_77_end_0, end_mask = cache_77_end_mask_0, squeeze_mask = cache_77_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_77_cast_fp16")]; + tensor cache_79_begin_0 = const()[name = string("cache_79_begin_0"), val = tensor([19, 0, 0, 0])]; + tensor cache_79_end_0 = const()[name = string("cache_79_end_0"), val = tensor([20, 1, 1024, 8])]; + tensor cache_79_end_mask_0 = const()[name = string("cache_79_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_79_squeeze_mask_0 = const()[name = string("cache_79_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_79_cast_fp16 = slice_by_index(begin = cache_79_begin_0, end = cache_79_end_0, end_mask = cache_79_end_mask_0, squeeze_mask = cache_79_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_79_cast_fp16")]; + tensor input_1015_axes_0 = const()[name = string("input_1015_axes_0"), val = tensor([-1])]; + tensor encoder_layers_19_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_19_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(382418560)))]; + tensor encoder_layers_19_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_19_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(382420672)))]; + tensor input_1015_cast_fp16 = layer_norm(axes = input_1015_axes_0, beta = encoder_layers_19_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_19_norm_feed_forward1_weight_to_fp16, x = input_1013_cast_fp16)[name = string("input_1015_cast_fp16")]; + tensor encoder_layers_19_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(382422784))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(386617152))))[name = string("encoder_layers_19_feed_forward1_linear1_weight_to_fp16_quantized")]; + tensor encoder_layers_19_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_19_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(386625408)))]; + tensor linear_172_cast_fp16 = linear(bias = encoder_layers_19_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_19_feed_forward1_linear1_weight_to_fp16_quantized, x = input_1015_cast_fp16)[name = string("linear_172_cast_fp16")]; + tensor input_1019_cast_fp16 = silu(x = linear_172_cast_fp16)[name = string("input_1019_cast_fp16")]; + tensor encoder_layers_19_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(386633664))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(390828032))))[name = string("encoder_layers_19_feed_forward1_linear2_weight_to_fp16_quantized")]; + tensor encoder_layers_19_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_19_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(390830144)))]; + tensor linear_173_cast_fp16 = linear(bias = encoder_layers_19_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_19_feed_forward1_linear2_weight_to_fp16_quantized, x = input_1019_cast_fp16)[name = string("linear_173_cast_fp16")]; + fp16 var_4502_to_fp16 = const()[name = string("op_4502_to_fp16"), val = fp16(0x1p-1)]; + tensor var_4503_cast_fp16 = mul(x = linear_173_cast_fp16, y = var_4502_to_fp16)[name = string("op_4503_cast_fp16")]; + tensor input_1025_cast_fp16 = add(x = input_1013_cast_fp16, y = var_4503_cast_fp16)[name = string("input_1025_cast_fp16")]; + tensor key_39_axes_0 = const()[name = string("key_39_axes_0"), val = tensor([-1])]; + tensor encoder_layers_19_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_19_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(390832256)))]; + tensor encoder_layers_19_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_19_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(390834368)))]; + tensor key_39_cast_fp16 = layer_norm(axes = key_39_axes_0, beta = encoder_layers_19_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_19_norm_self_att_weight_to_fp16, x = input_1025_cast_fp16)[name = string("key_39_cast_fp16")]; + bool input_1027_interleave_0 = const()[name = string("input_1027_interleave_0"), val = bool(false)]; + tensor input_1027_cast_fp16 = concat(axis = var_68, interleave = input_1027_interleave_0, values = (cache_77_cast_fp16, key_39_cast_fp16))[name = string("input_1027_cast_fp16")]; + tensor var_4525_begin_0 = const()[name = string("op_4525_begin_0"), val = tensor([0, 7, 0])]; + tensor var_4525_end_0 = const()[name = string("op_4525_end_0"), val = tensor([1, 42, 1024])]; + tensor var_4525_end_mask_0 = const()[name = string("op_4525_end_mask_0"), val = tensor([true, true, true])]; + tensor var_4525_cast_fp16 = slice_by_index(begin = var_4525_begin_0, end = var_4525_end_0, end_mask = var_4525_end_mask_0, x = cache_77_cast_fp16)[name = string("op_4525_cast_fp16")]; + bool var_4531_interleave_0 = const()[name = string("op_4531_interleave_0"), val = bool(false)]; + tensor var_4531_cast_fp16 = concat(axis = var_68, interleave = var_4531_interleave_0, values = (var_4525_cast_fp16, key_39_cast_fp16))[name = string("op_4531_cast_fp16")]; + tensor encoder_layers_19_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(390836480))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(391885120))))[name = string("encoder_layers_19_self_attn_linear_q_weight_to_fp16_quantized")]; + tensor encoder_layers_19_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_19_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(391887232)))]; + tensor linear_174_cast_fp16 = linear(bias = encoder_layers_19_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_19_self_attn_linear_q_weight_to_fp16_quantized, x = key_39_cast_fp16)[name = string("linear_174_cast_fp16")]; + tensor var_4536 = const()[name = string("op_4536"), val = tensor([1, -1, 8, 128])]; + tensor q_115_cast_fp16 = reshape(shape = var_4536, x = linear_174_cast_fp16)[name = string("q_115_cast_fp16")]; + tensor encoder_layers_19_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(391889344))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(392937984))))[name = string("encoder_layers_19_self_attn_linear_k_weight_to_fp16_quantized")]; + tensor encoder_layers_19_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_19_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(392940096)))]; + tensor linear_175_cast_fp16 = linear(bias = encoder_layers_19_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_19_self_attn_linear_k_weight_to_fp16_quantized, x = input_1027_cast_fp16)[name = string("linear_175_cast_fp16")]; + tensor var_4541 = const()[name = string("op_4541"), val = tensor([1, -1, 8, 128])]; + tensor k_77_cast_fp16 = reshape(shape = var_4541, x = linear_175_cast_fp16)[name = string("k_77_cast_fp16")]; + tensor encoder_layers_19_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(392942208))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(393990848))))[name = string("encoder_layers_19_self_attn_linear_v_weight_to_fp16_quantized")]; + tensor encoder_layers_19_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_19_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(393992960)))]; + tensor linear_176_cast_fp16 = linear(bias = encoder_layers_19_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_19_self_attn_linear_v_weight_to_fp16_quantized, x = input_1027_cast_fp16)[name = string("linear_176_cast_fp16")]; + tensor var_4546 = const()[name = string("op_4546"), val = tensor([1, -1, 8, 128])]; + tensor v_39_cast_fp16 = reshape(shape = var_4546, x = linear_176_cast_fp16)[name = string("v_39_cast_fp16")]; + tensor value_47_perm_0 = const()[name = string("value_47_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_19_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_19_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(393995072)))]; + tensor var_4559_cast_fp16 = add(x = q_115_cast_fp16, y = encoder_layers_19_self_attn_pos_bias_u_to_fp16)[name = string("op_4559_cast_fp16")]; + tensor encoder_layers_19_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_19_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(393997184)))]; + tensor var_4561_cast_fp16 = add(x = q_115_cast_fp16, y = encoder_layers_19_self_attn_pos_bias_v_to_fp16)[name = string("op_4561_cast_fp16")]; + tensor q_with_bias_v_39_perm_0 = const()[name = string("q_with_bias_v_39_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_501_transpose_x_0 = const()[name = string("x_501_transpose_x_0"), val = bool(false)]; + bool x_501_transpose_y_0 = const()[name = string("x_501_transpose_y_0"), val = bool(false)]; + tensor op_4563_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(393999296))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(394098688))))[name = string("op_4563_to_fp16_quantized")]; + tensor q_with_bias_v_39_cast_fp16 = transpose(perm = q_with_bias_v_39_perm_0, x = var_4561_cast_fp16)[name = string("transpose_191")]; + tensor x_501_cast_fp16 = matmul(transpose_x = x_501_transpose_x_0, transpose_y = x_501_transpose_y_0, x = q_with_bias_v_39_cast_fp16, y = op_4563_to_fp16_quantized)[name = string("x_501_cast_fp16")]; + tensor x_503_pad_0 = const()[name = string("x_503_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_503_mode_0 = const()[name = string("x_503_mode_0"), val = string("constant")]; + fp16 const_326_to_fp16 = const()[name = string("const_326_to_fp16"), val = fp16(0x0p+0)]; + tensor x_503_cast_fp16 = pad(constant_val = const_326_to_fp16, mode = x_503_mode_0, pad = x_503_pad_0, x = x_501_cast_fp16)[name = string("x_503_cast_fp16")]; + tensor var_4571 = const()[name = string("op_4571"), val = tensor([1, 8, -1, 7])]; + tensor x_505_cast_fp16 = reshape(shape = var_4571, x = x_503_cast_fp16)[name = string("x_505_cast_fp16")]; + tensor var_4575_begin_0 = const()[name = string("op_4575_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_4575_end_0 = const()[name = string("op_4575_end_0"), val = tensor([1, 8, 98, 7])]; + tensor var_4575_end_mask_0 = const()[name = string("op_4575_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_4575_cast_fp16 = slice_by_index(begin = var_4575_begin_0, end = var_4575_end_0, end_mask = var_4575_end_mask_0, x = x_505_cast_fp16)[name = string("op_4575_cast_fp16")]; + tensor var_4576 = const()[name = string("op_4576"), val = tensor([1, 8, 7, 97])]; + tensor matrix_bd_77_cast_fp16 = reshape(shape = var_4576, x = var_4575_cast_fp16)[name = string("matrix_bd_77_cast_fp16")]; + bool matrix_ac_39_transpose_x_0 = const()[name = string("matrix_ac_39_transpose_x_0"), val = bool(false)]; + bool matrix_ac_39_transpose_y_0 = const()[name = string("matrix_ac_39_transpose_y_0"), val = bool(false)]; + tensor transpose_134_perm_0 = const()[name = string("transpose_134_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_135_perm_0 = const()[name = string("transpose_135_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_135 = transpose(perm = transpose_135_perm_0, x = k_77_cast_fp16)[name = string("transpose_189")]; + tensor transpose_134 = transpose(perm = transpose_134_perm_0, x = var_4559_cast_fp16)[name = string("transpose_190")]; + tensor matrix_ac_39_cast_fp16 = matmul(transpose_x = matrix_ac_39_transpose_x_0, transpose_y = matrix_ac_39_transpose_y_0, x = transpose_134, y = transpose_135)[name = string("matrix_ac_39_cast_fp16")]; + tensor matrix_bd_79_begin_0 = const()[name = string("matrix_bd_79_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_79_end_0 = const()[name = string("matrix_bd_79_end_0"), val = tensor([1, 8, 7, 49])]; + tensor matrix_bd_79_end_mask_0 = const()[name = string("matrix_bd_79_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_79_cast_fp16 = slice_by_index(begin = matrix_bd_79_begin_0, end = matrix_bd_79_end_0, end_mask = matrix_bd_79_end_mask_0, x = matrix_bd_77_cast_fp16)[name = string("matrix_bd_79_cast_fp16")]; + tensor var_4585_cast_fp16 = add(x = matrix_ac_39_cast_fp16, y = matrix_bd_79_cast_fp16)[name = string("op_4585_cast_fp16")]; + fp16 _inversed_scores_77_y_0_to_fp16 = const()[name = string("_inversed_scores_77_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_77_cast_fp16 = mul(x = var_4585_cast_fp16, y = _inversed_scores_77_y_0_to_fp16)[name = string("_inversed_scores_77_cast_fp16")]; + tensor scores_79_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_77_cast_fp16, cond = mask_11)[name = string("scores_79_cast_fp16")]; + tensor var_4591_cast_fp16 = softmax(axis = var_59, x = scores_79_cast_fp16)[name = string("op_4591_cast_fp16")]; + tensor input_1029_cast_fp16 = select(a = var_44_to_fp16, b = var_4591_cast_fp16, cond = mask_11)[name = string("input_1029_cast_fp16")]; + bool x_507_transpose_x_0 = const()[name = string("x_507_transpose_x_0"), val = bool(false)]; + bool x_507_transpose_y_0 = const()[name = string("x_507_transpose_y_0"), val = bool(false)]; + tensor value_47_cast_fp16 = transpose(perm = value_47_perm_0, x = v_39_cast_fp16)[name = string("transpose_188")]; + tensor x_507_cast_fp16 = matmul(transpose_x = x_507_transpose_x_0, transpose_y = x_507_transpose_y_0, x = input_1029_cast_fp16, y = value_47_cast_fp16)[name = string("x_507_cast_fp16")]; + tensor var_4595_perm_0 = const()[name = string("op_4595_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_4596 = const()[name = string("op_4596"), val = tensor([1, -1, 1024])]; + tensor var_4595_cast_fp16 = transpose(perm = var_4595_perm_0, x = x_507_cast_fp16)[name = string("transpose_187")]; + tensor input_1031_cast_fp16 = reshape(shape = var_4596, x = var_4595_cast_fp16)[name = string("input_1031_cast_fp16")]; + tensor encoder_layers_19_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(394099008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(395147648))))[name = string("encoder_layers_19_self_attn_linear_out_weight_to_fp16_quantized")]; + tensor encoder_layers_19_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_19_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(395149760)))]; + tensor linear_178_cast_fp16 = linear(bias = encoder_layers_19_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_19_self_attn_linear_out_weight_to_fp16_quantized, x = input_1031_cast_fp16)[name = string("linear_178_cast_fp16")]; + tensor input_1035_cast_fp16 = add(x = input_1025_cast_fp16, y = linear_178_cast_fp16)[name = string("input_1035_cast_fp16")]; + tensor x_511_axes_0 = const()[name = string("x_511_axes_0"), val = tensor([-1])]; + tensor encoder_layers_19_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_19_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(395151872)))]; + tensor encoder_layers_19_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_19_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(395153984)))]; + tensor x_511_cast_fp16 = layer_norm(axes = x_511_axes_0, beta = encoder_layers_19_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_19_norm_conv_weight_to_fp16, x = input_1035_cast_fp16)[name = string("x_511_cast_fp16")]; + tensor input_1037_perm_0 = const()[name = string("input_1037_perm_0"), val = tensor([0, 2, 1])]; + string input_1039_pad_type_0 = const()[name = string("input_1039_pad_type_0"), val = string("valid")]; + tensor input_1039_strides_0 = const()[name = string("input_1039_strides_0"), val = tensor([1])]; + tensor input_1039_pad_0 = const()[name = string("input_1039_pad_0"), val = tensor([0, 0])]; + tensor input_1039_dilations_0 = const()[name = string("input_1039_dilations_0"), val = tensor([1])]; + int32 input_1039_groups_0 = const()[name = string("input_1039_groups_0"), val = int32(1)]; + tensor encoder_layers_19_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(395156096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397253312))))[name = string("encoder_layers_19_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_1037_cast_fp16 = transpose(perm = input_1037_perm_0, x = x_511_cast_fp16)[name = string("transpose_186")]; + tensor input_1039_cast_fp16 = conv(dilations = input_1039_dilations_0, groups = input_1039_groups_0, pad = input_1039_pad_0, pad_type = input_1039_pad_type_0, strides = input_1039_strides_0, weight = encoder_layers_19_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1037_cast_fp16)[name = string("input_1039_cast_fp16")]; + int32 x_513_split_num_splits_0 = const()[name = string("x_513_split_num_splits_0"), val = int32(2)]; + int32 x_513_split_axis_0 = const()[name = string("x_513_split_axis_0"), val = int32(1)]; + tensor x_513_split_cast_fp16_0, tensor x_513_split_cast_fp16_1 = split(axis = x_513_split_axis_0, num_splits = x_513_split_num_splits_0, x = input_1039_cast_fp16)[name = string("x_513_split_cast_fp16")]; + tensor x_513_split_1_sigmoid_cast_fp16 = sigmoid(x = x_513_split_cast_fp16_1)[name = string("x_513_split_1_sigmoid_cast_fp16")]; + tensor x_513_cast_fp16 = mul(x = x_513_split_cast_fp16_0, y = x_513_split_1_sigmoid_cast_fp16)[name = string("x_513_cast_fp16")]; + tensor input_1041_cast_fp16 = select(a = var_44_to_fp16, b = x_513_cast_fp16, cond = var_575)[name = string("input_1041_cast_fp16")]; + bool new_x_79_interleave_0 = const()[name = string("new_x_79_interleave_0"), val = bool(false)]; + tensor new_x_79_cast_fp16 = concat(axis = var_59, interleave = new_x_79_interleave_0, values = (cache_79_cast_fp16, input_1041_cast_fp16))[name = string("new_x_79_cast_fp16")]; + tensor var_4635_begin_0 = const()[name = string("op_4635_begin_0"), val = tensor([0, 0, 7])]; + tensor var_4635_end_0 = const()[name = string("op_4635_end_0"), val = tensor([1, 1024, 15])]; + tensor var_4635_end_mask_0 = const()[name = string("op_4635_end_mask_0"), val = tensor([true, true, true])]; + tensor var_4635_cast_fp16 = slice_by_index(begin = var_4635_begin_0, end = var_4635_end_0, end_mask = var_4635_end_mask_0, x = new_x_79_cast_fp16)[name = string("op_4635_cast_fp16")]; + string x_515_pad_type_0 = const()[name = string("x_515_pad_type_0"), val = string("valid")]; + int32 x_515_groups_0 = const()[name = string("x_515_groups_0"), val = int32(1024)]; + tensor x_515_strides_0 = const()[name = string("x_515_strides_0"), val = tensor([1])]; + tensor x_515_pad_0 = const()[name = string("x_515_pad_0"), val = tensor([0, 0])]; + tensor x_515_dilations_0 = const()[name = string("x_515_dilations_0"), val = tensor([1])]; + tensor encoder_layers_19_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397257472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397266752))))[name = string("encoder_layers_19_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_515_cast_fp16 = conv(dilations = x_515_dilations_0, groups = x_515_groups_0, pad = x_515_pad_0, pad_type = x_515_pad_type_0, strides = x_515_strides_0, weight = encoder_layers_19_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_79_cast_fp16)[name = string("x_515_cast_fp16")]; + tensor input_1043_perm_0 = const()[name = string("input_1043_perm_0"), val = tensor([0, 2, 1])]; + tensor x_517_axes_0 = const()[name = string("x_517_axes_0"), val = tensor([-1])]; + tensor encoder_layers_19_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_19_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397268864)))]; + tensor encoder_layers_19_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_19_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397270976)))]; + tensor input_1043_cast_fp16 = transpose(perm = input_1043_perm_0, x = x_515_cast_fp16)[name = string("transpose_185")]; + tensor x_517_cast_fp16 = layer_norm(axes = x_517_axes_0, beta = encoder_layers_19_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_19_conv_batch_norm_weight_to_fp16, x = input_1043_cast_fp16)[name = string("x_517_cast_fp16")]; + tensor input_1045_perm_0 = const()[name = string("input_1045_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1045_cast_fp16 = transpose(perm = input_1045_perm_0, x = x_517_cast_fp16)[name = string("transpose_184")]; + tensor input_1047_cast_fp16 = silu(x = input_1045_cast_fp16)[name = string("input_1047_cast_fp16")]; + string x_519_pad_type_0 = const()[name = string("x_519_pad_type_0"), val = string("valid")]; + tensor x_519_strides_0 = const()[name = string("x_519_strides_0"), val = tensor([1])]; + tensor x_519_pad_0 = const()[name = string("x_519_pad_0"), val = tensor([0, 0])]; + tensor x_519_dilations_0 = const()[name = string("x_519_dilations_0"), val = tensor([1])]; + int32 x_519_groups_0 = const()[name = string("x_519_groups_0"), val = int32(1)]; + tensor encoder_layers_19_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397273088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(398321728))))[name = string("encoder_layers_19_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_519_cast_fp16 = conv(dilations = x_519_dilations_0, groups = x_519_groups_0, pad = x_519_pad_0, pad_type = x_519_pad_type_0, strides = x_519_strides_0, weight = encoder_layers_19_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1047_cast_fp16)[name = string("x_519_cast_fp16")]; + tensor input_1049_perm_0 = const()[name = string("input_1049_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1049_cast_fp16 = transpose(perm = input_1049_perm_0, x = x_519_cast_fp16)[name = string("transpose_183")]; + tensor input_1051_cast_fp16 = add(x = input_1035_cast_fp16, y = input_1049_cast_fp16)[name = string("input_1051_cast_fp16")]; + tensor input_1053_axes_0 = const()[name = string("input_1053_axes_0"), val = tensor([-1])]; + tensor encoder_layers_19_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_19_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(398323840)))]; + tensor encoder_layers_19_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_19_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(398325952)))]; + tensor input_1053_cast_fp16 = layer_norm(axes = input_1053_axes_0, beta = encoder_layers_19_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_19_norm_feed_forward2_weight_to_fp16, x = input_1051_cast_fp16)[name = string("input_1053_cast_fp16")]; + tensor encoder_layers_19_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(398328064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402522432))))[name = string("encoder_layers_19_feed_forward2_linear1_weight_to_fp16_quantized")]; + tensor encoder_layers_19_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_19_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402530688)))]; + tensor linear_179_cast_fp16 = linear(bias = encoder_layers_19_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_19_feed_forward2_linear1_weight_to_fp16_quantized, x = input_1053_cast_fp16)[name = string("linear_179_cast_fp16")]; + tensor input_1057_cast_fp16 = silu(x = linear_179_cast_fp16)[name = string("input_1057_cast_fp16")]; + tensor encoder_layers_19_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402538944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(406733312))))[name = string("encoder_layers_19_feed_forward2_linear2_weight_to_fp16_quantized")]; + tensor encoder_layers_19_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_19_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(406735424)))]; + tensor linear_180_cast_fp16 = linear(bias = encoder_layers_19_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_19_feed_forward2_linear2_weight_to_fp16_quantized, x = input_1057_cast_fp16)[name = string("linear_180_cast_fp16")]; + fp16 var_4678_to_fp16 = const()[name = string("op_4678_to_fp16"), val = fp16(0x1p-1)]; + tensor var_4679_cast_fp16 = mul(x = linear_180_cast_fp16, y = var_4678_to_fp16)[name = string("op_4679_cast_fp16")]; + tensor input_1063_cast_fp16 = add(x = input_1051_cast_fp16, y = var_4679_cast_fp16)[name = string("input_1063_cast_fp16")]; + tensor input_1065_axes_0 = const()[name = string("input_1065_axes_0"), val = tensor([-1])]; + tensor encoder_layers_19_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_19_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(406737536)))]; + tensor encoder_layers_19_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_19_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(406739648)))]; + tensor input_1065_cast_fp16 = layer_norm(axes = input_1065_axes_0, beta = encoder_layers_19_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_19_norm_out_weight_to_fp16, x = input_1063_cast_fp16)[name = string("input_1065_cast_fp16")]; + tensor cache_81_begin_0 = const()[name = string("cache_81_begin_0"), val = tensor([20, 0, 0, 0])]; + tensor cache_81_end_0 = const()[name = string("cache_81_end_0"), val = tensor([21, 1, 42, 1024])]; + tensor cache_81_end_mask_0 = const()[name = string("cache_81_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_81_squeeze_mask_0 = const()[name = string("cache_81_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_81_cast_fp16 = slice_by_index(begin = cache_81_begin_0, end = cache_81_end_0, end_mask = cache_81_end_mask_0, squeeze_mask = cache_81_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_81_cast_fp16")]; + tensor cache_83_begin_0 = const()[name = string("cache_83_begin_0"), val = tensor([20, 0, 0, 0])]; + tensor cache_83_end_0 = const()[name = string("cache_83_end_0"), val = tensor([21, 1, 1024, 8])]; + tensor cache_83_end_mask_0 = const()[name = string("cache_83_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_83_squeeze_mask_0 = const()[name = string("cache_83_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_83_cast_fp16 = slice_by_index(begin = cache_83_begin_0, end = cache_83_end_0, end_mask = cache_83_end_mask_0, squeeze_mask = cache_83_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_83_cast_fp16")]; + tensor input_1067_axes_0 = const()[name = string("input_1067_axes_0"), val = tensor([-1])]; + tensor encoder_layers_20_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_20_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(406741760)))]; + tensor encoder_layers_20_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_20_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(406743872)))]; + tensor input_1067_cast_fp16 = layer_norm(axes = input_1067_axes_0, beta = encoder_layers_20_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_20_norm_feed_forward1_weight_to_fp16, x = input_1065_cast_fp16)[name = string("input_1067_cast_fp16")]; + tensor encoder_layers_20_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(406745984))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(410940352))))[name = string("encoder_layers_20_feed_forward1_linear1_weight_to_fp16_quantized")]; + tensor encoder_layers_20_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_20_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(410948608)))]; + tensor linear_181_cast_fp16 = linear(bias = encoder_layers_20_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_20_feed_forward1_linear1_weight_to_fp16_quantized, x = input_1067_cast_fp16)[name = string("linear_181_cast_fp16")]; + tensor input_1071_cast_fp16 = silu(x = linear_181_cast_fp16)[name = string("input_1071_cast_fp16")]; + tensor encoder_layers_20_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(410956864))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(415151232))))[name = string("encoder_layers_20_feed_forward1_linear2_weight_to_fp16_quantized")]; + tensor encoder_layers_20_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_20_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(415153344)))]; + tensor linear_182_cast_fp16 = linear(bias = encoder_layers_20_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_20_feed_forward1_linear2_weight_to_fp16_quantized, x = input_1071_cast_fp16)[name = string("linear_182_cast_fp16")]; + fp16 var_4715_to_fp16 = const()[name = string("op_4715_to_fp16"), val = fp16(0x1p-1)]; + tensor var_4716_cast_fp16 = mul(x = linear_182_cast_fp16, y = var_4715_to_fp16)[name = string("op_4716_cast_fp16")]; + tensor input_1077_cast_fp16 = add(x = input_1065_cast_fp16, y = var_4716_cast_fp16)[name = string("input_1077_cast_fp16")]; + tensor key_41_axes_0 = const()[name = string("key_41_axes_0"), val = tensor([-1])]; + tensor encoder_layers_20_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_20_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(415155456)))]; + tensor encoder_layers_20_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_20_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(415157568)))]; + tensor key_41_cast_fp16 = layer_norm(axes = key_41_axes_0, beta = encoder_layers_20_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_20_norm_self_att_weight_to_fp16, x = input_1077_cast_fp16)[name = string("key_41_cast_fp16")]; + bool input_1079_interleave_0 = const()[name = string("input_1079_interleave_0"), val = bool(false)]; + tensor input_1079_cast_fp16 = concat(axis = var_68, interleave = input_1079_interleave_0, values = (cache_81_cast_fp16, key_41_cast_fp16))[name = string("input_1079_cast_fp16")]; + tensor var_4738_begin_0 = const()[name = string("op_4738_begin_0"), val = tensor([0, 7, 0])]; + tensor var_4738_end_0 = const()[name = string("op_4738_end_0"), val = tensor([1, 42, 1024])]; + tensor var_4738_end_mask_0 = const()[name = string("op_4738_end_mask_0"), val = tensor([true, true, true])]; + tensor var_4738_cast_fp16 = slice_by_index(begin = var_4738_begin_0, end = var_4738_end_0, end_mask = var_4738_end_mask_0, x = cache_81_cast_fp16)[name = string("op_4738_cast_fp16")]; + bool var_4744_interleave_0 = const()[name = string("op_4744_interleave_0"), val = bool(false)]; + tensor var_4744_cast_fp16 = concat(axis = var_68, interleave = var_4744_interleave_0, values = (var_4738_cast_fp16, key_41_cast_fp16))[name = string("op_4744_cast_fp16")]; + tensor encoder_layers_20_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(415159680))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(416208320))))[name = string("encoder_layers_20_self_attn_linear_q_weight_to_fp16_quantized")]; + tensor encoder_layers_20_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_20_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(416210432)))]; + tensor linear_183_cast_fp16 = linear(bias = encoder_layers_20_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_20_self_attn_linear_q_weight_to_fp16_quantized, x = key_41_cast_fp16)[name = string("linear_183_cast_fp16")]; + tensor var_4749 = const()[name = string("op_4749"), val = tensor([1, -1, 8, 128])]; + tensor q_121_cast_fp16 = reshape(shape = var_4749, x = linear_183_cast_fp16)[name = string("q_121_cast_fp16")]; + tensor encoder_layers_20_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(416212544))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(417261184))))[name = string("encoder_layers_20_self_attn_linear_k_weight_to_fp16_quantized")]; + tensor encoder_layers_20_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_20_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(417263296)))]; + tensor linear_184_cast_fp16 = linear(bias = encoder_layers_20_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_20_self_attn_linear_k_weight_to_fp16_quantized, x = input_1079_cast_fp16)[name = string("linear_184_cast_fp16")]; + tensor var_4754 = const()[name = string("op_4754"), val = tensor([1, -1, 8, 128])]; + tensor k_81_cast_fp16 = reshape(shape = var_4754, x = linear_184_cast_fp16)[name = string("k_81_cast_fp16")]; + tensor encoder_layers_20_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(417265408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(418314048))))[name = string("encoder_layers_20_self_attn_linear_v_weight_to_fp16_quantized")]; + tensor encoder_layers_20_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_20_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(418316160)))]; + tensor linear_185_cast_fp16 = linear(bias = encoder_layers_20_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_20_self_attn_linear_v_weight_to_fp16_quantized, x = input_1079_cast_fp16)[name = string("linear_185_cast_fp16")]; + tensor var_4759 = const()[name = string("op_4759"), val = tensor([1, -1, 8, 128])]; + tensor v_41_cast_fp16 = reshape(shape = var_4759, x = linear_185_cast_fp16)[name = string("v_41_cast_fp16")]; + tensor value_49_perm_0 = const()[name = string("value_49_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_20_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_20_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(418318272)))]; + tensor var_4772_cast_fp16 = add(x = q_121_cast_fp16, y = encoder_layers_20_self_attn_pos_bias_u_to_fp16)[name = string("op_4772_cast_fp16")]; + tensor encoder_layers_20_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_20_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(418320384)))]; + tensor var_4774_cast_fp16 = add(x = q_121_cast_fp16, y = encoder_layers_20_self_attn_pos_bias_v_to_fp16)[name = string("op_4774_cast_fp16")]; + tensor q_with_bias_v_41_perm_0 = const()[name = string("q_with_bias_v_41_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_527_transpose_x_0 = const()[name = string("x_527_transpose_x_0"), val = bool(false)]; + bool x_527_transpose_y_0 = const()[name = string("x_527_transpose_y_0"), val = bool(false)]; + tensor op_4776_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(418322496))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(418421888))))[name = string("op_4776_to_fp16_quantized")]; + tensor q_with_bias_v_41_cast_fp16 = transpose(perm = q_with_bias_v_41_perm_0, x = var_4774_cast_fp16)[name = string("transpose_182")]; + tensor x_527_cast_fp16 = matmul(transpose_x = x_527_transpose_x_0, transpose_y = x_527_transpose_y_0, x = q_with_bias_v_41_cast_fp16, y = op_4776_to_fp16_quantized)[name = string("x_527_cast_fp16")]; + tensor x_529_pad_0 = const()[name = string("x_529_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_529_mode_0 = const()[name = string("x_529_mode_0"), val = string("constant")]; + fp16 const_339_to_fp16 = const()[name = string("const_339_to_fp16"), val = fp16(0x0p+0)]; + tensor x_529_cast_fp16 = pad(constant_val = const_339_to_fp16, mode = x_529_mode_0, pad = x_529_pad_0, x = x_527_cast_fp16)[name = string("x_529_cast_fp16")]; + tensor var_4784 = const()[name = string("op_4784"), val = tensor([1, 8, -1, 7])]; + tensor x_531_cast_fp16 = reshape(shape = var_4784, x = x_529_cast_fp16)[name = string("x_531_cast_fp16")]; + tensor var_4788_begin_0 = const()[name = string("op_4788_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_4788_end_0 = const()[name = string("op_4788_end_0"), val = tensor([1, 8, 98, 7])]; + tensor var_4788_end_mask_0 = const()[name = string("op_4788_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_4788_cast_fp16 = slice_by_index(begin = var_4788_begin_0, end = var_4788_end_0, end_mask = var_4788_end_mask_0, x = x_531_cast_fp16)[name = string("op_4788_cast_fp16")]; + tensor var_4789 = const()[name = string("op_4789"), val = tensor([1, 8, 7, 97])]; + tensor matrix_bd_81_cast_fp16 = reshape(shape = var_4789, x = var_4788_cast_fp16)[name = string("matrix_bd_81_cast_fp16")]; + bool matrix_ac_41_transpose_x_0 = const()[name = string("matrix_ac_41_transpose_x_0"), val = bool(false)]; + bool matrix_ac_41_transpose_y_0 = const()[name = string("matrix_ac_41_transpose_y_0"), val = bool(false)]; + tensor transpose_136_perm_0 = const()[name = string("transpose_136_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_137_perm_0 = const()[name = string("transpose_137_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_137 = transpose(perm = transpose_137_perm_0, x = k_81_cast_fp16)[name = string("transpose_180")]; + tensor transpose_136 = transpose(perm = transpose_136_perm_0, x = var_4772_cast_fp16)[name = string("transpose_181")]; + tensor matrix_ac_41_cast_fp16 = matmul(transpose_x = matrix_ac_41_transpose_x_0, transpose_y = matrix_ac_41_transpose_y_0, x = transpose_136, y = transpose_137)[name = string("matrix_ac_41_cast_fp16")]; + tensor matrix_bd_83_begin_0 = const()[name = string("matrix_bd_83_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_83_end_0 = const()[name = string("matrix_bd_83_end_0"), val = tensor([1, 8, 7, 49])]; + tensor matrix_bd_83_end_mask_0 = const()[name = string("matrix_bd_83_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_83_cast_fp16 = slice_by_index(begin = matrix_bd_83_begin_0, end = matrix_bd_83_end_0, end_mask = matrix_bd_83_end_mask_0, x = matrix_bd_81_cast_fp16)[name = string("matrix_bd_83_cast_fp16")]; + tensor var_4798_cast_fp16 = add(x = matrix_ac_41_cast_fp16, y = matrix_bd_83_cast_fp16)[name = string("op_4798_cast_fp16")]; + fp16 _inversed_scores_81_y_0_to_fp16 = const()[name = string("_inversed_scores_81_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_81_cast_fp16 = mul(x = var_4798_cast_fp16, y = _inversed_scores_81_y_0_to_fp16)[name = string("_inversed_scores_81_cast_fp16")]; + tensor scores_83_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_81_cast_fp16, cond = mask_11)[name = string("scores_83_cast_fp16")]; + tensor var_4804_cast_fp16 = softmax(axis = var_59, x = scores_83_cast_fp16)[name = string("op_4804_cast_fp16")]; + tensor input_1081_cast_fp16 = select(a = var_44_to_fp16, b = var_4804_cast_fp16, cond = mask_11)[name = string("input_1081_cast_fp16")]; + bool x_533_transpose_x_0 = const()[name = string("x_533_transpose_x_0"), val = bool(false)]; + bool x_533_transpose_y_0 = const()[name = string("x_533_transpose_y_0"), val = bool(false)]; + tensor value_49_cast_fp16 = transpose(perm = value_49_perm_0, x = v_41_cast_fp16)[name = string("transpose_179")]; + tensor x_533_cast_fp16 = matmul(transpose_x = x_533_transpose_x_0, transpose_y = x_533_transpose_y_0, x = input_1081_cast_fp16, y = value_49_cast_fp16)[name = string("x_533_cast_fp16")]; + tensor var_4808_perm_0 = const()[name = string("op_4808_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_4809 = const()[name = string("op_4809"), val = tensor([1, -1, 1024])]; + tensor var_4808_cast_fp16 = transpose(perm = var_4808_perm_0, x = x_533_cast_fp16)[name = string("transpose_178")]; + tensor input_1083_cast_fp16 = reshape(shape = var_4809, x = var_4808_cast_fp16)[name = string("input_1083_cast_fp16")]; + tensor encoder_layers_20_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(418422208))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(419470848))))[name = string("encoder_layers_20_self_attn_linear_out_weight_to_fp16_quantized")]; + tensor encoder_layers_20_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_20_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(419472960)))]; + tensor linear_187_cast_fp16 = linear(bias = encoder_layers_20_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_20_self_attn_linear_out_weight_to_fp16_quantized, x = input_1083_cast_fp16)[name = string("linear_187_cast_fp16")]; + tensor input_1087_cast_fp16 = add(x = input_1077_cast_fp16, y = linear_187_cast_fp16)[name = string("input_1087_cast_fp16")]; + tensor x_537_axes_0 = const()[name = string("x_537_axes_0"), val = tensor([-1])]; + tensor encoder_layers_20_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_20_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(419475072)))]; + tensor encoder_layers_20_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_20_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(419477184)))]; + tensor x_537_cast_fp16 = layer_norm(axes = x_537_axes_0, beta = encoder_layers_20_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_20_norm_conv_weight_to_fp16, x = input_1087_cast_fp16)[name = string("x_537_cast_fp16")]; + tensor input_1089_perm_0 = const()[name = string("input_1089_perm_0"), val = tensor([0, 2, 1])]; + string input_1091_pad_type_0 = const()[name = string("input_1091_pad_type_0"), val = string("valid")]; + tensor input_1091_strides_0 = const()[name = string("input_1091_strides_0"), val = tensor([1])]; + tensor input_1091_pad_0 = const()[name = string("input_1091_pad_0"), val = tensor([0, 0])]; + tensor input_1091_dilations_0 = const()[name = string("input_1091_dilations_0"), val = tensor([1])]; + int32 input_1091_groups_0 = const()[name = string("input_1091_groups_0"), val = int32(1)]; + tensor encoder_layers_20_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(419479296))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(421576512))))[name = string("encoder_layers_20_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_1089_cast_fp16 = transpose(perm = input_1089_perm_0, x = x_537_cast_fp16)[name = string("transpose_177")]; + tensor input_1091_cast_fp16 = conv(dilations = input_1091_dilations_0, groups = input_1091_groups_0, pad = input_1091_pad_0, pad_type = input_1091_pad_type_0, strides = input_1091_strides_0, weight = encoder_layers_20_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1089_cast_fp16)[name = string("input_1091_cast_fp16")]; + int32 x_539_split_num_splits_0 = const()[name = string("x_539_split_num_splits_0"), val = int32(2)]; + int32 x_539_split_axis_0 = const()[name = string("x_539_split_axis_0"), val = int32(1)]; + tensor x_539_split_cast_fp16_0, tensor x_539_split_cast_fp16_1 = split(axis = x_539_split_axis_0, num_splits = x_539_split_num_splits_0, x = input_1091_cast_fp16)[name = string("x_539_split_cast_fp16")]; + tensor x_539_split_1_sigmoid_cast_fp16 = sigmoid(x = x_539_split_cast_fp16_1)[name = string("x_539_split_1_sigmoid_cast_fp16")]; + tensor x_539_cast_fp16 = mul(x = x_539_split_cast_fp16_0, y = x_539_split_1_sigmoid_cast_fp16)[name = string("x_539_cast_fp16")]; + tensor input_1093_cast_fp16 = select(a = var_44_to_fp16, b = x_539_cast_fp16, cond = var_575)[name = string("input_1093_cast_fp16")]; + bool new_x_83_interleave_0 = const()[name = string("new_x_83_interleave_0"), val = bool(false)]; + tensor new_x_83_cast_fp16 = concat(axis = var_59, interleave = new_x_83_interleave_0, values = (cache_83_cast_fp16, input_1093_cast_fp16))[name = string("new_x_83_cast_fp16")]; + tensor var_4848_begin_0 = const()[name = string("op_4848_begin_0"), val = tensor([0, 0, 7])]; + tensor var_4848_end_0 = const()[name = string("op_4848_end_0"), val = tensor([1, 1024, 15])]; + tensor var_4848_end_mask_0 = const()[name = string("op_4848_end_mask_0"), val = tensor([true, true, true])]; + tensor var_4848_cast_fp16 = slice_by_index(begin = var_4848_begin_0, end = var_4848_end_0, end_mask = var_4848_end_mask_0, x = new_x_83_cast_fp16)[name = string("op_4848_cast_fp16")]; + string x_541_pad_type_0 = const()[name = string("x_541_pad_type_0"), val = string("valid")]; + int32 x_541_groups_0 = const()[name = string("x_541_groups_0"), val = int32(1024)]; + tensor x_541_strides_0 = const()[name = string("x_541_strides_0"), val = tensor([1])]; + tensor x_541_pad_0 = const()[name = string("x_541_pad_0"), val = tensor([0, 0])]; + tensor x_541_dilations_0 = const()[name = string("x_541_dilations_0"), val = tensor([1])]; + tensor encoder_layers_20_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(421580672))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(421589952))))[name = string("encoder_layers_20_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_541_cast_fp16 = conv(dilations = x_541_dilations_0, groups = x_541_groups_0, pad = x_541_pad_0, pad_type = x_541_pad_type_0, strides = x_541_strides_0, weight = encoder_layers_20_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_83_cast_fp16)[name = string("x_541_cast_fp16")]; + tensor input_1095_perm_0 = const()[name = string("input_1095_perm_0"), val = tensor([0, 2, 1])]; + tensor x_543_axes_0 = const()[name = string("x_543_axes_0"), val = tensor([-1])]; + tensor encoder_layers_20_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_20_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(421592064)))]; + tensor encoder_layers_20_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_20_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(421594176)))]; + tensor input_1095_cast_fp16 = transpose(perm = input_1095_perm_0, x = x_541_cast_fp16)[name = string("transpose_176")]; + tensor x_543_cast_fp16 = layer_norm(axes = x_543_axes_0, beta = encoder_layers_20_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_20_conv_batch_norm_weight_to_fp16, x = input_1095_cast_fp16)[name = string("x_543_cast_fp16")]; + tensor input_1097_perm_0 = const()[name = string("input_1097_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1097_cast_fp16 = transpose(perm = input_1097_perm_0, x = x_543_cast_fp16)[name = string("transpose_175")]; + tensor input_1099_cast_fp16 = silu(x = input_1097_cast_fp16)[name = string("input_1099_cast_fp16")]; + string x_545_pad_type_0 = const()[name = string("x_545_pad_type_0"), val = string("valid")]; + tensor x_545_strides_0 = const()[name = string("x_545_strides_0"), val = tensor([1])]; + tensor x_545_pad_0 = const()[name = string("x_545_pad_0"), val = tensor([0, 0])]; + tensor x_545_dilations_0 = const()[name = string("x_545_dilations_0"), val = tensor([1])]; + int32 x_545_groups_0 = const()[name = string("x_545_groups_0"), val = int32(1)]; + tensor encoder_layers_20_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(421596288))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(422644928))))[name = string("encoder_layers_20_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_545_cast_fp16 = conv(dilations = x_545_dilations_0, groups = x_545_groups_0, pad = x_545_pad_0, pad_type = x_545_pad_type_0, strides = x_545_strides_0, weight = encoder_layers_20_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1099_cast_fp16)[name = string("x_545_cast_fp16")]; + tensor input_1101_perm_0 = const()[name = string("input_1101_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1101_cast_fp16 = transpose(perm = input_1101_perm_0, x = x_545_cast_fp16)[name = string("transpose_174")]; + tensor input_1103_cast_fp16 = add(x = input_1087_cast_fp16, y = input_1101_cast_fp16)[name = string("input_1103_cast_fp16")]; + tensor input_1105_axes_0 = const()[name = string("input_1105_axes_0"), val = tensor([-1])]; + tensor encoder_layers_20_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_20_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(422647040)))]; + tensor encoder_layers_20_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_20_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(422649152)))]; + tensor input_1105_cast_fp16 = layer_norm(axes = input_1105_axes_0, beta = encoder_layers_20_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_20_norm_feed_forward2_weight_to_fp16, x = input_1103_cast_fp16)[name = string("input_1105_cast_fp16")]; + tensor encoder_layers_20_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(422651264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(426845632))))[name = string("encoder_layers_20_feed_forward2_linear1_weight_to_fp16_quantized")]; + tensor encoder_layers_20_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_20_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(426853888)))]; + tensor linear_188_cast_fp16 = linear(bias = encoder_layers_20_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_20_feed_forward2_linear1_weight_to_fp16_quantized, x = input_1105_cast_fp16)[name = string("linear_188_cast_fp16")]; + tensor input_1109_cast_fp16 = silu(x = linear_188_cast_fp16)[name = string("input_1109_cast_fp16")]; + tensor encoder_layers_20_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(426862144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(431056512))))[name = string("encoder_layers_20_feed_forward2_linear2_weight_to_fp16_quantized")]; + tensor encoder_layers_20_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_20_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(431058624)))]; + tensor linear_189_cast_fp16 = linear(bias = encoder_layers_20_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_20_feed_forward2_linear2_weight_to_fp16_quantized, x = input_1109_cast_fp16)[name = string("linear_189_cast_fp16")]; + fp16 var_4891_to_fp16 = const()[name = string("op_4891_to_fp16"), val = fp16(0x1p-1)]; + tensor var_4892_cast_fp16 = mul(x = linear_189_cast_fp16, y = var_4891_to_fp16)[name = string("op_4892_cast_fp16")]; + tensor input_1115_cast_fp16 = add(x = input_1103_cast_fp16, y = var_4892_cast_fp16)[name = string("input_1115_cast_fp16")]; + tensor input_1117_axes_0 = const()[name = string("input_1117_axes_0"), val = tensor([-1])]; + tensor encoder_layers_20_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_20_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(431060736)))]; + tensor encoder_layers_20_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_20_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(431062848)))]; + tensor input_1117_cast_fp16 = layer_norm(axes = input_1117_axes_0, beta = encoder_layers_20_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_20_norm_out_weight_to_fp16, x = input_1115_cast_fp16)[name = string("input_1117_cast_fp16")]; + tensor cache_85_begin_0 = const()[name = string("cache_85_begin_0"), val = tensor([21, 0, 0, 0])]; + tensor cache_85_end_0 = const()[name = string("cache_85_end_0"), val = tensor([22, 1, 42, 1024])]; + tensor cache_85_end_mask_0 = const()[name = string("cache_85_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_85_squeeze_mask_0 = const()[name = string("cache_85_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_85_cast_fp16 = slice_by_index(begin = cache_85_begin_0, end = cache_85_end_0, end_mask = cache_85_end_mask_0, squeeze_mask = cache_85_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_85_cast_fp16")]; + tensor cache_87_begin_0 = const()[name = string("cache_87_begin_0"), val = tensor([21, 0, 0, 0])]; + tensor cache_87_end_0 = const()[name = string("cache_87_end_0"), val = tensor([22, 1, 1024, 8])]; + tensor cache_87_end_mask_0 = const()[name = string("cache_87_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_87_squeeze_mask_0 = const()[name = string("cache_87_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_87_cast_fp16 = slice_by_index(begin = cache_87_begin_0, end = cache_87_end_0, end_mask = cache_87_end_mask_0, squeeze_mask = cache_87_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_87_cast_fp16")]; + tensor input_1119_axes_0 = const()[name = string("input_1119_axes_0"), val = tensor([-1])]; + tensor encoder_layers_21_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_21_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(431064960)))]; + tensor encoder_layers_21_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_21_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(431067072)))]; + tensor input_1119_cast_fp16 = layer_norm(axes = input_1119_axes_0, beta = encoder_layers_21_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_21_norm_feed_forward1_weight_to_fp16, x = input_1117_cast_fp16)[name = string("input_1119_cast_fp16")]; + tensor encoder_layers_21_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(431069184))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(435263552))))[name = string("encoder_layers_21_feed_forward1_linear1_weight_to_fp16_quantized")]; + tensor encoder_layers_21_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_21_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(435271808)))]; + tensor linear_190_cast_fp16 = linear(bias = encoder_layers_21_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_21_feed_forward1_linear1_weight_to_fp16_quantized, x = input_1119_cast_fp16)[name = string("linear_190_cast_fp16")]; + tensor input_1123_cast_fp16 = silu(x = linear_190_cast_fp16)[name = string("input_1123_cast_fp16")]; + tensor encoder_layers_21_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(435280064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(439474432))))[name = string("encoder_layers_21_feed_forward1_linear2_weight_to_fp16_quantized")]; + tensor encoder_layers_21_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_21_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(439476544)))]; + tensor linear_191_cast_fp16 = linear(bias = encoder_layers_21_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_21_feed_forward1_linear2_weight_to_fp16_quantized, x = input_1123_cast_fp16)[name = string("linear_191_cast_fp16")]; + fp16 var_4928_to_fp16 = const()[name = string("op_4928_to_fp16"), val = fp16(0x1p-1)]; + tensor var_4929_cast_fp16 = mul(x = linear_191_cast_fp16, y = var_4928_to_fp16)[name = string("op_4929_cast_fp16")]; + tensor input_1129_cast_fp16 = add(x = input_1117_cast_fp16, y = var_4929_cast_fp16)[name = string("input_1129_cast_fp16")]; + tensor key_43_axes_0 = const()[name = string("key_43_axes_0"), val = tensor([-1])]; + tensor encoder_layers_21_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_21_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(439478656)))]; + tensor encoder_layers_21_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_21_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(439480768)))]; + tensor key_43_cast_fp16 = layer_norm(axes = key_43_axes_0, beta = encoder_layers_21_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_21_norm_self_att_weight_to_fp16, x = input_1129_cast_fp16)[name = string("key_43_cast_fp16")]; + bool input_1131_interleave_0 = const()[name = string("input_1131_interleave_0"), val = bool(false)]; + tensor input_1131_cast_fp16 = concat(axis = var_68, interleave = input_1131_interleave_0, values = (cache_85_cast_fp16, key_43_cast_fp16))[name = string("input_1131_cast_fp16")]; + tensor var_4951_begin_0 = const()[name = string("op_4951_begin_0"), val = tensor([0, 7, 0])]; + tensor var_4951_end_0 = const()[name = string("op_4951_end_0"), val = tensor([1, 42, 1024])]; + tensor var_4951_end_mask_0 = const()[name = string("op_4951_end_mask_0"), val = tensor([true, true, true])]; + tensor var_4951_cast_fp16 = slice_by_index(begin = var_4951_begin_0, end = var_4951_end_0, end_mask = var_4951_end_mask_0, x = cache_85_cast_fp16)[name = string("op_4951_cast_fp16")]; + bool var_4957_interleave_0 = const()[name = string("op_4957_interleave_0"), val = bool(false)]; + tensor var_4957_cast_fp16 = concat(axis = var_68, interleave = var_4957_interleave_0, values = (var_4951_cast_fp16, key_43_cast_fp16))[name = string("op_4957_cast_fp16")]; + tensor encoder_layers_21_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(439482880))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(440531520))))[name = string("encoder_layers_21_self_attn_linear_q_weight_to_fp16_quantized")]; + tensor encoder_layers_21_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_21_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(440533632)))]; + tensor linear_192_cast_fp16 = linear(bias = encoder_layers_21_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_21_self_attn_linear_q_weight_to_fp16_quantized, x = key_43_cast_fp16)[name = string("linear_192_cast_fp16")]; + tensor var_4962 = const()[name = string("op_4962"), val = tensor([1, -1, 8, 128])]; + tensor q_127_cast_fp16 = reshape(shape = var_4962, x = linear_192_cast_fp16)[name = string("q_127_cast_fp16")]; + tensor encoder_layers_21_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(440535744))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(441584384))))[name = string("encoder_layers_21_self_attn_linear_k_weight_to_fp16_quantized")]; + tensor encoder_layers_21_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_21_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(441586496)))]; + tensor linear_193_cast_fp16 = linear(bias = encoder_layers_21_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_21_self_attn_linear_k_weight_to_fp16_quantized, x = input_1131_cast_fp16)[name = string("linear_193_cast_fp16")]; + tensor var_4967 = const()[name = string("op_4967"), val = tensor([1, -1, 8, 128])]; + tensor k_85_cast_fp16 = reshape(shape = var_4967, x = linear_193_cast_fp16)[name = string("k_85_cast_fp16")]; + tensor encoder_layers_21_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(441588608))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(442637248))))[name = string("encoder_layers_21_self_attn_linear_v_weight_to_fp16_quantized")]; + tensor encoder_layers_21_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_21_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(442639360)))]; + tensor linear_194_cast_fp16 = linear(bias = encoder_layers_21_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_21_self_attn_linear_v_weight_to_fp16_quantized, x = input_1131_cast_fp16)[name = string("linear_194_cast_fp16")]; + tensor var_4972 = const()[name = string("op_4972"), val = tensor([1, -1, 8, 128])]; + tensor v_43_cast_fp16 = reshape(shape = var_4972, x = linear_194_cast_fp16)[name = string("v_43_cast_fp16")]; + tensor value_51_perm_0 = const()[name = string("value_51_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_21_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_21_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(442641472)))]; + tensor var_4985_cast_fp16 = add(x = q_127_cast_fp16, y = encoder_layers_21_self_attn_pos_bias_u_to_fp16)[name = string("op_4985_cast_fp16")]; + tensor encoder_layers_21_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_21_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(442643584)))]; + tensor var_4987_cast_fp16 = add(x = q_127_cast_fp16, y = encoder_layers_21_self_attn_pos_bias_v_to_fp16)[name = string("op_4987_cast_fp16")]; + tensor q_with_bias_v_43_perm_0 = const()[name = string("q_with_bias_v_43_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_553_transpose_x_0 = const()[name = string("x_553_transpose_x_0"), val = bool(false)]; + bool x_553_transpose_y_0 = const()[name = string("x_553_transpose_y_0"), val = bool(false)]; + tensor op_4989_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(442645696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(442745088))))[name = string("op_4989_to_fp16_quantized")]; + tensor q_with_bias_v_43_cast_fp16 = transpose(perm = q_with_bias_v_43_perm_0, x = var_4987_cast_fp16)[name = string("transpose_173")]; + tensor x_553_cast_fp16 = matmul(transpose_x = x_553_transpose_x_0, transpose_y = x_553_transpose_y_0, x = q_with_bias_v_43_cast_fp16, y = op_4989_to_fp16_quantized)[name = string("x_553_cast_fp16")]; + tensor x_555_pad_0 = const()[name = string("x_555_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_555_mode_0 = const()[name = string("x_555_mode_0"), val = string("constant")]; + fp16 const_352_to_fp16 = const()[name = string("const_352_to_fp16"), val = fp16(0x0p+0)]; + tensor x_555_cast_fp16 = pad(constant_val = const_352_to_fp16, mode = x_555_mode_0, pad = x_555_pad_0, x = x_553_cast_fp16)[name = string("x_555_cast_fp16")]; + tensor var_4997 = const()[name = string("op_4997"), val = tensor([1, 8, -1, 7])]; + tensor x_557_cast_fp16 = reshape(shape = var_4997, x = x_555_cast_fp16)[name = string("x_557_cast_fp16")]; + tensor var_5001_begin_0 = const()[name = string("op_5001_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_5001_end_0 = const()[name = string("op_5001_end_0"), val = tensor([1, 8, 98, 7])]; + tensor var_5001_end_mask_0 = const()[name = string("op_5001_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_5001_cast_fp16 = slice_by_index(begin = var_5001_begin_0, end = var_5001_end_0, end_mask = var_5001_end_mask_0, x = x_557_cast_fp16)[name = string("op_5001_cast_fp16")]; + tensor var_5002 = const()[name = string("op_5002"), val = tensor([1, 8, 7, 97])]; + tensor matrix_bd_85_cast_fp16 = reshape(shape = var_5002, x = var_5001_cast_fp16)[name = string("matrix_bd_85_cast_fp16")]; + bool matrix_ac_43_transpose_x_0 = const()[name = string("matrix_ac_43_transpose_x_0"), val = bool(false)]; + bool matrix_ac_43_transpose_y_0 = const()[name = string("matrix_ac_43_transpose_y_0"), val = bool(false)]; + tensor transpose_138_perm_0 = const()[name = string("transpose_138_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_139_perm_0 = const()[name = string("transpose_139_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_139 = transpose(perm = transpose_139_perm_0, x = k_85_cast_fp16)[name = string("transpose_171")]; + tensor transpose_138 = transpose(perm = transpose_138_perm_0, x = var_4985_cast_fp16)[name = string("transpose_172")]; + tensor matrix_ac_43_cast_fp16 = matmul(transpose_x = matrix_ac_43_transpose_x_0, transpose_y = matrix_ac_43_transpose_y_0, x = transpose_138, y = transpose_139)[name = string("matrix_ac_43_cast_fp16")]; + tensor matrix_bd_87_begin_0 = const()[name = string("matrix_bd_87_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_87_end_0 = const()[name = string("matrix_bd_87_end_0"), val = tensor([1, 8, 7, 49])]; + tensor matrix_bd_87_end_mask_0 = const()[name = string("matrix_bd_87_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_87_cast_fp16 = slice_by_index(begin = matrix_bd_87_begin_0, end = matrix_bd_87_end_0, end_mask = matrix_bd_87_end_mask_0, x = matrix_bd_85_cast_fp16)[name = string("matrix_bd_87_cast_fp16")]; + tensor var_5011_cast_fp16 = add(x = matrix_ac_43_cast_fp16, y = matrix_bd_87_cast_fp16)[name = string("op_5011_cast_fp16")]; + fp16 _inversed_scores_85_y_0_to_fp16 = const()[name = string("_inversed_scores_85_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_85_cast_fp16 = mul(x = var_5011_cast_fp16, y = _inversed_scores_85_y_0_to_fp16)[name = string("_inversed_scores_85_cast_fp16")]; + tensor scores_87_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_85_cast_fp16, cond = mask_11)[name = string("scores_87_cast_fp16")]; + tensor var_5017_cast_fp16 = softmax(axis = var_59, x = scores_87_cast_fp16)[name = string("op_5017_cast_fp16")]; + tensor input_1133_cast_fp16 = select(a = var_44_to_fp16, b = var_5017_cast_fp16, cond = mask_11)[name = string("input_1133_cast_fp16")]; + bool x_559_transpose_x_0 = const()[name = string("x_559_transpose_x_0"), val = bool(false)]; + bool x_559_transpose_y_0 = const()[name = string("x_559_transpose_y_0"), val = bool(false)]; + tensor value_51_cast_fp16 = transpose(perm = value_51_perm_0, x = v_43_cast_fp16)[name = string("transpose_170")]; + tensor x_559_cast_fp16 = matmul(transpose_x = x_559_transpose_x_0, transpose_y = x_559_transpose_y_0, x = input_1133_cast_fp16, y = value_51_cast_fp16)[name = string("x_559_cast_fp16")]; + tensor var_5021_perm_0 = const()[name = string("op_5021_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_5022 = const()[name = string("op_5022"), val = tensor([1, -1, 1024])]; + tensor var_5021_cast_fp16 = transpose(perm = var_5021_perm_0, x = x_559_cast_fp16)[name = string("transpose_169")]; + tensor input_1135_cast_fp16 = reshape(shape = var_5022, x = var_5021_cast_fp16)[name = string("input_1135_cast_fp16")]; + tensor encoder_layers_21_self_attn_linear_out_weight_to_fp16 = const()[name = string("encoder_layers_21_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(442745408)))]; + tensor encoder_layers_21_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_21_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(444842624)))]; + tensor linear_196_cast_fp16 = linear(bias = encoder_layers_21_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_21_self_attn_linear_out_weight_to_fp16, x = input_1135_cast_fp16)[name = string("linear_196_cast_fp16")]; + tensor input_1139_cast_fp16 = add(x = input_1129_cast_fp16, y = linear_196_cast_fp16)[name = string("input_1139_cast_fp16")]; + tensor x_563_axes_0 = const()[name = string("x_563_axes_0"), val = tensor([-1])]; + tensor encoder_layers_21_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_21_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(444844736)))]; + tensor encoder_layers_21_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_21_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(444846848)))]; + tensor x_563_cast_fp16 = layer_norm(axes = x_563_axes_0, beta = encoder_layers_21_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_21_norm_conv_weight_to_fp16, x = input_1139_cast_fp16)[name = string("x_563_cast_fp16")]; + tensor input_1141_perm_0 = const()[name = string("input_1141_perm_0"), val = tensor([0, 2, 1])]; + string input_1143_pad_type_0 = const()[name = string("input_1143_pad_type_0"), val = string("valid")]; + tensor input_1143_strides_0 = const()[name = string("input_1143_strides_0"), val = tensor([1])]; + tensor input_1143_pad_0 = const()[name = string("input_1143_pad_0"), val = tensor([0, 0])]; + tensor input_1143_dilations_0 = const()[name = string("input_1143_dilations_0"), val = tensor([1])]; + int32 input_1143_groups_0 = const()[name = string("input_1143_groups_0"), val = int32(1)]; + tensor encoder_layers_21_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(444848960))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(446946176))))[name = string("encoder_layers_21_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_1141_cast_fp16 = transpose(perm = input_1141_perm_0, x = x_563_cast_fp16)[name = string("transpose_168")]; + tensor input_1143_cast_fp16 = conv(dilations = input_1143_dilations_0, groups = input_1143_groups_0, pad = input_1143_pad_0, pad_type = input_1143_pad_type_0, strides = input_1143_strides_0, weight = encoder_layers_21_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1141_cast_fp16)[name = string("input_1143_cast_fp16")]; + int32 x_565_split_num_splits_0 = const()[name = string("x_565_split_num_splits_0"), val = int32(2)]; + int32 x_565_split_axis_0 = const()[name = string("x_565_split_axis_0"), val = int32(1)]; + tensor x_565_split_cast_fp16_0, tensor x_565_split_cast_fp16_1 = split(axis = x_565_split_axis_0, num_splits = x_565_split_num_splits_0, x = input_1143_cast_fp16)[name = string("x_565_split_cast_fp16")]; + tensor x_565_split_1_sigmoid_cast_fp16 = sigmoid(x = x_565_split_cast_fp16_1)[name = string("x_565_split_1_sigmoid_cast_fp16")]; + tensor x_565_cast_fp16 = mul(x = x_565_split_cast_fp16_0, y = x_565_split_1_sigmoid_cast_fp16)[name = string("x_565_cast_fp16")]; + tensor input_1145_cast_fp16 = select(a = var_44_to_fp16, b = x_565_cast_fp16, cond = var_575)[name = string("input_1145_cast_fp16")]; + bool new_x_87_interleave_0 = const()[name = string("new_x_87_interleave_0"), val = bool(false)]; + tensor new_x_87_cast_fp16 = concat(axis = var_59, interleave = new_x_87_interleave_0, values = (cache_87_cast_fp16, input_1145_cast_fp16))[name = string("new_x_87_cast_fp16")]; + tensor var_5061_begin_0 = const()[name = string("op_5061_begin_0"), val = tensor([0, 0, 7])]; + tensor var_5061_end_0 = const()[name = string("op_5061_end_0"), val = tensor([1, 1024, 15])]; + tensor var_5061_end_mask_0 = const()[name = string("op_5061_end_mask_0"), val = tensor([true, true, true])]; + tensor var_5061_cast_fp16 = slice_by_index(begin = var_5061_begin_0, end = var_5061_end_0, end_mask = var_5061_end_mask_0, x = new_x_87_cast_fp16)[name = string("op_5061_cast_fp16")]; + string x_567_pad_type_0 = const()[name = string("x_567_pad_type_0"), val = string("valid")]; + int32 x_567_groups_0 = const()[name = string("x_567_groups_0"), val = int32(1024)]; + tensor x_567_strides_0 = const()[name = string("x_567_strides_0"), val = tensor([1])]; + tensor x_567_pad_0 = const()[name = string("x_567_pad_0"), val = tensor([0, 0])]; + tensor x_567_dilations_0 = const()[name = string("x_567_dilations_0"), val = tensor([1])]; + tensor encoder_layers_21_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(446950336))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(446959616))))[name = string("encoder_layers_21_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_567_cast_fp16 = conv(dilations = x_567_dilations_0, groups = x_567_groups_0, pad = x_567_pad_0, pad_type = x_567_pad_type_0, strides = x_567_strides_0, weight = encoder_layers_21_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_87_cast_fp16)[name = string("x_567_cast_fp16")]; + tensor input_1147_perm_0 = const()[name = string("input_1147_perm_0"), val = tensor([0, 2, 1])]; + tensor x_569_axes_0 = const()[name = string("x_569_axes_0"), val = tensor([-1])]; + tensor encoder_layers_21_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_21_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(446961728)))]; + tensor encoder_layers_21_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_21_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(446963840)))]; + tensor input_1147_cast_fp16 = transpose(perm = input_1147_perm_0, x = x_567_cast_fp16)[name = string("transpose_167")]; + tensor x_569_cast_fp16 = layer_norm(axes = x_569_axes_0, beta = encoder_layers_21_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_21_conv_batch_norm_weight_to_fp16, x = input_1147_cast_fp16)[name = string("x_569_cast_fp16")]; + tensor input_1149_perm_0 = const()[name = string("input_1149_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1149_cast_fp16 = transpose(perm = input_1149_perm_0, x = x_569_cast_fp16)[name = string("transpose_166")]; + tensor input_1151_cast_fp16 = silu(x = input_1149_cast_fp16)[name = string("input_1151_cast_fp16")]; + string x_571_pad_type_0 = const()[name = string("x_571_pad_type_0"), val = string("valid")]; + tensor x_571_strides_0 = const()[name = string("x_571_strides_0"), val = tensor([1])]; + tensor x_571_pad_0 = const()[name = string("x_571_pad_0"), val = tensor([0, 0])]; + tensor x_571_dilations_0 = const()[name = string("x_571_dilations_0"), val = tensor([1])]; + int32 x_571_groups_0 = const()[name = string("x_571_groups_0"), val = int32(1)]; + tensor encoder_layers_21_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(446965952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(448014592))))[name = string("encoder_layers_21_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_571_cast_fp16 = conv(dilations = x_571_dilations_0, groups = x_571_groups_0, pad = x_571_pad_0, pad_type = x_571_pad_type_0, strides = x_571_strides_0, weight = encoder_layers_21_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1151_cast_fp16)[name = string("x_571_cast_fp16")]; + tensor input_1153_perm_0 = const()[name = string("input_1153_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1153_cast_fp16 = transpose(perm = input_1153_perm_0, x = x_571_cast_fp16)[name = string("transpose_165")]; + tensor input_1155_cast_fp16 = add(x = input_1139_cast_fp16, y = input_1153_cast_fp16)[name = string("input_1155_cast_fp16")]; + tensor input_1157_axes_0 = const()[name = string("input_1157_axes_0"), val = tensor([-1])]; + tensor encoder_layers_21_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_21_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(448016704)))]; + tensor encoder_layers_21_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_21_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(448018816)))]; + tensor input_1157_cast_fp16 = layer_norm(axes = input_1157_axes_0, beta = encoder_layers_21_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_21_norm_feed_forward2_weight_to_fp16, x = input_1155_cast_fp16)[name = string("input_1157_cast_fp16")]; + tensor encoder_layers_21_feed_forward2_linear1_weight_to_fp16 = const()[name = string("encoder_layers_21_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(448020928)))]; + tensor encoder_layers_21_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_21_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(456409600)))]; + tensor linear_197_cast_fp16 = linear(bias = encoder_layers_21_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_21_feed_forward2_linear1_weight_to_fp16, x = input_1157_cast_fp16)[name = string("linear_197_cast_fp16")]; + tensor input_1161_cast_fp16 = silu(x = linear_197_cast_fp16)[name = string("input_1161_cast_fp16")]; + tensor encoder_layers_21_feed_forward2_linear2_weight_to_fp16 = const()[name = string("encoder_layers_21_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(456417856)))]; + tensor encoder_layers_21_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_21_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(464806528)))]; + tensor linear_198_cast_fp16 = linear(bias = encoder_layers_21_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_21_feed_forward2_linear2_weight_to_fp16, x = input_1161_cast_fp16)[name = string("linear_198_cast_fp16")]; + fp16 var_5104_to_fp16 = const()[name = string("op_5104_to_fp16"), val = fp16(0x1p-1)]; + tensor var_5105_cast_fp16 = mul(x = linear_198_cast_fp16, y = var_5104_to_fp16)[name = string("op_5105_cast_fp16")]; + tensor input_1167_cast_fp16 = add(x = input_1155_cast_fp16, y = var_5105_cast_fp16)[name = string("input_1167_cast_fp16")]; + tensor input_1169_axes_0 = const()[name = string("input_1169_axes_0"), val = tensor([-1])]; + tensor encoder_layers_21_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_21_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(464808640)))]; + tensor encoder_layers_21_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_21_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(464810752)))]; + tensor input_1169_cast_fp16 = layer_norm(axes = input_1169_axes_0, beta = encoder_layers_21_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_21_norm_out_weight_to_fp16, x = input_1167_cast_fp16)[name = string("input_1169_cast_fp16")]; + tensor cache_89_begin_0 = const()[name = string("cache_89_begin_0"), val = tensor([22, 0, 0, 0])]; + tensor cache_89_end_0 = const()[name = string("cache_89_end_0"), val = tensor([23, 1, 42, 1024])]; + tensor cache_89_end_mask_0 = const()[name = string("cache_89_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_89_squeeze_mask_0 = const()[name = string("cache_89_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_89_cast_fp16 = slice_by_index(begin = cache_89_begin_0, end = cache_89_end_0, end_mask = cache_89_end_mask_0, squeeze_mask = cache_89_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_89_cast_fp16")]; + tensor cache_91_begin_0 = const()[name = string("cache_91_begin_0"), val = tensor([22, 0, 0, 0])]; + tensor cache_91_end_0 = const()[name = string("cache_91_end_0"), val = tensor([23, 1, 1024, 8])]; + tensor cache_91_end_mask_0 = const()[name = string("cache_91_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_91_squeeze_mask_0 = const()[name = string("cache_91_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_91_cast_fp16 = slice_by_index(begin = cache_91_begin_0, end = cache_91_end_0, end_mask = cache_91_end_mask_0, squeeze_mask = cache_91_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_91_cast_fp16")]; + tensor input_1171_axes_0 = const()[name = string("input_1171_axes_0"), val = tensor([-1])]; + tensor encoder_layers_22_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_22_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(464812864)))]; + tensor encoder_layers_22_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_22_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(464814976)))]; + tensor input_1171_cast_fp16 = layer_norm(axes = input_1171_axes_0, beta = encoder_layers_22_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_22_norm_feed_forward1_weight_to_fp16, x = input_1169_cast_fp16)[name = string("input_1171_cast_fp16")]; + tensor encoder_layers_22_feed_forward1_linear1_weight_to_fp16 = const()[name = string("encoder_layers_22_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(464817088)))]; + tensor encoder_layers_22_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_22_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(473205760)))]; + tensor linear_199_cast_fp16 = linear(bias = encoder_layers_22_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_22_feed_forward1_linear1_weight_to_fp16, x = input_1171_cast_fp16)[name = string("linear_199_cast_fp16")]; + tensor input_1175_cast_fp16 = silu(x = linear_199_cast_fp16)[name = string("input_1175_cast_fp16")]; + tensor encoder_layers_22_feed_forward1_linear2_weight_to_fp16 = const()[name = string("encoder_layers_22_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(473214016)))]; + tensor encoder_layers_22_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_22_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(481602688)))]; + tensor linear_200_cast_fp16 = linear(bias = encoder_layers_22_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_22_feed_forward1_linear2_weight_to_fp16, x = input_1175_cast_fp16)[name = string("linear_200_cast_fp16")]; + fp16 var_5141_to_fp16 = const()[name = string("op_5141_to_fp16"), val = fp16(0x1p-1)]; + tensor var_5142_cast_fp16 = mul(x = linear_200_cast_fp16, y = var_5141_to_fp16)[name = string("op_5142_cast_fp16")]; + tensor input_1181_cast_fp16 = add(x = input_1169_cast_fp16, y = var_5142_cast_fp16)[name = string("input_1181_cast_fp16")]; + tensor key_45_axes_0 = const()[name = string("key_45_axes_0"), val = tensor([-1])]; + tensor encoder_layers_22_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_22_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(481604800)))]; + tensor encoder_layers_22_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_22_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(481606912)))]; + tensor key_45_cast_fp16 = layer_norm(axes = key_45_axes_0, beta = encoder_layers_22_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_22_norm_self_att_weight_to_fp16, x = input_1181_cast_fp16)[name = string("key_45_cast_fp16")]; + bool input_1183_interleave_0 = const()[name = string("input_1183_interleave_0"), val = bool(false)]; + tensor input_1183_cast_fp16 = concat(axis = var_68, interleave = input_1183_interleave_0, values = (cache_89_cast_fp16, key_45_cast_fp16))[name = string("input_1183_cast_fp16")]; + tensor var_5164_begin_0 = const()[name = string("op_5164_begin_0"), val = tensor([0, 7, 0])]; + tensor var_5164_end_0 = const()[name = string("op_5164_end_0"), val = tensor([1, 42, 1024])]; + tensor var_5164_end_mask_0 = const()[name = string("op_5164_end_mask_0"), val = tensor([true, true, true])]; + tensor var_5164_cast_fp16 = slice_by_index(begin = var_5164_begin_0, end = var_5164_end_0, end_mask = var_5164_end_mask_0, x = cache_89_cast_fp16)[name = string("op_5164_cast_fp16")]; + bool var_5170_interleave_0 = const()[name = string("op_5170_interleave_0"), val = bool(false)]; + tensor var_5170_cast_fp16 = concat(axis = var_68, interleave = var_5170_interleave_0, values = (var_5164_cast_fp16, key_45_cast_fp16))[name = string("op_5170_cast_fp16")]; + tensor encoder_layers_22_self_attn_linear_q_weight_to_fp16 = const()[name = string("encoder_layers_22_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(481609024)))]; + tensor encoder_layers_22_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_22_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483706240)))]; + tensor linear_201_cast_fp16 = linear(bias = encoder_layers_22_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_22_self_attn_linear_q_weight_to_fp16, x = key_45_cast_fp16)[name = string("linear_201_cast_fp16")]; + tensor var_5175 = const()[name = string("op_5175"), val = tensor([1, -1, 8, 128])]; + tensor q_133_cast_fp16 = reshape(shape = var_5175, x = linear_201_cast_fp16)[name = string("q_133_cast_fp16")]; + tensor encoder_layers_22_self_attn_linear_k_weight_to_fp16 = const()[name = string("encoder_layers_22_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483708352)))]; + tensor encoder_layers_22_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_22_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(485805568)))]; + tensor linear_202_cast_fp16 = linear(bias = encoder_layers_22_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_22_self_attn_linear_k_weight_to_fp16, x = input_1183_cast_fp16)[name = string("linear_202_cast_fp16")]; + tensor var_5180 = const()[name = string("op_5180"), val = tensor([1, -1, 8, 128])]; + tensor k_89_cast_fp16 = reshape(shape = var_5180, x = linear_202_cast_fp16)[name = string("k_89_cast_fp16")]; + tensor encoder_layers_22_self_attn_linear_v_weight_to_fp16 = const()[name = string("encoder_layers_22_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(485807680)))]; + tensor encoder_layers_22_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_22_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(487904896)))]; + tensor linear_203_cast_fp16 = linear(bias = encoder_layers_22_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_22_self_attn_linear_v_weight_to_fp16, x = input_1183_cast_fp16)[name = string("linear_203_cast_fp16")]; + tensor var_5185 = const()[name = string("op_5185"), val = tensor([1, -1, 8, 128])]; + tensor v_45_cast_fp16 = reshape(shape = var_5185, x = linear_203_cast_fp16)[name = string("v_45_cast_fp16")]; + tensor value_53_perm_0 = const()[name = string("value_53_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_22_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_22_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(487907008)))]; + tensor var_5198_cast_fp16 = add(x = q_133_cast_fp16, y = encoder_layers_22_self_attn_pos_bias_u_to_fp16)[name = string("op_5198_cast_fp16")]; + tensor encoder_layers_22_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_22_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(487909120)))]; + tensor var_5200_cast_fp16 = add(x = q_133_cast_fp16, y = encoder_layers_22_self_attn_pos_bias_v_to_fp16)[name = string("op_5200_cast_fp16")]; + tensor q_with_bias_v_45_perm_0 = const()[name = string("q_with_bias_v_45_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_579_transpose_x_0 = const()[name = string("x_579_transpose_x_0"), val = bool(false)]; + bool x_579_transpose_y_0 = const()[name = string("x_579_transpose_y_0"), val = bool(false)]; + tensor op_5202_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(487911232))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488010624))))[name = string("op_5202_to_fp16_quantized")]; + tensor q_with_bias_v_45_cast_fp16 = transpose(perm = q_with_bias_v_45_perm_0, x = var_5200_cast_fp16)[name = string("transpose_164")]; + tensor x_579_cast_fp16 = matmul(transpose_x = x_579_transpose_x_0, transpose_y = x_579_transpose_y_0, x = q_with_bias_v_45_cast_fp16, y = op_5202_to_fp16_quantized)[name = string("x_579_cast_fp16")]; + tensor x_581_pad_0 = const()[name = string("x_581_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_581_mode_0 = const()[name = string("x_581_mode_0"), val = string("constant")]; + fp16 const_365_to_fp16 = const()[name = string("const_365_to_fp16"), val = fp16(0x0p+0)]; + tensor x_581_cast_fp16 = pad(constant_val = const_365_to_fp16, mode = x_581_mode_0, pad = x_581_pad_0, x = x_579_cast_fp16)[name = string("x_581_cast_fp16")]; + tensor var_5210 = const()[name = string("op_5210"), val = tensor([1, 8, -1, 7])]; + tensor x_583_cast_fp16 = reshape(shape = var_5210, x = x_581_cast_fp16)[name = string("x_583_cast_fp16")]; + tensor var_5214_begin_0 = const()[name = string("op_5214_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_5214_end_0 = const()[name = string("op_5214_end_0"), val = tensor([1, 8, 98, 7])]; + tensor var_5214_end_mask_0 = const()[name = string("op_5214_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_5214_cast_fp16 = slice_by_index(begin = var_5214_begin_0, end = var_5214_end_0, end_mask = var_5214_end_mask_0, x = x_583_cast_fp16)[name = string("op_5214_cast_fp16")]; + tensor var_5215 = const()[name = string("op_5215"), val = tensor([1, 8, 7, 97])]; + tensor matrix_bd_89_cast_fp16 = reshape(shape = var_5215, x = var_5214_cast_fp16)[name = string("matrix_bd_89_cast_fp16")]; + bool matrix_ac_45_transpose_x_0 = const()[name = string("matrix_ac_45_transpose_x_0"), val = bool(false)]; + bool matrix_ac_45_transpose_y_0 = const()[name = string("matrix_ac_45_transpose_y_0"), val = bool(false)]; + tensor transpose_140_perm_0 = const()[name = string("transpose_140_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_141_perm_0 = const()[name = string("transpose_141_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_141 = transpose(perm = transpose_141_perm_0, x = k_89_cast_fp16)[name = string("transpose_162")]; + tensor transpose_140 = transpose(perm = transpose_140_perm_0, x = var_5198_cast_fp16)[name = string("transpose_163")]; + tensor matrix_ac_45_cast_fp16 = matmul(transpose_x = matrix_ac_45_transpose_x_0, transpose_y = matrix_ac_45_transpose_y_0, x = transpose_140, y = transpose_141)[name = string("matrix_ac_45_cast_fp16")]; + tensor matrix_bd_91_begin_0 = const()[name = string("matrix_bd_91_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_91_end_0 = const()[name = string("matrix_bd_91_end_0"), val = tensor([1, 8, 7, 49])]; + tensor matrix_bd_91_end_mask_0 = const()[name = string("matrix_bd_91_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_91_cast_fp16 = slice_by_index(begin = matrix_bd_91_begin_0, end = matrix_bd_91_end_0, end_mask = matrix_bd_91_end_mask_0, x = matrix_bd_89_cast_fp16)[name = string("matrix_bd_91_cast_fp16")]; + tensor var_5224_cast_fp16 = add(x = matrix_ac_45_cast_fp16, y = matrix_bd_91_cast_fp16)[name = string("op_5224_cast_fp16")]; + fp16 _inversed_scores_89_y_0_to_fp16 = const()[name = string("_inversed_scores_89_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_89_cast_fp16 = mul(x = var_5224_cast_fp16, y = _inversed_scores_89_y_0_to_fp16)[name = string("_inversed_scores_89_cast_fp16")]; + tensor scores_91_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_89_cast_fp16, cond = mask_11)[name = string("scores_91_cast_fp16")]; + tensor var_5230_cast_fp16 = softmax(axis = var_59, x = scores_91_cast_fp16)[name = string("op_5230_cast_fp16")]; + tensor input_1185_cast_fp16 = select(a = var_44_to_fp16, b = var_5230_cast_fp16, cond = mask_11)[name = string("input_1185_cast_fp16")]; + bool x_585_transpose_x_0 = const()[name = string("x_585_transpose_x_0"), val = bool(false)]; + bool x_585_transpose_y_0 = const()[name = string("x_585_transpose_y_0"), val = bool(false)]; + tensor value_53_cast_fp16 = transpose(perm = value_53_perm_0, x = v_45_cast_fp16)[name = string("transpose_161")]; + tensor x_585_cast_fp16 = matmul(transpose_x = x_585_transpose_x_0, transpose_y = x_585_transpose_y_0, x = input_1185_cast_fp16, y = value_53_cast_fp16)[name = string("x_585_cast_fp16")]; + tensor var_5234_perm_0 = const()[name = string("op_5234_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_5235 = const()[name = string("op_5235"), val = tensor([1, -1, 1024])]; + tensor var_5234_cast_fp16 = transpose(perm = var_5234_perm_0, x = x_585_cast_fp16)[name = string("transpose_160")]; + tensor input_1187_cast_fp16 = reshape(shape = var_5235, x = var_5234_cast_fp16)[name = string("input_1187_cast_fp16")]; + tensor encoder_layers_22_self_attn_linear_out_weight_to_fp16 = const()[name = string("encoder_layers_22_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488010944)))]; + tensor encoder_layers_22_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_22_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(490108160)))]; + tensor linear_205_cast_fp16 = linear(bias = encoder_layers_22_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_22_self_attn_linear_out_weight_to_fp16, x = input_1187_cast_fp16)[name = string("linear_205_cast_fp16")]; + tensor input_1191_cast_fp16 = add(x = input_1181_cast_fp16, y = linear_205_cast_fp16)[name = string("input_1191_cast_fp16")]; + tensor x_589_axes_0 = const()[name = string("x_589_axes_0"), val = tensor([-1])]; + tensor encoder_layers_22_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_22_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(490110272)))]; + tensor encoder_layers_22_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_22_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(490112384)))]; + tensor x_589_cast_fp16 = layer_norm(axes = x_589_axes_0, beta = encoder_layers_22_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_22_norm_conv_weight_to_fp16, x = input_1191_cast_fp16)[name = string("x_589_cast_fp16")]; + tensor input_1193_perm_0 = const()[name = string("input_1193_perm_0"), val = tensor([0, 2, 1])]; + string input_1195_pad_type_0 = const()[name = string("input_1195_pad_type_0"), val = string("valid")]; + tensor input_1195_strides_0 = const()[name = string("input_1195_strides_0"), val = tensor([1])]; + tensor input_1195_pad_0 = const()[name = string("input_1195_pad_0"), val = tensor([0, 0])]; + tensor input_1195_dilations_0 = const()[name = string("input_1195_dilations_0"), val = tensor([1])]; + int32 input_1195_groups_0 = const()[name = string("input_1195_groups_0"), val = int32(1)]; + tensor encoder_layers_22_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(490114496))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(492211712))))[name = string("encoder_layers_22_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_1193_cast_fp16 = transpose(perm = input_1193_perm_0, x = x_589_cast_fp16)[name = string("transpose_159")]; + tensor input_1195_cast_fp16 = conv(dilations = input_1195_dilations_0, groups = input_1195_groups_0, pad = input_1195_pad_0, pad_type = input_1195_pad_type_0, strides = input_1195_strides_0, weight = encoder_layers_22_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1193_cast_fp16)[name = string("input_1195_cast_fp16")]; + int32 x_591_split_num_splits_0 = const()[name = string("x_591_split_num_splits_0"), val = int32(2)]; + int32 x_591_split_axis_0 = const()[name = string("x_591_split_axis_0"), val = int32(1)]; + tensor x_591_split_cast_fp16_0, tensor x_591_split_cast_fp16_1 = split(axis = x_591_split_axis_0, num_splits = x_591_split_num_splits_0, x = input_1195_cast_fp16)[name = string("x_591_split_cast_fp16")]; + tensor x_591_split_1_sigmoid_cast_fp16 = sigmoid(x = x_591_split_cast_fp16_1)[name = string("x_591_split_1_sigmoid_cast_fp16")]; + tensor x_591_cast_fp16 = mul(x = x_591_split_cast_fp16_0, y = x_591_split_1_sigmoid_cast_fp16)[name = string("x_591_cast_fp16")]; + tensor input_1197_cast_fp16 = select(a = var_44_to_fp16, b = x_591_cast_fp16, cond = var_575)[name = string("input_1197_cast_fp16")]; + bool new_x_91_interleave_0 = const()[name = string("new_x_91_interleave_0"), val = bool(false)]; + tensor new_x_91_cast_fp16 = concat(axis = var_59, interleave = new_x_91_interleave_0, values = (cache_91_cast_fp16, input_1197_cast_fp16))[name = string("new_x_91_cast_fp16")]; + tensor var_5274_begin_0 = const()[name = string("op_5274_begin_0"), val = tensor([0, 0, 7])]; + tensor var_5274_end_0 = const()[name = string("op_5274_end_0"), val = tensor([1, 1024, 15])]; + tensor var_5274_end_mask_0 = const()[name = string("op_5274_end_mask_0"), val = tensor([true, true, true])]; + tensor var_5274_cast_fp16 = slice_by_index(begin = var_5274_begin_0, end = var_5274_end_0, end_mask = var_5274_end_mask_0, x = new_x_91_cast_fp16)[name = string("op_5274_cast_fp16")]; + string x_593_pad_type_0 = const()[name = string("x_593_pad_type_0"), val = string("valid")]; + int32 x_593_groups_0 = const()[name = string("x_593_groups_0"), val = int32(1024)]; + tensor x_593_strides_0 = const()[name = string("x_593_strides_0"), val = tensor([1])]; + tensor x_593_pad_0 = const()[name = string("x_593_pad_0"), val = tensor([0, 0])]; + tensor x_593_dilations_0 = const()[name = string("x_593_dilations_0"), val = tensor([1])]; + tensor encoder_layers_22_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(492215872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(492225152))))[name = string("encoder_layers_22_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_593_cast_fp16 = conv(dilations = x_593_dilations_0, groups = x_593_groups_0, pad = x_593_pad_0, pad_type = x_593_pad_type_0, strides = x_593_strides_0, weight = encoder_layers_22_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_91_cast_fp16)[name = string("x_593_cast_fp16")]; + tensor input_1199_perm_0 = const()[name = string("input_1199_perm_0"), val = tensor([0, 2, 1])]; + tensor x_595_axes_0 = const()[name = string("x_595_axes_0"), val = tensor([-1])]; + tensor encoder_layers_22_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_22_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(492227264)))]; + tensor encoder_layers_22_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_22_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(492229376)))]; + tensor input_1199_cast_fp16 = transpose(perm = input_1199_perm_0, x = x_593_cast_fp16)[name = string("transpose_158")]; + tensor x_595_cast_fp16 = layer_norm(axes = x_595_axes_0, beta = encoder_layers_22_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_22_conv_batch_norm_weight_to_fp16, x = input_1199_cast_fp16)[name = string("x_595_cast_fp16")]; + tensor input_1201_perm_0 = const()[name = string("input_1201_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1201_cast_fp16 = transpose(perm = input_1201_perm_0, x = x_595_cast_fp16)[name = string("transpose_157")]; + tensor input_1203_cast_fp16 = silu(x = input_1201_cast_fp16)[name = string("input_1203_cast_fp16")]; + string x_597_pad_type_0 = const()[name = string("x_597_pad_type_0"), val = string("valid")]; + tensor x_597_strides_0 = const()[name = string("x_597_strides_0"), val = tensor([1])]; + tensor x_597_pad_0 = const()[name = string("x_597_pad_0"), val = tensor([0, 0])]; + tensor x_597_dilations_0 = const()[name = string("x_597_dilations_0"), val = tensor([1])]; + int32 x_597_groups_0 = const()[name = string("x_597_groups_0"), val = int32(1)]; + tensor encoder_layers_22_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(492231488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(493280128))))[name = string("encoder_layers_22_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_597_cast_fp16 = conv(dilations = x_597_dilations_0, groups = x_597_groups_0, pad = x_597_pad_0, pad_type = x_597_pad_type_0, strides = x_597_strides_0, weight = encoder_layers_22_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1203_cast_fp16)[name = string("x_597_cast_fp16")]; + tensor input_1205_perm_0 = const()[name = string("input_1205_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1205_cast_fp16 = transpose(perm = input_1205_perm_0, x = x_597_cast_fp16)[name = string("transpose_156")]; + tensor input_1207_cast_fp16 = add(x = input_1191_cast_fp16, y = input_1205_cast_fp16)[name = string("input_1207_cast_fp16")]; + tensor input_1209_axes_0 = const()[name = string("input_1209_axes_0"), val = tensor([-1])]; + tensor encoder_layers_22_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_22_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(493282240)))]; + tensor encoder_layers_22_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_22_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(493284352)))]; + tensor input_1209_cast_fp16 = layer_norm(axes = input_1209_axes_0, beta = encoder_layers_22_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_22_norm_feed_forward2_weight_to_fp16, x = input_1207_cast_fp16)[name = string("input_1209_cast_fp16")]; + tensor encoder_layers_22_feed_forward2_linear1_weight_to_fp16 = const()[name = string("encoder_layers_22_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(493286464)))]; + tensor encoder_layers_22_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_22_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(501675136)))]; + tensor linear_206_cast_fp16 = linear(bias = encoder_layers_22_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_22_feed_forward2_linear1_weight_to_fp16, x = input_1209_cast_fp16)[name = string("linear_206_cast_fp16")]; + tensor input_1213_cast_fp16 = silu(x = linear_206_cast_fp16)[name = string("input_1213_cast_fp16")]; + tensor encoder_layers_22_feed_forward2_linear2_weight_to_fp16 = const()[name = string("encoder_layers_22_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(501683392)))]; + tensor encoder_layers_22_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_22_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(510072064)))]; + tensor linear_207_cast_fp16 = linear(bias = encoder_layers_22_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_22_feed_forward2_linear2_weight_to_fp16, x = input_1213_cast_fp16)[name = string("linear_207_cast_fp16")]; + fp16 var_5317_to_fp16 = const()[name = string("op_5317_to_fp16"), val = fp16(0x1p-1)]; + tensor var_5318_cast_fp16 = mul(x = linear_207_cast_fp16, y = var_5317_to_fp16)[name = string("op_5318_cast_fp16")]; + tensor input_1219_cast_fp16 = add(x = input_1207_cast_fp16, y = var_5318_cast_fp16)[name = string("input_1219_cast_fp16")]; + tensor input_1221_axes_0 = const()[name = string("input_1221_axes_0"), val = tensor([-1])]; + tensor encoder_layers_22_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_22_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(510074176)))]; + tensor encoder_layers_22_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_22_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(510076288)))]; + tensor input_1221_cast_fp16 = layer_norm(axes = input_1221_axes_0, beta = encoder_layers_22_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_22_norm_out_weight_to_fp16, x = input_1219_cast_fp16)[name = string("input_1221_cast_fp16")]; + tensor cache_93_begin_0 = const()[name = string("cache_93_begin_0"), val = tensor([23, 0, 0, 0])]; + tensor cache_93_end_0 = const()[name = string("cache_93_end_0"), val = tensor([24, 1, 42, 1024])]; + tensor cache_93_end_mask_0 = const()[name = string("cache_93_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_93_squeeze_mask_0 = const()[name = string("cache_93_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_93_cast_fp16 = slice_by_index(begin = cache_93_begin_0, end = cache_93_end_0, end_mask = cache_93_end_mask_0, squeeze_mask = cache_93_squeeze_mask_0, x = value_3_cast_fp16)[name = string("cache_93_cast_fp16")]; + tensor cache_begin_0 = const()[name = string("cache_begin_0"), val = tensor([23, 0, 0, 0])]; + tensor cache_end_0 = const()[name = string("cache_end_0"), val = tensor([24, 1, 1024, 8])]; + tensor cache_end_mask_0 = const()[name = string("cache_end_mask_0"), val = tensor([false, true, true, true])]; + tensor cache_squeeze_mask_0 = const()[name = string("cache_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor cache_cast_fp16 = slice_by_index(begin = cache_begin_0, end = cache_end_0, end_mask = cache_end_mask_0, squeeze_mask = cache_squeeze_mask_0, x = value_5_cast_fp16)[name = string("cache_cast_fp16")]; + tensor input_1223_axes_0 = const()[name = string("input_1223_axes_0"), val = tensor([-1])]; + tensor encoder_layers_23_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_23_norm_feed_forward1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(510078400)))]; + tensor encoder_layers_23_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_23_norm_feed_forward1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(510080512)))]; + tensor input_1223_cast_fp16 = layer_norm(axes = input_1223_axes_0, beta = encoder_layers_23_norm_feed_forward1_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_23_norm_feed_forward1_weight_to_fp16, x = input_1221_cast_fp16)[name = string("input_1223_cast_fp16")]; + tensor encoder_layers_23_feed_forward1_linear1_weight_to_fp16 = const()[name = string("encoder_layers_23_feed_forward1_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(510082624)))]; + tensor encoder_layers_23_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_layers_23_feed_forward1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(518471296)))]; + tensor linear_208_cast_fp16 = linear(bias = encoder_layers_23_feed_forward1_linear1_bias_to_fp16, weight = encoder_layers_23_feed_forward1_linear1_weight_to_fp16, x = input_1223_cast_fp16)[name = string("linear_208_cast_fp16")]; + tensor input_1227_cast_fp16 = silu(x = linear_208_cast_fp16)[name = string("input_1227_cast_fp16")]; + tensor encoder_layers_23_feed_forward1_linear2_weight_to_fp16 = const()[name = string("encoder_layers_23_feed_forward1_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(518479552)))]; + tensor encoder_layers_23_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_layers_23_feed_forward1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(526868224)))]; + tensor linear_209_cast_fp16 = linear(bias = encoder_layers_23_feed_forward1_linear2_bias_to_fp16, weight = encoder_layers_23_feed_forward1_linear2_weight_to_fp16, x = input_1227_cast_fp16)[name = string("linear_209_cast_fp16")]; + fp16 var_5354_to_fp16 = const()[name = string("op_5354_to_fp16"), val = fp16(0x1p-1)]; + tensor var_5355_cast_fp16 = mul(x = linear_209_cast_fp16, y = var_5354_to_fp16)[name = string("op_5355_cast_fp16")]; + tensor input_1233_cast_fp16 = add(x = input_1221_cast_fp16, y = var_5355_cast_fp16)[name = string("input_1233_cast_fp16")]; + tensor key_axes_0 = const()[name = string("key_axes_0"), val = tensor([-1])]; + tensor encoder_layers_23_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_23_norm_self_att_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(526870336)))]; + tensor encoder_layers_23_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_23_norm_self_att_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(526872448)))]; + tensor key_cast_fp16 = layer_norm(axes = key_axes_0, beta = encoder_layers_23_norm_self_att_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_23_norm_self_att_weight_to_fp16, x = input_1233_cast_fp16)[name = string("key_cast_fp16")]; + bool input_1235_interleave_0 = const()[name = string("input_1235_interleave_0"), val = bool(false)]; + tensor input_1235_cast_fp16 = concat(axis = var_68, interleave = input_1235_interleave_0, values = (cache_93_cast_fp16, key_cast_fp16))[name = string("input_1235_cast_fp16")]; + tensor var_5377_begin_0 = const()[name = string("op_5377_begin_0"), val = tensor([0, 7, 0])]; + tensor var_5377_end_0 = const()[name = string("op_5377_end_0"), val = tensor([1, 42, 1024])]; + tensor var_5377_end_mask_0 = const()[name = string("op_5377_end_mask_0"), val = tensor([true, true, true])]; + tensor var_5377_cast_fp16 = slice_by_index(begin = var_5377_begin_0, end = var_5377_end_0, end_mask = var_5377_end_mask_0, x = cache_93_cast_fp16)[name = string("op_5377_cast_fp16")]; + bool cache_last_channel_cur_interleave_0 = const()[name = string("cache_last_channel_cur_interleave_0"), val = bool(false)]; + tensor cache_last_channel_cur_cast_fp16 = concat(axis = var_68, interleave = cache_last_channel_cur_interleave_0, values = (var_5377_cast_fp16, key_cast_fp16))[name = string("cache_last_channel_cur_cast_fp16")]; + tensor encoder_layers_23_self_attn_linear_q_weight_to_fp16 = const()[name = string("encoder_layers_23_self_attn_linear_q_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(526874560)))]; + tensor encoder_layers_23_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_layers_23_self_attn_linear_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(528971776)))]; + tensor linear_210_cast_fp16 = linear(bias = encoder_layers_23_self_attn_linear_q_bias_to_fp16, weight = encoder_layers_23_self_attn_linear_q_weight_to_fp16, x = key_cast_fp16)[name = string("linear_210_cast_fp16")]; + tensor var_5388 = const()[name = string("op_5388"), val = tensor([1, -1, 8, 128])]; + tensor q_139_cast_fp16 = reshape(shape = var_5388, x = linear_210_cast_fp16)[name = string("q_139_cast_fp16")]; + tensor encoder_layers_23_self_attn_linear_k_weight_to_fp16 = const()[name = string("encoder_layers_23_self_attn_linear_k_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(528973888)))]; + tensor encoder_layers_23_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_layers_23_self_attn_linear_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531071104)))]; + tensor linear_211_cast_fp16 = linear(bias = encoder_layers_23_self_attn_linear_k_bias_to_fp16, weight = encoder_layers_23_self_attn_linear_k_weight_to_fp16, x = input_1235_cast_fp16)[name = string("linear_211_cast_fp16")]; + tensor var_5393 = const()[name = string("op_5393"), val = tensor([1, -1, 8, 128])]; + tensor k_93_cast_fp16 = reshape(shape = var_5393, x = linear_211_cast_fp16)[name = string("k_93_cast_fp16")]; + tensor encoder_layers_23_self_attn_linear_v_weight_to_fp16 = const()[name = string("encoder_layers_23_self_attn_linear_v_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531073216)))]; + tensor encoder_layers_23_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_layers_23_self_attn_linear_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(533170432)))]; + tensor linear_212_cast_fp16 = linear(bias = encoder_layers_23_self_attn_linear_v_bias_to_fp16, weight = encoder_layers_23_self_attn_linear_v_weight_to_fp16, x = input_1235_cast_fp16)[name = string("linear_212_cast_fp16")]; + tensor var_5398 = const()[name = string("op_5398"), val = tensor([1, -1, 8, 128])]; + tensor v_cast_fp16 = reshape(shape = var_5398, x = linear_212_cast_fp16)[name = string("v_cast_fp16")]; + tensor value_perm_0 = const()[name = string("value_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor encoder_layers_23_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_23_self_attn_pos_bias_u_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(533172544)))]; + tensor var_5411_cast_fp16 = add(x = q_139_cast_fp16, y = encoder_layers_23_self_attn_pos_bias_u_to_fp16)[name = string("op_5411_cast_fp16")]; + tensor encoder_layers_23_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_23_self_attn_pos_bias_v_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(533174656)))]; + tensor var_5413_cast_fp16 = add(x = q_139_cast_fp16, y = encoder_layers_23_self_attn_pos_bias_v_to_fp16)[name = string("op_5413_cast_fp16")]; + tensor q_with_bias_v_perm_0 = const()[name = string("q_with_bias_v_perm_0"), val = tensor([0, 2, -3, -1])]; + bool x_605_transpose_x_0 = const()[name = string("x_605_transpose_x_0"), val = bool(false)]; + bool x_605_transpose_y_0 = const()[name = string("x_605_transpose_y_0"), val = bool(false)]; + tensor op_5415_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(533176768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(533276160))))[name = string("op_5415_to_fp16_quantized")]; + tensor q_with_bias_v_cast_fp16 = transpose(perm = q_with_bias_v_perm_0, x = var_5413_cast_fp16)[name = string("transpose_155")]; + tensor x_605_cast_fp16 = matmul(transpose_x = x_605_transpose_x_0, transpose_y = x_605_transpose_y_0, x = q_with_bias_v_cast_fp16, y = op_5415_to_fp16_quantized)[name = string("x_605_cast_fp16")]; + tensor x_607_pad_0 = const()[name = string("x_607_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 1, 0])]; + string x_607_mode_0 = const()[name = string("x_607_mode_0"), val = string("constant")]; + fp16 const_378_to_fp16 = const()[name = string("const_378_to_fp16"), val = fp16(0x0p+0)]; + tensor x_607_cast_fp16 = pad(constant_val = const_378_to_fp16, mode = x_607_mode_0, pad = x_607_pad_0, x = x_605_cast_fp16)[name = string("x_607_cast_fp16")]; + tensor var_5423 = const()[name = string("op_5423"), val = tensor([1, 8, -1, 7])]; + tensor x_609_cast_fp16 = reshape(shape = var_5423, x = x_607_cast_fp16)[name = string("x_609_cast_fp16")]; + tensor var_5427_begin_0 = const()[name = string("op_5427_begin_0"), val = tensor([0, 0, 1, 0])]; + tensor var_5427_end_0 = const()[name = string("op_5427_end_0"), val = tensor([1, 8, 98, 7])]; + tensor var_5427_end_mask_0 = const()[name = string("op_5427_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_5427_cast_fp16 = slice_by_index(begin = var_5427_begin_0, end = var_5427_end_0, end_mask = var_5427_end_mask_0, x = x_609_cast_fp16)[name = string("op_5427_cast_fp16")]; + tensor var_5428 = const()[name = string("op_5428"), val = tensor([1, 8, 7, 97])]; + tensor matrix_bd_93_cast_fp16 = reshape(shape = var_5428, x = var_5427_cast_fp16)[name = string("matrix_bd_93_cast_fp16")]; + bool matrix_ac_transpose_x_0 = const()[name = string("matrix_ac_transpose_x_0"), val = bool(false)]; + bool matrix_ac_transpose_y_0 = const()[name = string("matrix_ac_transpose_y_0"), val = bool(false)]; + tensor transpose_142_perm_0 = const()[name = string("transpose_142_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_143_perm_0 = const()[name = string("transpose_143_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_143 = transpose(perm = transpose_143_perm_0, x = k_93_cast_fp16)[name = string("transpose_153")]; + tensor transpose_142 = transpose(perm = transpose_142_perm_0, x = var_5411_cast_fp16)[name = string("transpose_154")]; + tensor matrix_ac_cast_fp16 = matmul(transpose_x = matrix_ac_transpose_x_0, transpose_y = matrix_ac_transpose_y_0, x = transpose_142, y = transpose_143)[name = string("matrix_ac_cast_fp16")]; + tensor matrix_bd_begin_0 = const()[name = string("matrix_bd_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor matrix_bd_end_0 = const()[name = string("matrix_bd_end_0"), val = tensor([1, 8, 7, 49])]; + tensor matrix_bd_end_mask_0 = const()[name = string("matrix_bd_end_mask_0"), val = tensor([true, true, true, false])]; + tensor matrix_bd_cast_fp16 = slice_by_index(begin = matrix_bd_begin_0, end = matrix_bd_end_0, end_mask = matrix_bd_end_mask_0, x = matrix_bd_93_cast_fp16)[name = string("matrix_bd_cast_fp16")]; + tensor var_5437_cast_fp16 = add(x = matrix_ac_cast_fp16, y = matrix_bd_cast_fp16)[name = string("op_5437_cast_fp16")]; + fp16 _inversed_scores_93_y_0_to_fp16 = const()[name = string("_inversed_scores_93_y_0_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor _inversed_scores_93_cast_fp16 = mul(x = var_5437_cast_fp16, y = _inversed_scores_93_y_0_to_fp16)[name = string("_inversed_scores_93_cast_fp16")]; + tensor scores_cast_fp16 = select(a = var_45_to_fp16, b = _inversed_scores_93_cast_fp16, cond = mask_11)[name = string("scores_cast_fp16")]; + tensor var_5443_cast_fp16 = softmax(axis = var_59, x = scores_cast_fp16)[name = string("op_5443_cast_fp16")]; + tensor input_1237_cast_fp16 = select(a = var_44_to_fp16, b = var_5443_cast_fp16, cond = mask_11)[name = string("input_1237_cast_fp16")]; + bool x_611_transpose_x_0 = const()[name = string("x_611_transpose_x_0"), val = bool(false)]; + bool x_611_transpose_y_0 = const()[name = string("x_611_transpose_y_0"), val = bool(false)]; + tensor value_cast_fp16 = transpose(perm = value_perm_0, x = v_cast_fp16)[name = string("transpose_152")]; + tensor x_611_cast_fp16 = matmul(transpose_x = x_611_transpose_x_0, transpose_y = x_611_transpose_y_0, x = input_1237_cast_fp16, y = value_cast_fp16)[name = string("x_611_cast_fp16")]; + tensor var_5447_perm_0 = const()[name = string("op_5447_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_5448 = const()[name = string("op_5448"), val = tensor([1, -1, 1024])]; + tensor var_5447_cast_fp16 = transpose(perm = var_5447_perm_0, x = x_611_cast_fp16)[name = string("transpose_151")]; + tensor input_1239_cast_fp16 = reshape(shape = var_5448, x = var_5447_cast_fp16)[name = string("input_1239_cast_fp16")]; + tensor encoder_layers_23_self_attn_linear_out_weight_to_fp16 = const()[name = string("encoder_layers_23_self_attn_linear_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(533276480)))]; + tensor encoder_layers_23_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_layers_23_self_attn_linear_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(535373696)))]; + tensor linear_214_cast_fp16 = linear(bias = encoder_layers_23_self_attn_linear_out_bias_to_fp16, weight = encoder_layers_23_self_attn_linear_out_weight_to_fp16, x = input_1239_cast_fp16)[name = string("linear_214_cast_fp16")]; + tensor input_1243_cast_fp16 = add(x = input_1233_cast_fp16, y = linear_214_cast_fp16)[name = string("input_1243_cast_fp16")]; + tensor x_615_axes_0 = const()[name = string("x_615_axes_0"), val = tensor([-1])]; + tensor encoder_layers_23_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_23_norm_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(535375808)))]; + tensor encoder_layers_23_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_23_norm_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(535377920)))]; + tensor x_615_cast_fp16 = layer_norm(axes = x_615_axes_0, beta = encoder_layers_23_norm_conv_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_23_norm_conv_weight_to_fp16, x = input_1243_cast_fp16)[name = string("x_615_cast_fp16")]; + tensor input_1245_perm_0 = const()[name = string("input_1245_perm_0"), val = tensor([0, 2, 1])]; + string input_1247_pad_type_0 = const()[name = string("input_1247_pad_type_0"), val = string("valid")]; + tensor input_1247_strides_0 = const()[name = string("input_1247_strides_0"), val = tensor([1])]; + tensor input_1247_pad_0 = const()[name = string("input_1247_pad_0"), val = tensor([0, 0])]; + tensor input_1247_dilations_0 = const()[name = string("input_1247_dilations_0"), val = tensor([1])]; + int32 input_1247_groups_0 = const()[name = string("input_1247_groups_0"), val = int32(1)]; + tensor encoder_layers_23_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(535380032))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(537477248))))[name = string("encoder_layers_23_conv_pointwise_conv1_weight_to_fp16_quantized")]; + tensor input_1245_cast_fp16 = transpose(perm = input_1245_perm_0, x = x_615_cast_fp16)[name = string("transpose_150")]; + tensor input_1247_cast_fp16 = conv(dilations = input_1247_dilations_0, groups = input_1247_groups_0, pad = input_1247_pad_0, pad_type = input_1247_pad_type_0, strides = input_1247_strides_0, weight = encoder_layers_23_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1245_cast_fp16)[name = string("input_1247_cast_fp16")]; + int32 x_617_split_num_splits_0 = const()[name = string("x_617_split_num_splits_0"), val = int32(2)]; + int32 x_617_split_axis_0 = const()[name = string("x_617_split_axis_0"), val = int32(1)]; + tensor x_617_split_cast_fp16_0, tensor x_617_split_cast_fp16_1 = split(axis = x_617_split_axis_0, num_splits = x_617_split_num_splits_0, x = input_1247_cast_fp16)[name = string("x_617_split_cast_fp16")]; + tensor x_617_split_1_sigmoid_cast_fp16 = sigmoid(x = x_617_split_cast_fp16_1)[name = string("x_617_split_1_sigmoid_cast_fp16")]; + tensor x_617_cast_fp16 = mul(x = x_617_split_cast_fp16_0, y = x_617_split_1_sigmoid_cast_fp16)[name = string("x_617_cast_fp16")]; + tensor input_1249_cast_fp16 = select(a = var_44_to_fp16, b = x_617_cast_fp16, cond = var_575)[name = string("input_1249_cast_fp16")]; + bool new_x_interleave_0 = const()[name = string("new_x_interleave_0"), val = bool(false)]; + tensor new_x_cast_fp16 = concat(axis = var_59, interleave = new_x_interleave_0, values = (cache_cast_fp16, input_1249_cast_fp16))[name = string("new_x_cast_fp16")]; + tensor cache_last_time_cur_begin_0 = const()[name = string("cache_last_time_cur_begin_0"), val = tensor([0, 0, 7])]; + tensor cache_last_time_cur_end_0 = const()[name = string("cache_last_time_cur_end_0"), val = tensor([1, 1024, 15])]; + tensor cache_last_time_cur_end_mask_0 = const()[name = string("cache_last_time_cur_end_mask_0"), val = tensor([true, true, true])]; + tensor cache_last_time_cur_cast_fp16 = slice_by_index(begin = cache_last_time_cur_begin_0, end = cache_last_time_cur_end_0, end_mask = cache_last_time_cur_end_mask_0, x = new_x_cast_fp16)[name = string("cache_last_time_cur_cast_fp16")]; + string x_619_pad_type_0 = const()[name = string("x_619_pad_type_0"), val = string("valid")]; + int32 x_619_groups_0 = const()[name = string("x_619_groups_0"), val = int32(1024)]; + tensor x_619_strides_0 = const()[name = string("x_619_strides_0"), val = tensor([1])]; + tensor x_619_pad_0 = const()[name = string("x_619_pad_0"), val = tensor([0, 0])]; + tensor x_619_dilations_0 = const()[name = string("x_619_dilations_0"), val = tensor([1])]; + tensor encoder_layers_23_conv_depthwise_conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(537481408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(537490688))))[name = string("encoder_layers_23_conv_depthwise_conv_weight_to_fp16_quantized")]; + tensor x_619_cast_fp16 = conv(dilations = x_619_dilations_0, groups = x_619_groups_0, pad = x_619_pad_0, pad_type = x_619_pad_type_0, strides = x_619_strides_0, weight = encoder_layers_23_conv_depthwise_conv_weight_to_fp16_quantized, x = new_x_cast_fp16)[name = string("x_619_cast_fp16")]; + tensor input_1251_perm_0 = const()[name = string("input_1251_perm_0"), val = tensor([0, 2, 1])]; + tensor x_621_axes_0 = const()[name = string("x_621_axes_0"), val = tensor([-1])]; + tensor encoder_layers_23_conv_batch_norm_weight_to_fp16 = const()[name = string("encoder_layers_23_conv_batch_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(537492800)))]; + tensor encoder_layers_23_conv_batch_norm_bias_to_fp16 = const()[name = string("encoder_layers_23_conv_batch_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(537494912)))]; + tensor input_1251_cast_fp16 = transpose(perm = input_1251_perm_0, x = x_619_cast_fp16)[name = string("transpose_149")]; + tensor x_621_cast_fp16 = layer_norm(axes = x_621_axes_0, beta = encoder_layers_23_conv_batch_norm_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_23_conv_batch_norm_weight_to_fp16, x = input_1251_cast_fp16)[name = string("x_621_cast_fp16")]; + tensor input_1253_perm_0 = const()[name = string("input_1253_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1253_cast_fp16 = transpose(perm = input_1253_perm_0, x = x_621_cast_fp16)[name = string("transpose_148")]; + tensor input_1255_cast_fp16 = silu(x = input_1253_cast_fp16)[name = string("input_1255_cast_fp16")]; + string x_623_pad_type_0 = const()[name = string("x_623_pad_type_0"), val = string("valid")]; + tensor x_623_strides_0 = const()[name = string("x_623_strides_0"), val = tensor([1])]; + tensor x_623_pad_0 = const()[name = string("x_623_pad_0"), val = tensor([0, 0])]; + tensor x_623_dilations_0 = const()[name = string("x_623_dilations_0"), val = tensor([1])]; + int32 x_623_groups_0 = const()[name = string("x_623_groups_0"), val = int32(1)]; + tensor encoder_layers_23_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(537497024))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(538545664))))[name = string("encoder_layers_23_conv_pointwise_conv2_weight_to_fp16_quantized")]; + tensor x_623_cast_fp16 = conv(dilations = x_623_dilations_0, groups = x_623_groups_0, pad = x_623_pad_0, pad_type = x_623_pad_type_0, strides = x_623_strides_0, weight = encoder_layers_23_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1255_cast_fp16)[name = string("x_623_cast_fp16")]; + tensor input_1257_perm_0 = const()[name = string("input_1257_perm_0"), val = tensor([0, 2, 1])]; + tensor input_1257_cast_fp16 = transpose(perm = input_1257_perm_0, x = x_623_cast_fp16)[name = string("transpose_147")]; + tensor input_1259_cast_fp16 = add(x = input_1243_cast_fp16, y = input_1257_cast_fp16)[name = string("input_1259_cast_fp16")]; + tensor input_1261_axes_0 = const()[name = string("input_1261_axes_0"), val = tensor([-1])]; + tensor encoder_layers_23_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_23_norm_feed_forward2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(538547776)))]; + tensor encoder_layers_23_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_23_norm_feed_forward2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(538549888)))]; + tensor input_1261_cast_fp16 = layer_norm(axes = input_1261_axes_0, beta = encoder_layers_23_norm_feed_forward2_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_23_norm_feed_forward2_weight_to_fp16, x = input_1259_cast_fp16)[name = string("input_1261_cast_fp16")]; + tensor encoder_layers_23_feed_forward2_linear1_weight_to_fp16 = const()[name = string("encoder_layers_23_feed_forward2_linear1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(538552000)))]; + tensor encoder_layers_23_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_layers_23_feed_forward2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(546940672)))]; + tensor linear_215_cast_fp16 = linear(bias = encoder_layers_23_feed_forward2_linear1_bias_to_fp16, weight = encoder_layers_23_feed_forward2_linear1_weight_to_fp16, x = input_1261_cast_fp16)[name = string("linear_215_cast_fp16")]; + tensor input_1265_cast_fp16 = silu(x = linear_215_cast_fp16)[name = string("input_1265_cast_fp16")]; + tensor encoder_layers_23_feed_forward2_linear2_weight_to_fp16 = const()[name = string("encoder_layers_23_feed_forward2_linear2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(546948928)))]; + tensor encoder_layers_23_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_layers_23_feed_forward2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(555337600)))]; + tensor linear_216_cast_fp16 = linear(bias = encoder_layers_23_feed_forward2_linear2_bias_to_fp16, weight = encoder_layers_23_feed_forward2_linear2_weight_to_fp16, x = input_1265_cast_fp16)[name = string("linear_216_cast_fp16")]; + fp16 var_5530_to_fp16 = const()[name = string("op_5530_to_fp16"), val = fp16(0x1p-1)]; + tensor var_5531_cast_fp16 = mul(x = linear_216_cast_fp16, y = var_5530_to_fp16)[name = string("op_5531_cast_fp16")]; + tensor input_1271_cast_fp16 = add(x = input_1259_cast_fp16, y = var_5531_cast_fp16)[name = string("input_1271_cast_fp16")]; + tensor audio_signal_axes_0 = const()[name = string("audio_signal_axes_0"), val = tensor([-1])]; + tensor encoder_layers_23_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_23_norm_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(555339712)))]; + tensor encoder_layers_23_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_23_norm_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(555341824)))]; + tensor audio_signal_cast_fp16 = layer_norm(axes = audio_signal_axes_0, beta = encoder_layers_23_norm_out_bias_to_fp16, epsilon = var_42_to_fp16, gamma = encoder_layers_23_norm_out_weight_to_fp16, x = input_1271_cast_fp16)[name = string("audio_signal_cast_fp16")]; + int32 obj_5_axis_0 = const()[name = string("obj_5_axis_0"), val = int32(0)]; + tensor obj_5_cast_fp16 = stack(axis = obj_5_axis_0, values = (var_484_cast_fp16, var_697_cast_fp16, var_910_cast_fp16, var_1123_cast_fp16, var_1336_cast_fp16, var_1549_cast_fp16, var_1762_cast_fp16, var_1975_cast_fp16, var_2188_cast_fp16, var_2401_cast_fp16, var_2614_cast_fp16, var_2827_cast_fp16, var_3040_cast_fp16, var_3253_cast_fp16, var_3466_cast_fp16, var_3679_cast_fp16, var_3892_cast_fp16, var_4105_cast_fp16, var_4318_cast_fp16, var_4531_cast_fp16, var_4744_cast_fp16, var_4957_cast_fp16, var_5170_cast_fp16, cache_last_channel_cur_cast_fp16))[name = string("obj_5_cast_fp16")]; + int32 obj_7_axis_0 = const()[name = string("obj_7_axis_0"), val = int32(0)]; + tensor obj_7_cast_fp16 = stack(axis = obj_7_axis_0, values = (var_588_cast_fp16, var_801_cast_fp16, var_1014_cast_fp16, var_1227_cast_fp16, var_1440_cast_fp16, var_1653_cast_fp16, var_1866_cast_fp16, var_2079_cast_fp16, var_2292_cast_fp16, var_2505_cast_fp16, var_2718_cast_fp16, var_2931_cast_fp16, var_3144_cast_fp16, var_3357_cast_fp16, var_3570_cast_fp16, var_3783_cast_fp16, var_3996_cast_fp16, var_4209_cast_fp16, var_4422_cast_fp16, var_4635_cast_fp16, var_4848_cast_fp16, var_5061_cast_fp16, var_5274_cast_fp16, cache_last_time_cur_cast_fp16))[name = string("obj_7_cast_fp16")]; + tensor var_5547 = add(x = cache_len, y = max_audio_length_1)[name = string("op_5547")]; + string var_5547_promoted_to_fp16_dtype_0 = const()[name = string("op_5547_promoted_to_fp16_dtype_0"), val = string("fp16")]; + fp16 const_384_to_fp16 = const()[name = string("const_384_to_fp16"), val = fp16(-inf)]; + fp16 var_49_promoted_to_fp16 = const()[name = string("op_49_promoted_to_fp16"), val = fp16(0x1.5p+5)]; + tensor var_5547_to_fp16 = cast(dtype = var_5547_promoted_to_fp16_dtype_0, x = var_5547)[name = string("cast_9")]; + tensor clip_1_cast_fp16 = clip(alpha = const_384_to_fp16, beta = var_49_promoted_to_fp16, x = var_5547_to_fp16)[name = string("clip_1_cast_fp16")]; + int32 one_hot_1_batch_dims_0 = const()[name = string("one_hot_1_batch_dims_0"), val = int32(0)]; + bool one_hot_1_validate_indices_0 = const()[name = string("one_hot_1_validate_indices_0"), val = bool(false)]; + tensor to_onehot_identity_table_to_fp16 = const()[name = string("to_onehot_identity_table_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(555343936)))]; + string prompt_id_to_int16_dtype_0 = const()[name = string("prompt_id_to_int16_dtype_0"), val = string("int16")]; + string cast_230_dtype_0 = const()[name = string("cast_230_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor prompt_id_to_int16 = cast(dtype = prompt_id_to_int16_dtype_0, x = prompt_id)[name = string("cast_8")]; + tensor cast_230 = cast(dtype = cast_230_dtype_0, x = prompt_id_to_int16)[name = string("cast_7")]; + tensor greater_equal_0 = greater_equal(x = cast_230, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_2 = const()[name = string("slice_by_index_2"), val = int32(128)]; + tensor add_0 = add(x = cast_230, y = slice_by_index_2)[name = string("add_0")]; + tensor select_0 = select(a = cast_230, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_6")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_5")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(128)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 greater_equal_0_y_0_2 = const()[name = string("greater_equal_0_y_0_2"), val = int32(0)]; + tensor greater_equal_0_2 = greater_equal(x = select_0_1, y = greater_equal_0_y_0_2)[name = string("greater_equal_0_2")]; + int32 slice_by_index_0_1 = const()[name = string("slice_by_index_0_1"), val = int32(128)]; + tensor add_0_2 = add(x = select_0_1, y = slice_by_index_0_1)[name = string("add_0_2")]; + tensor select_0_2 = select(a = select_0_1, b = add_0_2, cond = greater_equal_0_2)[name = string("select_0_2")]; + int32 one_hot_1_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("one_hot_1_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor one_hot_1_cast_fp16_cast_uint16_cast_uint16 = gather(axis = one_hot_1_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = one_hot_1_batch_dims_0, indices = select_0_2, validate_indices = one_hot_1_validate_indices_0, x = to_onehot_identity_table_to_fp16)[name = string("one_hot_1_cast_fp16_cast_uint16_cast_uint16")]; + tensor var_5593_axes_0 = const()[name = string("op_5593_axes_0"), val = tensor([1])]; + tensor var_5593_cast_fp16 = expand_dims(axes = var_5593_axes_0, x = one_hot_1_cast_fp16_cast_uint16_cast_uint16)[name = string("op_5593_cast_fp16")]; + tensor one_hot_reps_0 = const()[name = string("one_hot_reps_0"), val = tensor([1, 7, 1])]; + tensor one_hot_cast_fp16 = tile(reps = one_hot_reps_0, x = var_5593_cast_fp16)[name = string("one_hot_cast_fp16")]; + int32 var_5602 = const()[name = string("op_5602"), val = int32(-1)]; + bool input_1273_interleave_0 = const()[name = string("input_1273_interleave_0"), val = bool(false)]; + tensor input_1273_cast_fp16 = concat(axis = var_5602, interleave = input_1273_interleave_0, values = (audio_signal_cast_fp16, one_hot_cast_fp16))[name = string("input_1273_cast_fp16")]; + tensor prompt_kernel_0_weight_to_fp16 = const()[name = string("prompt_kernel_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(555376768)))]; + tensor prompt_kernel_0_bias_to_fp16 = const()[name = string("prompt_kernel_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(560095424)))]; + tensor linear_217_cast_fp16 = linear(bias = prompt_kernel_0_bias_to_fp16, weight = prompt_kernel_0_weight_to_fp16, x = input_1273_cast_fp16)[name = string("linear_217_cast_fp16")]; + tensor input_cast_fp16 = relu(x = linear_217_cast_fp16)[name = string("input_cast_fp16")]; + tensor prompt_kernel_2_weight_to_fp16 = const()[name = string("prompt_kernel_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(560099584)))]; + tensor prompt_kernel_2_bias_to_fp16 = const()[name = string("prompt_kernel_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(564293952)))]; + tensor linear_218_cast_fp16 = linear(bias = prompt_kernel_2_bias_to_fp16, weight = prompt_kernel_2_weight_to_fp16, x = input_cast_fp16)[name = string("linear_218_cast_fp16")]; + tensor var_5615_perm_0 = const()[name = string("op_5615_perm_0"), val = tensor([0, 2, 1])]; + string var_5615_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_5615_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + string var_5620_dtype_0 = const()[name = string("op_5620_dtype_0"), val = string("int32")]; + tensor var_5623_perm_0 = const()[name = string("op_5623_perm_0"), val = tensor([1, 0, 2, 3])]; + string var_5623_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_5623_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor var_5626_perm_0 = const()[name = string("op_5626_perm_0"), val = tensor([1, 0, 2, 3])]; + string var_5626_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_5626_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + string var_5631_dtype_0 = const()[name = string("op_5631_dtype_0"), val = string("int32")]; + tensor cache_len_out = cast(dtype = var_5631_dtype_0, x = clip_1_cast_fp16)[name = string("cast_0")]; + tensor var_5626_cast_fp16 = transpose(perm = var_5626_perm_0, x = obj_7_cast_fp16)[name = string("transpose_144")]; + tensor cache_time_out = cast(dtype = var_5626_cast_fp16_to_fp32_dtype_0, x = var_5626_cast_fp16)[name = string("cast_1")]; + tensor var_5623_cast_fp16 = transpose(perm = var_5623_perm_0, x = obj_5_cast_fp16)[name = string("transpose_145")]; + tensor cache_channel_out = cast(dtype = var_5623_cast_fp16_to_fp32_dtype_0, x = var_5623_cast_fp16)[name = string("cast_2")]; + tensor encoded_length = cast(dtype = var_5620_dtype_0, x = clip_0_cast_fp16)[name = string("cast_3")]; + tensor var_5615_cast_fp16 = transpose(perm = var_5615_perm_0, x = linear_218_cast_fp16)[name = string("transpose_146")]; + tensor encoded = cast(dtype = var_5615_cast_fp16_to_fp32_dtype_0, x = var_5615_cast_fp16)[name = string("cast_4")]; + } -> (encoded, encoded_length, cache_channel_out, cache_time_out, cache_len_out); +} \ No newline at end of file diff --git a/multilingual/560ms/encoder.mlmodelc/weights/weight.bin b/multilingual/560ms/encoder.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..7b1ca90fb42a5ee3e6703488fa2d418b42814a01 --- /dev/null +++ b/multilingual/560ms/encoder.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:beaf958ec4cca237d94bf72f5afded9f1b3f6b93439b5d8e3b309e4ee1560e83 +size 564296064 diff --git a/multilingual/560ms/encoder.mlpackage/Data/com.apple.CoreML/model.mlmodel b/multilingual/560ms/encoder.mlpackage/Data/com.apple.CoreML/model.mlmodel new file mode 100644 index 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b/multilingual/560ms/joint.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a285566495ca331c28bd65cb8cd6869402e41b7e1acaca31afd91458a2070130 +size 243 diff --git a/multilingual/560ms/joint.mlmodelc/coremldata.bin b/multilingual/560ms/joint.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..a3a7df8b45babdc266afb4e770e305c0746798e2 --- /dev/null +++ b/multilingual/560ms/joint.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:38ad7913a24486cc3178df1a42d6a8233bcd54c5b42f59bc419ff9101bd19135 +size 341 diff --git a/multilingual/560ms/joint.mlmodelc/model.mil b/multilingual/560ms/joint.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..ed1622830370095ef3dc9ffc07f8ed95de1105d7 --- /dev/null +++ b/multilingual/560ms/joint.mlmodelc/model.mil @@ -0,0 +1,31 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.5.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] +{ + func main(tensor decoder, tensor encoder) { + tensor input_1_perm_0 = const()[name = string("input_1_perm_0"), val = tensor([0, 2, 1])]; + string encoder_to_fp16_dtype_0 = const()[name = string("encoder_to_fp16_dtype_0"), val = string("fp16")]; + tensor input_3_perm_0 = const()[name = string("input_3_perm_0"), val = tensor([0, 2, 1])]; + string decoder_to_fp16_dtype_0 = const()[name = string("decoder_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_enc_weight_to_fp16 = const()[name = string("module_enc_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor module_enc_bias_to_fp16 = const()[name = string("module_enc_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1310848)))]; + tensor encoder_to_fp16 = cast(dtype = encoder_to_fp16_dtype_0, x = encoder)[name = string("cast_2")]; + tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = encoder_to_fp16)[name = string("transpose_1")]; + tensor linear_0_cast_fp16 = linear(bias = module_enc_bias_to_fp16, weight = module_enc_weight_to_fp16, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor module_pred_weight_to_fp16 = const()[name = string("module_pred_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1312192)))]; + tensor module_pred_bias_to_fp16 = const()[name = string("module_pred_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2131456)))]; + tensor decoder_to_fp16 = cast(dtype = decoder_to_fp16_dtype_0, x = decoder)[name = string("cast_1")]; + tensor input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = decoder_to_fp16)[name = string("transpose_0")]; + tensor linear_1_cast_fp16 = linear(bias = module_pred_bias_to_fp16, weight = module_pred_weight_to_fp16, x = input_3_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_23_axes_0 = const()[name = string("op_23_axes_0"), val = tensor([2])]; + tensor var_23_cast_fp16 = expand_dims(axes = var_23_axes_0, x = linear_0_cast_fp16)[name = string("op_23_cast_fp16")]; + tensor var_25_axes_0 = const()[name = string("op_25_axes_0"), val = tensor([1])]; + tensor var_25_cast_fp16 = expand_dims(axes = var_25_axes_0, x = linear_1_cast_fp16)[name = string("op_25_cast_fp16")]; + tensor input_5_cast_fp16 = add(x = var_23_cast_fp16, y = var_25_cast_fp16)[name = string("input_5_cast_fp16")]; + tensor input_7_cast_fp16 = relu(x = input_5_cast_fp16)[name = string("input_7_cast_fp16")]; + tensor module_joint_net_2_weight_to_fp16 = const()[name = string("module_joint_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2132800)))]; + tensor module_joint_net_2_bias_to_fp16 = const()[name = string("module_joint_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18885504)))]; + tensor linear_2_cast_fp16 = linear(bias = module_joint_net_2_bias_to_fp16, weight = module_joint_net_2_weight_to_fp16, x = input_7_cast_fp16)[name = string("linear_2_cast_fp16")]; + string linear_2_cast_fp16_to_fp32_dtype_0 = const()[name = string("linear_2_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor logits = cast(dtype = linear_2_cast_fp16_to_fp32_dtype_0, x = linear_2_cast_fp16)[name = string("cast_0")]; + } -> (logits); +} \ No newline at end of file diff --git a/multilingual/560ms/joint.mlmodelc/weights/weight.bin b/multilingual/560ms/joint.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..0d899ae2b3a3c9be8967b683e91cd8ca7252c8ec --- /dev/null +++ b/multilingual/560ms/joint.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version 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b/multilingual/560ms/joint.mlpackage/Data/com.apple.CoreML/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f4c8ae93e304a187ebfa0b88c812b70e79b625a549727922e7f63d61c1c7b6dd +size 18911744 diff --git a/multilingual/560ms/joint.mlpackage/Manifest.json b/multilingual/560ms/joint.mlpackage/Manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..f97f8d4fd73506ae49bbd5bc163cd2079627721d --- /dev/null +++ b/multilingual/560ms/joint.mlpackage/Manifest.json @@ -0,0 +1,18 @@ +{ + "fileFormatVersion": "1.0.0", + "itemInfoEntries": { + "3F33ED2D-8E5C-4BBD-B243-7D003571E7E2": { + "author": "com.apple.CoreML", + "description": "CoreML Model Specification", + "name": "model.mlmodel", + "path": "com.apple.CoreML/model.mlmodel" + }, + "7D35F675-3334-491B-8264-00E768D11202": { + "author": "com.apple.CoreML", + "description": "CoreML Model Weights", + "name": "weights", + "path": "com.apple.CoreML/weights" + } + }, + "rootModelIdentifier": "3F33ED2D-8E5C-4BBD-B243-7D003571E7E2" +} diff --git a/multilingual/560ms/joint_noencproj_batched.mlmodelc/analytics/coremldata.bin b/multilingual/560ms/joint_noencproj_batched.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..e018ec9de1fd95cbb225a25d41f7166cc2650ccd --- /dev/null +++ b/multilingual/560ms/joint_noencproj_batched.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e5f4a9b0771be9af64fd93db7ceb42dbd305920b1260fe2219f1b046e84841cd +size 243 diff --git a/multilingual/560ms/joint_noencproj_batched.mlmodelc/coremldata.bin b/multilingual/560ms/joint_noencproj_batched.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..26d077246e6f610576835d6040df735a7222e4a5 --- /dev/null +++ b/multilingual/560ms/joint_noencproj_batched.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fc07c4c2de2b13127f406ee70373b2c178702a03755bdc7a2bd57e623b5e65c5 +size 406 diff --git a/multilingual/560ms/joint_noencproj_batched.mlmodelc/model.mil b/multilingual/560ms/joint_noencproj_batched.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..89b37c4bfd82ec0f8905ac19299fbe7a5f1d7e73 --- /dev/null +++ b/multilingual/560ms/joint_noencproj_batched.mlmodelc/model.mil @@ -0,0 +1,26 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.10.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] +{ + func main(tensor decoder, tensor encoder_proj) { + tensor input_1_perm_0 = const()[name = string("input_1_perm_0"), val = tensor([0, 2, 1])]; + string decoder_to_fp16_dtype_0 = const()[name = string("decoder_to_fp16_dtype_0"), val = string("fp16")]; + tensor joint_module_pred_weight_to_fp16 = const()[name = string("joint_module_pred_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor joint_module_pred_bias_to_fp16 = const()[name = string("joint_module_pred_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(819328)))]; + tensor decoder_to_fp16 = cast(dtype = decoder_to_fp16_dtype_0, x = decoder)[name = string("cast_2")]; + tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = decoder_to_fp16)[name = string("transpose_0")]; + tensor linear_0_cast_fp16 = linear(bias = joint_module_pred_bias_to_fp16, weight = joint_module_pred_weight_to_fp16, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_15_axes_0 = const()[name = string("op_15_axes_0"), val = tensor([2])]; + string encoder_proj_to_fp16_dtype_0 = const()[name = string("encoder_proj_to_fp16_dtype_0"), val = string("fp16")]; + tensor encoder_proj_to_fp16 = cast(dtype = encoder_proj_to_fp16_dtype_0, x = encoder_proj)[name = string("cast_1")]; + tensor var_15_cast_fp16 = expand_dims(axes = var_15_axes_0, x = encoder_proj_to_fp16)[name = string("op_15_cast_fp16")]; + tensor var_17_axes_0 = const()[name = string("op_17_axes_0"), val = tensor([1])]; + tensor var_17_cast_fp16 = expand_dims(axes = var_17_axes_0, x = linear_0_cast_fp16)[name = string("op_17_cast_fp16")]; + tensor input_3_cast_fp16 = add(x = var_15_cast_fp16, y = var_17_cast_fp16)[name = string("input_3_cast_fp16")]; + tensor input_5_cast_fp16 = relu(x = input_3_cast_fp16)[name = string("input_5_cast_fp16")]; + tensor joint_module_joint_net_2_weight_to_fp16 = const()[name = string("joint_module_joint_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(820672)))]; + tensor joint_module_joint_net_2_bias_to_fp16 = const()[name = string("joint_module_joint_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17573376)))]; + tensor linear_1_cast_fp16 = linear(bias = joint_module_joint_net_2_bias_to_fp16, weight = joint_module_joint_net_2_weight_to_fp16, x = input_5_cast_fp16)[name = string("linear_1_cast_fp16")]; + string linear_1_cast_fp16_to_fp32_dtype_0 = const()[name = string("linear_1_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor logits = cast(dtype = linear_1_cast_fp16_to_fp32_dtype_0, x = linear_1_cast_fp16)[name = string("cast_0")]; + } -> (logits); +} \ No newline at end of file diff --git a/multilingual/560ms/joint_noencproj_batched.mlmodelc/weights/weight.bin b/multilingual/560ms/joint_noencproj_batched.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..defecb9c76ab612924f900c8d498e0e5ff52cc43 --- /dev/null +++ b/multilingual/560ms/joint_noencproj_batched.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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b/multilingual/560ms/joint_noencproj_batched.mlpackage/Data/com.apple.CoreML/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8d6b104e9d6990c07d6cd41bafe27cae8d39cfe037ec701584c47af1094daeeb +size 17599616 diff --git a/multilingual/560ms/joint_noencproj_batched.mlpackage/Manifest.json b/multilingual/560ms/joint_noencproj_batched.mlpackage/Manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..f32ce8ce814e436e645a7f1306381c47fd849292 --- /dev/null +++ b/multilingual/560ms/joint_noencproj_batched.mlpackage/Manifest.json @@ -0,0 +1,18 @@ +{ + "fileFormatVersion": "1.0.0", + "itemInfoEntries": { + "55B094CA-55E5-480A-8B14-30A24DC3EEF0": { + "author": "com.apple.CoreML", + "description": "CoreML Model Weights", + "name": "weights", + "path": "com.apple.CoreML/weights" + }, + "CA02FD13-87CE-4425-9B49-DE8265EC1B54": { + "author": "com.apple.CoreML", + "description": "CoreML Model Specification", + "name": "model.mlmodel", + "path": "com.apple.CoreML/model.mlmodel" + } + }, + "rootModelIdentifier": "CA02FD13-87CE-4425-9B49-DE8265EC1B54" +} diff --git a/multilingual/560ms/metadata.json b/multilingual/560ms/metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..dc148148b47c1e6f4d5c469f9f7283e3d462ceec --- /dev/null +++ b/multilingual/560ms/metadata.json @@ -0,0 +1,197 @@ +{ + "model": "nvidia/nemotron-asr-streaming-multilingual-0.6b", + "model_class": "nemo.collections.asr.models.rnnt_bpe_models_prompt.EncDecRNNTBPEModelWithPrompt", + "sample_rate": 16000, + "mel_features": 128, + "chunk_mel_frames": 56, + "pre_encode_cache": 9, + "total_mel_frames": 65, + "att_context_size": [ + 42, + 13 + ], + "vocab_size": 13087, + "blank_idx": 13087, + "cache_channel_shape": [ + 1, + 24, + 42, + 1024 + ], + "cache_time_shape": [ + 1, + 24, + 1024, + 8 + ], + "decoder_hidden": 640, + "decoder_layers": 2, + "encoder_dim": 1024, + "num_prompts": 128, + "prompt_dictionary": { + "af-ZA": 54, + 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sha256:5ef90b3c4f6365cadfc6b88e1e03187356ce2383eeb8aa13cf58484699678e48 +size 431 diff --git a/multilingual/560ms/preprocessor.mlmodelc/model.mil b/multilingual/560ms/preprocessor.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..b1a0b2b9193c992de42e51245fc1ef433d345afc --- /dev/null +++ b/multilingual/560ms/preprocessor.mlmodelc/model.mil @@ -0,0 +1,122 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.10.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] +{ + func main(tensor audio, tensor audio_length) [FlexibleShapeInformation = tuple>>, tuple, ?>>>>((("DefaultShapes", {{"audio", [1, 1]}}), ("RangeDims", {{"audio", [[1, 1], [1, 1280000]]}})))] { + int32 var_9 = const()[name = string("op_9"), val = int32(1)]; + int32 var_10 = const()[name = string("op_10"), val = int32(160)]; + int32 var_12 = const()[name = string("op_12"), val = int32(0)]; + int32 var_33 = const()[name = string("op_33"), val = int32(512)]; + tensor var_34 = add(x = audio_length, y = var_33)[name = string("op_34")]; + int32 var_35 = const()[name = string("op_35"), val = int32(512)]; + tensor var_36 = sub(x = var_34, y = var_35)[name = string("op_36")]; + tensor floor_div_0 = floor_div(x = var_36, y = var_10)[name = string("floor_div_0")]; + tensor var_39 = equal(x = audio_length, y = var_12)[name = string("op_39")]; + tensor var_40 = const()[name = string("op_40"), val = tensor([0])]; + tensor mel_length = select(a = var_40, b = floor_div_0, cond = var_39)[name = string("seq_len")]; + string audio_to_fp16_dtype_0 = const()[name = string("audio_to_fp16_dtype_0"), val = string("fp16")]; + tensor audio_to_fp16 = cast(dtype = audio_to_fp16_dtype_0, x = audio)[name = string("cast_10")]; + tensor var_42_shape_cast_fp16 = shape(x = audio_to_fp16)[name = string("op_42_shape_cast_fp16")]; + int32 gather_0_axis_0 = const()[name = string("gather_0_axis_0"), val = int32(0)]; + int32 gather_0_batch_dims_0 = const()[name = string("gather_0_batch_dims_0"), val = int32(0)]; + bool gather_0_validate_indices_0 = const()[name = string("gather_0_validate_indices_0"), val = bool(false)]; + string var_42_shape_cast_fp16_to_int16_dtype_0 = const()[name = string("op_42_shape_cast_fp16_to_int16_dtype_0"), val = string("int16")]; + uint16 gather_0_indices_0_to_uint16 = const()[name = string("gather_0_indices_0_to_uint16"), val = uint16(1)]; + tensor var_42_shape_cast_fp16_to_int16 = cast(dtype = var_42_shape_cast_fp16_to_int16_dtype_0, x = var_42_shape_cast_fp16)[name = string("cast_9")]; + int16 gather_0_cast_uint16 = gather(axis = gather_0_axis_0, batch_dims = gather_0_batch_dims_0, indices = gather_0_indices_0_to_uint16, validate_indices = gather_0_validate_indices_0, x = var_42_shape_cast_fp16_to_int16)[name = string("gather_0_cast_uint16")]; + string gather_0_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_0_cast_uint16_to_int32_dtype_0"), val = string("int32")]; + int32 const_0 = const()[name = string("const_0"), val = int32(0)]; + int32 const_1 = const()[name = string("const_1"), val = int32(1)]; + int32 gather_0_cast_uint16_to_int32 = cast(dtype = gather_0_cast_uint16_to_int32_dtype_0, x = gather_0_cast_uint16)[name = string("cast_8")]; + tensor var_43 = range_1d(end = gather_0_cast_uint16_to_int32, start = const_0, step = const_1)[name = string("op_43")]; + tensor var_44_axes_0 = const()[name = string("op_44_axes_0"), val = tensor([0])]; + tensor var_44 = expand_dims(axes = var_44_axes_0, x = var_43)[name = string("op_44")]; + tensor var_45_axes_0 = const()[name = string("op_45_axes_0"), val = tensor([1])]; + tensor var_45 = expand_dims(axes = var_45_axes_0, x = audio_length)[name = string("op_45")]; + tensor timemask = less(x = var_44, y = var_45)[name = string("timemask")]; + tensor var_48_begin_0 = const()[name = string("op_48_begin_0"), val = tensor([0, 0])]; + tensor var_48_end_0 = const()[name = string("op_48_end_0"), val = tensor([1, 1])]; + tensor var_48_end_mask_0 = const()[name = string("op_48_end_mask_0"), val = tensor([true, false])]; + tensor var_48_squeeze_mask_0 = const()[name = string("op_48_squeeze_mask_0"), val = tensor([false, true])]; + tensor var_48_cast_fp16 = slice_by_index(begin = var_48_begin_0, end = var_48_end_0, end_mask = var_48_end_mask_0, squeeze_mask = var_48_squeeze_mask_0, x = audio_to_fp16)[name = string("op_48_cast_fp16")]; + tensor var_49_axes_0 = const()[name = string("op_49_axes_0"), val = tensor([1])]; + tensor var_49_cast_fp16 = expand_dims(axes = var_49_axes_0, x = var_48_cast_fp16)[name = string("op_49_cast_fp16")]; + tensor var_51_begin_0 = const()[name = string("op_51_begin_0"), val = tensor([0, 1])]; + tensor var_51_end_0 = const()[name = string("op_51_end_0"), val = tensor([1, 0])]; + tensor var_51_end_mask_0 = const()[name = string("op_51_end_mask_0"), val = tensor([true, true])]; + tensor var_51_cast_fp16 = slice_by_index(begin = var_51_begin_0, end = var_51_end_0, end_mask = var_51_end_mask_0, x = audio_to_fp16)[name = string("op_51_cast_fp16")]; + tensor var_53_begin_0 = const()[name = string("op_53_begin_0"), val = tensor([0, 0])]; + tensor var_53_end_0 = const()[name = string("op_53_end_0"), val = tensor([1, -1])]; + tensor var_53_end_mask_0 = const()[name = string("op_53_end_mask_0"), val = tensor([true, false])]; + tensor var_53_cast_fp16 = slice_by_index(begin = var_53_begin_0, end = var_53_end_0, end_mask = var_53_end_mask_0, x = audio_to_fp16)[name = string("op_53_cast_fp16")]; + fp16 var_54_to_fp16 = const()[name = string("op_54_to_fp16"), val = fp16(0x1.f0cp-1)]; + tensor var_55_cast_fp16 = mul(x = var_53_cast_fp16, y = var_54_to_fp16)[name = string("op_55_cast_fp16")]; + tensor var_56_cast_fp16 = sub(x = var_51_cast_fp16, y = var_55_cast_fp16)[name = string("op_56_cast_fp16")]; + bool x_3_interleave_0 = const()[name = string("x_3_interleave_0"), val = bool(false)]; + tensor x_3_cast_fp16 = concat(axis = var_9, interleave = x_3_interleave_0, values = (var_49_cast_fp16, var_56_cast_fp16))[name = string("x_3_cast_fp16")]; + tensor var_59 = logical_not(x = timemask)[name = string("op_59")]; + fp16 var_16_to_fp16 = const()[name = string("op_16_to_fp16"), val = fp16(0x0p+0)]; + tensor input_1_cast_fp16 = select(a = var_16_to_fp16, b = x_3_cast_fp16, cond = var_59)[name = string("input_1_cast_fp16")]; + tensor concat_1x = const()[name = string("concat_1x"), val = tensor([1, 1, -1])]; + tensor input_3_cast_fp16 = reshape(shape = concat_1x, x = input_1_cast_fp16)[name = string("input_3_cast_fp16")]; + tensor input_5_pad_0 = const()[name = string("input_5_pad_0"), val = tensor([0, 0, 0, 0, 256, 256])]; + string input_5_mode_0 = const()[name = string("input_5_mode_0"), val = string("constant")]; + fp16 const_3_to_fp16 = const()[name = string("const_3_to_fp16"), val = fp16(0x0p+0)]; + tensor input_5_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = input_5_mode_0, pad = input_5_pad_0, x = input_3_cast_fp16)[name = string("input_5_cast_fp16")]; + tensor concat_2x = const()[name = string("concat_2x"), val = tensor([1, -1])]; + tensor input_cast_fp16 = reshape(shape = concat_2x, x = input_5_cast_fp16)[name = string("input_cast_fp16")]; + tensor expand_dims_3 = const()[name = string("expand_dims_3"), val = tensor([160])]; + tensor expand_dims_4_axes_0 = const()[name = string("expand_dims_4_axes_0"), val = tensor([1])]; + tensor expand_dims_4_cast_fp16 = expand_dims(axes = expand_dims_4_axes_0, x = input_cast_fp16)[name = string("expand_dims_4_cast_fp16")]; + string conv_0_pad_type_0 = const()[name = string("conv_0_pad_type_0"), val = string("valid")]; + tensor conv_0_pad_0 = const()[name = string("conv_0_pad_0"), val = tensor([0, 0])]; + tensor conv_0_dilations_0 = const()[name = string("conv_0_dilations_0"), val = tensor([1])]; + int32 conv_0_groups_0 = const()[name = string("conv_0_groups_0"), val = int32(1)]; + tensor expand_dims_1_to_fp16 = const()[name = string("expand_dims_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor conv_0_cast_fp16 = conv(dilations = conv_0_dilations_0, groups = conv_0_groups_0, pad = conv_0_pad_0, pad_type = conv_0_pad_type_0, strides = expand_dims_3, weight = expand_dims_1_to_fp16, x = expand_dims_4_cast_fp16)[name = string("conv_0_cast_fp16")]; + string conv_1_pad_type_0 = const()[name = string("conv_1_pad_type_0"), val = string("valid")]; + tensor conv_1_pad_0 = const()[name = string("conv_1_pad_0"), val = tensor([0, 0])]; + tensor conv_1_dilations_0 = const()[name = string("conv_1_dilations_0"), val = tensor([1])]; + int32 conv_1_groups_0 = const()[name = string("conv_1_groups_0"), val = int32(1)]; + tensor expand_dims_2_to_fp16 = const()[name = string("expand_dims_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263296)))]; + tensor conv_1_cast_fp16 = conv(dilations = conv_1_dilations_0, groups = conv_1_groups_0, pad = conv_1_pad_0, pad_type = conv_1_pad_type_0, strides = expand_dims_3, weight = expand_dims_2_to_fp16, x = expand_dims_4_cast_fp16)[name = string("conv_1_cast_fp16")]; + int32 stack_0_axis_0 = const()[name = string("stack_0_axis_0"), val = int32(-1)]; + tensor stack_0_cast_fp16 = stack(axis = stack_0_axis_0, values = (conv_0_cast_fp16, conv_1_cast_fp16))[name = string("stack_0_cast_fp16")]; + fp16 var_19_promoted_to_fp16 = const()[name = string("op_19_promoted_to_fp16"), val = fp16(0x1p+1)]; + tensor var_74_cast_fp16 = pow(x = stack_0_cast_fp16, y = var_19_promoted_to_fp16)[name = string("op_74_cast_fp16")]; + tensor var_76_axes_0 = const()[name = string("op_76_axes_0"), val = tensor([-1])]; + bool var_76_keep_dims_0 = const()[name = string("op_76_keep_dims_0"), val = bool(false)]; + tensor var_76_cast_fp16 = reduce_sum(axes = var_76_axes_0, keep_dims = var_76_keep_dims_0, x = var_74_cast_fp16)[name = string("op_76_cast_fp16")]; + tensor x_11_cast_fp16 = identity(x = var_76_cast_fp16)[name = string("x_11_cast_fp16")]; + bool x_13_transpose_x_0 = const()[name = string("x_13_transpose_x_0"), val = bool(false)]; + bool x_13_transpose_y_0 = const()[name = string("x_13_transpose_y_0"), val = bool(false)]; + tensor const_4_to_fp16 = const()[name = string("const_4_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(526528)))]; + tensor x_13_cast_fp16 = matmul(transpose_x = x_13_transpose_x_0, transpose_y = x_13_transpose_y_0, x = const_4_to_fp16, y = x_11_cast_fp16)[name = string("x_13_cast_fp16")]; + fp16 var_83_to_fp16 = const()[name = string("op_83_to_fp16"), val = fp16(0x1p-24)]; + tensor var_84_cast_fp16 = add(x = x_13_cast_fp16, y = var_83_to_fp16)[name = string("op_84_cast_fp16")]; + fp32 x_epsilon_0 = const()[name = string("x_epsilon_0"), val = fp32(0x1p-149)]; + tensor x_cast_fp16 = log(epsilon = x_epsilon_0, x = var_84_cast_fp16)[name = string("x_cast_fp16")]; + tensor var_86_shape_cast_fp16 = shape(x = x_cast_fp16)[name = string("op_86_shape_cast_fp16")]; + int32 gather_5_batch_dims_0 = const()[name = string("gather_5_batch_dims_0"), val = int32(0)]; + bool gather_5_validate_indices_0 = const()[name = string("gather_5_validate_indices_0"), val = bool(false)]; + string var_86_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_86_shape_cast_fp16_to_uint16_dtype_0"), val = string("uint16")]; + int32 gather_5_cast_uint16_axis_0 = const()[name = string("gather_5_cast_uint16_axis_0"), val = int32(0)]; + uint16 select_0_to_uint16 = const()[name = string("select_0_to_uint16"), val = uint16(2)]; + tensor var_86_shape_cast_fp16_to_uint16 = cast(dtype = var_86_shape_cast_fp16_to_uint16_dtype_0, x = var_86_shape_cast_fp16)[name = string("cast_7")]; + uint16 gather_5_cast_uint16_cast_uint16 = gather(axis = gather_5_cast_uint16_axis_0, batch_dims = gather_5_batch_dims_0, indices = select_0_to_uint16, validate_indices = gather_5_validate_indices_0, x = var_86_shape_cast_fp16_to_uint16)[name = string("gather_5_cast_uint16_cast_uint16")]; + string gather_5_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_5_cast_uint16_to_int32_dtype_0"), val = string("int32")]; + int32 const_5 = const()[name = string("const_5"), val = int32(0)]; + int32 const_6 = const()[name = string("const_6"), val = int32(1)]; + int32 gather_5_cast_uint16_to_int32 = cast(dtype = gather_5_cast_uint16_to_int32_dtype_0, x = gather_5_cast_uint16_cast_uint16)[name = string("cast_6")]; + tensor mask_1 = range_1d(end = gather_5_cast_uint16_to_int32, start = const_5, step = const_6)[name = string("mask_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([0])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = mask_1)[name = string("expand_dims_0")]; + tensor var_91_axes_0 = const()[name = string("op_91_axes_0"), val = tensor([1])]; + tensor var_91 = expand_dims(axes = var_91_axes_0, x = mel_length)[name = string("op_91")]; + tensor mask = greater_equal(x = expand_dims_0, y = var_91)[name = string("mask")]; + tensor var_93_axes_0 = const()[name = string("op_93_axes_0"), val = tensor([1])]; + tensor var_93 = expand_dims(axes = var_93_axes_0, x = mask)[name = string("op_93")]; + tensor processed_signal_cast_fp16 = select(a = var_16_to_fp16, b = x_cast_fp16, cond = var_93)[name = string("processed_signal_cast_fp16")]; + string processed_signal_cast_fp16_to_fp32_dtype_0 = const()[name = string("processed_signal_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor mel = cast(dtype = processed_signal_cast_fp16_to_fp32_dtype_0, x = processed_signal_cast_fp16)[name = string("cast_5")]; + } -> (mel, mel_length); +} \ No newline at end of file diff --git a/multilingual/560ms/preprocessor.mlmodelc/weights/weight.bin b/multilingual/560ms/preprocessor.mlmodelc/weights/weight.bin new file mode 100644 index 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"com.apple.CoreML/model.mlmodel" + }, + "B1E3045B-7172-4628-BD63-F2960CEFBA16": { + "author": "com.apple.CoreML", + "description": "CoreML Model Weights", + "name": "weights", + "path": "com.apple.CoreML/weights" + } + }, + "rootModelIdentifier": "571B03EE-25FB-448D-B106-8C0685101326" +} diff --git a/multilingual/560ms/tokenizer.json b/multilingual/560ms/tokenizer.json new file mode 100644 index 0000000000000000000000000000000000000000..5c9c31a266fd62950553b9d5fef65598813f55e0 --- /dev/null +++ b/multilingual/560ms/tokenizer.json @@ -0,0 +1,13089 @@ +{ + "0": "", + "1": "", + "2": "\u2581", + "3": "\u0438", + "4": ".", + "5": "\u0435", + "6": ",", + "7": "\u0430", + "8": "\u0441", + "9": "\u043e", + "10": "\u043d", + "11": "\u0442", + "12": "\u0442\u0430", + "13": "\u044f", + "14": "\u043a", + "15": "\u2581\u043d\u0430", + "16": "\u043b", + "17": "\u0443", + "18": "\u0437", + "19": "\u0440", + "20": "\u0442\u043e", + "21": "\u043d\u0430", + "22": "\u2581\u0434\u0430", + "23": "\u0432\u0430", + "24": "\u0440\u0430", + "25": "\u0434", + "26": "e", + "27": "\u043a\u0430", + "28": "\u2581\u0437\u0430", + "29": "\u043d\u043e", + "30": "\u043c", + "31": "\u043d\u0438", + "32": "\u044a", + "33": "\u043c\u0435", + "34": "t", + "35": "\u0441\u0442", + "36": "\u043f", + "37": "\u2581\u043f\u043e", + "38": "a", + "39": "\u043d\u0435", + "40": "s", + "41": "\u2581\u0441\u0435", + "42": "\u0440\u0435", + "43": "\u0432", + "44": "\u043a\u043e", + "45": "\u2581\u0432", + "46": "o", + "47": "i", + "48": "\u2581\u0441", + "49": "\u0433", + "50": "\u0442\u0435", + "51": "\u043b\u0438", + "52": "\u0447\u0435", + "53": "\u0431", + "54": "\u2581\u0438", + "55": "\u0442\u0438", + "56": "\u0436", + "57": "\u2581\u043e\u0442", + "58": "\u0447", + "59": "r", + "60": "\u0438\u0442\u0435", + "61": "\u043c\u0430", + "62": "\u0445", + "63": "\u0432\u0435", + "64": "\u0432\u0438", + "65": "\u0440\u0438", + "66": "l", + "67": "\u0448", + "68": "u", + "69": "\u0432\u043e", + "70": "\u2581\u0435", + "71": "d", + "72": "\u0434\u0438", + "73": "c", + "74": "\u0433\u0430", + "75": "\u043c\u0438", + "76": "\u043b\u0430", + "77": "\u043f\u043e", + "78": "\u2581\u0441\u044a", + "79": "\u0439", + "80": "\u0449\u0435", + "81": "\u043b\u0435", + "82": "\u0440\u043e", + "83": "\u0434\u0435", + "84": "\u0433\u043e", + "85": "h", + "86": "m", + "87": "\u0446\u0438", + "88": "\u0449", + "89": "\u2581\u043f\u0440\u043e", + "90": "\u2581\u0438\u0437", + "91": "\u0434\u0430", + "92": "\u043c\u043e", + "93": "\u0441\u0438", + "94": "\u0418", + "95": "\u2581\u0442\u043e\u0432\u0430", + "96": "\u043d\u0438\u044f", + "97": "p", + "98": "\u043d\u0438\u0442\u0435", + "99": "\u0435\u043d\u0438\u0435", + "100": "\u043b\u043e", + "101": "\u041d", + "102": "\u0444", + "103": "\u2581\u0434\u043e", + "104": "\u041f", + "105": "\u2581\u043f\u0440\u0435\u0434", + "106": "\u2581\u043f\u0440\u0438", + "107": "\u043f\u0440\u0430\u0432", + "108": "\u0421", + "109": "\u2581\u0441\u0430", + "110": "\u2581\u0440\u0430\u0437", + "111": "\u0449\u043e", + "112": "\u2581\u043e\u0431", + "113": "n", + "114": "\u2581\u0438\u043c\u0430", + "115": "\u043f\u0430", + "116": "\u0441\u0442\u0430", + "117": "\u0412", + "118": "\u2581\u0442\u0440\u044f\u0431\u0432\u0430", + "119": "g", + "120": "\u043d\u043e\u0441\u0442", + "121": "f", + "122": "\u0431\u0438", + "123": "\u0447\u0430", + "124": "en", + "125": "in", + "126": "\u0446\u0438\u044f", + "127": "\u0432\u044a\u0440", + "128": "on", + "129": "y", + "130": "er", + "131": "an", + "132": "\u0422", + "133": "w", + "134": "\u0432\u0430\u043d\u0435", + "135": "\u041a", + "136": "\u2581\u043a\u043e\u0438\u0442\u043e", + "137": "\u2581\u043c\u043d\u043e\u0433\u043e", + "138": "\u2581\u043f\u0440\u0435", + "139": "0", + "140": "b", + "141": "v", + "142": "\u0434\u044a\u0440\u0436\u0430", + "143": "\u0446\u0435", + "144": "\u0410", + "145": "\u0436\u0434\u0430", + "146": "\u2581\u0411\u043b\u0430\u0433\u043e\u0434\u0430\u0440\u044f", + "147": "2", + "148": "\u041c", + "149": "1", + "150": "\u2581the", + "151": "\u041e", + "152": "\u2581\u043c\u043e\u0436\u0435", + "153": "\u0414", + "154": "\u0441\u043b\u0435\u0434", + "155": "\u0433\u043e\u0432\u043e\u0440", + "156": "\u0440\u0430\u0431\u043e\u0442", + "157": "\u2581\u0432\u044a\u043f\u0440\u043e\u0441", + "158": "\u0434\u043e\u0431", + "159": "\u0417", + "160": "\u0446", + "161": "k", + "162": "\u2581\u0422\u043e\u0432\u0430", + "163": "?", + "164": "\u2581\u0431\u044a\u0434\u0435", + "165": "\u2581\u043a\u043e\u043c\u0438\u0441\u0438\u044f", + "166": "\u0415", + "167": "\u2581\u0442\u043e\u0437\u0438", + "168": "\u2581\u0442\u0435\u0437\u0438", + "169": "\u2581\u0433\u043e\u0441\u043f\u043e\u0434\u0438\u043d", + "170": "\u2581\u0432\u044a\u0437", + "171": "\u2581\u0415\u0432\u0440\u043e\u043f\u0435\u0439\u0441\u043a\u0438\u044f", + "172": "\u0411", + "173": "\u0413", + "174": "\u0420", + "175": "\u044e", + "176": "3", + "177": "5", + "178": "q", + "179": "I", + "180": "\u00e9", + "181": "4", + "182": "z", + "183": "A", + "184": "6", + "185": "j", + "186": "E", + "187": "7", + "188": "\u0429", + "189": "T", + "190": "8", + "191": "9", + "192": "\u041b", + "193": "S", + "194": "\u0424", + "195": "x", + "196": "C", + "197": "\u0425", + "198": "\u044c", + "199": "M", + "200": "P", + "201": "\u0423", + "202": "D", + "203": "B", + "204": "\u0427", + "205": "U", + "206": "W", + "207": "\u0428", + "208": "\u0426", + "209": "N", + "210": "O", + "211": "G", + "212": "\u00e0", + "213": "F", + "214": "L", + "215": "\u00e8", + "216": "V", + "217": "R", + "218": "\u0627", + "219": "\u042e", + "220": "\u00f3", + "221": "\u042f", + "222": "H", + "223": "\u03b1", + "224": "\u00fc", + "225": "\u00e4", + "226": "\u0644", + "227": "\u0416", + "228": "J", + "229": "\u00ed", + "230": "\u03c4", + "231": "\u03b9", + "232": "\u00e1", + "233": "\u03bf", + "234": "K", + "235": "\u03b5", + "236": "\u064a", + "237": "\u00ea", + "238": "Y", + "239": "\u03bd", + "240": "\u0646", + "241": "\u00f6", + "242": "\u0645", + "243": "\u00e7", + "244": "\u03c1", + "245": "\u0419", + "246": "\u0648", + "247": "\u03c3", + "248": "\u03c0", + "249": "\u0119", + "250": "\u03c5", + "251": "\u062a", + "252": "\u03b7", + "253": "\u0631", + "254": "\u03bc", + "255": "\u03ba", + "256": "", + "257": "st", + "258": "ch", + "259": "n\u00ed", + "260": "\u2581s", + "261": "le", + "262": "li", + "263": "\u2581po", + "264": "\u2581v", + "265": "\u017e", + "266": "\u010d", + "267": "\u2581to", + "268": "no", + "269": "to", + "270": "\u2581z", + "271": "me", + "272": "\u2581se", + "273": "\u2581a", + "274": "te", + "275": "\u2581je", + "276": "ho", + "277": "\u2581pro", + "278": "\u016f", + "279": "n\u011b", + "280": "ro", + "281": "\u2581na", + "282": "ce", + "283": "\u2581o", + "284": "la", + "285": "\u0161", + "286": "\u2581ne", + "287": "ni", + "288": "ra", + "289": "ti", + "290": "lo", + "291": "ko", + "292": "\u2581\u017ee", + "293": "n\u00e1", + "294": "po", + "295": "je", + "296": "\u011b", + "297": "de", + "298": "na", + "299": "mi", + "300": "\u2581do", + "301": "ci", + "302": "\u2581k", + "303": "ku", + "304": "\u0159e", + "305": "\u2581by", + "306": "ve", + "307": "\u2581za", + "308": "m\u011b", + "309": "\u2581A", + "310": "\u00fd", + "311": "re", + "312": "v\u00e1", + "313": "ou", + "314": "vo", + "315": "n\u00e9", + "316": "va", + "317": "\u017ee", + "318": "mo", + "319": "v\u011b", + "320": "j\u00ed", + "321": "t\u011b", + "322": "v\u00fd", + "323": "\u2581tak", + "324": "ze", + "325": "\u0159\u00ed", + "326": "ne", + "327": "\u0161e", + "328": "\u2581vy", + "329": "ka", + "330": "ji", + "331": "ky", + "332": "r\u00e1", + "333": "ovat", + "334": "\u2581ob", + "335": "c\u00ed", + "336": "\u2581jak", + "337": "\u2581p\u0159e", + "338": "ny", + "339": "v\u00ed", + "340": "n\u00fd", + "341": "vi", + "342": "\u2581in", + "343": "pr\u00e1v", + "344": "\u00fa", + "345": "\u2581co", + "346": "\u2581tak\u00e9", + "347": "ent", + "348": "\u2581pan", + "349": "\u2581D\u011bkuji", + "350": "\u2581kter\u00e9", + "351": "\u0159i", + "352": "\u2581aby", + "353": "\u2581p\u0159\u00ed", + "354": "\u2581p\u0159i", + "355": "prav", + "356": "\u0159", + "357": "vrop", + "358": "\u2581bude", + "359": "\u2581roz", + "360": "\u2581jsou", + "361": "ov\u00e9", + "362": "\u2581jsme", + "363": "sk\u00e9", + "364": "ov\u00e1n\u00ed", + "365": "\u2581tady", + "366": "sk\u00fd", + "367": "d\u011bl", + "368": "\u2581mus\u00ed", + "369": "Z", + "370": "klad", + "371": "\u2581tedy", + "372": "dob", + "373": "\u2581To", + "374": "\u00e1ln\u00ed", + "375": "\u2581Je", + "376": "\u2581st\u00e1t", + "377": "\u0148", + "378": "oval", + "379": "\u2581proto\u017ee", + "380": "\u2581jsem", + "381": "\u2581kter\u00fd", + "382": "p\u0159edsed", + "383": "\u2581b\u00fdt", + "384": "\u010f", + "385": "\u010c", + "386": "\u0165", + "387": "\u0160", + "388": "\u0158", + "389": "\u017d", + "390": "\u0103", + "391": "\u0142", + "392": "\u017c", + "393": "\u0105", + "394": "X", + "395": "\u00da", + "396": "\u015b", + "397": "", + "398": "\u2581for", + "399": "\u2581det", + "400": "\u2581at", + "401": "\u00e6", + "402": "et", + "403": "\u2581og", + "404": "\u2581vi", + "405": "al", + "406": "\u2581de", + "407": "\u2581der", + "408": "\u2581til", + "409": "or", + "410": "\u2581er", + "411": "om", + "412": "\u00e5", + "413": "\u00f8", + "414": "and", + "415": "\u2581har", + "416": "at", + "417": "\u2581f", + "418": "\u2581i", + "419": "\u2581s\u00e5", + "420": "\u2581af", + "421": "ge", + "422": "ar", + "423": "is", + "424": "ing", + "425": "\u2581med", + "426": "\u2581p\u00e5", + "427": "\u2581be", + "428": "un", + "429": "lig", + "430": "\u2581ikke", + "431": "\u2581man", + "432": "ig", + "433": "\u2581som", + "434": "\u00f8r", + "435": "\u2581Og", + "436": "el", + "437": "ag", + "438": "\u2581skal", + "439": "erne", + "440": "\u2581Det", + "441": "\u2581den", + "442": "ste", + "443": "ning", + "444": "\u2581jeg", + "445": "id", + "446": "\u2581kan", + "447": "\u2581ogs\u00e5", + "448": "\u2581vil", + "449": "ske", + "450": "iv", + "451": "\u2581ud", + "452": "\u2581her", + "453": "ion", + "454": "am", + "455": "ur", + "456": "for", + "457": "\u2581pr", + "458": "else", + "459": "\u2581sig", + "460": "\u2581men", + "461": "\u2581ind", + "462": "\u2581jo", + "463": "ende", + "464": "\u2581v\u00e6re", + "465": "\u2581Vi", + "466": "ation", + "467": "\u2581m\u00e5", + "468": "mme", + "469": "ighed", + "470": "tage", + "471": "\u2581op", + "472": "\u2581Jeg", + "473": "\u2581hvor", + "474": "\u2581ved", + "475": "\u2581f\u00e5", + "476": "\u2581fra", + "477": "\u2581over", + "478": "\u2581have", + "479": "kke", + "480": "\u2581meget", + "481": "\u2581S\u00e5", + "482": "\u2581Tak", + "483": "\u2581noget", + "484": "\u2581alle", + "485": "brug", + "486": "\u2581komme", + "487": "\u2581Men", + "488": "\u2581var", + "489": "hold", + "490": "arbejde", + "491": "\u2581eller", + "492": "\u2581vores", + "493": "\u2581frem", + "494": "\u2581alts\u00e5", + "495": "\u2581vigtig", + "496": "v\u00e6r", + "497": "\u2581EU", + "498": "\u2581g\u00f8re", + "499": "\u2581nogle", + "500": "skab", + "501": "\u2581sp\u00f8rgsm\u00e5l", + "502": "\u2581kunne", + "503": "\u2581kommissionen", + "504": "\u2581hvis", + "505": "\u00d8", + "506": "\u00c6", + "507": "\u03c2", + "508": "\u03bb", + "509": "\u03af", + "510": "\u03cc", + "511": "\u0131", + "512": "\u03ad", + "513": "\u03ac", + "514": "\u03c9", + "515": "\u03b3", + "516": "\u03b4", + "517": "\u03ae", + "518": "", + "519": "\u2581die", + "520": "\u2581und", + "521": "\u2581das", + "522": "sch", + "523": "\u2581ist", + "524": "\u2581ich", + "525": "\u2581ein", + "526": "\u2581ge", + "527": "ung", + "528": "it", + "529": "\u2581wir", + "530": "\u2581zu", + "531": "\u2581so", + "532": "\u2581da", + "533": "\u2581S", + "534": "\u2581auch", + "535": "gen", + "536": "\u2581nicht", + "537": "\u2581W", + "538": "\u2581B", + "539": "\u2581E", + "540": "\u2581F", + "541": "ll", + "542": "\u2581es", + "543": "\u2581K", + "544": "ie", + "545": "au", + "546": "\u2581P", + "547": "ich", + "548": "\u2581eine", + "549": "lich", + "550": "ck", + "551": "ten", + "552": "mal", + "553": "ein", + "554": "\u2581T", + "555": "\u2581dann", + "556": "\u2581Und", + "557": "\u2581mit", + "558": "\u2581auf", + "559": "hr", + "560": "ter", + "561": "tz", + "562": "\u2581dass", + "563": "\u2581G", + "564": "ben", + "565": "um", + "566": "us", + "567": "cht", + "568": "il", + "569": "\u2581Das", + "570": "\u2581diese", + "571": "\u2581noch", + "572": "\u2581jetzt", + "573": "ut", + "574": "\u2581ver", + "575": "kt", + "576": "\u2581Ich", + "577": "\u2581hier", + "578": "\u2581hat", + "579": "\u2581haben", + "580": "\u2581von", + "581": "ri", + "582": "ach", + "583": "ol", + "584": "\u2581Da", + "585": "\u2581als", + "586": "sp", + "587": "\u2581f\u00fcr", + "588": "ell", + "589": "\u2581sich", + "590": "\u2581was", + "591": "\u2581ja", + "592": "uch", + "593": "\u2581kann", + "594": "\u2581sind", + "595": "wi", + "596": "\u2581aus", + "597": "rei", + "598": "\u2581wie", + "599": "\u2581Ge", + "600": "und", + "601": "\u2581St", + "602": "isch", + "603": "\u2581sie", + "604": "\u2581Ja", + "605": "\u2581du", + "606": "\u2581war", + "607": "\u2581im", + "608": "\u2581dem", + "609": "\u2581aber", + "610": "\u2581oder", + "611": "\u00df", + "612": "\u2581Sch", + "613": "\u2581uns", + "614": "\u2581habe", + "615": "\u2581wenn", + "616": "\u2581wo", + "617": "\u2581bei", + "618": "\u2581ihr", + "619": "\u2581Ma", + "620": "zu", + "621": "\u2581schon", + "622": "\u2581De", + "623": "\u2581Sie", + "624": "\u2581\u00fcber", + "625": "\u2581vor", + "626": "\u2581Die", + "627": "\u2581ganz", + "628": "iert", + "629": "\u2581Le", + "630": "\u2581viel", + "631": "\u2581In", + "632": "\u2581Also", + "633": "\u2581Ver", + "634": "\u2581sehr", + "635": "\u2581Re", + "636": "halt", + "637": "\u2581einfach", + "638": "\u2581werden", + "639": "\u2581sein", + "640": "\u2581Wir", + "641": "\u2581nur", + "642": "\u2581immer", + "643": "ieren", + "644": "\u2581muss", + "645": "\u2581wieder", + "646": "\u2581mir", + "647": "\u2581gut", + "648": "\u2581mehr", + "649": "\u2581Mi", + "650": "\u2581nach", + "651": "\u2581Ha", + "652": "\u2581weil", + "653": "\u2581Aber", + "654": "kommen", + "655": "\u2581gibt", + "656": "\u2581meine", + "657": "\u2581andere", + "658": "\u2581k\u00f6nnen", + "659": "\u2581machen", + "660": "\u2581nat\u00fcrlich", + "661": "\u2581bisschen", + "662": "\u2581durch", + "663": "sehen", + "664": "\u2581weiter", + "665": "\u2581keine", + "666": "\u2581sagen", + "667": "\u2581wirklich", + "668": "\u2581eigentlich", + "669": "\u2581jede", + "670": "schaft", + "671": "\u2581glaube", + "672": "\u00dc", + "673": "", + "674": "\u03c7", + "675": "\u03c4\u03b1", + "676": "\u2581\u03bd\u03b1", + "677": "\u03b5\u03b9", + "678": "\u2581\u03ba\u03b1\u03b9", + "679": "\u03bc\u03b1", + "680": "\u03b2", + "681": "\u03c3\u03b7", + "682": "\u03c4\u03b5", + "683": "\u03ce", + "684": "\u03b8", + "685": "\u03c6", + "686": "\u03c0\u03bf", + "687": "\u03cd", + "688": "\u2581\u03c4\u03bf", + "689": "\u03af\u03b1", + "690": "\u03c4\u03b9", + "691": "\u03b1\u03bd", + "692": "\u03bf\u03c5", + "693": "\u03c1\u03b1", + "694": "\u2581\u03b3\u03b9\u03b1", + "695": "\u03b5\u03af", + "696": "\u03c4\u03b7", + "697": "\u03be", + "698": "\u03ba\u03b1", + "699": "\u2581\u03c4\u03b7\u03bd", + "700": "\u2581\u03c4\u03b7", + "701": "\u03bc\u03b5", + "702": "\u03c4\u03bf", + "703": "\u03bf\u03cd", + "704": "\u2581\u03c4\u03bf\u03c5", + "705": "\u2581\u03c0\u03c1\u03bf", + "706": "\u2581\u03bc\u03b5", + "707": "\u03b6", + "708": "\u2581\u03b8\u03b1", + "709": "\u2581\u03b5\u03af\u03bd\u03b1\u03b9", + "710": "\u03c1\u03bf", + "711": "\u03c9\u03bd", + "712": "\u03bc\u03ad", + "713": "\u2581\u03c0\u03bf\u03c5", + "714": "\u03b9\u03b1", + "715": "\u03bd\u03bf", + "716": "\u03b9\u03ba\u03ae", + "717": "\u03ce\u03bd", + "718": "\u03c1\u03b9", + "719": "\u03b8\u03b5", + "720": "\u0395", + "721": "\u03c1\u03af", + "722": "\u2581\u03cc\u03c4\u03b9", + "723": "\u03bf\u03c5\u03bc\u03b5", + "724": "\u2581\u03b1\u03c0\u03cc", + "725": "\u03bb\u03bf", + "726": "\u03c1\u03ac", + "727": "\u03b9\u03bf", + "728": "\u2581\u03c4\u03c9\u03bd", + "729": "\u03b5\u03c5", + "730": "\u03bb\u03b7", + "731": "\u03bf\u03c5\u03bd", + "732": "\u0391", + "733": "\u2581\u03c3\u03b5", + "734": "\u03a0", + "735": "\u2581\u03c3\u03c5\u03bd", + "736": "\u03c6\u03bf\u03c1", + "737": "\u2581\u03b4\u03b5\u03bd", + "738": "\u03a3", + "739": "\u2581\u03c3\u03c4\u03bf", + "740": "\u2581\u03b4\u03b9", + "741": "\u03c4\u03ac", + "742": "\u2581\u03b1\u03c5\u03c4\u03cc", + "743": "\u2581\u03b4\u03b9\u03b1", + "744": "\u03b9\u03c3\u03c4", + "745": "\u2581\u03c0\u03bf\u03bb\u03cd", + "746": "\u2581\u03c0\u03c1\u03ad\u03c0\u03b5\u03b9", + "747": "\u2581\u03c3\u03c4\u03b7\u03bd", + "748": "\u03c3\u03bf\u03c5\u03bc\u03b5", + "749": "\u03b9\u03ba\u03ac", + "750": "\u03a4", + "751": "\u2581\u03b5\u03c0", + "752": "\u039a", + "753": "\u03c8", + "754": "\u2581\u03b1\u03c0\u03bf", + "755": "\u2581\u03bf\u03b9", + "756": "\u03b5\u03c4\u03b1\u03b9", + "757": "\u2581\u03b5\u03c0\u03b9", + "758": "\u2581\u03a5\u03c0\u03cc\u03c4\u03b9\u03c4\u03bb\u03bf\u03b9", + "759": "\u2581AUTHORWAVE", + "760": "\u03bf\u03cd\u03bc\u03b5", + "761": "\u03b9\u03ba\u03cc", + "762": "\u2581\u039a\u03b1\u03b9", + "763": "\u03c0\u03c1\u03cc", + "764": "\u2581\u0395\u03c5\u03c7\u03b1\u03c1\u03b9\u03c3\u03c4\u03ce", + "765": "\u2581\u03bc\u03b9\u03b1", + "766": "\u2581\u03ad\u03bd\u03b1", + "767": "\u2581\u03c3\u03c5\u03bc", + "768": "\u039c", + "769": "\u2581\u03c0\u03b5\u03c1\u03b9", + "770": "\u2581\u03b1\u03c5\u03c4\u03ae", + "771": "\u03ae\u03c3\u03b5\u03b9", + "772": "\u039f", + "773": "\u03b9\u03ba\u03ad", + "774": "\u2581\u03ba\u03b1\u03c4\u03ac", + "775": "\u0393", + "776": "\u0398", + "777": "\u2581\u0395\u03c5\u03c1\u03c9\u03c0\u03b1\u03ca\u03ba\u03ae", + "778": "\u2581\u03ad\u03c7\u03bf\u03c5\u03bc\u03b5", + "779": "\u2581\u03b1\u03bb\u03bb\u03ac", + "780": "\u03b5\u03c1\u03b3", + "781": "\u0397", + "782": "\u2581\u03b8\u03ad\u03bc\u03b1", + "783": "\u03bf\u03bb\u03bf\u03b3", + "784": "\u03cc\u03c4\u03b7\u03c4\u03b1", + "785": "\u2581\u03ad\u03c7\u03b5\u03b9", + "786": "\u03c0\u03bf\u03bb\u03b9\u03c4", + "787": "\u0394", + "788": "\u2581\u03bb\u03bf\u03b9\u03c0\u03cc\u03bd", + "789": "\u03bf\u03bd\u03c4\u03b1\u03b9", + "790": "\u039d", + "791": "\u03c6\u03ad\u03c1", + "792": "\u2581\u0395\u03c0\u03b9\u03c4\u03c1\u03bf\u03c0\u03ae", + "793": "\u2581\u03b1\u03c5\u03c4\u03ac", + "794": "\u2581\u0388\u03bd\u03c9\u03c3\u03b7", + "795": "\u03a5", + "796": "\u03ca", + "797": "\u2581\u0394\u03b5\u03bd", + "798": "\u2581\u03ad\u03c7\u03bf\u03c5\u03bd", + "799": "\u2581\u03c5\u03c0\u03ac\u03c1\u03c7\u03b5\u03b9", + "800": "\u0392", + "801": "\u0399", + "802": "\u039b", + "803": "\u03a6", + "804": "\u03a1", + "805": "\u03a7", + "806": "\u039e", + "807": "\u03a9", + "808": "\u0396", + "809": "\u03a8", + "810": "\u0389", + "811": "\u0386", + "812": "\u038c", + "813": "\u0388", + "814": "", + "815": "ma", + "816": "ta", + "817": "se", + "818": "da", + "819": "si", + "820": "\u2581on", + "821": "\u00f5", + "822": "ks", + "823": "ga", + "824": "\u2581et", + "825": "\u2581ka", + "826": "he", + "827": "mu", + "828": "tu", + "829": "ha", + "830": "ja", + "831": "gi", + "832": "\u2581selle", + "833": "\u2581ole", + "834": "nd", + "835": "oo", + "836": "gu", + "837": "ju", + "838": "est", + "839": "\u2581ei", + "840": "\u2581pa", + "841": "nud", + "842": "\u2581v\u00e4ga", + "843": "\u2581see", + "844": "tud", + "845": "\u2581pea", + "846": "nda", + "847": "\u00e4r", + "848": "\u2581Euroopa", + "849": "\u2581kui", + "850": "vad", + "851": "ke", + "852": "sta", + "853": "sed", + "854": "\u2581v\u00f5i", + "855": "di", + "856": "\u2581saa", + "857": "mise", + "858": "\u2581siis", + "859": "\u2581su", + "860": "ide", + "861": "pool", + "862": "val", + "863": "tus", + "864": "\u2581seda", + "865": "\u2581Me", + "866": "\u2581vastu", + "867": "\u2581j\u00e4", + "868": "\u2581tule", + "869": "selt", + "870": "ment", + "871": "\u2581kes", + "872": "ndus", + "873": "\u2581t\u00f6\u00f6", + "874": "\u2581k\u00f5ik", + "875": "dus", + "876": "\u2581m\u00f5", + "877": "eeri", + "878": "\u2581meie", + "879": "\u2581meil", + "880": "\u2581ning", + "881": "v\u00f5t", + "882": "\u2581mida", + "883": "\u2581arv", + "884": "\u2581See", + "885": "takse", + "886": "\u2581vaja", + "887": "\u2581osa", + "888": "\u00f5igus", + "889": "\u2581nende", + "890": "\u2581n\u00fc\u00fcd", + "891": "\u2581aasta", + "892": "tsiooni", + "893": "\u2581inim", + "894": "\u2581need", + "895": "tsus", + "896": "riigi", + "897": "\u2581t\u00e4h", + "898": "\u2581Liidu", + "899": "\u2581v\u00e4lja", + "900": "\u00c4", + "901": "\u00d5", + "902": "\u00e3", + "903": "Q", + "904": "\u0107", + "905": "\u0639", + "906": "\u00f1", + "907": "", + "908": "t\u00e4", + "909": "ssa", + "910": "lla", + "911": "\u2581ett\u00e4", + "912": "ksi", + "913": "ty", + "914": "ki", + "915": "v\u00e4", + "916": "pa", + "917": "lle", + "918": "lu", + "919": "tta", + "920": "st\u00e4", + "921": "isi", + "922": "ise", + "923": "ll\u00e4", + "924": "kin", + "925": "n\u00e4", + "926": "\u00e4\u00e4n", + "927": "kse", + "928": "tte", + "929": "j\u00e4", + "930": "tt\u00e4", + "931": "ss\u00e4", + "932": "ista", + "933": "inen", + "934": "k\u00e4", + "935": "llis", + "936": "t\u00f6", + "937": "\u2581my\u00f6s", + "938": "vu", + "939": "taan", + "940": "\u2581t\u00e4m\u00e4", + "941": "\u2581voi", + "942": "utta", + "943": "iden", + "944": "nyt", + "945": "\u2581niin", + "946": "\u2581Kiitos", + "947": "\u2581ovat", + "948": "h\u00e4n", + "949": "suu", + "950": "\u2581toimi", + "951": "aika", + "952": "\u2581T\u00e4m\u00e4", + "953": "\u2581p\u00e4\u00e4", + "954": "\u2581mutta", + "955": "\u2581k\u00e4y", + "956": "\u2581t\u00e4ss\u00e4", + "957": "\u2581asia", + "958": "\u2581T\u00e4", + "959": "\u2581jotka", + "960": "\u2581ty\u00f6", + "961": "neet", + "962": "\u2581t\u00e4ytyy", + "963": "\u2581sitten", + "964": "\u2581Euroopan", + "965": "\u2581puolesta", + "966": "\u2581halua", + "967": "\u2581siit\u00e4", + "968": "\u2581komissio", + "969": "\u2581hyv\u00e4", + "970": "\u2581hyvin", + "971": "\u2581puhu", + "972": "\u2581meid\u00e4n", + "973": "\u2581vastaan", + "974": "\u2581t\u00e4rke\u00e4", + "975": "\u2581kaikki", + "976": "\u2581Kiitoksia", + "977": "\u2581viel\u00e4", + "978": "\u2581muut", + "979": "\u2581paljon", + "980": "mahdollis", + "981": "parlament", + "982": "\u2581pit\u00e4isi", + "983": "\u2581hyv\u00e4ksy", + "984": "\u2581puheenjohtaja", + "985": "\u2581liitty", + "986": "\u0101", + "987": "\u10d0", + "988": "\u10d8", + "989": "\u012b", + "990": "\u0113", + "991": "\u00eb", + "992": "\u10d4", + "993": "", + "994": "\u2581est", + "995": "\u2581c", + "996": "\u2581d", + "997": "\u2581la", + "998": "\u2581p", + "999": "\u2581que", + "1000": "\u2581en", + "1001": "\u2581le", + "1002": "\u2581\u00e0", + "1003": "es", + "1004": "\u2581l", + "1005": "\u2581un", + "1006": "\u2581pas", + "1007": "\u2581les", + "1008": "\u2581qui", + "1009": "\u2581il", + "1010": "\u2581vous", + "1011": "\u2581des", + "1012": "\u2581ce", + "1013": "\u2581qu", + "1014": "\u2581pour", + "1015": "\u2581n", + "1016": "\u2581par", + "1017": "\u2581\u00e7a", + "1018": "\u2581une", + "1019": "\u2581b", + "1020": "ant", + "1021": "\u2581j", + "1022": "ais", + "1023": "ez", + "1024": "\u2581dans", + "1025": "\u2581va", + "1026": "\u2581C", + "1027": "tre", + "1028": "ir", + "1029": "elle", + "1030": "eur", + "1031": "\u2581sur", + "1032": "\u2581re", + "1033": "\u2581con", + "1034": "\u2581ma", + "1035": "\u2581Et", + "1036": "\u2581au", + "1037": "ement", + "1038": "tion", + "1039": "t\u00e9", + "1040": "\u2581tout", + "1041": "mp", + "1042": "ique", + "1043": "\u2581plus", + "1044": "eux", + "1045": "\u2581d\u00e9", + "1046": "\u2581fait", + "1047": "qu", + "1048": "\u2581ai", + "1049": "\u2581comme", + "1050": "ens", + "1051": "ac", + "1052": "\u2581l\u00e0", + "1053": "\u2581si", + "1054": "ait", + "1055": "che", + "1056": "\u2581mais", + "1057": "que", + "1058": "ul", + "1059": "\u2581avec", + "1060": "\u2581bien", + "1061": "\u2581tu", + "1062": "age", + "1063": "\u2581mon", + "1064": "\u2581Donc", + "1065": "end", + "1066": "\u2581faire", + "1067": "\u2581\u00eatre", + "1068": "ver", + "1069": "\u2581peu", + "1070": "\u2581m\u00eame", + "1071": "tra", + "1072": "cha", + "1073": "\u2581nous", + "1074": "\u2581donc", + "1075": "\u2581sont", + "1076": "\u2581moi", + "1077": "ille", + "1078": "ff", + "1079": "\u2581ex", + "1080": "ien", + "1081": "\u2581Il", + "1082": "\u2581tr\u00e8s", + "1083": "\u2581cette", + "1084": "im", + "1085": "it\u00e9", + "1086": "\u2581dire", + "1087": "\u2581peut", + "1088": "ance", + "1089": "aire", + "1090": "m\u00e9", + "1091": "\u2581app", + "1092": "\u2581aussi", + "1093": "\u2581petit", + "1094": "aux", + "1095": "\u2581parce", + "1096": "onne", + "1097": "mb", + "1098": "man", + "1099": "\u2581On", + "1100": "\u2581quand", + "1101": "\u2581autre", + "1102": "\u00f4", + "1103": "\u2581chose", + "1104": "\u2581puis", + "1105": "\u2581\u00e9tait", + "1106": "ndre", + "1107": "port", + "1108": "\u2581vraiment", + "1109": "ence", + "1110": "\u2581Mais", + "1111": "\u00ee", + "1112": "\u2581avoir", + "1113": "form", + "1114": "\u2581faut", + "1115": "\u2581Alors", + "1116": "ign", + "1117": "\u2581o\u00f9", + "1118": "pr\u00e8s", + "1119": "\u2581beaucoup", + "1120": "ture", + "1121": "\u00fb", + "1122": "\u00c7", + "1123": "\u00e2", + "1124": "\u00f9", + "1125": "", + "1126": "sz", + "1127": "\u2581az", + "1128": "\u2581hogy", + "1129": "\u0151", + "1130": "\u00e1s", + "1131": "ok", + "1132": "gy", + "1133": "ek", + "1134": "\u00e1l", + "1135": "\u00e9s", + "1136": "em", + "1137": "\u00e1r", + "1138": "\u2581meg", + "1139": "\u2581\u00e9s", + "1140": "\u2581is", + "1141": "\u2581ez", + "1142": "\u2581egy", + "1143": "os", + "1144": "ak", + "1145": "ban", + "1146": "nak", + "1147": "\u00edt", + "1148": "ik", + "1149": "unk", + "1150": "\u2581nem", + "1151": "oz", + "1152": "\u00fcl", + "1153": "\u00e1n", + "1154": "\u00e1t", + "1155": "cs", + "1156": "\u00e9l", + "1157": "\u00e9r", + "1158": "nek", + "1159": "\u2581mi", + "1160": "szer", + "1161": "bb", + "1162": "\u2581K\u00f6sz\u00f6n\u00f6m", + "1163": "s\u00e9g", + "1164": "\u2581kell", + "1165": "\u00e9n", + "1166": "hat", + "1167": "\u2581ha", + "1168": "s\u00e1g", + "1169": "\u2581sz\u00e9pen", + "1170": "\u00e9rt", + "1171": "\u00e9k", + "1172": "ott", + "1173": "\u00f6n", + "1174": "\u00e9p", + "1175": "el\u0151", + "1176": "\u00fcnk", + "1177": "\u2581van", + "1178": "\u2581ki", + "1179": "\u2581fel", + "1180": "\u00e9ny", + "1181": "v\u00e9", + "1182": "leg", + "1183": "eket", + "1184": "\u2581Az", + "1185": "juk", + "1186": "\u2581k\u00f6z", + "1187": "\u0171", + "1188": "\u2581nagyon", + "1189": "\u2581tud", + "1190": "\u2581jelen", + "1191": "\u2581amely", + "1192": "\u2581lehet", + "1193": "\u2581ami", + "1194": "\u2581k\u00e9rd\u00e9s", + "1195": "\u2581ellen", + "1196": "tart", + "1197": "r\u0151l", + "1198": "\u00c9", + "1199": "orsz\u00e1g", + "1200": "rend", + "1201": "r\u00f3l", + "1202": "\u2581vagy", + "1203": "\u2581fontos", + "1204": "\u2581Eur\u00f3pai", + "1205": "\u2581akkor", + "1206": "\u2581jog", + "1207": "\u2581fog", + "1208": "fogad", + "1209": "kapcsol", + "1210": "\u2581r\u00e9sz", + "1211": "\u00e1ci\u00f3", + "1212": "\u2581volt", + "1213": "\u2581eln\u00f6k", + "1214": "\u2581bizotts\u00e1g", + "1215": "\u2581gondol", + "1216": "\u2581olyan", + "1217": "\u2581illetve", + "1218": "\u2581tag\u00e1llam", + "1219": "\u2581pedig", + "1220": "\u2581Teh\u00e1t", + "1221": "\u2581eur\u00f3pai", + "1222": "\u2581sz\u00fcks\u00e9g", + "1223": "szavaz", + "1224": "\u2581teh\u00e1t", + "1225": "k\u00f6vetkez", + "1226": "\u2581\u00f6ssze", + "1227": "\u2581biztos", + "1228": "\u00d6", + "1229": "\u00c1", + "1230": "\u00cd", + "1231": "\u0150", + "1232": "", + "1233": "\u2581u", + "1234": "\u2581bi", + "1235": "\u2581sa", + "1236": "\u0107e", + "1237": "\u2581od", + "1238": "ru", + "1239": "\u2581iz", + "1240": "go", + "1241": "nje", + "1242": "sti", + "1243": "\u0111", + "1244": "\u2581pri", + "1245": "ima", + "1246": "nu", + "1247": "\u2581pre", + "1248": "\u2581Hvala", + "1249": "lje", + "1250": "\u2581\u0161to", + "1251": "\u010di", + "1252": "nja", + "1253": "zi", + "1254": "vr", + "1255": "\u0107i", + "1256": "\u010de", + "1257": "ca", + "1258": "\u2581koji", + "1259": "ba", + "1260": "\u2581raz", + "1261": "\u05d9", + "1262": "\u05d5", + "1263": "\u05d4", + "1264": "\u062f", + "1265": "\u05dc", + "1266": "\u0629", + "1267": "\u0628", + "1268": "\u0647", + "1269": "\u0623", + "1270": "\u05d0", + "1271": "\u0633", + "1272": "\u0643", + "1273": "\u05ea", + "1274": "\u05e8", + "1275": "\u021b", + "1276": "\u05de", + "1277": "\u0642", + "1278": "\u05e9", + "1279": "", + "1280": "\u2581di", + "1281": "\u2581e", + "1282": "\u2581che", + "1283": "\u2581\u00e8", + "1284": "co", + "1285": "\u2581per", + "1286": "\u2581al", + "1287": "\u2581non", + "1288": "do", + "1289": "gli", + "1290": "so", + "1291": "amo", + "1292": "sa", + "1293": "ndo", + "1294": "\u2581una", + "1295": "fi", + "1296": "pi", + "1297": "nti", + "1298": "tto", + "1299": "tro", + "1300": "\u2581fa", + "1301": "chi", + "1302": "\u2581qua", + "1303": "zione", + "1304": "bi", + "1305": "\u2581del", + "1306": "mente", + "1307": "pe", + "1308": "ssi", + "1309": "\u2581ri", + "1310": "\u2581sono", + "1311": "\u2581me", + "1312": "\u2581questo", + "1313": "nte", + "1314": "tti", + "1315": "t\u00e0", + "1316": "\u2581nel", + "1317": "\u2581anche", + "1318": "sso", + "1319": "\u2581perch\u00e9", + "1320": "\u2581pi\u00f9", + "1321": "nta", + "1322": "\u2581come", + "1323": "cu", + "1324": "\u2581quindi", + "1325": "ggi", + "1326": "nza", + "1327": "sto", + "1328": "\u2581ho", + "1329": "\u00f2", + "1330": "\u2581della", + "1331": "gra", + "1332": "\u2581fare", + "1333": "spe", + "1334": "cco", + "1335": "nde", + "1336": "mento", + "1337": "fe", + "1338": "gio", + "1339": "pu", + "1340": "\u2581questa", + "1341": "\u2581tra", + "1342": "zza", + "1343": "sci", + "1344": "\u2581ba", + "1345": "\u2581dei", + "1346": "\u2581poi", + "1347": "sco", + "1348": "stra", + "1349": "\u2581quel", + "1350": "qui", + "1351": "\u2581delle", + "1352": "\u2581cosa", + "1353": "\u2581molto", + "1354": "sse", + "1355": "zioni", + "1356": "\u2581vol", + "1357": "\u2581inter", + "1358": "sce", + "1359": "\u2581fatto", + "1360": "\u2581com", + "1361": "\u2581quello", + "1362": "\u2581essere", + "1363": "\u2581due", + "1364": "\u2581abbiamo", + "1365": "\u2581comp", + "1366": "\u2581tutti", + "1367": "\u00ec", + "1368": "\u2581prima", + "1369": "\u2581parte", + "1370": "\u2581cos\u00ec", + "1371": "\u2581sempre", + "1372": "\u2581tutto", + "1373": "\u2581video", + "1374": "\u2581maglia", + "1375": "\u2581imp", + "1376": "\u2581cui", + "1377": "\u2581dove", + "1378": "\u2581col", + "1379": "\u2581Quindi", + "1380": "sione", + "1381": "rebbe", + "1382": "scri", + "1383": "", + "1384": "\u0117", + "1385": "ai", + "1386": "\u0173", + "1387": "\u2581ir", + "1388": "as", + "1389": "\u012f", + "1390": "\u2581kad", + "1391": "\u0117s", + "1392": "\u2581tai", + "1393": "\u016b", + "1394": "t\u0173", + "1395": "\u2581yra", + "1396": "i\u0173", + "1397": "uo", + "1398": "\u2581ko", + "1399": "\u2581i\u0161", + "1400": "tin", + "1401": "\u2581vis", + "1402": "\u010dia", + "1403": "\u2581kuri", + "1404": "d\u0117", + "1405": "ly", + "1406": "gal", + "1407": "\u2581\u0161i", + "1408": "iau", + "1409": "jo", + "1410": "tar", + "1411": "yb", + "1412": "\u2581Ir", + "1413": "\u2581tik", + "1414": "ijos", + "1415": "sak", + "1416": "\u2581turi", + "1417": "oje", + "1418": "\u2581Tai", + "1419": "j\u0173", + "1420": "\u2581apie", + "1421": "\u2581nu", + "1422": "\u2581mes", + "1423": "\u2581u\u017e", + "1424": "i\u0161k", + "1425": "\u2581gali", + "1426": "\u2581d\u0117l", + "1427": "\u2581labai", + "1428": "imas", + "1429": "klaus", + "1430": "laik", + "1431": "\u2581Europos", + "1432": "\u2581a\u0161", + "1433": "veik", + "1434": "\u2581b\u016bt\u0173", + "1435": "darb", + "1436": "\u2581kaip", + "1437": "\u2581teis", + "1438": "\u2581daug", + "1439": "\u2581tikrai", + "1440": "\u2581pra", + "1441": "reik", + "1442": "\u2581buvo", + "1443": "tur\u0117", + "1444": "\u2581valstyb", + "1445": "\u2581reikia", + "1446": "\u2581b\u016bti", + "1447": "\u2581A\u0161", + "1448": "\u2581m\u016bs\u0173", + "1449": "\u2581j\u016bs", + "1450": "vyk", + "1451": "\u2581A\u010di\u016b", + "1452": "cija", + "1453": "\u012e", + "1454": "\u0146", + "1455": "", + "1456": "\u2581no", + "1457": "j\u0101", + "1458": "iem", + "1459": "t\u0101", + "1460": "\u0101k", + "1461": "\u2581ar", + "1462": "\u0101m", + "1463": "\u2581pie", + "1464": "ies", + "1465": "ot", + "1466": "k\u0101", + "1467": "\u013c", + "1468": "tr", + "1469": "\u2581t\u0101", + "1470": "\u012bt", + "1471": "n\u0101", + "1472": "\u2581uz", + "1473": "\u2581tas", + "1474": "\u0113t", + "1475": "dz", + "1476": "\u2581ar\u012b", + "1477": "\u2581vien", + "1478": "\u2581jau", + "1479": "\u2581k\u0101", + "1480": "\u2581ie", + "1481": "gad", + "1482": "\u2581kur", + "1483": "\u2581kas", + "1484": "\u2581Un", + "1485": "\u2581m\u0113s", + "1486": "iet", + "1487": "d\u0101", + "1488": "\u012bg", + "1489": "\u2581Ta", + "1490": "\u2581k\u0101d", + "1491": "kaut", + "1492": "\u0113m", + "1493": "\u2581lie", + "1494": "umu", + "1495": "ties", + "1496": "dar", + "1497": "l\u0113", + "1498": "\u2581vai", + "1499": "\u2581bija", + "1500": "\u2581mums", + "1501": "\u2581tad", + "1502": "\u2581bet", + "1503": "\u012bba", + "1504": "\u2581ga", + "1505": "\u2581Latvijas", + "1506": "ija", + "1507": "kr", + "1508": "v\u0113", + "1509": "sim", + "1510": "\u2581\u0161o", + "1511": "dien", + "1512": "gan", + "1513": "\u012bgi", + "1514": "\u2581ap", + "1515": "\u0123", + "1516": "\u2581b\u016bt", + "1517": "dom\u0101", + "1518": "\u2581tev", + "1519": "m\u0113r", + "1520": "\u2581daudz", + "1521": "\u2581aiz", + "1522": "\u2581T\u0101", + "1523": "\u2581t\u0101d", + "1524": "\u2581tur", + "1525": "\u2581mon\u0113t", + "1526": "\u2581v\u0113l", + "1527": "\u2581laik", + "1528": "\u2581cilv\u0113", + "1529": "\u2581nav", + "1530": "\u2581lab", + "1531": "\u2581\u013coti", + "1532": "aug", + "1533": "\u2581l\u012bdz", + "1534": "\u2581lai", + "1535": "\u0161ana", + "1536": "\u2581Nu", + "1537": "\u2581vi\u0146a", + "1538": "\u2581savu", + "1539": "\u2581cit", + "1540": "teik", + "1541": "\u2581darb", + "1542": "\u2581Ne", + "1543": "zin", + "1544": "\u2581pirm", + "1545": "\u2581Latvi", + "1546": "\u2581tie\u0161", + "1547": "\u2581vi\u0146i", + "1548": "\u0113ja", + "1549": "dz\u012bvo", + "1550": "\u2581vi\u0146\u0161", + "1551": "\u2581pils\u0113", + "1552": "in\u0101t", + "1553": "\u2581vi\u0146u", + "1554": "\u2581tagad", + "1555": "k\u0101rt", + "1556": "\u2581pats", + "1557": "\u2581vair\u0101k", + "1558": "reiz", + "1559": "\u2581tikai", + "1560": "sakta", + "1561": "\u2581bij", + "1562": "\u2581Vi\u0146", + "1563": "\u2581sev", + "1564": "\u2581m\u0101j", + "1565": "v\u0113rt", + "1566": "\u258120", + "1567": "\u2581ce\u013c", + "1568": "tiek", + "1569": "iski", + "1570": "\u2581dz\u012bv", + "1571": "\u2581k\u0101p\u0113c", + "1572": "\u2581Bet", + "1573": "\u2581p\u0113c", + "1574": "\u2581noz\u012bm\u0113", + "1575": "niek", + "1576": "\u012bb\u0101", + "1577": "\u2581pal\u012bdz", + "1578": "\u2581protams", + "1579": "\u2581stils", + "1580": "\u2581vajadz", + "1581": "\u2581att\u012bst\u012b", + "1582": "\u2581svar\u012bg", + "1583": "\u2581sievie", + "1584": "\u2581grib", + "1585": "\u2581da\u017e\u0101d", + "1586": "\u2581valst", + "1587": "\u2581banka", + "1588": "\u2581iesp\u0113ja", + "1589": "\u2581bez", + "1590": "pr\u0101t", + "1591": "v\u0113rt\u012bb", + "1592": "\u2581person", + "1593": "pasaules", + "1594": "\u2581varb\u016bt", + "1595": "\u2581vienk\u0101r\u0161i", + "1596": "\u2581nauda", + "1597": "mekl\u0113", + "1598": "brauc", + "1599": "\u2581nevar", + "1600": "\u0101cijas", + "1601": "sp\u0113j", + "1602": "\u0137", + "1603": "", + "1604": "\u2581een", + "1605": "\u2581het", + "1606": "\u2581dat", + "1607": "\u2581we", + "1608": "\u2581ik", + "1609": "ij", + "1610": "\u2581En", + "1611": "\u2581te", + "1612": "\u2581ook", + "1613": "\u2581niet", + "1614": "\u2581dan", + "1615": "\u2581zo", + "1616": "\u2581voor", + "1617": "\u2581met", + "1618": "\u2581aan", + "1619": "\u2581zijn", + "1620": "\u2581Ik", + "1621": "\u2581wel", + "1622": "\u2581wat", + "1623": "aar", + "1624": "\u2581ze", + "1625": "ken", + "1626": "\u2581heb", + "1627": "der", + "1628": "ui", + "1629": "den", + "1630": "\u2581daar", + "1631": "\u2581maar", + "1632": "op", + "1633": "\u2581heel", + "1634": "\u2581nog", + "1635": "\u2581Dus", + "1636": "oor", + "1637": "\u2581hebben", + "1638": "\u2581uit", + "1639": "\u2581of", + "1640": "ven", + "1641": "\u2581Maar", + "1642": "\u2581Dat", + "1643": "\u2581gaan", + "1644": "elijk", + "1645": "\u2581naar", + "1646": "\u2581moet", + "1647": "acht", + "1648": "\u2581waar", + "1649": "\u2581dus", + "1650": "\u2581ben", + "1651": "\u2581goed", + "1652": "\u2581Het", + "1653": "\u2581even", + "1654": "ond", + "1655": "eld", + "1656": "\u2581dit", + "1657": "\u2581wil", + "1658": "rij", + "1659": "\u2581echt", + "1660": "\u2581doen", + "1661": "\u2581gewoon", + "1662": "lijk", + "1663": "tijd", + "1664": "\u2581meer", + "1665": "\u2581mijn", + "1666": "\u2581We", + "1667": "\u2581gaat", + "1668": "werk", + "1669": "\u2581hoe", + "1670": "uw", + "1671": "\u2581eigenlijk", + "1672": "\u2581deze", + "1673": "zelf", + "1674": "vol", + "1675": "\u2581veel", + "1676": "atie", + "1677": "\u2581kunnen", + "1678": "\u2581door", + "1679": "llen", + "1680": "\u2581mee", + "1681": "\u2581onder", + "1682": "\u2581toe", + "1683": "\u2581zit", + "1684": "\u2581mensen", + "1685": "\u2581hij", + "1686": "\u2581denk", + "1687": "\u2581zie", + "1688": "\u2581heeft", + "1689": "\u2581kl", + "1690": "nnen", + "1691": "\u2581zien", + "1692": "komen", + "1693": "\u2581natuurlijk", + "1694": "heid", + "1695": "\u2581Dan", + "1696": "\u2581vind", + "1697": "\u2581wordt", + "1698": "\u2581iets", + "1699": "\u2581maken", + "1700": "\u2581doe", + "1701": "\u2581Wat", + "1702": "\u2581wij", + "1703": "\u2581beetje", + "1704": "\u2581worden", + "1705": "\u2581Want", + "1706": "\u2581twee", + "1707": "\u2581hem", + "1708": "\u2581had", + "1709": "\u2581jullie", + "1710": "\u2581Als", + "1711": "\u2581kijken", + "1712": "\u2581toch", + "1713": "\u2581tot", + "1714": "nieuw", + "1715": "lang", + "1716": "\u2581Nou", + "1717": "\u2581krijg", + "1718": "houd", + "1719": "\u2581hele", + "1720": "\u2581allemaal", + "1721": "\u2581want", + "1722": "\u2581zeggen", + "1723": "\u2581leuk", + "1724": "", + "1725": "nie", + "1726": "\u2581w", + "1727": "cz", + "1728": "wa", + "1729": "\u2581si\u0119", + "1730": "\u2581jest", + "1731": "my", + "1732": "\u0142a", + "1733": "cie", + "1734": "czy", + "1735": "\u2581nie", + "1736": "wie", + "1737": "\u2581wy", + "1738": "nia", + "1739": "wo", + "1740": "rze", + "1741": "\u0142o", + "1742": "\u2581\u017ce", + "1743": "dzi", + "1744": "ej", + "1745": "\u00f3w", + "1746": "dzie", + "1747": "\u2581prze", + "1748": "\u015bci", + "1749": "by", + "1750": "za", + "1751": "dy", + "1752": "ry", + "1753": "\u0144", + "1754": "j\u0105", + "1755": "we", + "1756": "cze", + "1757": "owa", + "1758": "ego", + "1759": "\u017ce", + "1760": "cy", + "1761": "rzy", + "1762": "mie", + "1763": "\u2581przy", + "1764": "\u0142y", + "1765": "rz", + "1766": "szy", + "1767": "sze", + "1768": "\u015b\u0107", + "1769": "wia", + "1770": "zy", + "1771": "\u017cy", + "1772": "\u2581tutaj", + "1773": "j\u0119", + "1774": "pie", + "1775": "nych", + "1776": "\u2581tym", + "1777": "\u2581mo\u017ce", + "1778": "cji", + "1779": "\u2581pod", + "1780": "\u2581ale", + "1781": "\u2581tego", + "1782": "owy", + "1783": "uje", + "1784": "\u2581bo", + "1785": "\u2581by\u0142", + "1786": "n\u0105", + "1787": "bie", + "1788": "sy", + "1789": "\u2581te\u017c", + "1790": "\u2581bardzo", + "1791": "\u2581s\u0105", + "1792": "\u2581b\u0119dzie", + "1793": "\u2581Po", + "1794": "ski", + "1795": "\u2581kt\u00f3re", + "1796": "\u017a", + "1797": "\u2581ju\u017c", + "1798": "\u2581dla", + "1799": "\u0142em", + "1800": "nego", + "1801": "\u2581Nie", + "1802": "\u2581No", + "1803": "\u2581praw", + "1804": "cja", + "1805": "\u2581ten", + "1806": "\u2581takie", + "1807": "owa\u0107", + "1808": "\u2581kt\u00f3ry", + "1809": "\u2581w\u0142a\u015bnie", + "1810": "\u2581jeszcze", + "1811": "\u2581tam", + "1812": "\u2581\u017ceby", + "1813": "\u2581by\u0107", + "1814": "\u2581wi\u0119c", + "1815": "\u2581czyli", + "1816": "\u2581sobie", + "1817": "\u2581sam", + "1818": "\u2581tylko", + "1819": "\u2581tej", + "1820": "\u2581spraw", + "1821": "\u2581Na", + "1822": "\u2581m\u00f3wi", + "1823": "\u2581osob", + "1824": "\u2581czas", + "1825": "\u2581prac", + "1826": "\u2581Czy", + "1827": "\u2581prostu", + "1828": "\u2581teraz", + "1829": "st\u0119p", + "1830": "\u2581Was", + "1831": "\u2581my\u015bl", + "1832": "\u2581powiedz", + "1833": "\u2581zrobi", + "1834": "li\u015bmy", + "1835": "\u2581jakie\u015b", + "1836": "aj\u0105c", + "1837": "\u2581widz", + "1838": "\u2581kart", + "1839": "\u2581musi", + "1840": "\u2581pyta", + "1841": "", + "1842": "pt", + "1843": "PT", + "1844": "<", + "1845": ">", + "1846": "-", + "1847": "\u2581\u00e9", + "1848": "\u2581n\u00e3o", + "1849": "\u2581eu", + "1850": "\u2581um", + "1851": "\u2581voc\u00ea", + "1852": "\u2581para", + "1853": "\u00e3o", + "1854": "\u2581aqui", + "1855": "\u2581uma", + "1856": "\u00e7\u00e3o", + "1857": "\u2581ca", + "1858": "\u2581pe", + "1859": "\u2581tem", + "1860": "\u2581em", + "1861": "\u2581gente", + "1862": "\u2581O", + "1863": "\u2581ele", + "1864": "pre", + "1865": "ria", + "1866": "\u2581fo", + "1867": "mos", + "1868": "nho", + "1869": "\u2581Ent\u00e3o", + "1870": "bo", + "1871": "io", + "1872": "nha", + "1873": "\u2581isso", + "1874": "\u2581por", + "1875": "\u2581muito", + "1876": "nto", + "1877": "\u2581Eu", + "1878": "\u2581est\u00e1", + "1879": "idade", + "1880": "\u2581a\u00ed", + "1881": "be", + "1882": "\u2581esse", + "1883": "\u2581pode", + "1884": "\u2581como", + "1885": "ente", + "1886": "\u2581tamb\u00e9m", + "1887": "\u2581essa", + "1888": "lha", + "1889": "\u2581j\u00e1", + "1890": "\u2581mas", + "1891": "\u2581pessoa", + "1892": "qua", + "1893": "\u2581n\u00e9", + "1894": "\u2581fazer", + "1895": "\u2581t\u00e1", + "1896": "lho", + "1897": "\u2581l\u00e1", + "1898": "fica", + "1899": "\u2581vou", + "1900": "\u2581porque", + "1901": "\u2581Se", + "1902": "\u2581fala", + "1903": "\u2581coisa", + "1904": "\u2581N\u00e3o", + "1905": "...", + "1906": "\u2581s\u00f3", + "1907": "\u2581n\u00f3s", + "1908": "\u00e7o", + "1909": "\u2581Por", + "1910": "\u2581assim", + "1911": "\u2581Co", + "1912": "iza", + "1913": "\u2581bem", + "1914": "\u2581todo", + "1915": "eira", + "1916": "\u2581sua", + "1917": "\u00eancia", + "1918": "\u00e7\u00f5es", + "1919": "\u2581Voc\u00ea", + "1920": "\u2581tudo", + "1921": "\u2581agora", + "1922": "eiro", + "1923": "\u00e1rio", + "1924": "\u2581at\u00e9", + "1925": "\u2581mesmo", + "1926": "\u2581vamos", + "1927": "\u2581quando", + "1928": "ciona", + "1929": "", + "1930": "\u2581\u00een", + "1931": "\u021bi", + "1932": "\u2581s\u0103", + "1933": "\u2581\u0219i", + "1934": "\u2581cu", + "1935": "\u2581c\u0103", + "1936": "\u2581care", + "1937": "\u2581mai", + "1938": "r\u0103", + "1939": "sc", + "1940": "c\u0103", + "1941": "\u2581am", + "1942": "are", + "1943": "\u2581din", + "1944": "\u2581fi", + "1945": "\u2581este", + "1946": "t\u0103", + "1947": "\u2581pentru", + "1948": "rea", + "1949": "\u0219ti", + "1950": "\u0219", + "1951": "ele", + "1952": "du", + "1953": "\u2581M", + "1954": "\u2581fac", + "1955": "\u00e2n", + "1956": "\u2581sunt", + "1957": "\u2581I", + "1958": "\u2581acest", + "1959": "ului", + "1960": "lor", + "1961": "\u2581mult", + "1962": "\u0219i", + "1963": "\u2581mo", + "1964": "\u2581fost", + "1965": "per", + "1966": "\u2581foarte", + "1967": "\u2581\u0218i", + "1968": "\u2581m\u0103", + "1969": "s\u0103", + "1970": "cur", + "1971": "tor", + "1972": "\u2581cum", + "1973": "inte", + "1974": "at\u0103", + "1975": "\u0219te", + "1976": "\u2581dac\u0103", + "1977": "\u00e2nd", + "1978": "\u2581subliniere", + "1979": "\u2581dar", + "1980": "\u2581sau", + "1981": "tat", + "1982": "ori", + "1983": "\u2581v\u0103", + "1984": "\u2581asta", + "1985": "n\u0103", + "1986": "\u2581prim", + "1987": "\u2581a\u0219a", + "1988": "eaz\u0103", + "1989": "\u2581\u00eentr", + "1990": "\u2581spun", + "1991": "\u2581lui", + "1992": "\u2581sub", + "1993": "itate", + "1994": "\u2581aici", + "1995": "\u2581bine", + "1996": "\u2581c\u00e2nd", + "1997": "\u2581prin", + "1998": "\u2581alt", + "1999": "\u2581nici", + "2000": "stru", + "2001": "\u2581c\u00e2t", + "2002": "\u2581vede", + "2003": "fer", + "2004": "\u2581dup\u0103", + "2005": "\u2581ju", + "2006": "\u2581despre", + "2007": "\u2581timp", + "2008": "\u2581acum", + "2009": "\u2581poate", + "2010": "\u2581spus", + "2011": "\u2581lucru", + "2012": "\u2581f\u0103cut", + "2013": "p\u0103r", + "2014": "\u2581urm\u0103", + "2015": "\u2581atunci", + "2016": "\u2581fr", + "2017": "\u2581chiar", + "2018": "\u2581\u00eencep", + "2019": "\u0218", + "2020": "\u00ce", + "2021": "", + "2022": "\u2581\u043d\u0435", + "2023": "\u044b", + "2024": "\u0442\u044c", + "2025": "\u2581\u044d\u0442\u043e", + "2026": "\u0436\u0435", + "2027": "\u2581\u0447\u0442\u043e", + "2028": "\u2581\u0442\u043e", + "2029": "\u043b\u044c", + "2030": "\u2581\u043e", + "2031": "\u2581\u0443", + "2032": "\u0430\u0442\u044c", + "2033": "\u2581\u0442\u0430\u043a", + "2034": "\u2581\u043a\u0430\u043a", + "2035": "\u043a\u0438", + "2036": "\u0441\u044f", + "2037": "\u0435\u043c", + "2038": "\u2581\u0432\u044b", + "2039": "\u2581\u0431\u044b", + "2040": "\u2581\u0432\u0441\u0435", + "2041": "\u0440\u0443", + "2042": "\u0431\u043e", + "2043": "\u2581\u0418", + "2044": "\u2581\u0432\u043e\u0442", + "2045": "\u043a\u0443", + "2046": "\u2581\u0412", + "2047": "\u0447\u0438", + "2048": "\u043e\u0439", + "2049": "\u043c\u0443", + "2050": "\u2581\u0441\u043e", + "2051": "\u0442\u044b", + "2052": "\u043d\u0443", + "2053": "\u0441\u044c", + "2054": "\u2581\u0435\u0441\u0442\u044c", + "2055": "\u0442\u0443", + "2056": "\u043d\u044b", + "2057": "\u0448\u0435", + "2058": "\u2581\u043c\u044b", + "2059": "\u0434\u0443", + "2060": "\u0438\u0442\u044c", + "2061": "\u044d", + "2062": "\u0434\u0435\u043b", + "2063": "\u043b\u044f", + "2064": "\u043c\u0435\u043d", + "2065": "\u0436\u0438", + "2066": "\u0441\u0442\u043e", + "2067": "\u0445\u043e", + "2068": "\u0441\u0442\u0432", + "2069": "\u0432\u044b", + "2070": "\u0432\u0435\u0440", + "2071": "\u0437\u043d\u0430", + "2072": "\u0441\u0442\u0438", + "2073": "\u0448\u0438", + "2074": "\u0435\u0442\u0441\u044f", + "2075": "\u0443\u044e", + "2076": "\u0440\u044b", + "2077": "\u0445\u043e\u0434", + "2078": "\u0430\u0435\u0442", + "2079": "\u043d\u044b\u0439", + "2080": "\u043f\u0435\u0440", + "2081": "\u2581\u041f\u043e", + "2082": "\u043b\u0443\u0447", + "2083": "\u043d\u044b\u0435", + "2084": "\u0442\u043e\u0440", + "2085": "\u2581\u0442\u0430\u043c", + "2086": "\u2581\u0431\u0443\u0434\u0435\u0442", + "2087": "\u2581\u0441\u0430\u043c", + "2088": "\u2581\u0434\u043b\u044f", + "2089": "\u2581\u043e\u0447\u0435\u043d\u044c", + "2090": "\u0435\u043d\u0438\u044f", + "2091": "\u0430\u044e\u0442", + "2092": "\u2581\u041d\u0443", + "2093": "\u2581\u042d\u0442\u043e", + "2094": "\u2581\u0414\u0430", + "2095": "\u2581\u043c\u0435\u043d\u044f", + "2096": "\u2581\u0435\u0441\u043b\u0438", + "2097": "\u2581\u0422\u043e", + "2098": "\u0435\u043d\u044c", + "2099": "\u043d\u044b\u0445", + "2100": "\u2581\u0435\u0449\u0435", + "2101": "\u2581\u0432\u0430\u043c", + "2102": "\u2581\u043f\u0435\u0440\u0435", + "2103": "\u2581\u0437\u0434\u0435\u0441\u044c", + "2104": "\u2581\u043f\u0440\u043e\u0441\u0442\u043e", + "2105": "\u2581\u0412\u043e\u0442", + "2106": "\u2581\u041d\u043e", + "2107": "\u2581\u0447\u0442\u043e\u0431\u044b", + "2108": "\u0441\u043c\u043e\u0442\u0440", + "2109": "\u2581\u0441\u0435\u0439\u0447\u0430\u0441", + "2110": "\u2581\u043c\u043e\u0436\u0435\u0442", + "2111": "\u2581\u044d\u0442\u0438", + "2112": "\u0430\u043b\u044c\u043d\u043e", + "2113": "\u0434\u043e\u043b", + "2114": "\u2581\u041d\u0430", + "2115": "\u2581\u0422\u0430\u043a", + "2116": "\u2581\u043a\u043e\u0433\u0434\u0430", + "2117": "\u0451", + "2118": "\u0430\u0439\u0442\u0435", + "2119": "\u043f\u0438\u0441", + "2120": "\u0442\u0435\u043b\u044c\u043d\u043e", + "2121": "\u0435\u0448\u044c", + "2122": "\u2581\u0434\u0440\u0443\u0433", + "2123": "\u042d", + "2124": "", + "2125": "ov", + "2126": "\u013e", + "2127": "sk", + "2128": "\u2581aj", + "2129": "ob", + "2130": "t\u00e1", + "2131": "a\u0165", + "2132": "\u2581bol", + "2133": "\u2581s\u00fa", + "2134": "\u2581ako", + "2135": "\u017ei", + "2136": "\u2581sme", + "2137": "\u2581V", + "2138": "ali", + "2139": "\u2581alebo", + "2140": "\u2581\u010do", + "2141": "i\u0165", + "2142": "\u2581m\u00e1", + "2143": "\u00fdch", + "2144": "\u2581z\u00e1", + "2145": "\u2581tie", + "2146": "\u2581nejak", + "2147": "\u2581v\u00fd", + "2148": "\u010das", + "2149": "nov", + "2150": "rov", + "2151": "\u2581ktor\u00e9", + "2152": "aj\u00fa", + "2153": "ova\u0165", + "2154": "\u2581ke\u010f", + "2155": "\u2581str", + "2156": "\u2581\u0161kol", + "2157": "n\u00fa", + "2158": "\u2581ktor", + "2159": "\u2581vlastne", + "2160": "\u2581pr\u00ed", + "2161": "nej", + "2162": "\u2581ve\u013emi", + "2163": "\u0161ie", + "2164": "rob", + "2165": "\u2581tr", + "2166": "n\u00fdch", + "2167": "enie", + "2168": "\u2581spo", + "2169": "\u2581rok", + "2170": "osti", + "2171": "\u2581t\u00fdm", + "2172": "\u2581m\u00f4\u017ee", + "2173": "\u2581ktor\u00fd", + "2174": "os\u0165", + "2175": "\u2581projekt", + "2176": "\u2581kon", + "2177": "\u2581vzdel\u00e1va", + "2178": "\u2581Tak\u017ee", + "2179": "\u2581e\u0161te", + "2180": "\u2581t\u00fdch", + "2181": "\u2581mal", + "2182": "\u2581cel", + "2183": "\u2581potom", + "2184": "\u2581svoj", + "2185": "enia", + "2186": "\u00e1lne", + "2187": "ie\u0165", + "2188": "\u2581teda", + "2189": "jedn", + "2190": "sled", + "2191": "\u2581mo\u017eno", + "2192": "\u2581v\u00e1m", + "2193": "chod", + "2194": "uj\u00fa", + "2195": "tvor", + "2196": "\u2581druh", + "2197": "\u2581Slovensk", + "2198": "h\u013ead", + "2199": "stup", + "2200": "\u2581\u013eud\u00ed", + "2201": "\u2581napr\u00edklad", + "2202": "\u2581ve\u013ek", + "2203": "\u2581nie\u010do", + "2204": "\u010e", + "2205": "", + "2206": "sl", + "2207": "lj", + "2208": "kot", + "2209": "ih", + "2210": "\u2581svet", + "2211": "\u2581ta", + "2212": "\u2581tako", + "2213": "\u2581kar", + "2214": "\u2581nek", + "2215": "jih", + "2216": "udi", + "2217": "\u2581vse", + "2218": "\u2581drug", + "2219": "\u2581ima", + "2220": "kaj", + "2221": "\u2581smo", + "2222": "del", + "2223": "\u2581sem", + "2224": "\u2581lahko", + "2225": "\u2581samo", + "2226": "\u2581ve\u010d", + "2227": "nih", + "2228": "\u2581dr\u017eav", + "2229": "\u2581zelo", + "2230": "\u2581zdaj", + "2231": "\u2581razum", + "2232": "\u2581\u0161e", + "2233": "\u2581tega", + "2234": "\u2581ljudi", + "2235": "\u2581pred", + "2236": "\u2581sta", + "2237": "nost", + "2238": "\u2581ampak", + "2239": "\u2581novinar", + "2240": "\u2581naprej", + "2241": "\u2581mora", + "2242": "\u2581Vs", + "2243": "krat", + "2244": "\u2581Ampak", + "2245": "\u2581vedno", + "2246": "\u2581velik", + "2247": "\u2581kako", + "2248": "\u2581najbolj", + "2249": "ziroma", + "2250": "\u2581vsi", + "2251": "\u2581nekaj", + "2252": "\u2581kater", + "2253": "\u2581res", + "2254": "\u2581tukaj", + "2255": "\u2581dogaja", + "2256": "\u2581svoje", + "2257": "\u2581let", + "2258": "daj", + "2259": "\u2581pripri\u010da", + "2260": "\u2581\u010dlovek", + "2261": "\u2581ho\u010de", + "2262": "\u2581vojn", + "2263": "\u2581Pre", + "2264": "\u2581dobr", + "2265": "ljan", + "2266": "\u2581moj", + "2267": "\u2581dejansko", + "2268": "\u2581ljudje", + "2269": "\u2581mediji", + "2270": "\u2581prot", + "2271": "\u2581narav", + "2272": "bilo", + "2273": "\u2581Afrik", + "2274": "\u2581vzhod", + "2275": "\u2581\u010dlove\u0161tva", + "2276": "\u2581kriz", + "2277": "\u2581pogled", + "2278": "\u2581medije", + "2279": "poved", + "2280": "\u2581za\u010del", + "2281": "\u2581ve\u010din", + "2282": "imajo", + "2283": "\u2581Ljudje", + "2284": "\u2581dru\u017eb", + "2285": "\u2581govorim", + "2286": "\u2581informacij", + "2287": "\u2581kultur", + "2288": "\u2581bli\u017enj", + "2289": "\u2581podobno", + "2290": "\u2581njihov", + "2291": "\u2581konc", + "2292": "\u2581pisa", + "2293": "\u2581zaveda", + "2294": "\u2581vsak", + "2295": "\u017eivel", + "2296": "\u2581funkcionira", + "2297": "\u2581internet", + "2298": "\u2581islamsk", + "2299": "\u2581film", + "2300": "\u2581otroci", + "2301": "\u2581prihaja", + "2302": "\u2581politi\u010dn", + "2303": "\u2581popoln", + "2304": "\u2581Velik", + "2305": "\u2581druga\u010den", + "2306": "\u2581recimo", + "2307": "\u2581resnic", + "2308": "solutno", + "2309": "\u2581Bli\u017en", + "2310": "\u2581Evropsk", + "2311": "\u2581muslimani", + "2312": "\u2581nadzoruje", + "2313": "\u2581socialne", + "2314": "\u2581zgodovin", + "2315": "\u2581\u010dlove\u0161k", + "2316": "\u2581\u017eivljenj", + "2317": "\u2581prijatelj", + "2318": "\u2581vendar", + "2319": "\u2581ljudem", + "2320": "\u2581\u0161tevil", + "2321": "\u2581Sirij", + "2322": "", + "2323": "\u2581att", + "2324": "\u2581och", + "2325": "\u2581\u00e4r", + "2326": "\u2581f\u00f6r", + "2327": "\u2581h\u00e4r", + "2328": "\u2581jag", + "2329": "\u00e4n", + "2330": "\u2581till", + "2331": "\u2581h", + "2332": "\u2581inte", + "2333": "\u2581Och", + "2334": "\u2581av", + "2335": "\u2581om", + "2336": "\u2581ska", + "2337": "\u2581ut", + "2338": "\u2581ett", + "2339": "all", + "2340": "\u2581ocks\u00e5", + "2341": "\u2581Jag", + "2342": "era", + "2343": "pp", + "2344": "\u2581upp", + "2345": "\u2581d\u00e5", + "2346": "\u2581d\u00e4r", + "2347": "\u2581lite", + "2348": "\u00e5r", + "2349": "sam", + "2350": "isk", + "2351": "het", + "2352": "f\u00f6r", + "2353": "\u2581kommer", + "2354": "\u2581vill", + "2355": "\u00f6r", + "2356": "erna", + "2357": "ande", + "2358": "s\u00e4tt", + "2359": "\u2581finns", + "2360": "\u2581n\u00e4r", + "2361": "\u2581vara", + "2362": "ade", + "2363": "s\u00f6k", + "2364": "\u2581hur", + "2365": "\u2581vad", + "2366": "bil", + "2367": "\u2581g\u00f6ra", + "2368": "\u2581f\u00e5r", + "2369": "verk", + "2370": "\u2581mycket", + "2371": "\u2581v\u00e4l", + "2372": "kom", + "2373": "\u2581g\u00f6r", + "2374": "\u2581ni", + "2375": "\u2581bara", + "2376": "\u2581fr\u00e5n", + "2377": "st\u00e4ll", + "2378": "\u2581v\u00e4ldigt", + "2379": "\u2581min", + "2380": "\u2581olika", + "2381": "\u2581alla", + "2382": "lev", + "2383": "\u2581fram", + "2384": "\u2581kanske", + "2385": "\u2581v\u00e5r", + "2386": "\u2581tid", + "2387": "skap", + "2388": "h\u00e5ll", + "2389": "\u2581F\u00f6r", + "2390": "\u2581g\u00e5r", + "2391": "\u2581blir", + "2392": "\u2581under", + "2393": "\u2581l\u00e4r", + "2394": "\u2581ny", + "2395": "\u2581D\u00e5", + "2396": "\u2581b\u00f6rja", + "2397": "r\u00e4tt", + "2398": "\u2581\u00f6ver", + "2399": "\u2581oss", + "2400": "\u2581exempel", + "2401": "\u2581skulle", + "2402": "g\u00e5ng", + "2403": "\u2581kunna", + "2404": "\u2581andra", + "2405": "\u2581n\u00e5gon", + "2406": "\u2581jobba", + "2407": "land", + "2408": "\u2581n\u00e5got", + "2409": "\u2581beh\u00f6ver", + "2410": "\u2581s\u00e4ga", + "2411": "klar", + "2412": "\u2581m\u00e5nga", + "2413": "\u2581skriv", + "2414": "\u2581anv\u00e4nda", + "2415": "\u2581sj\u00e4lv", + "2416": "\u2581samma", + "2417": "l\u00e4gg", + "2418": "\u2581m\u00e5ste", + "2419": "\u2581efter", + "2420": "text", + "2421": "\u2581prata", + "2422": "\u2581klicka", + "2423": "\u2581hitta", + "2424": "\u2581tror", + "2425": "\u2581n\u00e5gonting", + "2426": "fr\u00e5ga", + "2427": "\u2581titta", + "2428": "\u2581tycker", + "2429": "\u2581ganska", + "2430": "\u2581j\u00e4tte", + "2431": "\u2581Vad", + "2432": "\u2581genom", + "2433": "\u2581\u00e4ven", + "2434": "\u2581t\u00e4nker", + "2435": "arbete", + "2436": "\u2581faktiskt", + "2437": "person", + "2438": "\u2581komma", + "2439": "bygg", + "2440": "", + "2441": "\u0456", + "2442": "\u043d\u0456", + "2443": "\u0454", + "2444": "\u0457", + "2445": "\u2581\u0437", + "2446": "\u2581\u0449\u043e", + "2447": "\u0432\u0456", + "2448": "\u0440\u0456", + "2449": "\u0446\u0456", + "2450": "\u2581\u0456", + "2451": "\u043b\u0456", + "2452": "\u043c\u0456", + "2453": "\u0431\u0443", + "2454": "\u0434\u0456", + "2455": "\u043e\u0433\u043e", + "2456": "\u2581\u0432\u0438", + "2457": "\u2581\u0446\u0435", + "2458": "\u0435\u0440", + "2459": "\u0441\u0456", + "2460": "\u2581\u044f\u043a", + "2461": "\u043e\u043c\u0443", + "2462": "\u2581\u0432\u0456\u0434", + "2463": "\u0434\u043e", + "2464": "\u0442\u0456", + "2465": "\u0456\u043d", + "2466": "\u0431\u0430", + "2467": "\u2581\u0406", + "2468": "\u043f\u0456", + "2469": "\u043f\u0435", + "2470": "\u0435\u043d\u043d\u044f", + "2471": "\u043d\u044c", + "2472": "\u043a\u0456", + "2473": "\u0437\u0430", + "2474": "\u043d\u0438\u0445", + "2475": "\u2581\u043f\u0456\u0434", + "2476": "\u043d\u0438\u0439", + "2477": "\u0440\u0430\u0437", + "2478": "\u043d\u044f", + "2479": "\u043b\u044e", + "2480": "\u043f\u0438", + "2481": "\u0441\u043e", + "2482": "\u0431\u0456", + "2483": "\u0442\u044c\u0441\u044f", + "2484": "\u2581\u0440\u043e\u0437", + "2485": "\u0441\u0442\u0456", + "2486": "\u2581\u044f\u043a\u0456", + "2487": "\u0443\u0432\u0430\u0442\u0438", + "2488": "\u2581\u043d\u0430\u0441", + "2489": "\u0430\u043d\u043d\u044f", + "2490": "\u043d\u043e\u0433\u043e", + "2491": "\u2581\u0432\u043e\u043d\u0438", + "2492": "\u0430\u044e\u0442\u044c", + "2493": "\u2581\u0434\u0443\u0436\u0435", + "2494": "\u2581\u0417\u0430", + "2495": "\u043b\u0443", + "2496": "\u043a\u0456\u0432", + "2497": "\u2581\u041c\u0438", + "2498": "\u2581\u0442\u043e\u043c\u0443", + "2499": "\u2581\u0431\u0443\u0434\u0435", + "2500": "\u2581\u0432\u0436\u0435", + "2501": "\u2581\u0426\u0435", + "2502": "\u0446\u044c", + "2503": "\u2581\u0447\u0430\u0441", + "2504": "\u0456\u0441\u0442\u044c", + "2505": "\u0446\u044f", + "2506": "\u2581\u0431\u0443\u043b\u043e", + "2507": "\u2581\u0430\u043b\u0435", + "2508": "\u0431\u0456\u043b\u044c\u0448", + "2509": "\u043f\u0440\u0430\u0446", + "2510": "\u0442\u0440\u0438\u043c", + "2511": "\u0430\u0454\u043c\u043e", + "2512": "\u0430\u0454\u0442\u044c\u0441\u044f", + "2513": "\u2581\u0442\u0443\u0442", + "2514": "\u0443\u044e\u0442\u044c", + "2515": "\u0430\u0446\u0456\u0457", + "2516": "\u2581\u044f\u043a\u0438\u0439", + "2517": "\u043c\u0435\u043d\u0442", + "2518": "\u2581\u043b\u044e\u0434\u0438", + "2519": "\u0443\u0432\u0430\u043d\u043d\u044f", + "2520": "\u2581\u044f\u043a\u0449\u043e", + "2521": "\u0444\u043e\u0440", + "2522": "\u2581\u0431\u0435\u0437", + "2523": "\u0443\u043a\u0440\u0430\u0457\u043d", + "2524": "\u0443\u0432\u0430\u043b\u0438", + "2525": "\u0440\u043e\u0437\u0443\u043c\u0456", + "2526": "\u0404", + "2527": "\u0407", + "2528": "\u0406", + "2529": "", + "2530": "\u0641", + "2531": "\u062d", + "2532": "\u0650", + "2533": "\u064f", + "2534": "\u062c", + "2535": "\u2581\u0627\u0644", + "2536": "\u0635", + "2537": "\u2581\u0648", + "2538": "\u0652", + "2539": "\u0637", + "2540": "\u0634", + "2541": "\u064e", + "2542": "\u062e", + "2543": "\u0632", + "2544": "\u0627\u0646", + "2545": "\u2581\u0623", + "2546": "\u0636", + "2547": "\u0627\u0644", + "2548": "\u2581\u0628", + "2549": "\u2581\u0627\u0644\u0645", + "2550": "\u2581\u0641\u064a", + "2551": "\u2581\u0645\u0646", + "2552": "\u0649", + "2553": "\u0627\u062a", + "2554": "\u064e\u0651", + "2555": "\u064a\u0646", + "2556": "\u0647\u0627", + "2557": "\u064a\u0629", + "2558": "\u062b", + "2559": "\u063a", + "2560": "\u2581\u0645", + "2561": "\u0627\u0631", + "2562": "\u2581\u0648\u064e", + "2563": "\u0644\u0627", + "2564": "\u0630", + "2565": "\u0648\u0644", + "2566": "\u0626", + "2567": "\u064e\u0627", + "2568": "\u0627\u0645", + "2569": "\u0648\u0646", + "2570": "\u0648\u0627", + "2571": "\u2581\u0639\u0644\u0649", + "2572": "\u2581\u0648\u0627\u0644", + "2573": "\u2581\u0627\u0644\u0652", + "2574": "\u0646\u0627", + "2575": "\u2581\u0627\u0644\u0623", + "2576": "\u0645\u0627", + "2577": "\u064a\u0631", + "2578": "\u0644\u0650", + "2579": "\u0638", + "2580": "\u0627\u0621", + "2581": "\u0644\u064e", + "2582": "\u0648\u0631", + "2583": "\u2581\u0627\u0644\u062a", + "2584": "\u2581\u0623\u0646", + "2585": "\u0627\u0628", + "2586": "\u0645\u064e", + "2587": "\u0643\u064e", + "2588": "\u062a\u064e", + "2589": "\u0651", + "2590": "\u0647\u0645", + "2591": "\u0639\u064e", + "2592": "\u0627\u062f", + "2593": "\u2581\u0625", + "2594": "\u0646\u0652", + "2595": "\u2581\u0623\u064e", + "2596": "\u0627\u0633", + "2597": "\u2581\u0627\u0644\u0633", + "2598": "\u064f\u0648", + "2599": "\u0628\u0650", + "2600": "\u2581\u0627\u0644\u0639", + "2601": "\u0622", + "2602": "\u064a\u0647", + "2603": "\u0650\u0651", + "2604": "\u2581\u0644\u0644", + "2605": "\u064d", + "2606": "\u2581\u0627\u0644\u062d", + "2607": "\u0646\u064e", + "2608": "\u064a\u0627", + "2609": "\u2581\u0641\u0649", + "2610": "\u2581\u0628\u0627\u0644", + "2611": "\u0648\u0645", + "2612": "\u2581\u0639\u0646", + "2613": "\u2581\u0627\u0644\u0646", + "2614": "\u0645\u064e\u0627", + "2615": "\u064a\u062f", + "2616": "\u2581\u0645\u0627", + "2617": "\u0627\u0639", + "2618": "\u064e\u064a\u0652", + "2619": "\u0627\u064b", + "2620": "\u064b\u0627", + "2621": "\u2581\u0645\u0639", + "2622": "\u0633\u062a", + "2623": "\u0645\u064f", + "2624": "\u2581\u0627\u0644\u0634", + "2625": "\u0634\u0631", + "2626": "\u2581\u0643\u0627\u0646", + "2627": "\u0625", + "2628": "\u0625\u0650", + "2629": "\u0627\u0641", + "2630": "\u0627\u062d", + "2631": "\u064c", + "2632": "\u062d\u064e", + "2633": "\u0630\u0627", + "2634": "\u2581\u0641\u0650\u064a", + "2635": "\u0631\u0628", + "2636": "\u2581\u064a\u0639\u0646\u064a", + "2637": "\u2581\u064a\u064e", + "2638": "\u064e\u0629\u0650", + "2639": "\u2581\u0627\u0644\u0642", + "2640": "\u0642\u064e", + "2641": "\u0646\u064e\u0627", + "2642": "\u064f\u0651", + "2643": "\u2581\u0627\u0644\u062c", + "2644": "\u0633\u064e", + "2645": "\u2581\u0627\u0644\u0628", + "2646": "\u2581\u0627\u0644\u062f", + "2647": "\u0645\u0652", + "2648": "\u0645\u0631", + "2649": "\u064f\u0648\u0646\u064e", + "2650": "\u0624", + "2651": "\u2581\u0627\u0644\u0627", + "2652": "\u0645\u0650", + "2653": "\u064e\u0648\u0652", + "2654": "\u0628\u0631", + "2655": "\u2581\u0628\u064a", + "2656": "\u2581\u0627\u0644\u0631", + "2657": "\u0628\u064e", + "2658": "\u0647\u064e\u0627", + "2659": "\u0647\u064f", + "2660": "\u0648\u0642", + "2661": "\u0627\u062c", + "2662": "\u2581\u0641\u064e", + "2663": "\u2581\u0622\u0647", + "2664": "\u2581\u0627\u0644\u0641", + "2665": "\u0652\u062a\u064e", + "2666": "\u2581\u0643\u0644", + "2667": "\u2581\u0627\u0644\u0635", + "2668": "\u2581\u0625\u0644\u0649", + "2669": "\u2581\u0647\u0648", + "2670": "\u2581\u0645\u0650\u0646\u0652", + "2671": "\u0648\u062f", + "2672": "\u0648\u0628", + "2673": "\u2581\u0648\u0623", + "2674": "\u062e\u0644", + "2675": "\u0631\u064e", + "2676": "\u062d\u062f", + "2677": "\u064a\u0645", + "2678": "\u2581\u0627\u0644\u0625", + "2679": "\u062f\u064e", + "2680": "\u0641\u064e", + "2681": "\u0647\u064f\u0645\u0652", + "2682": "\u0646\u0650", + "2683": "\u062c\u064e", + "2684": "\u064f\u0648\u0627", + "2685": "\u0641\u0631", + "2686": "\u064e\u0639\u0652", + "2687": "\u2581\u0623\u0648", + "2688": "\u2581\u0625\u0646", + "2689": "\u0648\u0633", + "2690": "\u0644\u064e\u0627", + "2691": "\u062c\u0645", + "2692": "\u0650\u064a\u0646\u064e", + "2693": "\u064e\u0651\u0627", + "2694": "\u0648\u0641", + "2695": "\u0648\u062c", + "2696": "\u2581\u0627\u0644\u062e", + "2697": "\u0639\u0645\u0644", + "2698": "\u2581\u0644\u0645", + "2699": "\u064e\u0627\u062a\u0650", + "2700": "\u2581\u0647\u0630\u0627", + "2701": "\u2581\u0623\u064e\u0646\u0652", + "2702": "\u2581\u0645\u0634", + "2703": "\u2581\u0628\u0639\u062f", + "2704": "\u2581\u0627\u0644\u0652\u0645\u064f", + "2705": "\u2581\u0627\u0644\u0637", + "2706": "\u0650\u0647\u0650", + "2707": "\u2581\u0627\u0644\u0644\u0649", + "2708": "\u0621", + "2709": "\u2581\u0627\u0644\u0644\u064a", + "2710": "\u2581\u0639\u064e\u0644\u064e\u0649", + "2711": "\u0652\u062a\u0650", + "2712": "\u2581\u0627\u0644\u0652\u0623\u064e", + "2713": "\u0630\u064e\u0627", + "2714": "\u064e\u0631\u064e", + "2715": "\u2581\u0623\u0646\u0627", + "2716": "\u0643\u064f\u0645\u0652", + "2717": "\u2581\u0627\u0644\u0652\u0645\u064e", + "2718": "\u2581\u0625\u0650\u0646\u064e\u0651", + "2719": "\u0652\u0631\u064e", + "2720": "\u2581\u0647\u0630\u0647", + "2721": "\u064e\u0644\u064e", + "2722": "\u064e\u0631\u0652", + "2723": "\u2581\u0627\u0633\u062a", + "2724": "\u2581\u0645\u0635\u0631", + "2725": "\u0650\u064a\u064e", + "2726": "\u0652\u0631\u0650", + "2727": "\u064e\u062d\u0652", + "2728": "\u0631\u0650\u064a", + "2729": "\u064e\u062f\u0652", + "2730": "\u2581\u0645\u0650\u0646\u064e", + "2731": "\u2581\u0648\u064e\u0644\u064e", + "2732": "\u2581\u0648\u064e\u0627\u0644\u0652", + "2733": "\u2581\u0643\u0645\u0627", + "2734": "\u0628\u0642\u0649", + "2735": "\u062f\u0650\u064a", + "2736": "\u2581\u0627\u0644\u0644\u0647", + "2737": "\u2581\u0627\u0644\u064e\u0651\u0630\u0650\u064a", + "2738": "\u2581\u0627\u0644\u0630\u064a", + "2739": "\u0639\u0631\u0641", + "2740": "\u2581\u0627\u0644\u0652\u0639\u064e", + "2741": "\u064e\u0647\u064f", + "2742": "\u0634\u0639\u0631", + "2743": "\u2581\u0644\u0643\u0646", + "2744": "\u0639\u0644\u0645", + "2745": "\u064e\u0629\u064f", + "2746": "\u064b", + "2747": "\u2581\u0646\u0641\u0633", + "2748": "\u0650\u064a\u064e\u0651\u0629\u0650", + "2749": "\u064e\u062a\u0652", + "2750": "\u2581\u0648\u064e\u0623\u064e", + "2751": "\u064e\u0629\u064d", + "2752": "\u0645\u062b\u0644", + "2753": "\u2581\u063a\u064a\u0631", + "2754": "\u0627\u0626\u064a", + "2755": "\u2581\u0625\u0650\u0644\u064e\u0649", + "2756": "\u2581\u0648\u0627\u062d\u062f", + "2757": "\u2581\u0623\u064e\u0646\u064e\u0651", + "2758": "\u2581\u0647\u064e\u0630\u064e\u0627", + "2759": "\u2581\u0630\u0644\u0643", + "2760": "\u064e\u0629\u064e", + "2761": "\u2581\u062d\u062a\u0649", + "2762": "\u2581\u0647\u064e\u0644\u0652", + "2763": "\u061f", + "2764": "\u060c", + "2765": "vy", + "2766": "\u2581byl", + "2767": "\u0147", + "2768": "\u0164", + "2769": "\u00d3", + "2770": "\u00e6r", + "2771": "\u2581blev", + "2772": "ft", + "2773": "lige", + "2774": "ved", + "2775": "'", + "2776": "\u00c5", + "2777": "\u2581H", + "2778": "\u2581D", + "2779": "aus", + "2780": "\u2581N", + "2781": "\u2581Be", + "2782": "mm", + "2783": "ab", + "2784": "\u2581Er", + "2785": "ssen", + "2786": "hl", + "2787": "hn", + "2788": "ischen", + "2789": "\u2581wurde", + "2790": "rie", + "2791": "lei", + "2792": "\u2581An", + "2793": "\u2581Ein", + "2794": "etz", + "2795": "rau", + "2796": "ische", + "2797": "\u00e4h", + "2798": "\u2581mein", + "2799": "\u2581So", + "2800": "\u2581hatte", + "2801": "\u2581unter", + "2802": "\u2581Zu", + "2803": "\u2581ihn", + "2804": "\u2581Jahr", + "2805": "\u2581zwei", + "2806": "keit", + "2807": "\u2581ihm", + "2808": "\u2581Aus", + "2809": "", + "2810": "\u2581you", + "2811": "\u2581that", + "2812": "\u2581and", + "2813": "\u2581can", + "2814": "\u2581it", + "2815": "\u2581your", + "2816": "ed", + "2817": "\u2581Okay", + "2818": "\u2581just", + "2819": "ay", + "2820": "\u2581Yeah", + "2821": "\u2581with", + "2822": "th", + "2823": "\u2581Thank", + "2824": "\u2581thank", + "2825": "\u2581help", + "2826": "\u2581please", + "2827": "\u2581one", + "2828": "\u2581there", + "2829": "ic", + "2830": "\u2581much", + "2831": "\u2581what", + "2832": "\u2581my", + "2833": "hi", + "2834": "\u2581will", + "2835": "\u2581would", + "2836": "\u2581if", + "2837": "\u2581two", + "2838": "\u2581this", + "2839": "\u2581he", + "2840": "\u2581go", + "2841": "\u2581all", + "2842": "\u2581Oh", + "2843": "\u2581like", + "2844": "\u2581very", + "2845": "\u2581The", + "2846": "\u2581today", + "2847": "\u2581not", + "2848": "\u2581yeah", + "2849": "\u2581take", + "2850": "ight", + "2851": "ex", + "2852": "\u2581Ok", + "2853": "\u2581seven", + "2854": "\u2581number", + "2855": "\u2581know", + "2856": "\u2581about", + "2857": "\u2581four", + "2858": "\u2581okay", + "2859": "\u2581name", + "2860": "\u2581And", + "2861": "\u2581five", + "2862": "\u2581How", + "2863": "\u2581account", + "2864": "\u2581any", + "2865": "\u2581three", + "2866": "\u2581could", + "2867": "\u2581up", + "2868": "\u2581get", + "2869": "\u2581phone", + "2870": "\u2581great", + "2871": "\u2581six", + "2872": "\u2581eight", + "2873": "\u2581now", + "2874": "\u2581nine", + "2875": "\u2581That", + "2876": "\u2581address", + "2877": "\u2581look", + "2878": "\u2581call", + "2879": "ill", + "2880": "\u2581You", + "2881": "\u2581but", + "2882": "\u2581got", + "2883": "\u2581don", + "2884": "\u2581email", + "2885": "\u2581calling", + "2886": "\u2581problem", + "2887": "\u2581right", + "2888": "\u2581good", + "2889": "\u2581well", + "2890": "\u2581out", + "2891": "\u2581What", + "2892": "\u2581how", + "2893": "\u2581really", + "2894": "\u2581anything", + "2895": "\u2581actually", + "2896": "\u2581from", + "2897": "\u2581think", + "2898": "\u2581time", + "2899": "\u2581some", + "2900": "\u2581ask", + "2901": "\u2581else", + "2902": "other", + "2903": "\u2581fine", + "2904": "able", + "2905": "\u2581Good", + "2906": "\u2581when", + "2907": "\u2581full", + "2908": "\u2581confirm", + "2909": "\u2581give", + "2910": "\u2581more", + "2911": "ever", + "2912": "\u2581month", + "2913": "\u2581information", + "2914": "\u2581sure", + "2915": "\u2581survey", + "2916": "\u2581sorry", + "2917": "\u2581send", + "2918": "\u2581through", + "2919": "\u2581check", + "2920": "\u2581long", + "2921": "\u2581birth", + "2922": "\u2581should", + "2923": "\u2581twenty", + "2924": "\u2581make", + "2925": "\u2581zero", + "2926": "ful", + "2927": "\u2581store", + "2928": "\u2581policy", + "2929": "\u2581back", + "2930": "\u2581again", + "2931": "\u2581first", + "2932": "\u2581Could", + "2933": "\u2581work", + "2934": "\u2581afternoon", + "2935": "\u2581after", + "2936": "\u2581insurance", + "2937": "\u2581customer", + "2938": "\u2581payment", + "2939": "\u2581question", + "2940": "\u2581receive", + "2941": "\u2581possible", + "2942": "\u2581moment", + "2943": "\u2581system", + "2944": "\u2581change", + "2945": "\u2581hundred", + "2946": "\u2581nineteen", + "2947": "", + "2948": "\u2581.", + "2949": "\u2581,", + "2950": "\u2581st", + "2951": "\u2581are", + "2952": "ow", + "2953": "ive", + "2954": "ate", + "2955": "ad", + "2956": "ect", + "2957": "\u2581they", + "2958": "\u2581as", + "2959": "ng", + "2960": "ity", + "2961": "ther", + "2962": "act", + "2963": "ist", + "2964": "\u2581our", + "2965": "\u2581sp", + "2966": "ally", + "2967": "\u2581his", + "2968": "\u2581But", + "2969": "\u2581has", + "2970": "\u2581also", + "2971": "\u2581which", + "2972": "\u2581He", + "2973": "\u2581uh", + "2974": "day", + "2975": "\u2581people", + "2976": "\u2581who", + "2977": "\u2581thing", + "2978": "\u2581because", + "2979": "\u2581other", + "2980": "ough", + "2981": "\u2581part", + "2982": "\u2581say", + "2983": "\u2581year", + "2984": "side", + "2985": "\"", + "2986": "", + "2987": "\u2581y", + "2988": "\u2581el", + "2989": "ci\u00f3n", + "2990": "\u2581Es", + "2991": "res", + "2992": "\u2581los", + "2993": "\u2581La", + "2994": "dos", + "2995": "\u00eda", + "2996": "\u2581El", + "2997": "\u2581las", + "2998": "\u2581m\u00e1s", + "2999": "men", + "3000": "\u00f1o", + "3001": "\u2581esta", + "3002": "idad", + "3003": "par", + "3004": "\u00bf", + "3005": "r\u00eda", + "3006": "\u2581fue", + "3007": "rio", + "3008": "enta", + "3009": "\u00f3n", + "3010": "cho", + "3011": "ciones", + "3012": "ble", + "3013": "\u2581Ca", + "3014": "\u2581muy", + "3015": "\u2581tambi\u00e9n", + "3016": "\u2581tiene", + "3017": "\u00f1a", + "3018": "\u2581Su", + "3019": "\u2581pero", + "3020": "\u2581son", + "3021": "encia", + "3022": "si\u00f3n", + "3023": "\u2581hay", + "3024": "\u2581puede", + "3025": "ncia", + "3026": "\u2581mucho", + "3027": "\u2581Si", + "3028": "\u2581pues", + "3029": "miento", + "3030": "\u2581Con", + "3031": "ones", + "3032": "ecto", + "3033": "iendo", + "3034": "\u2581d\u00eda", + "3035": "\u2581sobre", + "3036": "\u2581primer", + "3037": "\u2581qu\u00e9", + "3038": "\u2581gusta", + "3039": "\u2581San", + "3040": "\u2581hacer", + "3041": "cional", + "3042": "\u2581verdad", + "3043": "\u2581persona", + "3044": "\u2581pasa", + "3045": "\u2581mejor", + "3046": "qu\u00ed", + "3047": "\u2581Fue", + "3048": "\u2581Com", + "3049": "\u2581ciudad", + "3050": "\u00d1", + "3051": "", + "3052": "cia", + "3053": "\u2581lo", + "3054": "\u2581Y", + "3055": "ron", + "3056": "les", + "3057": "\u2581mu", + "3058": "cio", + "3059": "\u2581yo", + "3060": "bu", + "3061": "\u2581s\u00ed", + "3062": "\u2581Pero", + "3063": "\u2581as\u00ed", + "3064": "", + "3065": "r\u00e9", + "3066": "\u00e9e", + "3067": "\u2581Les", + "3068": "nt", + "3069": "our", + "3070": "\u2581Ce", + "3071": "com", + "3072": "\u2581Elle", + "3073": "\u2581Cet", + "3074": "ux", + "3075": "ale", + "3076": "ier", + "3077": "ction", + "3078": "\u2581cha", + "3079": "\u2581pr\u00e9", + "3080": "\u2581deux", + "3081": "if", + "3082": "l\u00e9", + "3083": "\u00e8re", + "3084": "i\u00e8re", + "3085": "iste", + "3086": "\u2581parti", + "3087": "\u2581\u00e9t\u00e9", + "3088": "cette", + "3089": "avec", + "3090": "\u2581tou", + "3091": "jour", + "3092": "app", + "3093": "cul", + "3094": "\u2581\u00e9gale", + "3095": "aine", + "3096": "gue", + "3097": "\u2581tr\u00e8", + "3098": "\u2581nombre", + "3099": "\u2581\u00e9tai", + "3100": "tout", + "3101": "\u2581grand", + "3102": "\u2581commun", + "3103": "Une", + "3104": "\u0153", + "3105": "\u00ef", + "3106": "\u00c0", + "3107": "\u0152", + "3108": "\u00c8", + "3109": "\u014d", + "3110": "\u00ff", + "3111": "\u014c", + "3112": "\u00d4", + "3113": "\u00ca", + "3114": "\u00c2", + "3115": "\u2581m", + "3116": "av", + "3117": "ouv", + "3118": "\u00eat", + "3119": "ois", + "3120": "pri", + "3121": "voir", + "3122": "sion", + "3123": "ix", + "3124": "ang", + "3125": "\u00e9tait", + "3126": "ard", + "3127": "aient", + "3128": "\u0106", + "3129": "\u0130", + "3130": "\u00d9", + "3131": "\u00db", + "3132": "\u00cb", + "3133": "\u00cf", + "3134": "", + "3135": "\u05d1", + "3136": "\u05e2", + "3137": "\u05e7", + "3138": "\u05d7", + "3139": "\u05db", + "3140": "\u05d3", + "3141": "\u05e0", + "3142": "\u2581\u05d1", + "3143": "\u05d9\u05dd", + "3144": "\u05d2", + "3145": "\u2581\u05de", + "3146": "\u05e1", + "3147": "\u05dd", + "3148": "\u05d5\u05ea", + "3149": "\u05e6", + "3150": "\u05e4", + "3151": "\u05d8", + "3152": "\u05d5\u05e8", + "3153": "\u05d6", + "3154": "\u2581\u05dc", + "3155": "\u05e0\u05d9", + "3156": "\u2581\u05e9\u05dc", + "3157": "\u2581\u05d4\u05de", + "3158": "\u05df", + "3159": "\u05da", + "3160": "\u05de\u05d5", + "3161": "\u05d1\u05d9", + "3162": "\u05e0\u05d5", + "3163": "\u05d5\u05dc", + "3164": "\u05dc\u05d9", + "3165": "\u05d1\u05e8", + "3166": "\u05d3\u05d9", + "3167": "\u2581\u05d0\u05ea", + "3168": "\u2581\u05e2\u05dc", + "3169": "\u05e9\u05d9", + "3170": "\u05de\u05e9", + "3171": "\u05d5\u05df", + "3172": "\u05d9\u05e8", + "3173": "\u05e0\u05d4", + "3174": "\u05d9\u05ea", + "3175": "\u05e4\u05e8", + "3176": "\u05e3", + "3177": "\u05db\u05dc", + "3178": "\u2581\u05d4\u05d5\u05d0", + "3179": "\u05de\u05d9", + "3180": "\u05d5\u05d1", + "3181": "\u05e8\u05d5", + "3182": "\u2581\u05d1\u05de", + "3183": "\u05e4\u05d9", + "3184": "\u05d0\u05d9", + "3185": "\u2581\u05d4\u05e9", + "3186": "\u05d7\u05d9", + "3187": "\u05d7\u05d5", + "3188": "\u05dc\u05d5", + "3189": "\u05d1\u05e2", + "3190": "\u2581\u05d4\u05d0", + "3191": "\u05e7\u05e8", + "3192": "\u2581\u05dc\u05d0", + "3193": "\u05e0\u05d9\u05dd", + "3194": "\u05e1\u05d9", + "3195": "\u05e8\u05d9", + "3196": "\u2581\u05dc\u05d4", + "3197": "\u05e9\u05e8", + "3198": "\u05d5\u05d3", + "3199": "\u05d9\u05df", + "3200": "\u05d5\u05e4", + "3201": "\u05d0\u05dc", + "3202": "\u2581\u05d4\u05d7", + "3203": "\u05d3\u05e8", + "3204": "\u05e0\u05d5\u05ea", + "3205": "\u2581\u05d4\u05e2", + "3206": "\u05e8\u05d9\u05dd", + "3207": "\u05e4\u05d5", + "3208": "\u05e6\u05d9", + "3209": "\u2581\u05dc\u05de", + "3210": "\u05d0\u05e8", + "3211": "\u05d0\u05d5\u05ea", + "3212": "\u05d8\u05d9", + "3213": "\u2581\u05d4\u05e1", + "3214": "\u05d9\u05d5\u05ea", + "3215": "\u05db\u05d9", + "3216": "\u05e5", + "3217": "\u2581\u05d0\u05d5", + "3218": "\u2581\u05d5\u05d4", + "3219": "\u2581\u05d6\u05d4", + "3220": "\u2581\u05d4\u05d9\u05d0", + "3221": "\u2581\u05d4\u05e6", + "3222": "\u05de\u05e8", + "3223": "\u05e4\u05e2", + "3224": "\u2581\u05d4\u05e4", + "3225": "\u05db\u05df", + "3226": "\u2581\u05d4\u05d9\u05d4", + "3227": "\u05d8\u05e8", + "3228": "\u05d6\u05e8", + "3229": "\u2581\u05e9\u05e0", + "3230": "\u05d0\u05d7\u05e8", + "3231": "\u2581\u05e8\u05d1", + "3232": "\u2581\u05d6\u05d5", + "3233": "\u2581\u05d4\u05e8", + "3234": "\u05de\u05d9\u05dd", + "3235": "\u2581\u05d5\u05de", + "3236": "\u05e8\u05d0\u05e9", + "3237": "\u2581\u05dc\u05d0\u05d7\u05e8", + "3238": "\u05d7\u05dc\u05e7", + "3239": "\u05de\u05df", + "3240": "\u2581\u05d4\u05d9\u05d5", + "3241": "\u05de\u05e1\u05e4\u05e8", + "3242": "\u2581\u05d9\u05d5\u05ea\u05e8", + "3243": "\u05d0\u05d7\u05d3", + "3244": "\u2581\u05d4\u05d9\u05d9\u05ea", + "3245": "\u05e2\u05e6\u05de", + "3246": "\u05de\u05e7\u05d5\u05dd", + "3247": "", + "3248": "\u093e", + "3249": "\u0930", + "3250": "\u0928", + "3251": "\u0915", + "3252": "\u0938", + "3253": "\u0964", + "3254": "\u093f", + "3255": "\u092e", + "3256": "\u0940", + "3257": "\u0932", + "3258": "\u0947", + "3259": "\u092a", + "3260": "\u2581\u0939\u0948", + "3261": "\u094d", + "3262": "\u0939", + "3263": "\u091c", + "3264": "\u0935", + "3265": "\u0924", + "3266": "\u0902", + "3267": "\u091f", + "3268": "\u094b", + "3269": "\u0941", + "3270": "\u0917", + "3271": "\u2581\u0915\u0947", + "3272": "\u2581\u092c", + "3273": "\u2581\u092e\u0947\u0902", + "3274": "\u0936", + "3275": "\u0928\u0947", + "3276": "\u0942", + "3277": "\u092f", + "3278": "\u0928\u093e", + "3279": "\u0924\u093e", + "3280": "\u0926", + "3281": "\u091a", + "3282": "\u2581\u0906", + "3283": "\u092c", + "3284": "\u2581\u0915\u0930", + "3285": "\u2581\u0915\u0940", + "3286": "\u2581\u0905", + "3287": "\u0930\u094d", + "3288": "\u2581\u0939\u094b", + "3289": "\u2581\u0914\u0930", + "3290": "\u090f", + "3291": "\u0916", + "3292": "\u2581\u0924\u094b", + "3293": "\u2581\u0939\u0948\u0902", + "3294": "\u2581\u0938\u0947", + "3295": "\u2581\u0915\u093e", + "3296": "\u094b\u0902", + "3297": "\u2581\u0915\u094b", + "3298": "\u2581\u0915\u093f", + "3299": "\u0924\u0947", + "3300": "\u092b", + "3301": "\u0927", + "3302": "\u0930\u093e", + "3303": "\u0935\u093e", + "3304": "\u2581\u091c\u093e", + "3305": "\u0921", + "3306": "\u0948", + "3307": "\u2581\u0928\u0939\u0940\u0902", + "3308": "\u0909", + "3309": "\u094d\u092f", + "3310": "\u0908", + "3311": "\u2581\u092d\u0940", + "3312": "\u0915\u093e", + "3313": "\u2581\u0926", + "3314": "\u0921\u093c", + "3315": "\u0915\u0947", + "3316": "\u0930\u0940", + "3317": "\u0924\u0940", + "3318": "\u0907", + "3319": "\u2581\u090f\u0915", + "3320": "\u094d\u0930", + "3321": "\u2581\u0907\u0938", + "3322": "\u2581\u092a\u094d\u0930", + "3323": "\u2581\u0909\u0938", + "3324": "\u092f\u093e", + "3325": "\u2581\u092a\u0930", + "3326": "\u092e\u093e", + "3327": "\u092d", + "3328": "\u0947\u0902", + "3329": "\u0932\u0947", + "3330": "\u2581\u0935\u094b", + "3331": "\u0932\u093e", + "3332": "\u094c", + "3333": "\u0938\u0947", + "3334": "\u2581\u0939\u092e", + "3335": "\u2581\u091c\u094b", + "3336": "\u0915\u094d", + "3337": "\u0917\u093e", + "3338": "\u0923", + "3339": "\u2581\u0935\u093f", + "3340": "\u0939\u093e", + "3341": "\u0928\u0940", + "3342": "\u2581\u0906\u092a", + "3343": "\u093f\u092f\u093e", + "3344": "\u2581\u092e\u0948\u0902", + "3345": "\u0902\u0917", + "3346": "\u0938\u094d", + "3347": "\u2581\u0939\u0940", + "3348": "\u0925", + "3349": "\u0930\u0947", + "3350": "\u2581\u092a\u093e", + "3351": "\u093f\u0924", + "3352": "\u0949", + "3353": "\u092d\u093e", + "3354": "\u0938\u0940", + "3355": "\u0901", + "3356": "\u2581\u092f\u0947", + "3357": "\u0915\u094d\u0937", + "3358": "\u091b", + "3359": "\u2581\u0925\u093e", + "3360": "\u0924\u093f", + "3361": "\u2581\u0932\u093f\u090f", + "3362": "\u2581\u0926\u0947", + "3363": "\u0932\u0940", + "3364": "\u2581\u0915\u094d\u092f\u093e", + "3365": "\u2581\u0938\u0902", + "3366": "\u0937", + "3367": "\u2581\u092f\u0939", + "3368": "\u2581\u0939\u093e\u0901", + "3369": "\u0920", + "3370": "\u0924\u094d\u0930", + "3371": "\u0902\u0926", + "3372": "\u0918", + "3373": "\u2581\u092c\u0939\u0941\u0924", + "3374": "\u2581\u0938\u092e", + "3375": "\u094d\u092f\u093e", + "3376": "\u2581\u0932\u0917", + "3377": "\u2581\u0926\u094b", + "3378": "\u093c", + "3379": "\u2581\u0926\u0947\u0916", + "3380": "\u0913", + "3381": "\u0926\u093e", + "3382": "\u2581\u0928\u093f", + "3383": "\u0902\u0921", + "3384": "\u0926\u0940", + "3385": "\u2581\u0930\u0939\u0947", + "3386": "\u2581\u0932\u094b\u0917", + "3387": "\u2581\u092c\u093e\u0924", + "3388": "\u2581\u0915\u0941\u091b", + "3389": "\u093e\u0907", + "3390": "\u2581\u0905\u091a\u094d\u091b\u093e", + "3391": "\u2581\u0938\u0941", + "3392": "\u2581\u0938\u093e\u0925", + "3393": "\u2581\u0915\u0939\u093e", + "3394": "\u2581\u0915\u093f\u092f\u093e", + "3395": "\u0938\u094d\u091f", + "3396": "\u2581\u0938\u092c", + "3397": "\u0922\u093c", + "3398": "\u2581\u0930\u0939\u093e", + "3399": "\u2581\u0917\u092f\u093e", + "3400": "\u2581\u092b\u093f\u0930", + "3401": "\u2581\u092a\u0947", + "3402": "\u2581\u0905\u092c", + "3403": "\u0938\u094d\u0925", + "3404": "\u2581\u091c\u0940", + "3405": "\u2581\u091a\u0932", + "3406": "\u2581\u092c\u093e\u0930", + "3407": "\u2581\u0925\u0947", + "3408": "\u0938\u094d\u0924", + "3409": "\u2581\u0925\u0940", + "3410": "\u2581\u092e\u093f\u0932", + "3411": "\u2581\u0915\u094b\u0908", + "3412": "\u0943", + "3413": "\u2581\u092e\u0924\u0932\u092c", + "3414": "\u093f\u092f\u094b\u0902", + "3415": "\u2581\u0939\u0942\u0901", + "3416": "\u2581\u0905\u092d\u0940", + "3417": "\u0947\u0902\u0917\u0947", + "3418": "\u2581\u092c\u094b\u0932", + "3419": "\u091d", + "3420": "\u2581\u0930\u0939\u0940", + "3421": "\u091a\u093e\u0930", + "3422": "\u2581\u0905\u092a\u0928\u0947", + "3423": "\u2581\u092c\u093e\u0926", + "3424": "\u2581\u0932\u0947\u0915\u093f\u0928", + "3425": "\u0924\u094d\u0924", + "3426": "\u0910", + "3427": "\u2581\u092e\u0941\u091d\u0947", + "3428": "\u2581\u092e\u0947\u0930\u0947", + "3429": "\u0911", + "3430": "!", + "3431": "\u0906", + "3432": "\u090a", + "3433": "\u0922", + "3434": "\u091e", + "3435": "\u0905", + "3436": "\u0903", + "3437": "\u0914", + "3438": "\u090b", + "3439": "\u0945", + "3440": "\u0919", + "3441": "\u090d", + "3442": "\u0950", + "3443": "\u0960", + "3444": "\u0931", + "3445": "\u00cc", + "3446": "", + "3447": "\u2581\u3044", + "3448": "\u2581\u3002", + "3449": "\u2581\u3001", + "3450": "\u2581\u306e", + "3451": "\u2581\u3046", + "3452": "\u2581\u3093", + "3453": "\u2581\u306a", + "3454": "\u2581\u304b", + "3455": "\u2581\u3067", + "3456": "\u2581\u3063", + "3457": "\u2581\u3066", + "3458": "\u2581\u3042", + "3459": "\u2581\u305f", + "3460": "\u2581\u3068", + "3461": "\u2581\u3059", + "3462": "\u2581\u308b", + "3463": "\u2581\u306f", + "3464": "\u2581\u306b", + "3465": "\u2581\u3057", + "3466": "\u2581\u305d", + "3467": "\u2581\u3082", + "3468": "\u2581\u30fc", + "3469": "\u2581\u307e", + "3470": "\u2581\u304c", + "3471": "\u2581\u306d", + "3472": "\u2581\u3089", + "3473": "\u2581\u308c", + "3474": "\u2581\u3060", + "3475": "\u2581\u30f3", + "3476": "\u2581\u3053", + "3477": "\u2581\u3088", + "3478": "\u2581\u308a", + "3479": "\u2581\u3092", + "3480": "\u4ee5", + "3481": "\u4ed5", + "3482": "\u2581\u53cb", + "3483": "\u2581\u6771", + "3484": "\u2581\u9055", + "3485": "\u2581\u6587", + "3486": "\u2581\u30a1", + "3487": "\u2581\u30ce", + "3488": "\u2581\u6210", + "3489": "\u2581\u660e", + "3490": "\u2581\u4e16", + "3491": "\u2581\u5f37", + "3492": "\u2581\u66f2", + "3493": "\u2581\u8868", + "3494": "\u2581\u6708", + "3495": "\u2581\u60c5", + "3496": "\u2581\u6d3b", + "3497": "\u2581\u753a", + "3498": "\u2581\u4ed8", + "3499": "\u2581\u3075", + "3500": "\u2581\u3072", + "3501": "\u2581\u8cb7", + "3502": "\u2581\u9023", + "3503": "\u2581\u3080", + "3504": "\u2581\u533a", + "3505": "\u2581\u30da", + "3506": "\u2581\u78ba", + "3507": "\u2581\u6d41", + "3508": "\u2581\u671f", + "3509": "\u2581\u6d77", + "3510": "\u2581\u8a2d", + "3511": "\u2581\u8a9e", + "3512": "\u2581\u66f8", + "3513": "\u2581\u6599", + "3514": "\u2581\u8981", + "3515": "\u2581\u79d1", + "3516": "\u2581\u80b2", + "3517": "\u2581\u30b4", + "3518": "\u2581\u5b89", + "3519": "\u2581\u516d", + "3520": "\u2581\u6709", + "3521": "\u2581\u30b2", + "3522": "\u2581\u539f", + "3523": "\u2581\u80fd", + "3524": "\u2581\u58f2", + "3525": "\u2581\u611b", + "3526": "\u2581\u4eac", + "3527": "\u2581\u5236", + "3528": "\u2581\u30b6", + "3529": "\u2581\u826f", + "3530": "\u2581\u30ae", + "3531": "\u2581\u30e4", + "3532": "\u2581\u4e03", + "3533": "\u2581\u7121", + "3534": "\u2581\u8003", + "3535": "\u2581\u7279", + "3536": "\u2581\u767e", + "3537": "\u2581\u5c11", + "3538": "\u2581\u53c2", + "3539": "\u2581\u7537", + "3540": "\u2581\u4fdd", + "3541": "\u2581\u5712", + "3542": "\u666e", + "3543": "\u2581\u4ed6", + "3544": "\u2581\u30a9", + "3545": "\u2581\u6b21", + "3546": "\u2581\u512a", + "3547": "\u2581\u304e", + "3548": "\u2581\u8abf", + "3549": "\u2581\u6f14", + "3550": "\u2581\u53e3", + "3551": "\u2581\u98a8", + "3552": "\u2581\u9001", + "3553": "\u2581\u904b", + "3554": "\u2581\u99c5", + "3555": "\u2581\u5c40", + "3556": "\u53d7", + "3557": "\u2581\u7f6e", + "3558": "\u2581\u90fd", + "3559": "\u2581\u4fe1", + "3560": "\u2581\u7f8e", + "3561": "\u2581\u89aa", + "3562": "\u2581\u3005", + "3563": "\u2581\u96f6", + "3564": "\u2581\u5143", + "3565": "\u2581\u59cb", + "3566": "\u2581\u9078", + "3567": "\u2581\u5de5", + "3568": "\u2581\u754c", + "3569": "\u2581\u8eab", + "3570": "\u2581\u5e83", + "3571": "\u2581\u5411", + "3572": "\u2581\u7d44", + "3573": "\u2581\u5728", + "3574": "\u2581\u5354", + "3575": "\u2581\u6c34", + "3576": "\u2581\u5dde", + "3577": "\u2581\u4f11", + "3578": "\u2581\u548c", + "3579": "\u2581\u653e", + "3580": "\u2581\u69d8", + "3581": "\u2581\u7d42", + "3582": "\u2581\u969b", + "3583": "\u2581\u52a0", + "3584": "\u2581\u5357", + "3585": "\u2581\u5207", + "3586": "\u2581\u4e0d", + "3587": "\u2581\u50d5", + "3588": "\u2581\u4f8b", + "3589": "\u2581\u65e9", + "3590": "\u2581\u65cf", + "3591": "\u2581\u3047", + "3592": "\u2581\u7d4c", + "3593": "\u2581\u4f9b", + "3594": "\u2581\u5f62", + "3595": "\u2581\u767d", + "3596": "\u2581\u6728", + "3597": "\u2581\u7b49", + "3598": "\u2581\u5929", + "3599": "\u2581\u5229", + "3600": "\u2581\u73fe", + "3601": "\u2581\u5fdc", + "3602": "\u2581\u9928", + "3603": "\u2581\u5404", + "3604": "\u2581\u70b9", + "3605": "\u2581\u52d9", + "3606": "\u2581\u30f4", + "3607": "\u2581\u771f", + "3608": "\u2581\u6307", + "3609": "\u2581\u671d", + "3610": "\u2581\u97f3", + "3611": "\u2581\u4e88", + "3612": "\u2581\u5e73", + "3613": "\u2581\u984c", + "3614": "\u2581\u4f4f", + "3615": "\u2581\u5186", + "3616": "\u2581\u4f1d", + "3617": "\u2581\u56e3", + "3618": "\u2581\u6751", + "3619": "\u2581\u76f8", + "3620": "\u2581\u8853", + "3621": "\u2581\u5e30", + "3622": "\u2581\u53e4", + "3623": "\u2581\u8ab0", + "3624": "\u2581\u53ef", + "3625": "\u2581\u592b", + "3626": "\u2581\u5f7c", + "3627": "\u2581\u533b", + "3628": "\u2581\u7a7a", + "3629": "\u2581\u8cde", + "3630": "\u2581\u653f", + "3631": "\u2581\u4ea4", + "3632": "\u2581\u6c11", + "3633": "\u2581\u6c7a", + "3634": "\u5c02", + "3635": "\u2581\u7523", + "3636": "\u2581\u9650", + "3637": "\u2581\u795e", + "3638": "\u2581\u57fa", + "3639": "\u2581\u60aa", + "3640": "\u2581\u9662", + "3641": "\u2581\u7cfb", + "3642": "\u2581\u5f15", + "3643": "\u2581\u65c5", + "3644": "\u2581\u6280", + "3645": "\u2581\u53f0", + "3646": "\u2581\u52dd", + "3647": "\u2581\u3086", + "3648": "\u2581\u57df", + "3649": "\u2581\u518d", + "3650": "\u2581\u554f", + "3651": "\u2581\u8272", + "3652": "\u2581\u30d2", + "3653": "\u4f01", + "3654": "\u767b", + "3655": "\u7dcf", + "3656": "\u6539", + "3657": "\u63a2", + "3658": "\u7570", + "3659": "\u5b8c", + "3660": "\u4f0a", + "3661": "\u9811", + "3662": "\u6df1", + "3663": "\u7af6", + "3664": "\u63a8", + "3665": "\u8fb2", + "3666": "\u6628", + "3667": "\u639b", + "3668": "\u5bc4", + "3669": "\u4ed9", + "3670": "\u5371", + "3671": "\u8f9e", + "3672": "\u6f2b", + "3673": "\u57fc", + "3674": "\u8a73", + "3675": "\u5e7c", + "3676": "\u6271", + "3677": "\u4f59", + "3678": "\u63cf", + "3679": "\u63a1", + "3680": "\u88ab", + "3681": "\u30f6", + "3682": "\u4ff3", + "3683": "\u6803", + "3684": "\u56fa", + "3685": "\u526f", + "3686": "\u6df7", + "3687": "\u6551", + "3688": "\u7518", + "3689": "\u4e92", + "3690": "\u9589", + "3691": "\u75b2", + "3692": "\u4e9c", + "3693": "\u501f", + "3694": "\u690d", + "3695": "\u8cac", + "3696": "\u4eee", + "3697": "\u8da3", + "3698": "\u8f9b", + "3699": "\u8131", + "3700": "\u6050", + "3701": "\u6ce3", + "3702": "\u5951", + "3703": "\u60b2", + "3704": "\u96a0", + "3705": "\u662d", + "3706": "\u9ebb", + "3707": "\u54f2", + "3708": "\u5ba3", + "3709": "\u6c96", + "3710": "\u60a9", + "3711": "\u6d6e", + "3712": "\u8af8", + "3713": "\u5de8", + "3714": "\u5348", + "3715": "\u5360", + "3716": "\u8ddd", + "3717": "\u7e4b", + "3718": "\u6e0b", + "3719": "\u5fd9", + "3720": "\u6c5a", + "3721": "\u5ef6", + "3722": "\u5192", + "3723": "\u8a2a", + "3724": "\u6cbf", + "3725": "\u552f", + "3726": "\u6279", + "3727": "\u90f5", + "3728": "\u4f9d", + "3729": "\u63da", + "3730": "\u52c7", + "3731": "\u8a95", + "3732": "\u67d4", + "3733": "\u50be", + "3734": "\u5bc2", + "3735": "\u8a89", + "3736": "\u61f8", + "3737": "\u9ec4", + "3738": "\u90a6", + "3739": "\u81e8", + "3740": "\u5b09", + "3741": "\u7dba", + "3742": "\u5d29", + "3743": "\u8cfc", + "3744": "\u6d45", + "3745": "\u7e70", + "3746": "\u7dad", + "3747": "\u55ab", + "3748": "\u7a3c", + "3749": "\u71c3", + "3750": "\u65e2", + "3751": "\u8e0f", + "3752": "\u55a7", + "3753": "\u61a7", + "3754": "\u795d", + "3755": "\u6f01", + "3756": "\u8352", + "3757": "\u7dca", + "3758": "\u7372", + "3759": "\u98fe", + "3760": "\u70ad", + "3761": "\u642d", + "3762": "\u52aa", + "3763": "\u72d9", + "3764": "\u8a34", + "3765": "\u5bc5", + "3766": "\u9867", + "3767": "\u6311", + "3768": "\u61d0", + "3769": "\u72ed", + "3770": "\u96f0", + "3771": "\u62db", + "3772": "\u5857", + "3773": "\u6392", + "3774": "\u963f", + "3775": "\u596a", + "3776": "\u96c7", + "3777": "\u57cb", + "3778": "\u5c65", + "3779": "\u4fb5", + "3780": "\u61b2", + "3781": "\u8a72", + "3782": "\u786c", + "3783": "\u8caf", + "3784": "\u80f8", + "3785": "\u983b", + "3786": "\u52e7", + "3787": "\u9b45", + "3788": "\u5fe0", + "3789": "\u8328", + "3790": "\u6291", + "3791": "\u9a5a", + "3792": "\u75e9", + "3793": "\u5996", + "3794": "\u63c3", + "3795": "\u885d", + "3796": "\u54c0", + "3797": "\u829d", + "3798": "\u504f", + "3799": "\u5f27", + "3800": "\u4ef0", + "3801": "\u6f70", + "3802": "\u6dbc", + "3803": "\u8ae6", + "3804": "\u98fd", + "3805": "\u598a", + "3806": "\u633f", + "3807": "\u8010", + "3808": "\u8ce2", + "3809": "\u902e", + "3810": "\u62ab", + "3811": "\u6d69", + "3812": "\u900f", + "3813": "\u6328", + "3814": "\u4fc3", + "3815": "\u667a", + "3816": "\u507d", + "3817": "\u62d3", + "3818": "\u63a7", + "3819": "\u64a4", + "3820": "\u6f5c", + "3821": "\u6817", + "3822": "\u5553", + "3823": "\u7fa8", + "3824": "\u8d08", + "3825": "\u52b1", + "3826": "\u4f3a", + "3827": "\u5410", + "3828": "\u5faa", + "3829": "\u9700", + "3830": "\u6442", + "3831": "\u6dfb", + "3832": "\u7ffb", + "3833": "\u7761", + "3834": "\u5b64", + "3835": "\u7b20", + "3836": "\u6606", + "3837": "\u583a", + "3838": "\u6c88", + "3839": "\u4fd7", + "3840": "\u51fd", + "3841": "\u5302", + "3842": "\u906d", + "3843": "\u6eb6", + "3844": "\u52f2", + "3845": "\u7f70", + "3846": "\u8a87", + "3847": "\u659c", + "3848": "\u935b", + "3849": "\u8cb0", + "3850": "\u7de9", + "3851": "\u62bd", + "3852": "\u7652", + "3853": "\u53e9", + "3854": "\u4f46", + "3855": "\u683d", + "3856": "\u8cbf", + "3857": "\u8107", + "3858": "\u5036", + "3859": "\u9022", + "3860": "\u5949", + "3861": "\u662f", + "3862": "\u8912", + "3863": "\u9271", + "3864": "\u8cbc", + "3865": "\u4f73", + "3866": "\u75be", + "3867": "\u5e61", + "3868": "\u67b6", + "3869": "\u546a", + "3870": "\u4e32", + "3871": "\u5e7e", + "3872": "\u6c99", + "3873": "\u62d2", + "3874": "\u8105", + "3875": "\u8b21", + "3876": "\u631f", + "3877": "\u62cd", + "3878": "\u938c", + "3879": "\u80c3", + "3880": "\u99b4", + "3881": "\u9077", + "3882": "\u5197", + "3883": "\u7b51", + "3884": "\u6f2c", + "3885": "\u6068", + "3886": "\u6bb4", + "3887": "\u66c7", + "3888": "\u7fcc", + "3889": "\u8ecc", + "3890": "\u5378", + "3891": "\u6b53", + "3892": "\u6de1", + "3893": "\u6f0f", + "3894": "\u8986", + "3895": "\u72e9", + "3896": "\u755c", + "3897": "\u84b8", + "3898": "\u854e", + "3899": "\u6cf0", + "3900": "\u7d1b", + "3901": "\u7d5e", + "3902": "\u8a50", + "3903": "\u6905", + "3904": "\u6052", + "3905": "\u5132", + "3906": "\u64ec", + "3907": "\u53d4", + "3908": "\u53ec", + "3909": "\u5e7d", + "3910": "\u80ba", + "3911": "\u7b87", + "3912": "\u80a5", + "3913": "\u758e", + "3914": "\u9676", + "3915": "\u65e8", + "3916": "\u90b8", + "3917": "\u5449", + "3918": "\u51c6", + "3919": "\u8df3", + "3920": "\u757f", + "3921": "\u5ef7", + "3922": "\u920d", + "3923": "\u6e9c", + "3924": "\u6170", + "3925": "\u72a0", + "3926": "\u7e4a", + "3927": "\u82b3", + "3928": "\u7272", + "3929": "\u773a", + "3930": "\u90ca", + "3931": "\u618e", + "3932": "\u514b", + "3933": "\u731b", + "3934": "\u63aa", + "3935": "\u9a19", + "3936": "\u6d78", + "3937": "\u6148", + "3938": "\u52a3", + "3939": "\u93ae", + "3940": "\u8650", + "3941": "\u8e74", + "3942": "\u82d7", + "3943": "\u9665", + "3944": "\u5f90", + "3945": "\u62ed", + "3946": "\u58cc", + "3947": "\u614c", + "3948": "\u6349", + "3949": "\u819c", + "3950": "\u508d", + "3951": "\u565b", + "3952": "\u819a", + "3953": "\u6f20", + "3954": "\u606d", + "3955": "\u81a8", + "3956": "\u6a3d", + "3957": "\u820c", + "3958": "\u611a", + "3959": "\u7881", + "3960": "\u82a6", + "3961": "\u5eca", + "3962": "\u5674", + "3963": "\u7f8a", + "3964": "\u85ab", + "3965": "\u7be0", + "3966": "\u59a5", + "3967": "\u78ef", + "3968": "\u6851", + "3969": "\u7092", + "3970": "\u62d8", + "3971": "\u690e", + "3972": "\u7c98", + "3973": "\u5208", + "3974": "\u8061", + "3975": "\u537f", + "3976": "\u80e1", + "3977": "\u5d07", + "3978": "\u84b2", + "3979": "\u5270", + "3980": "\u745e", + "3981": "\u6e13", + "3982": "\u8ced", + "3983": "\u6e67", + "3984": "\u70f9", + "3985": "\u51dd", + "3986": "\u7d3a", + "3987": "\u9038", + "3988": "\u7261", + "3989": "\u58a8", + "3990": "\u840c", + "3991": "\u622f", + "3992": "\u8429", + "3993": "\u79e9", + "3994": "\u6367", + "3995": "\u69fb", + "3996": "\u8154", + "3997": "\u8776", + "3998": "\u8d05", + "3999": "\u7a4f", + "4000": "\u6562", + "4001": "\u64c1", + "4002": "\u8d74", + "4003": "\u78d0", + "4004": "\u58ee", + "4005": "\u8a93", + "4006": "\u62b9", + "4007": "\u6ea2", + "4008": "\u53f1", + "4009": "\u53f6", + "4010": "\u59a8", + "4011": "\u6cb8", + "4012": "\u7d33", + "4013": "\u963b", + "4014": "\u5984", + "4015": "\u6590", + "4016": "\u5983", + "4017": "\u5de7", + "4018": "\u540a", + "4019": "\u60da", + "4020": "\u8236", + "4021": "\u52ff", + "4022": "\u61c7", + "4023": "\u7525", + "4024": "\u60dc", + "4025": "\u7b39", + "4026": "\u6b86", + "4027": "\u6fe1", + "4028": "\u60e3", + "4029": "\u6020", + "4030": "\u6dc0", + "4031": "\u5265", + "4032": "\u66d6", + "4033": "\u6b64", + "4034": "\u85dd", + "4035": "\u8fb0", + "4036": "\u632b", + "4037": "\u66ab", + "4038": "\u6155", + "4039": "\u78a7", + "4040": "\u5634", + "4041": "\u3062", + "4042": "\u6d2a", + "4043": "\u865c", + "4044": "\u9065", + "4045": "\u92ed", + "4046": "\u5a2f", + "4047": "\u814e", + "4048": "\u871c", + "4049": "\u8a02", + "4050": "\u74e6", + "4051": "\u5944", + "4052": "\u64ad", + "4053": "\u75d5", + "4054": "\u7db4", + "4055": "\u7a40", + "4056": "\u9699", + "4057": "\u5384", + "4058": "\u5448", + "4059": "\u66f0", + "4060": "\u5d16", + "4061": "\u64e6", + "4062": "\u70cf", + "4063": "\u62c9", + "4064": "\u8861", + "4065": "\u6731", + "4066": "\u5606", + "4067": "\u8339", + "4068": "\u5cef", + "4069": "\u6ff1", + "4070": "\u84bc", + "4071": "\u30f1", + "4072": "\u6d12", + "4073": "\u85a9", + "4074": "\u8acf", + "4075": "\u55c5", + "4076": "\u689d", + "4077": "\u8096", + "4078": "\u785d", + "4079": "\u8a63", + "4080": "\u8cd1", + "4081": "\u67a2", + "4082": "\u6e9d", + "4083": "\u7a00", + "4084": "\u6a58", + "4085": "\u7766", + "4086": "\u9673", + "4087": "\u91e7", + "4088": "\u91b8", + "4089": "\u55aa", + "4090": "\u67af", + "4091": "\u6881", + "4092": "\u86cd", + "4093": "\u7ce7", + "4094": "\u90ed", + "4095": "\u7058", + "4096": "\u723d", + "4097": "\u7c97", + "4098": "\u8702", + "4099": "\u636e", + "4100": "\u5112", + "4101": "\u80a1", + "4102": "\u978d", + "4103": "\u61f2", + "4104": "\u5b54", + "4105": "\u6f06", + "4106": "\u8499", + "4107": "\u693f", + "4108": "\u7345", + "4109": "\u73c8", + "4110": "\u7554", + "4111": "\u9a28", + "4112": "\u675c", + "4113": "\u7984", + "4114": "\u52c3", + "4115": "\u9ac4", + "4116": "\u5f0a", + "4117": "\u77ef", + "4118": "\u9df2", + "4119": "\u58ec", + "4120": "\u6666", + "4121": "\u6e15", + "4122": "\u85cd", + "4123": "\u533f", + "4124": "\u582a", + "4125": "\u7aaa", + "4126": "\u5289", + "4127": "\u6182", + "4128": "\u5091", + "4129": "\u63b4", + "4130": "\u540e", + "4131": "\u916a", + "4132": "\u5176", + "4133": "\u82eb", + "4134": "\u30c5", + "4135": "\u63c9", + "4136": "\u73a9", + "4137": "\u80f4", + "4138": "\u8910", + "4139": "\u8afe", + "4140": "\u5598", + "4141": "\u559a", + "4142": "\u8594", + "4143": "\u8cc4", + "4144": "\u7fe0", + "4145": "\u5023", + "4146": "\u576a", + "4147": "\u6109", + "4148": "\u6276", + "4149": "\u670b", + "4150": "\u5351", + "4151": "\u66fe", + "4152": "\u786b", + "4153": "\u51a8", + "4154": "\u5b78", + "4155": "\u6c7d", + "4156": "\u837b", + "4157": "\u8461", + "4158": "\u6eba", + "4159": "\u8fbf", + "4160": "\u4e91", + "4161": "\u5fcc", + "4162": "\u7815", + "4163": "\u6734", + "4164": "\u6a8e", + "4165": "\u9320", + "4166": "\u5e63", + "4167": "\u80af", + "4168": "\u81b5", + "4169": "\u52c5", + "4170": "\u65bc", + "4171": "\u7947", + "4172": "\u8304", + "4173": "\u6591", + "4174": "\u50c5", + "4175": "\u8a60", + "4176": "\u96bc", + "4177": "\u98e2", + "4178": "\u7a3d", + "4179": "\u5dba", + "4180": "\u6df5", + "4181": "\u8b83", + "4182": "\u7aae", + "4183": "\u7be4", + "4184": "\u97fb", + "4185": "\u6897", + "4186": "\u72f8", + "4187": "\u69cd", + "4188": "\u8b17", + "4189": "\u8ab9", + "4190": "\u9010", + "4191": "\u53d9", + "4192": "\u5420", + "4193": "\u725f", + "4194": "\u9838", + "4195": "\u52fe", + "4196": "\u717d", + "4197": "\u7460", + "4198": "\u4fb6", + "4199": "\u68b6", + "4200": "\u8997", + "4201": "\u95a4", + "4202": "\u51a5", + "4203": "\u5dfe", + "4204": "\u5f04", + "4205": "\u83e9", + "4206": "\u8526", + "4207": "\u99a8", + "4208": "\u6fc1", + "4209": "\u714e", + "4210": "\u8218", + "4211": "\u6876", + "4212": "\u79e6", + "4213": "\u9061", + "4214": "\u5806", + "4215": "\u6afb", + "4216": "\u6e07", + "4217": "\u77ad", + "4218": "\u81c6", + "4219": "\u4fe3", + "4220": "\u7169", + "4221": "\u54b3", + "4222": "\u5506", + "4223": "\u60f9", + "4224": "\u6775", + "4225": "\u7c9f", + "4226": "\u9091", + "4227": "\u553e", + "4228": "\u6756", + "4229": "\u6960", + "4230": "\u6b6a", + "4231": "\u711a", + "4232": "\u8fb1", + "4233": "\u559d", + "4234": "\u6e58", + "4235": "\u76f2", + "4236": "\u8b39", + "4237": "\u8e2a", + "4238": "\u965b", + "4239": "\u589f", + "4240": "\u64b0", + "4241": "\u6ccc", + "4242": "\u6f15", + "4243": "\u8a6b", + "4244": "\u771e", + "4245": "\u90c1", + "4246": "\u6e1a", + "4247": "\u8210", + "4248": "\u8235", + "4249": "\u8e8a", + "4250": "\u58f9", + "4251": "\u5c6f", + "4252": "\u7435", + "4253": "\u7436", + "4254": "\u7a92", + "4255": "\u82af", + "4256": "\u8e87", + "4257": "\u4e1e", + "4258": "\u7262", + "4259": "\u8305", + "4260": "\u5f57", + "4261": "\u699b", + "4262": "\u7b95", + "4263": "\u82ad", + "4264": "\u918d", + "4265": "\u9190", + "4266": "\u9945", + "4267": "\u5815", + "4268": "\u5deb", + "4269": "\u6a9c", + "4270": "\u914c", + "4271": "\u96eb", + "4272": "\u6b3e", + "4273": "\u9d3b", + "4274": "\u4f10", + "4275": "\u7901", + "4276": "\u7a83", + "4277": "\u8389", + "4278": "\u929a", + "4279": "\u6191", + "4280": "\u639f", + "4281": "\u6492", + "4282": "\u6a0b", + "4283": "\u7336", + "4284": "\u868a", + "4285": "\u88fe", + "4286": "\u96cc", + "4287": "\u6216", + "4288": "\u643e", + "4289": "\u6cc4", + "4290": "\u7109", + "4291": "\u7940", + "4292": "\u7b8b", + "4293": "\u919c", + "4294": "\u9d5c", + "4295": "\u51f1", + "4296": "\u5c16", + "4297": "\u6c23", + "4298": "\u75d2", + "4299": "\u830e", + "4300": "\u745b", + "4301": "\u602f", + "4302": "\u698e", + "4303": "\u6feb", + "4304": "\u7099", + "4305": "\u97ad", + "4306": "\u9b4f", + "4307": "\u4f5b", + "4308": "\u51b6", + "4309": "\u55dc", + "4310": "\u5750", + "4311": "\u6144", + "4312": "\u61c9", + "4313": "\u6c50", + "4314": "\u73c2", + "4315": "\u8fc5", + "4316": "\u62f7", + "4317": "\u9019", + "4318": "\u5ae1", + "4319": "\u60bc", + "4320": "\u637b", + "4321": "\u6a3a", + "4322": "\u85c1", + "4323": "\u932c", + "4324": "\u50ad", + "4325": "\u5243", + "4326": "\u5d4c", + "4327": "\u727d", + "4328": "\u937c", + "4329": "\u4fae", + "4330": "\u5f59", + "4331": "\u6bec", + "4332": "\u4ea8", + "4333": "\u4f86", + "4334": "\u5b8d", + "4335": "\u8a1b", + "4336": "\u9ab8", + "4337": "\u4ec7", + "4338": "\u5df4", + "4339": "\u6c3e", + "4340": "\u71e6", + "4341": "\u783a", + "4342": "\u79df", + "4343": "\u8549", + "4344": "\u5614", + "4345": "\u6703", + "4346": "\u67da", + "4347": "\u69cc", + "4348": "\u83ab", + "4349": "\u88d4", + "4350": "\u91d8", + "4351": "\u51a4", + "4352": "\u51b4", + "4353": "\u64ab", + "4354": "\u8d0b", + "4355": "\u30f5", + "4356": "\u4e9b", + "4357": "\u4f43", + "4358": "\u72d0", + "4359": "\u56a2", + "4360": "\u92f3", + "4361": "\u5dbd", + "4362": "\u9f4b", + "4363": "\u51f9", + "4364": "\u54fa", + "4365": "\u57f4", + "4366": "\u65fa", + "4367": "\u86cb", + "4368": "\u8cdc", + "4369": "\u4f0d", + "4370": "\u545f", + "4371": "\u5937", + "4372": "\u5dbc", + "4373": "\u6c4e", + "4374": "\u9739", + "4375": "\u5875", + "4376": "\u6101", + "4377": "\u8106", + "4378": "\u97ee", + "4379": "\u540f", + "4380": "\u5957", + "4381": "\u5993", + "4382": "\u68b1", + "4383": "\u6d1b", + "4384": "\u6f31", + "4385": "\u725d", + "4386": "\u798d", + "4387": "\u7d21", + "4388": "\u810a", + "4389": "\u8cd3", + "4390": "\u586b", + "4391": "\u673d", + "4392": "\u6a2b", + "4393": "\u8299", + "4394": "\u84c9", + "4395": "\u9310", + "4396": "\u5835", + "4397": "\u5f14", + "4398": "\u633d", + "4399": "\u6955", + "4400": "\u6c72", + "4401": "\u5294", + "4402": "\u5eb8", + "4403": "\u694a", + "4404": "\u7826", + "4405": "\u9c57", + "4406": "\u61a4", + "4407": "\u634f", + "4408": "\u6d29", + "4409": "\u723e", + "4410": "\u750d", + "4411": "\u817f", + "4412": "\u9c52", + "4413": "\u685f", + "4414": "\u6a7f", + "4415": "\u82db", + "4416": "\u982c", + "4417": "\u55da", + "4418": "\u5751", + "4419": "\u5b75", + "4420": "\u5e87", + "4421": "\u68a2", + "4422": "\u6b05", + "4423": "\u7560", + "4424": "\u7a7f", + "4425": "\u8513", + "4426": "\u8d99", + "4427": "\u927e", + "4428": "\u4f51", + "4429": "\u5dcc", + "4430": "\u5f77", + "4431": "\u65a7", + "4432": "\u68d8", + "4433": "\u6dd8", + "4434": "\u7b94", + "4435": "\u7d2f", + "4436": "\u8729", + "4437": "\u908a", + "4438": "\u9ebf", + "4439": "\u5713", + "4440": "\u66a2", + "4441": "\u69ae", + "4442": "\u6b89", + "4443": "\u6d8c", + "4444": "\u7aea", + "4445": "\u8aee", + "4446": "\u96db", + "4447": "\u9bf5", + "4448": "\u4e14", + "4449": "\u5347", + "4450": "\u5954", + "4451": "\u5ce8", + "4452": "\u7149", + "4453": "\u7791", + "4454": "\u8276", + "4455": "\u840e", + "4456": "\u8568", + "4457": "\u85aa", + "4458": "\u8f0c", + "4459": "\u5dfd", + "4460": "\u66f3", + "4461": "\u6cab", + "4462": "\u82a5", + "4463": "\u8511", + "4464": "\u93a7", + "4465": "\u9f20", + "4466": "\u51ea", + "4467": "\u5c51", + "4468": "\u5d14", + "4469": "\u5d6f", + "4470": "\u6a59", + "4471": "\u6e38", + "4472": "\u7a1c", + "4473": "\u8072", + "4474": "\u511a", + "4475": "\u695a", + "4476": "\u8006", + "4477": "\u82b9", + "4478": "\u83d6", + "4479": "\u88f3", + "4480": "\u9017", + "4481": "\u905c", + "4482": "\u9640", + "4483": "\u4ff8", + "4484": "\u5a29", + "4485": "\u5cd9", + "4486": "\u6190", + "4487": "\u6241", + "4488": "\u626e", + "4489": "\u6faa", + "4490": "\u7729", + "4491": "\u7f75", + "4492": "\u8036", + "4493": "\u8058", + "4494": "\u9c3b", + "4495": "\u309d", + "4496": "\u56c3", + "4497": "\u5f7f", + "4498": "\u6167", + "4499": "\u66dd", + "4500": "\u6fe0", + "4501": "\u8309", + "4502": "\u976d", + "4503": "\u9daf", + "4504": "\u9e92", + "4505": "\u30f0", + "4506": "\u4e5e", + "4507": "\u50b2", + "4508": "\u54e8", + "4509": "\u5f6c", + "4510": "\u73c0", + "4511": "\u79e4", + "4512": "\u84ec", + "4513": "\u8ebe", + "4514": "\u9075", + "4515": "\u51f8", + "4516": "\u53a9", + "4517": "\u6168", + "4518": "\u698a", + "4519": "\u6c8c", + "4520": "\u75b1", + "4521": "\u8fe6", + "4522": "\u53e1", + "4523": "\u543b", + "4524": "\u5b2c", + "4525": "\u5d69", + "4526": "\u660f", + "4527": "\u8171", + "4528": "\u8888", + "4529": "\u9c39", + "4530": "\u57e0", + "4531": "\u5be1", + "4532": "\u5cfb", + "4533": "\u5df7", + "4534": "\u62d7", + "4535": "\u62d9", + "4536": "\u63c4", + "4537": "\u63f6", + "4538": "\u65a1", + "4539": "\u6962", + "4540": "\u6dcb", + "4541": "\u722c", + "4542": "\u7425", + "4543": "\u805a", + "4544": "\u80da", + "4545": "\u81a0", + "4546": "\u8292", + "4547": "\u8703", + "4548": "\u87ba", + "4549": "\u9910", + "4550": "\u9dfa", + "4551": "\u51e0", + "4552": "\u52ab", + "4553": "\u5321", + "4554": "\u63d6", + "4555": "\u6b3d", + "4556": "\u7422", + "4557": "\u7825", + "4558": "\u877f", + "4559": "\u8adc", + "4560": "\u8ae7", + "4561": "\u8dbe", + "4562": "\u50d1", + "4563": "\u5a9a", + "4564": "\u5b5f", + "4565": "\u5b95", + "4566": "\u5bd3", + "4567": "\u5f8a", + "4568": "\u5f98", + "4569": "\u6357", + "4570": "\u66d9", + "4571": "\u7e82", + "4572": "\u7fc1", + "4573": "\u81bf", + "4574": "\u85ea", + "4575": "\u8a0a", + "4576": "\u8fc2", + "4577": "\u932b", + "4578": "\u4fef", + "4579": "\u5a3c", + "4580": "\u689f", + "4581": "\u6e3e", + "4582": "\u6ffe", + "4583": "\u79bf", + "4584": "\u7ce0", + "4585": "\u8180", + "4586": "\u82c5", + "4587": "\u8877", + "4588": "\u8c79", + "4589": "\u9798", + "4590": "\u9eb9", + "4591": "\u9ece", + "4592": "\u6abb", + "4593": "\u6e25", + "4594": "\u9149", + "4595": "\u97a0", + "4596": "\u567a", + "4597": "\u60f0", + "4598": "\u646f", + "4599": "\u65db", + "4600": "\u6bc0", + "4601": "\u6d38", + "4602": "\u6dd1", + "4603": "\u71fb", + "4604": "\u77b0", + "4605": "\u7ac8", + "4606": "\u7cfe", + "4607": "\u86d9", + "4608": "\u8e44", + "4609": "\u502d", + "4610": "\u536f", + "4611": "\u56c1", + "4612": "\u5830", + "4613": "\u6652", + "4614": "\u6a13", + "4615": "\u72db", + "4616": "\u84fc", + "4617": "\u86db", + "4618": "\u8718", + "4619": "\u8b33", + "4620": "\u52be", + "4621": "\u5403", + "4622": "\u5484", + "4623": "\u5631", + "4624": "\u6070", + "4625": "\u60b6", + "4626": "\u69c7", + "4627": "\u7325", + "4628": "\u7396", + "4629": "\u792b", + "4630": "\u7977", + "4631": "\u7ad9", + "4632": "\u7ae3", + "4633": "\u7d68", + "4634": "\u7e1e", + "4635": "\u966a", + "4636": "\u4e58", + "4637": "\u53e2", + "4638": "\u5c39", + "4639": "\u61be", + "4640": "\u62ee", + "4641": "\u633a", + "4642": "\u6582", + "4643": "\u6714", + "4644": "\u701e", + "4645": "\u7587", + "4646": "\u77a5", + "4647": "\u7a63", + "4648": "\u7f79", + "4649": "\u8aeb", + "4650": "\u9013", + "4651": "\u96f9", + "4652": "\u981a", + "4653": "\u4f3d", + "4654": "\u5eff", + "4655": "\u60df", + "4656": "\u63bb", + "4657": "\u6523", + "4658": "\u6bb2", + "4659": "\u6c5d", + "4660": "\u6d59", + "4661": "\u806f", + "4662": "\u8a54", + "4663": "\u96bb", + "4664": "\u9801", + "4665": "\u9913", + "4666": "\u50b3", + "4667": "\u51b2", + "4668": "\u65a5", + "4669": "\u7e3d", + "4670": "\u8151", + "4671": "\u92f8", + "4672": "\u9695", + "4673": "\u9812", + "4674": "\u9837", + "4675": "\u4ec0", + "4676": "\u54ed", + "4677": "\u5718", + "4678": "\u5851", + "4679": "\u59e6", + "4680": "\u5bf5", + "4681": "\u615f", + "4682": "\u6b12", + "4683": "\u7953", + "4684": "\u79bd", + "4685": "\u7c50", + "4686": "\u8695", + "4687": "\u8ce6", + "4688": "\u8f62", + "4689": "\u912d", + "4690": "\u92d2", + "4691": "\u985b", + "4692": "\u9c48", + "4693": "\u4e11", + "4694": "\u5b30", + "4695": "\u5ba6", + "4696": "\u5be6", + "4697": "\u5c4d", + "4698": "\u67e9", + "4699": "\u6d9b", + "4700": "\u7473", + "4701": "\u75bc", + "4702": "\u7aa9", + "4703": "\u7dfb", + "4704": "\u811b", + "4705": "\u936c", + "4706": "\u4eab", + "4707": "\u53ad", + "4708": "\u54bd", + "4709": "\u5632", + "4710": "\u6a05", + "4711": "\u71ed", + "4712": "\u75d9", + "4713": "\u7624", + "4714": "\u7e23", + "4715": "\u808b", + "4716": "\u809b", + "4717": "\u8654", + "4718": "\u895f", + "4719": "\u9583", + "4720": "\u9b6f", + "4721": "\u55a9", + "4722": "\u55fd", + "4723": "\u56a5", + "4724": "\u58d5", + "4725": "\u601c", + "4726": "\u634c", + "4727": "\u7b4f", + "4728": "\u7baa", + "4729": "\u7e6d", + "4730": "\u85cf", + "4731": "\u86fe", + "4732": "\u8a03", + "4733": "\u8caa", + "4734": "\u98af", + "4735": "\u531d", + "4736": "\u5480", + "4737": "\u548e", + "4738": "\u56bc", + "4739": "\u5c53", + "4740": "\u5e9a", + "4741": "\u6115", + "4742": "\u6ef8", + "4743": "\u707c", + "4744": "\u7b25", + "4745": "\u8700", + "4746": "\u8a36", + "4747": "\u8a85", + "4748": "\u8d14", + "4749": "\u91ac", + "4750": "\u9c10", + "4751": "\u4fc4", + "4752": "\u5026", + "4753": "\u5039", + "4754": "\u5239", + "4755": "\u5699", + "4756": "\u5859", + "4757": "\u685d", + "4758": "\u6adb", + "4759": "\u7119", + "4760": "\u76e7", + "4761": "\u7ac4", + "4762": "\u7d18", + "4763": "\u7d62", + "4764": "\u83f0", + "4765": "\u8466", + "4766": "\u849c", + "4767": "\u8541", + "4768": "\u8599", + "4769": "\u8606", + "4770": "\u8b01", + "4771": "\u8fa3", + "4772": "\u9761", + "4773": "\u99d5", + "4774": "\u9d0e", + "4775": "\u4ec4", + "4776": "\u4f98", + "4777": "\u5016", + "4778": "\u5080", + "4779": "\u50fb", + "4780": "\u5121", + "4781": "\u524b", + "4782": "\u5f45", + "4783": "\u6802", + "4784": "\u6854", + "4785": "\u68b5", + "4786": "\u6ef2", + "4787": "\u6fb3", + "4788": "\u6fe4", + "4789": "\u7368", + "4790": "\u7577", + "4791": "\u75d4", + "4792": "\u7626", + "4793": "\u7960", + "4794": "\u79b0", + "4795": "\u81a3", + "4796": "\u834f", + "4797": "\u8944", + "4798": "\u8a25", + "4799": "\u8de8", + "4800": "\u8e93", + "4801": "\u90b1", + "4802": "\u9264", + "4803": "\u93d1", + "4804": "\u95ca", + "4805": "\u96c9", + "4806": "\u9d6c", + "4807": "\u53db", + "4808": "\u543c", + "4809": "\u59d0", + "4810": "\u5f4c", + "4811": "\u66fc", + "4812": "\u6c83", + "4813": "\u6f23", + "4814": "\u6f38", + "4815": "\u700b", + "4816": "\u721b", + "4817": "\u7690", + "4818": "\u7c3e", + "4819": "\u7fe1", + "4820": "\u82d3", + "4821": "\u839e", + "4822": "\u84d1", + "4823": "\u857e", + "4824": "\u874b", + "4825": "\u8766", + "4826": "\u892a", + "4827": "\u9119", + "4828": "\u914b", + "4829": "\u92e4", + "4830": "\u937e", + "4831": "\u9435", + "4832": "\u5191", + "4833": "\u557c", + "4834": "\u5617", + "4835": "\u5c4f", + "4836": "\u65af", + "4837": "\u6900", + "4838": "\u6e20", + "4839": "\u71be", + "4840": "\u7280", + "4841": "\u76ba", + "4842": "\u7768", + "4843": "\u78cb", + "4844": "\u7b67", + "4845": "\u7cca", + "4846": "\u837c", + "4847": "\u83b1", + "4848": "\u8fa8", + "4849": "\u901e", + "4850": "\u9081", + "4851": "\u936e", + "4852": "\u968b", + "4853": "\u9786", + "4854": "\u978b", + "4855": "\u4e56", + "4856": "\u55df", + "4857": "\u5700", + "4858": "\u5fd6", + "4859": "\u60e0", + "4860": "\u61ba", + "4861": "\u6518", + "4862": "\u6727", + "4863": "\u675e", + "4864": "\u69d9", + "4865": "\u6b98", + "4866": "\u6deb", + "4867": "\u7015", + "4868": "\u70b8", + "4869": "\u71d0", + "4870": "\u7b50", + "4871": "\u7ff3", + "4872": "\u813e", + "4873": "\u81c0", + "4874": "\u8b49", + "4875": "\u9318", + "4876": "\u9d2c", + "4877": "\u308e", + "4878": "\u4e8e", + "4879": "\u5055", + "4880": "\u54ac", + "4881": "\u5516", + "4882": "\u555c", + "4883": "\u5703", + "4884": "\u58fd", + "4885": "\u59da", + "4886": "\u59e5", + "4887": "\u5a49", + "4888": "\u5b0c", + "4889": "\u5b55", + "4890": "\u5c60", + "4891": "\u5cb1", + "4892": "\u5ed3", + "4893": "\u61ab", + "4894": "\u621f", + "4895": "\u6309", + "4896": "\u637a", + "4897": "\u6853", + "4898": "\u6939", + "4899": "\u6977", + "4900": "\u6ac2", + "4901": "\u704c", + "4902": "\u71d7", + "4903": "\u7526", + "4904": "\u788d", + "4905": "\u795f", + "4906": "\u79ae", + "4907": "\u7a79", + "4908": "\u7b4d", + "4909": "\u7c17", + "4910": "\u814b", + "4911": "\u832b", + "4912": "\u8494", + "4913": "\u8afa", + "4914": "\u8cb6", + "4915": "\u9059", + "4916": "\u9211", + "4917": "\u9328", + "4918": "\u9771", + "4919": "\u98c4", + "4920": "\u9af7", + "4921": "\u9d60", + "4922": "\u9f0e", + "4923": "\u4ea6", + "4924": "\u4f47", + "4925": "\u5072", + "4926": "\u526a", + "4927": "\u5271", + "4928": "\u57d2", + "4929": "\u59f6", + "4930": "\u5c0d", + "4931": "\u5e47", + "4932": "\u5fbd", + "4933": "\u606b", + "4934": "\u652b", + "4935": "\u6b78", + "4936": "\u72e1", + "4937": "\u77bc", + "4938": "\u786f", + "4939": "\u7afa", + "4940": "\u7b0f", + "4941": "\u7bdd", + "4942": "\u7c00", + "4943": "\u7c7e", + "4944": "\u7f6b", + "4945": "\u807e", + "4946": "\u8139", + "4947": "\u8521", + "4948": "\u8557", + "4949": "\u876e", + "4950": "\u8cfd", + "4951": "\u8d16", + "4952": "\u8fad", + "4953": "\u92ea", + "4954": "\u9b93", + "4955": "\u9c2f", + "4956": "\u9c3a", + "4957": "\u4e24", + "4958": "\u4e4e", + "4959": "\u5118", + "4960": "\u530d", + "4961": "\u5310", + "4962": "\u5686", + "4963": "\u5f1b", + "4964": "\u5fa8", + "4965": "\u60e1", + "4966": "\u619a", + "4967": "\u6698", + "4968": "\u68c9", + "4969": "\u6a02", + "4970": "\u6bb7", + "4971": "\u6beb", + "4972": "\u6c40", + "4973": "\u70d9", + "4974": "\u72c4", + "4975": "\u73ea", + "4976": "\u7433", + "4977": "\u74e3", + "4978": "\u7b8f", + "4979": "\u7e5a", + "4980": "\u8207", + "4981": "\u822b", + "4982": "\u8237", + "4983": "\u8317", + "4984": "\u849f", + "4985": "\u84bb", + "4986": "\u86ed", + "4987": "\u88a2", + "4988": "\u8956", + "4989": "\u8966", + "4990": "\u8cf4", + "4991": "\u8d04", + "4992": "\u8e59", + "4993": "\u8f4d", + "4994": "\u8f9f", + "4995": "\u8faf", + "4996": "\u9182", + "4997": "\u9187", + "4998": "\u947d", + "4999": "\u9846", + "5000": "\u9870", + "5001": "\u9c2d", + "5002": "\u51f0", + "5003": "\u5475", + "5004": "\u566a", + "5005": "\u5bf6", + "5006": "\u61fa", + "5007": "\u6372", + "5008": "\u63a0", + "5009": "\u69b4", + "5010": "\u71df", + "5011": "\u7370", + "5012": "\u754f", + "5013": "\u755d", + "5014": "\u7566", + "5015": "\u76c8", + "5016": "\u7827", + "5017": "\u7a62", + "5018": "\u7d06", + "5019": "\u7fc6", + "5020": "\u803d", + "5021": "\u8205", + "5022": "\u8569", + "5023": "\u86f8", + "5024": "\u8882", + "5025": "\u893b", + "5026": "\u8eaf", + "5027": "\u8fed", + "5028": "\u9005", + "5029": "\u9082", + "5030": "\u9089", + "5031": "\u920e", + "5032": "\u929b", + "5033": "\u95dc", + "5034": "\u9e1e", + "5035": "\u9f67", + "5036": "\u4ea5", + "5037": "\u52f8", + "5038": "\u543d", + "5039": "\u54a5", + "5040": "\u5967", + "5041": "\u598d", + "5042": "\u5a62", + "5043": "\u5c24", + "5044": "\u5c41", + "5045": "\u6134", + "5046": "\u65b7", + "5047": "\u65f1", + "5048": "\u6688", + "5049": "\u67b7", + "5050": "\u67d8", + "5051": "\u6ac3", + "5052": "\u6adf", + "5053": "\u6bd8", + "5054": "\u6c6a", + "5055": "\u6f74", + "5056": "\u6fb1", + "5057": "\u7164", + "5058": "\u7194", + "5059": "\u7576", + "5060": "\u777e", + "5061": "\u7893", + "5062": "\u7a84", + "5063": "\u7bc1", + "5064": "\u7c2a", + "5065": "\u7e79", + "5066": "\u7ff9", + "5067": "\u8000", + "5068": "\u8387", + "5069": "\u83f4", + "5070": "\u8602", + "5071": "\u8737", + "5072": "\u8904", + "5073": "\u890c", + "5074": "\u8b2c", + "5075": "\u8ce3", + "5076": "\u8eb0", + "5077": "\u8ecb", + "5078": "\u903c", + "5079": "\u93ac", + "5080": "\u975c", + "5081": "\u9b43", + "5082": "\u9b9f", + "5083": "\u9cf6", + "5084": "\u9f5f", + "5085": "\u9f6c", + "5086": "\u301c", + "5087": "\u30ee", + "5088": "\u4e9f", + "5089": "\u4ec6", + "5090": "\u51cb", + "5091": "\u54a4", + "5092": "\u5544", + "5093": "\u57dc", + "5094": "\u5a11", + "5095": "\u5a36", + "5096": "\u6089", + "5097": "\u620a", + "5098": "\u620e", + "5099": "\u64bc", + "5100": "\u64f2", + "5101": "\u6578", + "5102": "\u6726", + "5103": "\u687f", + "5104": "\u6a1f", + "5105": "\u6aae", + "5106": "\u6c81", + "5107": "\u6d63", + "5108": "\u6d9c", + "5109": "\u6ed3", + "5110": "\u703e", + "5111": "\u71e7", + "5112": "\u7232", + "5113": "\u733e", + "5114": "\u7464", + "5115": "\u7469", + "5116": "\u766c", + "5117": "\u776b", + "5118": "\u77ee", + "5119": "\u788c", + "5120": "\u7a1f", + "5121": "\u7a4e", + "5122": "\u7be5", + "5123": "\u7bf3", + "5124": "\u7cb9", + "5125": "\u7dec", + "5126": "\u7f77", + "5127": "\u7f9e", + "5128": "\u8216", + "5129": "\u847a", + "5130": "\u8acd", + "5131": "\u8af7", + "5132": "\u8b04", + "5133": "\u8da8", + "5134": "\u8e4a", + "5135": "\u8e81", + "5136": "\u8f3b", + "5137": "\u900d", + "5138": "\u970d", + "5139": "\u9b06", + "5140": "\u9baa", + "5141": "\u9ef4", + "5142": "\u4f7b", + "5143": "\u5167", + "5144": "\u51c9", + "5145": "\u525d", + "5146": "\u52d2", + "5147": "\u5396", + "5148": "\u53b6", + "5149": "\u5538", + "5150": "\u5556", + "5151": "\u5885", + "5152": "\u592d", + "5153": "\u5ba5", + "5154": "\u5be2", + "5155": "\u5df2", + "5156": "\u608d", + "5157": "\u62c7", + "5158": "\u6350", + "5159": "\u6426", + "5160": "\u649a", + "5161": "\u64a5", + "5162": "\u64d4", + "5163": "\u652a", + "5164": "\u665d", + "5165": "\u6753", + "5166": "\u6763", + "5167": "\u6787", + "5168": "\u6867", + "5169": "\u6930", + "5170": "\u6a47", + "5171": "\u6b23", + "5172": "\u6cd7", + "5173": "\u6db8", + "5174": "\u6df9", + "5175": "\u6e2d", + "5176": "\u6eff", + "5177": "\u6f58", + "5178": "\u6fd4", + "5179": "\u6fd8", + "5180": "\u6fdf", + "5181": "\u70ac", + "5182": "\u7147", + "5183": "\u71a8", + "5184": "\u71f5", + "5185": "\u72fd", + "5186": "\u73bb", + "5187": "\u763b", + "5188": "\u7647", + "5189": "\u779e", + "5190": "\u7895", + "5191": "\u79a7", + "5192": "\u79be", + "5193": "\u79c9", + "5194": "\u7d72", + "5195": "\u7d89", + "5196": "\u7e0b", + "5197": "\u7e37", + "5198": "\u7e6b", + "5199": "\u81fa", + "5200": "\u8271", + "5201": "\u856a", + "5202": "\u867b", + "5203": "\u8778", + "5204": "\u89ba", + "5205": "\u8a1d", + "5206": "\u8abc", + "5207": "\u8b6f", + "5208": "\u8f15", + "5209": "\u9438", + "5210": "\u958f", + "5211": "\u9a5b", + "5212": "\u9ad9", + "5213": "\u9b18", + "5214": "\u9b4d", + "5215": "\u9b4e", + "5216": "\u9bf0", + "5217": "\u9bf1", + "5218": "\u9d61", + "5219": "\u9e1a", + "5220": "\u9edb", + "5221": "\u9f3e", + "5222": "\u4e9e", + "5223": "\u4f83", + "5224": "\u4fad", + "5225": "\u4fce", + "5226": "\u5011", + "5227": "\u52de", + "5228": "\u5319", + "5229": "\u541e", + "5230": "\u54b8", + "5231": "\u54c8", + "5232": "\u564e", + "5233": "\u5664", + "5234": "\u56d3", + "5235": "\u58de", + "5236": "\u5abd", + "5237": "\u5ff8", + "5238": "\u5ffd", + "5239": "\u6029", + "5240": "\u604d", + "5241": "\u6063", + "5242": "\u60c7", + "5243": "\u61ae", + "5244": "\u622a", + "5245": "\u6258", + "5246": "\u64bb", + "5247": "\u6572", + "5248": "\u658c", + "5249": "\u660a", + "5250": "\u6919", + "5251": "\u69ce", + "5252": "\u6d8e", + "5253": "\u6dee", + "5254": "\u6dfa", + "5255": "\u6e5b", + "5256": "\u6eaf", + "5257": "\u6f09", + "5258": "\u6f6f", + "5259": "\u6fb9", + "5260": "\u7114", + "5261": "\u711c", + "5262": "\u7156", + "5263": "\u71d4", + "5264": "\u7337", + "5265": "\u736a", + "5266": "\u73ca", + "5267": "\u743f", + "5268": "\u745a", + "5269": "\u751c", + "5270": "\u752b", + "5271": "\u7564", + "5272": "\u7586", + "5273": "\u766a", + "5274": "\u76ea", + "5275": "\u77a0", + "5276": "\u783f", + "5277": "\u7957", + "5278": "\u798a", + "5279": "\u7aba", + "5280": "\u7b08", + "5281": "\u7b19", + "5282": "\u7bad", + "5283": "\u7c38", + "5284": "\u80e4", + "5285": "\u81cd", + "5286": "\u821b", + "5287": "\u827e", + "5288": "\u8318", + "5289": "\u83aa", + "5290": "\u8403", + "5291": "\u8431", + "5292": "\u848b", + "5293": "\u8597", + "5294": "\u85f9", + "5295": "\u86ce", + "5296": "\u86ef", + "5297": "\u8815", + "5298": "\u88b1", + "5299": "\u8977", + "5300": "\u89af", + "5301": "\u89c0", + "5302": "\u8a48", + "5303": "\u8aa6", + "5304": "\u8acc", + "5305": "\u8ae4", + "5306": "\u8b7d", + "5307": "\u8c50", + "5308": "\u8cce", + "5309": "\u8ce4", + "5310": "\u8d6d", + "5311": "\u8dcb", + "5312": "\u8e42", + "5313": "\u8e99", + "5314": "\u8f46", + "5315": "\u8f64", + "5316": "\u9041", + "5317": "\u9248", + "5318": "\u9249", + "5319": "\u932e", + "5320": "\u96d9", + "5321": "\u98ee", + "5322": "\u991e", + "5323": "\u9952", + "5324": "\u9957", + "5325": "\u99c8", + "5326": "\u99dd", + "5327": "\u9a57", + "5328": "\u9d44", + "5329": "\u9dd7", + "5330": "\u9eb4", + "5331": "\u9ed1", + "5332": "\ud857\udc4b", + "5333": "\u4e15", + "5334": "\u4e2a", + "5335": "\u4e99", + "5336": "\u4eb0", + "5337": "\u4efd", + "5338": "\u5047", + "5339": "\u50d6", + "5340": "\u50ed", + "5341": "\u524c", + "5342": "\u528d", + "5343": "\u52bf", + "5344": "\u5377", + "5345": "\u53c3", + "5346": "\u548b", + "5347": "\u54ab", + "5348": "\u54ea", + "5349": "\u5583", + "5350": "\u55ae", + "5351": "\u56b4", + "5352": "\u56c2", + "5353": "\u56d1", + "5354": "\u57b3", + "5355": "\u5852", + "5356": "\u58d8", + "5357": "\u5919", + "5358": "\u5934", + "5359": "\u5987", + "5360": "\u59b2", + "5361": "\u59c6", + "5362": "\u5ae3", + "5363": "\u5be5", + "5364": "\u5bf9", + "5365": "\u5c07", + "5366": "\u5c08", + "5367": "\u5d5c", + "5368": "\u5e08", + "5369": "\u5e1a", + "5370": "\u5e36", + "5371": "\u5e96", + "5372": "\u5eec", + "5373": "\u5f61", + "5374": "\u5f9e", + "5375": "\u5fb7", + "5376": "\u60fb", + "5377": "\u613f", + "5378": "\u6147", + "5379": "\u618a", + "5380": "\u61c3", + "5381": "\u61ff", + "5382": "\u6208", + "5383": "\u6230", + "5384": "\u6237", + "5385": "\u6289", + "5386": "\u62c2", + "5387": "\u62cc", + "5388": "\u62d4", + "5389": "\u6369", + "5390": "\u63ac", + "5391": "\u6451", + "5392": "\u6493", + "5393": "\u64b9", + "5394": "\u652c", + "5395": "\u6656", + "5396": "\u678c", + "5397": "\u6837", + "5398": "\u68b3", + "5399": "\u69ff", + "5400": "\u6a31", + "5401": "\u6a84", + "5402": "\u6aa2", + "5403": "\u6aaa", + "5404": "\u6aac", + "5405": "\u6ab8", + "5406": "\u6ae8", + "5407": "\u6b1d", + "5408": "\u6c9b", + "5409": "\u6cbd", + "5410": "\u6d35", + "5411": "\u6da6", + "5412": "\u6e8c", + "5413": "\u6ec9", + "5414": "\u6eef", + "5415": "\u6efe", + "5416": "\u6f11", + "5417": "\u6f32", + "5418": "\u6f6d", + "5419": "\u7165", + "5420": "\u71fc", + "5421": "\u7252", + "5422": "\u72f7", + "5423": "\u7463", + "5424": "\u7511", + "5425": "\u758b", + "5426": "\u75cd", + "5427": "\u75f0", + "5428": "\u7672", + "5429": "\u767c", + "5430": "\u76c2", + "5431": "\u775b", + "5432": "\u77dc", + "5433": "\u77e9", + "5434": "\u787c", + "5435": "\u78a9", + "5436": "\u7941", + "5437": "\u798e", + "5438": "\u79b9", + "5439": "\u7b1e", + "5440": "\u7b45", + "5441": "\u7b86", + "5442": "\u7c11", + "5443": "\u7cae", + "5444": "\u7d45", + "5445": "\u7d7d", + "5446": "\u7d93", + "5447": "\u7da0", + "5448": "\u7dac", + "5449": "\u7db8", + "5450": "\u7dd8", + "5451": "\u7e12", + "5452": "\u7e61", + "5453": "\u7e69", + "5454": "\u7e6a", + "5455": "\u7e8c", + "5456": "\u7eb8", + "5457": "\u7ec8", + "5458": "\u804a", + "5459": "\u8070", + "5460": "\u8085", + "5461": "\u80c4", + "5462": "\u820d", + "5463": "\u8229", + "5464": "\u8258", + "5465": "\u8278", + "5466": "\u83eb", + "5467": "\u8514", + "5468": "\u851a", + "5469": "\u860a", + "5470": "\u863f", + "5471": "\u86de", + "5472": "\u870a", + "5473": "\u8753", + "5474": "\u8755", + "5475": "\u87c4", + "5476": "\u87e0", + "5477": "\u884d", + "5478": "\u88dd", + "5479": "\u89bd", + "5480": "\u89bf", + "5481": "\u8a3b", + "5482": "\u8ac4", + "5483": "\u8b74", + "5484": "\u8b80", + "5485": "\u8b93", + "5486": "\u8bf7", + "5487": "\u8c6c", + "5488": "\u8c98", + "5489": "\u8d39", + "5490": "\u8d6b", + "5491": "\u8de3", + "5492": "\u8e89", + "5493": "\u8efe", + "5494": "\u8f49", + "5495": "\u8ff8", + "5496": "\u8ff9", + "5497": "\u914a", + "5498": "\u9169", + "5499": "\u91aa", + "5500": "\u923f", + "5501": "\u929c", + "5502": "\u934d", + "5503": "\u943a", + "5504": "\u945a", + "5505": "\u94bf", + "5506": "\u95bb", + "5507": "\u95ee", + "5508": "\u965e", + "5509": "\u96dc", + "5510": "\u9706", + "5511": "\u9730", + "5512": "\u97cb", + "5513": "\u985a", + "5514": "\u9986", + "5515": "\u99c1", + "5516": "\u99f1", + "5517": "\u9a55", + "5518": "\u9b51", + "5519": "\u93b9", + "5520": "\u6248", + "5521": "\u9e7c", + "5522": "\u9c24", + "5523": "\u8757", + "5524": "\u6777", + "5525": "\u66c9", + "5526": "\u9c67", + "5527": "\u9c47", + "5528": "\u9214", + "5529": "\u6eaa", + "5530": "\u65a4", + "5531": "\u734f", + "5532": "\u6670", + "5533": "\u76d2", + "5534": "\u5e5f", + "5535": "\u8f5f", + "5536": "\u8ad2", + "5537": "\u7b92", + "5538": "\u75e3", + "5539": "\u9ea9", + "5540": "\u699c", + "5541": "\u9b92", + "5542": "\u5398", + "5543": "\u8cc2", + "5544": "\u84a1", + "5545": "\u85af", + "5546": "\u6a80", + "5547": "\u8e35", + "5548": "\u5366", + "5549": "\u7962", + "5550": "\u60b8", + "5551": "\u7b48", + "5552": "\u76c3", + "5553": "\u67a1", + "5554": "\u87a2", + "5555": "\u9b41", + "5556": "\u7fb9", + "5557": "\u6bef", + "5558": "\u7bed", + "5559": "\u7621", + "5560": "\u5653", + "5561": "\u535c", + "5562": "\u7d2c", + "5563": "\u58f7", + "5564": "\u55e3", + "5565": "\u80f1", + "5566": "\u96c1", + "5567": "\u6634", + "5568": "\u6602", + "5569": "\u647a", + "5570": "\u8b02", + "5571": "\u818f", + "5572": "\u7d9c", + "5573": "\u87fb", + "5574": "\u81e5", + "5575": "\u9bab", + "5576": "\u6ad3", + "5577": "\u88df", + "5578": "\u59be", + "5579": "\u74dc", + "5580": "\u9eb5", + "5581": "\u87f2", + "5582": "\u9e78", + "5583": "\u515c", + "5584": "\u7e8f", + "5585": "\u9306", + "5586": "\u88b4", + "5587": "\u74e2", + "5588": "\u4e19", + "5589": "\u7aff", + "5590": "\u5962", + "5591": "\u852d", + "5592": "\u67ca", + "5593": "\u55ac", + "5594": "\u9921", + "5595": "\u8fc4", + "5596": "\u676d", + "5597": "\u7c95", + "5598": "\u64e2", + "5599": "\u9784", + "5600": "\u8e5f", + "5601": "\u7e55", + "5602": "\u8087", + "5603": "\u9742", + "5604": "\u907d", + "5605": "\u57c3", + "5606": "\u6813", + "5607": "\u751a", + "5608": "\u714c", + "5609": "\u67f5", + "5610": "\u51cc", + "5611": "\u853d", + "5612": "\u71c8", + "5613": "\u9949", + "5614": "\u91c7", + "5615": "\u8463", + "5616": "\u696f", + "5617": "\u57a2", + "5618": "\u6e26", + "5619": "\u6bc5", + "5620": "\u6028", + "5621": "\u5687", + "5622": "\u9e9f", + "5623": "\u67d1", + "5624": "\u6689", + "5625": "\u7dcb", + "5626": "\u75e2", + "5627": "\u6893", + "5628": "\u6e4a", + "5629": "\u901d", + "5630": "\u7aaf", + "5631": "\u5740", + "5632": "\u7e4d", + "5633": "\u63c6", + "5634": "\u60e7", + "5635": "\u5df3", + "5636": "\u58fa", + "5637": "\u7483", + "5638": "\u80b4", + "5639": "\u8098", + "5640": "\u9b8e", + "5641": "\u8a6e", + "5642": "\u514e", + "5643": "\u9aed", + "5644": "\u8471", + "5645": "\u5840", + "5646": "\u53ea", + "5647": "\u7ca5", + "5648": "\u8a23", + "5649": "\u6284", + "5650": "\u5f10", + "5651": "\u5446", + "5652": "\u8338", + "5653": "\u5ec9", + "5654": "\u7078", + "5655": "\u681e", + "5656": "\u5e25", + "5657": "\u82fa", + "5658": "\u6953", + "5659": "\u724c", + "5660": "\u7d79", + "5661": "\u68af", + "5662": "\u6234", + "5663": "\u4e98", + "5664": "\u5bb5", + "5665": "\u8b5a", + "5666": "\u5efb", + "5667": "\u9bdb", + "5668": "\u99b3", + "5669": "\u51e7", + "5670": "\u7a14", + "5671": "\u7f60", + "5672": "\u9192", + "5673": "\u75b9", + "5674": "\u7dbb", + "5675": "\u589c", + "5676": "\u9262", + "5677": "\u72d7", + "5678": "\u6912", + "5679": "\u4ed4", + "5680": "\u7cde", + "5681": "\u8d66", + "5682": "\u8404", + "5683": "\u82d4", + "5684": "\u7027", + "5685": "\u8823", + "5686": "\u59d1", + "5687": "\u8017", + "5688": "\u51db", + "5689": "\u98f4", + "5690": "\u68fa", + "5691": "\u60a6", + "5692": "\u9bad", + "5693": "\u87f9", + "5694": "\u7709", + "5695": "\u6816", + "5696": "\u9bc9", + "5697": "\u8587", + "5698": "\u541f", + "5699": "\u9591", + "5700": "\u86ee", + "5701": "\u85fb", + "5702": "\u7a9f", + "5703": "\u8c8c", + "5704": "\u5a7f", + "5705": "\u817a", + "5706": "\u75fa", + "5707": "\u9688", + "5708": "\u81fc", + "5709": "\u7d10", + "5710": "\u7dbf", + "5711": "\u69fd", + "5712": "\u9be8", + "5713": "\u7409", + "5714": "\u53c9", + "5715": "\u4ff5", + "5716": "\u7259", + "5717": "\u831c", + "5718": "\u7432", + "5719": "\u5e16", + "5720": "\u906e", + "5721": "\u6ef4", + "5722": "\u932f", + "5723": "\u907c", + "5724": "\u9bd6", + "5725": "\u59dc", + "5726": "\u8749", + "5727": "\u9813", + "5728": "\u7897", + "5729": "\u732a", + "5730": "\u9a30", + "5731": "\u5b9b", + "5732": "\u914e", + "5733": "\u71d5", + "5734": "\u9cf3", + "5735": "\u5ac9", + "5736": "\u5766", + "5737": "\u6c70", + "5738": "\u9d28", + "5739": "\u8f3f", + "5740": "\u984e", + "5741": "\u8aed", + "5742": "\u760d", + "5743": "\u6841", + "5744": "\u842c", + "5745": "\u904d", + "5746": "\u67d0", + "5747": "\u9756", + "5748": "\u58f1", + "5749": "\u971e", + "5750": "\u865a", + "5751": "\u5e06", + "5752": "\u7a6b", + "5753": "\u81b3", + "5754": "\u9ba8", + "5755": "\u6681", + "5756": "\u62d0", + "5757": "\u5b8b", + "5758": "\u51e1", + "5759": "\u6ce1", + "5760": "\u5451", + "5761": "\u9ce9", + "5762": "\u55b0", + "5763": "\u56da", + "5764": "\u59ea", + "5765": "\u584a", + "5766": "\u59ac", + "5767": "\u7d17", + "5768": "\u74f6", + "5769": "\u5c3a", + "5770": "\u77db", + "5771": "\u5ee3", + "5772": "\u9e93", + "5773": "\u84cb", + "5774": "\u6f02", + "5775": "\u6643", + "5776": "\u5f84", + "5777": "\u5146", + "5778": "\u67ff", + "5779": "\u4fa0", + "5780": "\u9b31", + "5781": "\u5bf8", + "5782": "\u638c", + "5783": "\u5b9c", + "5784": "\u8ce0", + "5785": "\u6f84", + "5786": "\u674f", + "5787": "\u59fb", + "5788": "\u53a8", + "5789": "\u95a5", + "5790": "\u68f2", + "5791": "\u4faf", + "5792": "\u731f", + "5793": "\u674e", + "5794": "\u7985", + "5795": "\u8b19", + "5796": "\u86c7", + "5797": "\u80c6", + "5798": "\u30c2", + "5799": "\u6627", + "5800": "\u971c", + "5801": "\u845b", + "5802": "\u65ac", + "5803": "\u7c60", + "5804": "\u66f9", + "5805": "\u60e8", + "5806": "\u7e2b", + "5807": "\u7070", + "5808": "\u6842", + "5809": "\u8fbb", + "5810": "\u864e", + "5811": "\u7c92", + "5812": "\u7b1b", + "5813": "\u5507", + "5814": "\u9175", + "5815": "\u80ce", + "5816": "\u722a", + "5817": "\u73e0", + "5818": "\u76fe", + "5819": "\u6bbb", + "5820": "\u9418", + "5821": "\u925b", + "5822": "\u9685", + "5823": "\u821f", + "5824": "\u9285", + "5825": "\u570b", + "5826": "\u9326", + "5827": "\u70c8", + "5828": "\u9df9", + "5829": "\u92fc", + "5830": "\u6795", + "5831": "\u5824", + "5832": "\u8a1f", + "5833": "\u51f6", + "5834": "\u673a", + "5835": "\u5eb6", + "5836": "\u5c3c", + "5837": "\u5589", + "5838": "\u6850", + "5839": "\u819d", + "5840": "\u58c7", + "5841": "\u84c4", + "5842": "\u82bd", + "5843": "\u8607", + "5844": "\u7bb8", + "5845": "\u5ce0", + "5846": "\u8c9e", + "5847": "\u7089", + "5848": "\u5ce1", + "5849": "\u7d46", + "5850": "\u6ecb", + "5851": "\u8896", + "5852": "\u74a7", + "5853": "\u5609", + "5854": "\u7f36", + "5855": "\u8679", + "5856": "\u88f8", + "5857": "\u8015", + "5858": "\u60a0", + "5859": "\u8475", + "5860": "\u642c", + "5861": "\u664b", + "5862": "\u5f26", + "5863": "\u990c", + "5864": "\u8247", + "5865": "\u4eae", + "5866": "\u816b", + "5867": "\u72fc", + "5868": "\u697c", + "5869": "\u9905", + "5870": "\u723a", + "5871": "\u53c8", + "5872": "\u4f8d", + "5873": "\u68df", + "5874": "\u596e", + "5875": "\u50e7", + "5876": "\u84ee", + "5877": "\u828b", + "5878": "\u7573", + "5879": "\u5bb4", + "5880": "\u99ff", + "5881": "\u916c", + "5882": "\u68da", + "5883": "\u5256", + "5884": "\u8cca", + "5885": "\u8870", + "5886": "\u5841", + "5887": "\u8b5c", + "5888": "\u65cb", + "5889": "\u8b90", + "5890": "\u80aa", + "5891": "\u8178", + "5892": "\u83f1", + "5893": "\u95b2", + "5894": "\u7b52", + "5895": "\u54c9", + "5896": "\u9675", + "5897": "\u5a46", + "5898": "\u6b04", + "5899": "\u9855", + "5900": "\u9042", + "5901": "\u7e1b", + "5902": "\u8ef8", + "5903": "\u585e", + "5904": "\u5b8f", + "5905": "\u7def", + "5906": "\u7434", + "5907": "\u5bb0", + "5908": "\u91dc", + "5909": "\u862d", + "5910": "\u9298", + "5911": "\u6f64", + "5912": "\u66a6", + "5913": "\u4f0e", + "5914": "\u75f4", + "5915": "\u73b2", + "5916": "\u75ab", + "5917": "\u660c", + "5918": "\u73ed", + "5919": "\u5f80", + "5920": "\u5203", + "5921": "\u6f54", + "5922": "\u6d32", + "5923": "\u5982", + "5924": "\u5cac", + "5925": "\u7950", + "5926": "\u67cf", + "5927": "\u518a", + "5928": "\u96c0", + "5929": "\u88c2", + "5930": "\u53eb", + "5931": "\u7f85", + "5932": "\u7c8b", + "5933": "\u67f1", + "5934": "\u7948", + "5935": "\u6566", + "5936": "\u30f2", + "5937": "\u5c09", + "5938": "\u3045", + "5939": "\u7db1", + "5940": "\u4e4f", + "5941": "\u6b3a", + "5942": "\u66fd", + "5943": "\u6df3", + "5944": "\u7fd4", + "5945": "\u628a", + "5946": "\u6b96", + "5947": "\u6daf", + "5948": "\u6212", + "5949": "\u5a92", + "5950": "\u7b26", + "5951": "\u9162", + "5952": "\u9177", + "5953": "\u8c9d", + "5954": "\u5cf0", + "5955": "\u5bdb", + "5956": "\u96b7", + "5957": "\u733f", + "5958": "\u764c", + "5959": "\u7dbe", + "5960": "\u6ce5", + "5961": "\u7c9b", + "5962": "\u6249", + "5963": "\u5a20", + "5964": "\u8f14", + "5965": "\u76bf", + "5966": "\u9f13", + "5967": "\u719f", + "5968": "\u6717", + "5969": "\u99d2", + "5970": "\u92ad", + "5971": "\u82d1", + "5972": "\u9396", + "5973": "\u809d", + "5974": "\u5782", + "5975": "\u5104", + "5976": "\u78c1", + "5977": "\u6d1e", + "5978": "\u95c7", + "5979": "\u8987", + "5980": "\u51a0", + "5981": "\u58b3", + "5982": "\u4e3c", + "5983": "\u5be7", + "5984": "\u77b3", + "5985": "\u7656", + "5986": "\u525b", + "5987": "\u83ca", + "5988": "\u5b22", + "5989": "\u9047", + "5990": "\u80a2", + "5991": "\u654f", + "5992": "\u5c0b", + "5993": "\u72c2", + "5994": "\u67f4", + "5995": "\u5f6b", + "5996": "\u5805", + "5997": "\u679d", + "5998": "\u7d2b", + "5999": "\u62fe", + "6000": "\u5d8b", + "6001": "\u9084", + "6002": "\u7e26", + "6003": "\u80de", + "6004": "\u6069", + "6005": "\u3043", + "6006": "\u91c8", + "6007": "\u5c3b", + "6008": "\u5eb5", + "6009": "\u5a01", + "6010": "\u5c1a", + "6011": "\u62f3", + "6012": "\u64b2", + "6013": "\u5320", + "6014": "\u6676", + "6015": "\u61a9", + "6016": "\u7965", + "6017": "\u7832", + "6018": "\u7126", + "6019": "\u5c3f", + "6020": "\u9b42", + "6021": "\u7a42", + "6022": "\u8f44", + "6023": "\u62dd", + "6024": "\u91a4", + "6025": "\u658e", + "6026": "\u621a", + "6027": "\u5de3", + "6028": "\u572d", + "6029": "\u9727", + "6030": "\u6f6e", + "6031": "\u57f9", + "6032": "\u5f81", + "6033": "\u5f25", + "6034": "\u5b5d", + "6035": "\u8150", + "6036": "\u8ca2", + "6037": "\u6ca1", + "6038": "\u68cb", + "6039": "\u5f70", + "6040": "\u5e3d", + "6041": "\u83cc", + "6042": "\u7891", + "6043": "\u6597", + "6044": "\u63fa", + "6045": "\u7cf8", + "6046": "\u9d8f", + "6047": "\u9f3b", + "6048": "\u7235", + "6049": "\u85a6", + "6050": "\u808c", + "6051": "\u5c48", + "6052": "\u7d0b", + "6053": "\u67a0", + "6054": "\u57a3", + "6055": "\u65ec", + "6056": "\u614e", + "6057": "\u968f", + "6058": "\u8ed2", + "6059": "\u4e59", + "6060": "\u7384", + "6061": "\u5200", + "6062": "\u8df5", + "6063": "\u4f0f", + "6064": "\u5642", + "6065": "\u5e84", + "6066": "\u78e8", + "6067": "\u9694", + "6068": "\u9686", + "6069": "\u7a74", + "6070": "\u76c6", + "6071": "\u8ca7", + "6072": "\u9375", + "6073": "\u5cb3", + "6074": "\u616e", + "6075": "\u5374", + "6076": "\u62f6", + "6077": "\u5f13", + "6078": "\u5373", + "6079": "\u59d3", + "6080": "\u6398", + "6081": "\u6d6a", + "6082": "\u8b72", + "6083": "\u6589", + "6084": "\u9a0e", + "6085": "\u5968", + "6086": "\u8b00", + "6087": "\u5854", + "6088": "\u6ed1", + "6089": "\u5098", + "6090": "\u96f7", + "6091": "\u4fca", + "6092": "\u8edf", + "6093": "\u8f1d", + "6094": "\u6458", + "6095": "\u6176", + "6096": "\u6c57", + "6097": "\u6fa4", + "6098": "\u7bc7", + "6099": "\u8b0e", + "6100": "\u5e7b", + "6101": "\u9903", + "6102": "\u5339", + "6103": "\u543e", + "6104": "\u93e1", + "6105": "\u68d2", + "6106": "\u6d99", + "6107": "\u8cc3", + "6108": "\u8302", + "6109": "\u609f", + "6110": "\u5504", + "6111": "\u846c", + "6112": "\u6469", + "6113": "\u7c3f", + "6114": "\u6e09", + "6115": "\u511f", + "6116": "\u50da", + "6117": "\u65ed", + "6118": "\u8102", + "6119": "\u5e8f", + "6120": "\u8cab", + "6121": "\u3049", + "6122": "\u8aa4", + "6123": "\u7ffc", + "6124": "\u5bee", + "6125": "\u6edd", + "6126": "\u6c37", + "6127": "\u91dd", + "6128": "\u96c5", + "6129": "\u502b", + "6130": "\u85e9", + "6131": "\u5237", + "6132": "\u9663", + "6133": "\u7551", + "6134": "\u5a9b", + "6135": "\u5c3d", + "6136": "\u8cdb", + "6137": "\u50b5", + "6138": "\u8aa0", + "6139": "\u716e", + "6140": "\u6843", + "6141": "\u52d8", + "6142": "\u7a32", + "6143": "\u68c4", + "6144": "\u8170", + "6145": "\u83c5", + "6146": "\u7344", + "6147": "\u67f3", + "6148": "\u7ca7", + "6149": "\u5f18", + "6150": "\u9db4", + "6151": "\u80a9", + "6152": "\u9283", + "6153": "\u6c41", + "6154": "\u706f", + "6155": "\u773c", + "6156": "\u7db2", + "6157": "\u5c01", + "6158": "\u564c", + "6159": "\u7a3f", + "6160": "\u9644", + "6161": "\u676f", + "6162": "\u6094", + "6163": "\u9ea6", + "6164": "\u6e7f", + "6165": "\u9774", + "6166": "\u307a", + "6167": "\u6b8a", + "6168": "\u62b5", + "6169": "\u790e", + "6170": "\u8c5a", + "6171": "\u9ed9", + "6172": "\u7de0", + "6173": "\u9154", + "6174": "\u4e43", + "6175": "\u71e5", + "6176": "\u934b", + "6177": "\u8c6a", + "6178": "\u8a0e", + "6179": "\u6fc3", + "6180": "\u7d05", + "6181": "\u7968", + "6182": "\u708e", + "6183": "\u76ae", + "6184": "\u7e2e", + "6185": "\u5fb9", + "6186": "\u6749", + "6187": "\u8f03", + "6188": "\u7a1a", + "6189": "\u5800", + "6190": "\u5e33", + "6191": "\u5fcd", + "6192": "\u4f2f", + "6193": "\u8a17", + "6194": "\u77e2", + "6195": "\u8a69", + "6196": "\u8feb", + "6197": "\u5076", + "6198": "\u6838", + "6199": "\u5100", + "6200": "\u53cc", + "6201": "\u5230", + "6202": "\u524a", + "6203": "\u6db2", + "6204": "\u99c6", + "6205": "\u4e80", + "6206": "\u8972", + "6207": "\u8846", + "6208": "\u5510", + "6209": "\u7cd6", + "6210": "\u5de1", + "6211": "\u7e41", + "6212": "\u81ed", + "6213": "\u708a", + "6214": "\u9670", + "6215": "\u8155", + "6216": "\u6d44", + "6217": "\u5629", + "6218": "\u54b2", + "6219": "\u76d7", + "6220": "\u8108", + "6221": "\u6ede", + "6222": "\u7267", + "6223": "\u574a", + "6224": "\u5305", + "6225": "\u81f3", + "6226": "\u679a", + "6227": "\u5049", + "6228": "\u81f4", + "6229": "\u8a13", + "6230": "\u8ca8", + "6231": "\u8033", + "6232": "\u6f22", + "6233": "\u65d7", + "6234": "\u5df1", + "6235": "\u6247", + "6236": "\u6885", + "6237": "\u63e1", + "6238": "\u6b27", + "6239": "\u8584", + "6240": "\u6065", + "6241": "\u732e", + "6242": "\u9810", + "6243": "\u4ec1", + "6244": "\u9f8d", + "6245": "\u8a70", + "6246": "\u73cd", + "6247": "\u5f69", + "6248": "\u5feb", + "6249": "\u6cbc", + "6250": "\u6bd2", + "6251": "\u4e39", + "6252": "\u53e5", + "6253": "\u9234", + "6254": "\u91e3", + "6255": "\u7e01", + "6256": "\u5fae", + "6257": "\u5999", + "6258": "\u62ec", + "6259": "\u6669", + "6260": "\u7c89", + "6261": "\u9eba", + "6262": "\u5353", + "6263": "\u570f", + "6264": "\u517c", + "6265": "\u6b20", + "6266": "\u8e0a", + "6267": "\u8133", + "6268": "\u7adc", + "6269": "\u9cf4", + "6270": "\u5f66", + "6271": "\u5ac1", + "6272": "\u9a12", + "6273": "\u9aea", + "6274": "\u8acb", + "6275": "\u5375", + "6276": "\u640d", + "6277": "\u8377", + "6278": "\u68a8", + "6279": "\u5531", + "6280": "\u5d50", + "6281": "\u5e4c", + "6282": "\u4f34", + "6283": "\u624d", + "6284": "\u961c", + "6285": "\u81d3", + "6286": "\u7363", + "6287": "\u7bb1", + "6288": "\u7956", + "6289": "\u7532", + "6290": "\u6d74", + "6291": "\u5c0a", + "6292": "\u907f", + "6293": "\u6607", + "6294": "\u718a", + "6295": "\u58c1", + "6296": "\u4e18", + "6297": "\u6790", + "6298": "\u5b6b", + "6299": "\u5e72", + "6300": "\u7687", + "6301": "\u539a", + "6302": "\u4ead", + "6303": "\u970a", + "6304": "\u7b46", + "6305": "\u627f", + "6306": "\u5747", + "6307": "\u878d", + "6308": "\u5f8b", + "6309": "\u7dd1", + "6310": "\u5426", + "6311": "\u9b3c", + "6312": "\u587e", + "6313": "\u811a", + "6314": "\u9808", + "6315": "\u90aa", + "6316": "\u888b", + "6317": "\u6e56", + "6318": "\u4e73", + "6319": "\u88d5", + "6320": "\u63ee", + "6321": "\u51cd", + "6322": "\u6ec5", + "6323": "\u4e7e", + "6324": "\u3074", + "6325": "\u7fbd", + "6326": "\u6162", + "6327": "\u5019", + "6328": "\u62e1", + "6329": "\u6fef", + "6330": "\u8cb8", + "6331": "\u7802", + "6332": "\u656c", + "6333": "\u6982", + "6334": "\u5e81", + "6335": "\u7159", + "6336": "\u57f7", + "6337": "\u5e95", + "6338": "\u88c1", + "6339": "\u558b", + "6340": "\u9e97", + "6341": "\u9732", + "6342": "\u96f2", + "6343": "\u9aa8", + "6344": "\u500d", + "6345": "\u6bbf", + "6346": "\u5947", + "6347": "\u6613", + "6348": "\u5c64", + "6349": "\u6577", + "6350": "\u5e55", + "6351": "\u6bdb", + "6352": "\u7206", + "6353": "\u6687", + "6354": "\u68b0", + "6355": "\u8cb4", + "6356": "\u96a3", + "6357": "\u8f38", + "6358": "\u3077", + "6359": "\u67c4", + "6360": "\u59eb", + "6361": "\u7bc4", + "6362": "\u63b2", + "6363": "\u5618", + "6364": "\u585a", + "6365": "\u5264", + "6366": "\u5145", + "6367": "\u4f75", + "6368": "\u5974", + "6369": "\u675f", + "6370": "\u5893", + "6371": "\u702c", + "6372": "\u520a", + "6373": "\u8863", + "6374": "\u629e", + "6375": "\u7e04", + "6376": "\u77ac", + "6377": "\u5c04", + "6378": "\u83d3", + "6379": "\u52df", + "6380": "\u4e71", + "6381": "\u8fce", + "6382": "\u62b1", + "6383": "\u6c38", + "6384": "\u7af9", + "6385": "\u9178", + "6386": "\u523a", + "6387": "\u95a3", + "6388": "\u90f7", + "6389": "\u4e5f", + "6390": "\u61b6", + "6391": "\u5263", + "6392": "\u529f", + "6393": "\u9e7f", + "6394": "\u725b", + "6395": "\u79d8", + "6396": "\u4ecf", + "6397": "\u96c4", + "6398": "\u866b", + "6399": "\u5584", + "6400": "\u5c4a", + "6401": "\u8266", + "6402": "\u7247", + "6403": "\u8907", + "6404": "\u70ba", + "6405": "\u6cf3", + "6406": "\u5b9d", + "6407": "\u6fc0", + "6408": "\u5e79", + "6409": "\u81e3", + "6410": "\u4e4b", + "6411": "\u6691", + "6412": "\u6d66", + "6413": "\u770b", + "6414": "\u7591", + "6415": "\u8a98", + "6416": "\u66b4", + "6417": "\u8056", + "6418": "\u6368", + "6419": "\u677f", + "6420": "\u685c", + "6421": "\u7834", + "6422": "\u9769", + "6423": "\u5e0c", + "6424": "\u5e45", + "6425": "\u5442", + "6426": "\u6298", + "6427": "\u8a3a", + "6428": "\u4f38", + "6429": "\u60d1", + "6430": "\u6e2c", + "6431": "\u99d0", + "6432": "\u7a93", + "6433": "\u7d00", + "6434": "\u820e", + "6435": "\u7f72", + "6436": "\u60a3", + "6437": "\u5cb8", + "6438": "\u7e3e", + "6439": "\u6e7e", + "6440": "\u5c90", + "6441": "\u6a39", + "6442": "\u7d0d", + "6443": "\u79c0", + "6444": "\u514d", + "6445": "\u8b1d", + "6446": "\u6c60", + "6447": "\u7981", + "6448": "\u80cc", + "6449": "\u8e8d", + "6450": "\u8074", + "6451": "\u6297", + "6452": "\u8c46", + "6453": "\u7a0e", + "6454": "\u594f", + "6455": "\u8349", + "6456": "\u5f3e", + "6457": "\u6075", + "6458": "\u8001", + "6459": "\u793c", + "6460": "\u89d2", + "6461": "\u7ae5", + "6462": "\u5be9", + "6463": "\u88cf", + "6464": "\u5439", + "6465": "\u7720", + "6466": "\u6b6f", + "6467": "\u62e0", + "6468": "\u5bd2", + "6469": "\u6163", + "6470": "\u89e6", + "6471": "\u98fc", + "6472": "\u8358", + "6473": "\u7fa4", + "6474": "\u8ff7", + "6475": "\u6cca", + "6476": "\u5b97", + "6477": "\u65e6", + "6478": "\u50b7", + "6479": "\u984d", + "6480": "\u5869", + "6481": "\u5238", + "6482": "\u5e8a", + "6483": "\u9759", + "6484": "\u7559", + "6485": "\u8457", + "6486": "\u6cb9", + "6487": "\u8a8c", + "6488": "\u7f6a", + "6489": "\u7d14", + "6490": "\u8179", + "6491": "\u5075", + "6492": "\u5247", + "6493": "\u58ca", + "6494": "\u672d", + "6495": "\u8f2a", + "6496": "\u6383", + "6497": "\u707d", + "6498": "\u95d8", + "6499": "\u5f31", + "6500": "\u523b", + "6501": "\u822a", + "6502": "\u7b54", + "6503": "\u6804", + "6504": "\u59ff", + "6505": "\u4ea1", + "6506": "\u7e54", + "6507": "\u6557", + "6508": "\u7ae0", + "6509": "\u5438", + "6510": "\u4ee4", + "6511": "\u9bae", + "6512": "\u88dc", + "6513": "\u5915", + "6514": "\u635c", + "6515": "\u6012", + "6516": "\u6a21", + "6517": "\u76ca", + "6518": "\u559c", + "6519": "\u83ef", + "6520": "\u7d75", + "6521": "\u7533", + "6522": "\u76e4", + "6523": "\u8efd", + "6524": "\u7a4d", + "6525": "\u6a19", + "6526": "\u968e", + "6527": "\u7701", + "6528": "\u5bc6", + "6529": "\u9805", + "6530": "\u732b", + "6531": "\u5f93", + "6532": "\u975e", + "6533": "\u5e1d", + "6534": "\u5b63", + "6535": "\u6355", + "6536": "\u515a", + "6537": "\u6211", + "6538": "\u5727", + "6539": "\u9999", + "6540": "\u7b4b", + "6541": "\u8f29", + "6542": "\u7c4d", + "6543": "\u4e01", + "6544": "\u62bc", + "6545": "\u5c3e", + "6546": "\u97d3", + "6547": "\u64cd", + "6548": "\u6697", + "6549": "\u75c7", + "6550": "\u6563", + "6551": "\u7a81", + "6552": "\u9069", + "6553": "\u96d1", + "6554": "\u8de1", + "6555": "\u53b3", + "6556": "\u4e86", + "6557": "\u9ce5", + "6558": "\u9003", + "6559": "\u8b1b", + "6560": "\u6674", + "6561": "\u5fb4", + "6562": "\u5211", + "6563": "\u99c4", + "6564": "\u5009", + "6565": "\u56f0", + "6566": "\u77ed", + "6567": "\u5a66", + "6568": "\u9063", + "6569": "\u7565", + "6570": "\u9f62", + "6571": "\u9707", + "6572": "\u6575", + "6573": "\u8535", + "6574": "\u535a", + "6575": "\u8840", + "6576": "\u6e80", + "6577": "\u5fd7", + "6578": "\u8217", + "6579": "\u5b99", + "6580": "\u90e1", + "6581": "\u90a3", + "6582": "\u5bff", + "6583": "\u907a", + "6584": "\u79cb", + "6585": "\u6975", + "6586": "\u91cc", + "6587": "\u5ec3", + "6588": "\u56e0", + "6589": "\u5178", + "6590": "\u67d3", + "6591": "\u5f92", + "6592": "\u5dfb", + "6593": "\u9802", + "6594": "\u5742", + "6595": "\u8d85", + "6596": "\u6cb3", + "6597": "\u76db", + "6598": "\u72ac", + "6599": "\u8c4a", + "6600": "\u7aef", + "6601": "\u7d39", + "6602": "\u9996", + "6603": "\u6e6f", + "6604": "\u967d", + "6605": "\u7cbe", + "6606": "\u7949", + "6607": "\u6b73", + "6608": "\u7df4", + "6609": "\u6c5f", + "6610": "\u602a", + "6611": "\u5370", + "6612": "\u7b97", + "6613": "\u7d19", + "6614": "\u6255", + "6615": "\u6c42", + "6616": "\u969c", + "6617": "\u7c21", + "6618": "\u5fa1", + "6619": "\u9014", + "6620": "\u5275", + "6621": "\u8cc0", + "6622": "\u8239", + "6623": "\u5802", + "6624": "\u83dc", + "6625": "\u30a5", + "6626": "\u52e4", + "6627": "\u75db", + "6628": "\u4e26", + "6629": "\u666f", + "6630": "\u96ea", + "6631": "\u7bc0", + "6632": "\u9451", + "6633": "\u6d5c", + "6634": "\u663c", + "6635": "\u6e05", + "6636": "\u629c", + "6637": "\u52e2", + "6638": "\u66ae", + "6639": "\u9280", + "6640": "\u76df", + "6641": "\u9b5a", + "6642": "\u7387", + "6643": "\u6d0b", + "6644": "\u5bfa", + "6645": "\u5f01", + "6646": "\u7686", + "6647": "\u5fb3", + "6648": "\u8336", + "6649": "\u7b11", + "6650": "\u6e21", + "6651": "\u5948", + "6652": "\u9806", + "6653": "\u6cc1", + "6654": "\u8ac7", + "6655": "\u821e", + "6656": "\u6848", + "6657": "\u5ca9", + "6658": "\u8ca0", + "6659": "\u65e7", + "6660": "\u8ca1", + "6661": "\u8a31", + "6662": "\u6545", + "6663": "\u51ac", + "6664": "\u6a2a", + "6665": "\u5965", + "6666": "\u8a33", + "6667": "\u6bd4", + "6668": "\u56f2", + "6669": "\u505c", + "6670": "\u7bc9", + "6671": "\u6ce2", + "6672": "\u59b9", + "6673": "\u6797", + "6674": "\u6696", + "6675": "\u7d22", + "6676": "\u8d64", + "6677": "\u7d66", + "6678": "\u672b", + "6679": "\u50ac", + "6680": "\u6b66", + "6681": "\u6d17", + "6682": "\u9045", + "6683": "\u8ff0", + "6684": "\u9ed2", + "6685": "\u72af", + "6686": "\u5de6", + "6687": "\u6e90", + "6688": "\u9b54", + "6689": "\u7d30", + "6690": "\u4e45", + "6691": "\u4e0e", + "6692": "\u6e1b", + "6693": "\u7d1a", + "6694": "\u8cbb", + "6695": "\u8d8a", + "6696": "\u5dee", + "6697": "\u59bb", + "6698": "\u9818", + "6699": "\u885b", + "6700": "\u4e38", + "6701": "\u7d61", + "6702": "\u968a", + "6703": "\u85ac", + "6704": "\u6c0f", + "6705": "\u671b", + "6706": "\u4f3c", + "6707": "\u5c31", + "6708": "\u53f3", + "6709": "\u6761", + "6710": "\u5e03", + "6711": "\u51e6", + "6712": "\u8c37", + "6713": "\u7b56", + "6714": "\u52b9", + "6715": "\u5fd8", + "6716": "\u71b1", + "6717": "\u5fa9", + "6718": "\u59c9", + "6719": "\u30cc", + "6720": "\u632f", + "6721": "\u8ab2", + "6722": "\u898f", + "6723": "\u5012", + "6724": "\u6e2f", + "6725": "\u6ce8", + "6726": "\u68ee", + "6727": "\u9632", + "6728": "\u7d99", + "6729": "\u9000", + "6730": "\u6839", + "6731": "\u706b", + "6732": "\u66ff", + "6733": "\u9678", + "6734": "\u53bb", + "6735": "\u8996", + "6736": "\u6574", + "6737": "\u6e96", + "6738": "\u5ead", + "6739": "\u30be", + "6740": "\u72ec", + "6741": "\u6483", + "6742": "\u5150", + "6743": "\u6a4b", + "6744": "\u307d", + "6745": "\u63db", + "6746": "\u5ff5", + "6747": "\u8b58", + "6748": "\u306c", + "6749": "\u6253", + "6750": "\u6d25", + "6751": "\u96e8", + "6752": "\u5e78", + "6753": "\u542b", + "6754": "\u796d", + "6755": "\u97ff", + "6756": "\u52b4", + "6757": "\u51c4", + "6758": "\u5c06", + "6759": "\u5b98", + "6760": "\u82e6", + "6761": "\u8ffd", + "6762": "\u9060", + "6763": "\u672a", + "6764": "\u8ca9", + "6765": "\u5a18", + "6766": "\u8857", + "6767": "\u66dc", + "6768": "\u7a0b", + "6769": "\u63d0", + "6770": "\u7389", + "6771": "\u5224", + "6772": "\u79fb", + "6773": "\u653b", + "6774": "\u4f4e", + "6775": "\u88c5", + "6776": "\u65ad", + "6777": "\u53ca", + "6778": "\u8a3c", + "6779": "\u8c61", + "6780": "\u5b88", + "6781": "\u9752", + "6782": "\u5bcc", + "6783": "\u623b", + "6784": "\u8a5e", + "6785": "\u5409", + "6786": "\u6295", + "6787": "\u6b74", + "6788": "\u6ca2", + "6789": "\u8f09", + "6790": "\u5177", + "6791": "\u5eab", + "6792": "\u9664", + "6793": "\u74b0", + "6794": "\u5c55", + "6795": "\u5352", + "6796": "\u4e89", + "6797": "\u5931", + "6798": "\u623f", + "6799": "\u6625", + "6800": "\u6319", + "6801": "\u6f5f", + "6802": "\u8fd4", + "6803": "\u99ac", + "6804": "\u6b32", + "6805": "\u6750", + "6806": "\u6238", + "6807": "\u56f3", + "6808": "\u5bdd", + "6809": "\u990a", + "6810": "\u713c", + "6811": "\u5c0e", + "6812": "\u5922", + "6813": "\u7c73", + "6814": "\u51b7", + "6815": "\u606f", + "6816": "\u5175", + "6817": "\u5e2d", + "6818": "\u6e08", + "6819": "\u5287", + "6820": "\u63f4", + "6821": "\u98ef", + "6822": "\u592e", + "6823": "\u967a", + "6824": "\u670d", + "6825": "\u614b", + "6826": "\u8d70", + "6827": "\u8a55", + "6828": "\u5c45", + "6829": "\u6a29", + "6830": "\u8ad6", + "6831": "\u5f1f", + "6832": "\u3085", + "6833": "\u5883", + "6834": "\u5bdf", + "6835": "\u6388", + "6836": "\u983c", + "6837": "\u6d3e", + "6838": "\u64ae", + "6839": "\u7d20", + "6840": "\u4fee", + "6841": "\u7b2c", + "6842": "\u8cea", + "6843": "\u544a", + "6844": "\u8208", + "6845": "\u79d2", + "6846": "\u5b87", + "6847": "\u8089", + "6848": "\u5144", + "6849": "\u50cf", + "6850": "\u79f0", + "6851": "\u5024", + "6852": "\u982d", + "6853": "\u9031", + "6854": "\u7763", + "6855": "\u6d88", + "6856": "\u5b85", + "6857": "\u82b8", + "6858": "\u9854", + "6859": "\u8aad", + "6860": "\u4ef2", + "6861": "\u904a", + "6862": "\u8a66", + "6863": "\u901f", + "6864": "\u9152", + "6865": "\u5bbf", + "6866": "\u96e2", + "6867": "\u677e", + "6868": "\u5897", + "6869": "\u6bba", + "6870": "\u9244", + "6871": "\u53f8", + "6872": "\u5bb3", + "6873": "\u5272", + "6874": "\u77f3", + "6875": "\u590f", + "6876": "\u7248", + "6877": "\u4f50", + "6878": "\u52a9", + "6879": "\u82f1", + "6880": "\u53f7", + "6881": "\u60f3", + "6882": "\u7ba1", + "6883": "\u6025", + "6884": "\u9803", + "6885": "\u3065", + "6886": "\u82e5", + "6887": "\u604b", + "6888": "\u9020", + "6889": "\u53f2", + "6890": "\u6cc9", + "6891": "\u91cf", + "6892": "\u88fd", + "6893": "\u5e9c", + "6894": "\u8db3", + "6895": "\u6016", + "6896": "\u738b", + "6897": "\u59d4", + "6898": "\u4e21", + "6899": "\u8fba", + "6900": "\u6b8b", + "6901": "\u9006", + "6902": "\u5099", + "6903": "\u8ecd", + "6904": "\u8b66", + "6905": "\u67fb", + "6906": "\u5217", + "6907": "\u7de8", + "6908": "\u6bb5", + "6909": "\u53cd", + "6910": "\u30bc", + "6911": "\u643a", + "6912": "\u6b69", + "6913": "\u682a", + "6914": "\u5668", + "6915": "\u5ea7", + "6916": "\u98db", + "6917": "\u4e08", + "6918": "\u82b1", + "6919": "\u4fa1", + "6920": "\u76e3", + "6921": "\u5d0e", + "6922": "\u85e4", + "6923": "\u30d8", + "6924": "\u5468", + "6925": "\u6bce", + "6926": "\u7d71", + "6927": "\u53ce", + "6928": "\u843d", + "6929": "\u661f", + "6930": "\u964d", + "6931": "\u62c5", + "6932": "\u5074", + "6933": "\u7642", + "6934": "\u5e2b", + "6935": "\u5199", + "6936": "\u985e", + "6937": "\u547d", + "6938": "\u4ecb", + "6939": "\u9858", + "6940": "\u8b77", + "6941": "\u57ce", + "6942": "\u6b7b", + "6943": "\u679c", + "6944": "\u962a", + "6945": "\u4efb", + "6946": "\u66f4", + "6947": "\u5e38", + "6948": "\u4fbf", + "6949": "\u305c", + "6950": "\u691c", + "6951": "\u904e", + "6952": "\u8cc7", + "6953": "\u50cd", + "6954": "\u8a8d", + "6955": "\u822c", + "6956": "\u793a", + "6957": "\u5ba2", + "6958": "\u7fd2", + "6959": "\u7a76", + "6960": "\u534a", + "6961": "\u9332", + "6962": "\u5b57", + "6963": "\u6614", + "6964": "\u5eb7", + "6965": "\u90ce", + "6966": "\u5f71", + "6967": "\u899a", + "6968": "\u578b", + "6969": "\u58f0", + "6970": "\u4ef6", + "6971": "\u7fa9", + "6972": "\u65bd", + "6973": "\u798f", + "6974": "\u5bb9", + "6975": "\u8def", + "6976": "\u547c", + "6977": "\u5f79", + "6978": "\u5358", + "6979": "\u4e95", + "6980": "\u72b6", + "6981": "\u5efa", + "6982": "\u7531", + "6983": "\u5c5e", + "6984": "\u52c9", + "6985": "\u571f", + "6986": "\u8449", + "6987": "\u8d77", + "6988": "\u89a7", + "6989": "\u914d", + "6990": "\u5f35", + "6991": "\u63a5", + "6992": "\u8fbc", + "6993": "\u5f85", + "6994": "\u5ba4", + "6995": "\u75c5", + "6996": "\u5e2f", + "6997": "\u5acc", + "6998": "\u5a5a", + "6999": "\u5149", + "7000": "\u500b", + "7001": "\u8077", + "7002": "\u55b6", + "7003": "\u307c", + "7004": "\u7814", + "7005": "\u8a08", + "7006": "\u76f4", + "7007": "\u96e3", + "7008": "\u305e", + "7009": "\u7d76", + "7010": "\u30e8", + "7011": "\u7167", + "7012": "\u897f", + "7013": "\u7d04", + "7014": "\u5b58", + "7015": "\u9a13", + "7016": "\u6cbb", + "7017": "\u7236", + "7018": "\u89e3", + "7019": "\u5ca1", + "7020": "\u8ee2", + "7021": "\u5546", + "7022": "\u9032", + "7023": "\u4fc2", + "7024": "\u8aac", + "7025": "\u89b3", + "7026": "\u7403", + "7027": "\u4e57", + "7028": "\u5bae", + "7029": "\u652f", + "7030": "\u5f97", + "7031": "\u541b", + "7032": "\u8b70", + "7033": "\u5065", + "7034": "\u9580", + "7035": "\u6b62", + "7036": "\u91cd", + "7037": "\u6e29", + "7038": "\u7dd2", + "7039": "\u7740", + "7040": "\u98f2", + "7041": "\u6bcd", + "7042": "\u58eb", + "7043": "\u3056", + "7044": "\u96c6", + "7045": "\u4e07", + "7046": "\u592a", + "7047": "\u7d9a", + "7048": "\u7dda", + "7049": "\u7a2e", + "7050": "\u683c", + "7051": "\u4f4d", + "7052": "\u30e6", + "7053": "\u6b4c", + "7054": "\u591c", + "7055": "\u5171", + "7056": "\u6b63", + "7057": "\u5fc5", + "7058": "\u30d2", + "7059": "\u8272", + "7060": "\u554f", + "7061": "\u518d", + "7062": "\u57df", + "7063": "\u3086", + "7064": "\u52dd", + "7065": "\u53f0", + "7066": "\u6280", + "7067": "\u65c5", + "7068": "\u5f15", + "7069": "\u7cfb", + "7070": "\u9662", + "7071": "\u60aa", + "7072": "\u57fa", + "7073": "\u795e", + "7074": "\u9650", + "7075": "\u7523", + "7076": "\u6c7a", + "7077": "\u6c11", + "7078": "\u4ea4", + "7079": "\u653f", + "7080": "\u8cde", + "7081": "\u7a7a", + "7082": "\u533b", + "7083": "\u5f7c", + "7084": "\u592b", + "7085": "\u53ef", + "7086": "\u8ab0", + "7087": "\u53e4", + "7088": "\u5e30", + "7089": "\u8853", + "7090": "\u76f8", + "7091": "\u6751", + "7092": "\u56e3", + "7093": "\u4f1d", + "7094": "\u5186", + "7095": "\u4f4f", + "7096": "\u984c", + "7097": "\u5e73", + "7098": "\u4e88", + "7099": "\u97f3", + "7100": "\u671d", + "7101": "\u6307", + "7102": "\u771f", + "7103": "\u30f4", + "7104": "\u52d9", + "7105": "\u70b9", + "7106": "\u5404", + "7107": "\u9928", + "7108": "\u5fdc", + "7109": "\u73fe", + "7110": "\u5229", + "7111": "\u5929", + "7112": "\u7b49", + "7113": "\u6728", + "7114": "\u767d", + "7115": "\u5f62", + "7116": "\u4f9b", + "7117": "\u7d4c", + "7118": "\u3047", + "7119": "\u65cf", + "7120": "\u65e9", + "7121": "\u4f8b", + "7122": "\u50d5", + "7123": "\u4e0d", + "7124": "\u5207", + "7125": "\u5357", + "7126": "\u52a0", + "7127": "\u969b", + "7128": "\u7d42", + "7129": "\u69d8", + "7130": "\u653e", + "7131": "\u548c", + "7132": "\u4f11", + "7133": "\u5dde", + "7134": "\u6c34", + "7135": "\u5354", + "7136": "\u5728", + "7137": "\u7d44", + "7138": "\u5411", + "7139": "\u5e83", + "7140": "\u8eab", + "7141": "\u754c", + "7142": "\u5de5", + "7143": "\u9078", + "7144": "\u59cb", + "7145": "\u5143", + "7146": "\u96f6", + "7147": "\u3005", + "7148": "\u89aa", + "7149": "\u7f8e", + "7150": "\u4fe1", + "7151": "\u90fd", + "7152": "\u7f6e", + "7153": "\u5c40", + "7154": "\u99c5", + "7155": "\u904b", + "7156": "\u9001", + "7157": "\u98a8", + "7158": "\u53e3", + "7159": "\u6f14", + "7160": "\u8abf", + "7161": "\u304e", + "7162": "\u512a", + "7163": "\u6b21", + "7164": "\u30a9", + "7165": "\u4ed6", + "7166": "\u5712", + "7167": "\u4fdd", + "7168": "\u7537", + "7169": "\u53c2", + "7170": "\u5c11", + "7171": "\u767e", + "7172": "\u7279", + "7173": "\u8003", + "7174": "\u7121", + "7175": "\u4e03", + "7176": "\u30e4", + "7177": "\u30ae", + "7178": "\u826f", + "7179": "\u30b6", + "7180": "\u5236", + "7181": "\u4eac", + "7182": "\u611b", + "7183": "\u58f2", + "7184": "\u80fd", + "7185": "\u539f", + "7186": "\u30b2", + "7187": "\u6709", + "7188": "\u516d", + "7189": "\u5b89", + "7190": "\u30b4", + "7191": "\u80b2", + "7192": "\u79d1", + "7193": "\u8981", + "7194": "\u6599", + "7195": "\u66f8", + "7196": "\u8a9e", + "7197": "\u8a2d", + "7198": "\u6d77", + "7199": "\u671f", + "7200": "\u6d41", + "7201": "\u78ba", + "7202": "\u30da", + "7203": "\u533a", + "7204": "\u3080", + "7205": "\u9023", + "7206": "\u8cb7", + "7207": "\u3072", + "7208": "\u3075", + "7209": "\u4ed8", + "7210": "\u753a", + "7211": "\u6d3b", + "7212": "\u60c5", + "7213": "\u6708", + "7214": "\u8868", + "7215": "\u66f2", + "7216": "\u5f37", + "7217": "\u4e16", + "7218": "\u660e", + "7219": "\u6210", + "7220": "\u30ce", + "7221": "\u30a1", + "7222": "\u6587", + "7223": "\u9055", + "7224": "\u6771", + "7225": "\u53cb", + "7226": "\u610f", + "7227": "\u529b", + "7228": "\u5f0f", + "7229": "\u6cd5", + "7230": "\u5831", + "7231": "\u54e1", + "7232": "\u5fc3", + "7233": "\u5c4b", + "7234": "\u54c1", + "7235": "\u5317", + "7236": "\u5148", + "7237": "\u5cf6", + "7238": "\u5473", + "7239": "\u5ddd", + "7240": "\u958b", + "7241": "\u5343", + "7242": "\u95a2", + "7243": "\u516b", + "7244": "\u96fb", + "7245": "\u7136", + "7246": "\u5ea6", + "7247": "\u4ffa", + "7248": "\u9054", + "7249": "\u9762", + "7250": "\u4e5d", + "7251": "\u6570", + "7252": "\u53d6", + "7253": "\u697d", + "7254": "\u91d1", + "7255": "\u6027", + "7256": "\u91ce", + "7257": "\u5225", + "7258": "\u6226", + "7259": "\u516c", + "7260": "\u6a5f", + "7261": "\u9053", + "7262": "\u76ee", + "7263": "\u8a18", + "7264": "\u3073", + "7265": "\u767a", + "7266": "\u5bfe", + "7267": "\u7acb", + "7268": "\u521d", + "7269": "\u5316", + "7270": "\u30bd", + "7271": "\u56db", + "7272": "\u30ef", + "7273": "\u7530", + "7274": "\u6301", + "7275": "\u30ac", + "7276": "\u8eca", + "7277": "\u756a", + "7278": "\u30d4", + "7279": "\u805e", + "7280": "\u56de", + "7281": "\u3041", + "7282": "\u3076", + "7283": "\u30d9", + "7284": "\u4e94", + "7285": "\u3052", + "7286": "\u5b9f", + "7287": "\u30dc", + "7288": "\u5e97", + "7289": "\u5c0f", + "7290": "\u5b9a", + "7291": "\u30e2", + "7292": "\u9577", + "7293": "\u65b0", + "7294": "\u30cf", + "7295": "\u30b1", + "7296": "\u5916", + "7297": "\u30dd", + "7298": "\u8fd1", + "7299": "\u6240", + "7300": "\u3078", + "7301": "\u770c", + "7302": "\u540c", + "7303": "\u30cd", + "7304": "\u5185", + "7305": "\u5973", + "7306": "\u30db", + "7307": "\u4f53", + "7308": "\u597d", + "7309": "\u30c4", + "7310": "\u30bb", + "7311": "\u77e5", + "7312": "\u5c71", + "7313": "\u6765", + "7314": "\u30a7", + "7315": "\u4f7f", + "7316": "\u30e7", + "7317": "\u30ba", + "7318": "\u4e3b", + "7319": "\u52d5", + "7320": "\u7406", + "7321": "\u7269", + "7322": "\u6620", + "7323": "\u8005", + "7324": "\u3050", + "7325": "\u7684", + "7326": "\u4ee3", + "7327": "\u5909", + "7328": "\u6559", + "7329": "\u793e", + "7330": "\u7528", + "7331": "\u8a71", + "7332": "\u540d", + "7333": "\u69cb", + "7334": "\u9ad8", + "7335": "\u6700", + "7336": "\u305a", + "7337": "\u30df", + "7338": "\u6821", + "7339": "\u30c0", + "7340": "\u98df", + "7341": "\u5f8c", + "7342": "\u624b", + "7343": "\u4e09", + "7344": "\u901a", + "7345": "\u611f", + "7346": "\u5408", + "7347": "\u591a", + "7348": "\u696d", + "7349": "\u5165", + "7350": "\u30a8", + "7351": "\u5834", + "7352": "\u3079", + "7353": "\u4e0a", + "7354": "\u5bb6", + "7355": "\u79c1", + "7356": "\u5e74", + "7357": "\u9593", + "7358": "\u753b", + "7359": "\u524d", + "7360": "\u4e0b", + "7361": "\u30e3", + "7362": "\u5730", + "7363": "\u4e8c", + "7364": "\u30a6", + "7365": "\u30ca", + "7366": "\u30d3", + "7367": "\u81ea", + "7368": "\u5168", + "7369": "\u30d1", + "7370": "\u7d50", + "7371": "\u30d6", + "7372": "\u30e5", + "7373": "\u5e02", + "7374": "\u30b5", + "7375": "\u6c17", + "7376": "\u65b9", + "7377": "\u30c7", + "7378": "\u5341", + "7379": "\u30ad", + "7380": "\u5f53", + "7381": "\u56fd", + "7382": "\u4f5c", + "7383": "\u30a3", + "7384": "\u90e8", + "7385": "\u30aa", + "7386": "\u30cb", + "7387": "\u30c1", + "7388": "\u30e0", + "7389": "\u30b0", + "7390": "\u30e1", + "7391": "\u3054", + "7392": "\u5b50", + "7393": "\u3070", + "7394": "\u751f", + "7395": "\u307b", + "7396": "\u3071", + "7397": "\u305b", + "7398": "\u4f55", + "7399": "\u51fa", + "7400": "\u8a00", + "7401": "\u4eca", + "7402": "\u30d0", + "7403": "\u4e8b", + "7404": "\u4e2d", + "7405": "\u30d7", + "7406": "\u6642", + "7407": "\u30b3", + "7408": "\u898b", + "7409": "\u30c6", + "7410": "\u4f1a", + "7411": "\u30de", + "7412": "\u30ab", + "7413": "\u601d", + "7414": "\u30ed", + "7415": "\u30b8", + "7416": "\u30d5", + "7417": "\u30b7", + "7418": "\u3081", + "7419": "\u30ec", + "7420": "\u30c9", + "7421": "\u5206", + "7422": "\u3087", + "7423": "\u308d", + "7424": "\u5b66", + "7425": "\u884c", + "7426": "\u30bf", + "7427": "\u5927", + "7428": "\u3064", + "7429": "\u672c", + "7430": "\u65e5", + "7431": "\u308f", + "7432": "\u4e00", + "7433": "\u30af", + "7434": "\u307f", + "7435": "\u30ea", + "7436": "\u30a2", + "7437": "\u30c3", + "7438": "\u4eba", + "7439": "\u30e9", + "7440": "\uff1f", + "7441": "\u304a", + "7442": "\u3058", + "7443": "\u30a4", + "7444": "\u30eb", + "7445": "\u30c8", + "7446": "\u3083", + "7447": "\u304d", + "7448": "\u3055", + "7449": "\u3061", + "7450": "\u3084", + "7451": "\u30b9", + "7452": "\u3069", + "7453": "\u3051", + "7454": "\u304f", + "7455": "\u3048", + "7456": "\u3092", + "7457": "\u308a", + "7458": "\u3088", + "7459": "\u3053", + "7460": "\u30f3", + "7461": "\u3060", + "7462": "\u308c", + "7463": "\u3089", + "7464": "\u306d", + "7465": "\u304c", + "7466": "\u307e", + "7467": "\u30fc", + "7468": "\u3082", + "7469": "\u305d", + "7470": "\u3057", + "7471": "\u306b", + "7472": "\u306f", + "7473": "\u308b", + "7474": "\u3059", + "7475": "\u3068", + "7476": "\u305f", + "7477": "\u3042", + "7478": "\u3066", + "7479": "\u3063", + "7480": "\u3067", + "7481": "\u304b", + "7482": "\u306a", + "7483": "\u3093", + "7484": "\u3046", + "7485": "\u306e", + "7486": "\u3001", + "7487": "\u3002", + "7488": "\u3044", + "7489": "", + "7490": "\uc774", + "7491": "\uac00", + "7492": "\uc744", + "7493": "\ub294", + "7494": "\uc5d0", + "7495": "\ub3c4", + "7496": "\uace0", + "7497": "\uc758", + "7498": "\uc9c0", + "7499": "\ub97c", + "7500": "\u2581\uadf8", + "7501": "\ub2e4", + "7502": "\uc740", + "7503": "\uae30", + "7504": "\ud55c", + "7505": "\uc5b4", + "7506": "\uc2dc", + "7507": "\uc790", + "7508": "\uc11c", + "7509": "\ub85c", + "7510": "\ud574", + "7511": "\ub9ac", + "7512": "\uc694", + "7513": "\uc0ac", + "7514": "\u2581\ubb50", + "7515": "\uc778", + "7516": "\uac8c", + "7517": "\uc5d0\uc11c", + "7518": "\u2581\uc774\uc81c", + "7519": "\uc815", + "7520": "\ud558", + "7521": "\u2581\uc5b4", + "7522": "\u2581\uac70", + "7523": "\ud558\ub294", + "7524": "\ub098", + "7525": "\ub300", + "7526": "\u2581\uc880", + "7527": "\ud558\uace0", + "7528": "\ub9cc", + "7529": "\u2581\uc218", + "7530": "\u2581\uc544", + "7531": "\uc7a5", + "7532": "\uba74", + "7533": "\uc73c\ub85c", + "7534": "\uc6d0", + "7535": "\uc57c", + "7536": "\uc8fc", + "7537": "\uacfc", + "7538": "\uc0c1", + "7539": "\uad6c", + "7540": "\uc2a4", + "7541": "\uc77c", + "7542": "\u2581\uadf8\ub7f0", + "7543": "\ub77c", + "7544": "\uc218", + "7545": "\ud560", + "7546": "\uc544", + "7547": "\ub4e4", + "7548": "\u2581\uc774\ub7f0", + "7549": "\u2581\uc608", + "7550": "\uac70", + "7551": "\u2581\uc9c0\uae08", + "7552": "\uc131", + "7553": "\u2581\ubcf4", + "7554": "\u2581\uc548", + "7555": "\ubcf4", + "7556": "\u2581\ub610", + "7557": "\ub3d9", + "7558": "\uc18c", + "7559": "\uc2e0", + "7560": "\u2581\uc788\ub294", + "7561": "\uc2ed", + "7562": "\u2581\uac83", + "7563": "\uac04", + "7564": "\uc81c", + "7565": "\ub294\ub370", + "7566": "\uac74", + "7567": "\u2581\ub300", + "7568": "\ubd80", + "7569": "\ud654", + "7570": "\uc804", + "7571": "\u2581\uc804", + "7572": "\u2581\uc774\ub807\uac8c", + "7573": "\u2581\uc77c", + "7574": "\u2581\uadfc\ub370", + "7575": "\ub4e4\uc774", + "7576": "\u2581\uadf8\ub798\uc11c", + "7577": "\ub370", + "7578": "\ud588", + "7579": "\uce58", + "7580": "\uc120", + "7581": "\ub4dc", + "7582": "\u2581\ub9ce\uc774", + "7583": "\uc138", + "7584": "\uc9c4", + "7585": "\uc5f0", + "7586": "\uc5ec", + "7587": "\uad00", + "7588": "\ubd84", + "7589": "\u2581\ub124", + "7590": "\ub9c8", + "7591": "\uc624", + "7592": "\ubbf8", + "7593": "\uc704", + "7594": "\uc8e0", + "7595": "\uc2b5\ub2c8\ub2e4", + "7596": "\uacc4", + "7597": "\uc2dd", + "7598": "\ubb34", + "7599": "\uc788", + "7600": "\ubb38", + "7601": "\ub2f9", + "7602": "\uc7ac", + "7603": "\ub144", + "7604": "\uccb4", + "7605": "\u2581\ub098", + "7606": "\uc640", + "7607": "\uc6b0", + "7608": "\ub77c\uace0", + "7609": "\uc2e4", + "7610": "\u2581\ub54c", + "7611": "\ub2e8", + "7612": "\ud1b5", + "7613": "\uc601", + "7614": "\u2581\uc8fc", + "7615": "\uc801", + "7616": "\uba85", + "7617": "\u2581\uc54a", + "7618": "\u2581\ub9d0", + "7619": "\u2581\uc624", + "7620": "\u2581\uc788\ub2e4", + "7621": "\ud574\uc11c", + "7622": "\ub824", + "7623": "\u2581\uc5b4\ub5a4", + "7624": "\ubc29", + "7625": "\uc0b0", + "7626": "\u2581\uc6b0\ub9ac", + "7627": "\ucc28", + "7628": "\u2581\uc800", + "7629": "\ubb3c", + "7630": "\ub2c8", + "7631": "\u2581\uc544\ub2c8", + "7632": "\u2581\ub354", + "7633": "\u2581\uc0ac", + "7634": "\ubc18", + "7635": "\ub2c8\ub2e4", + "7636": "\uc810", + "7637": "\u2581\ube44", + "7638": "\ud2b8", + "7639": "\u2581\uc74c", + "7640": "\uc6a9", + "7641": "\uc5c5", + "7642": "\uacbd", + "7643": "\uc0dd", + "7644": "\uc801\uc73c\ub85c", + "7645": "\uacf5", + "7646": "\u2581\ub0b4", + "7647": "\u2581\uadf8\ub9ac\uace0", + "7648": "\uad6d", + "7649": "\ub7ec", + "7650": "\uc548", + "7651": "\ube44", + "7652": "\uae4c\uc9c0", + "7653": "\ub2c8\uae4c", + "7654": "\uae08", + "7655": "\uc6b4", + "7656": "\u2581\uc774\uac8c", + "7657": "\u2581\uacf5", + "7658": "\ub0b4", + "7659": "\ud68c", + "7660": "\u2581\uc798", + "7661": "\ud558\uac8c", + "7662": "\ud589", + "7663": "\uc870", + "7664": "\ubaa8", + "7665": "\uac10", + "7666": "\uac00\uc9c0\uace0", + "7667": "\u2581\ub9c9", + "7668": "\uc9d1", + "7669": "\ub41c", + "7670": "\uac83", + "7671": "\ubc1c", + "7672": "\ud559", + "7673": "\uc2ec", + "7674": "\ub358", + "7675": "\ubc31", + "7676": "\u2581\uc720", + "7677": "\ub77c\ub294", + "7678": "\ub0a8", + "7679": "\u2581\ub54c\ubb38\uc5d0", + "7680": "\u2581\uadf8\ub7ec\ub2c8\uae4c", + "7681": "\ub418\ub294", + "7682": "\uc785\ub2c8\ub2e4", + "7683": "\ud0c0", + "7684": "\uad50", + "7685": "\u2581\ub4e4\uc5b4", + "7686": "\u2581\uc5c6", + "7687": "\uc5b4\uc694", + "7688": "\ubc95", + "7689": "\uc801\uc778", + "7690": "\uc5ed", + "7691": "\u2581\uc0dd\uac01", + "7692": "\ub9e4", + "7693": "\ubbfc", + "7694": "\ud55c\ub2e4", + "7695": "\u2581\uac19\uc740", + "7696": "\u2581\uadf8\ub0e5", + "7697": "\ubc30", + "7698": "\ub974", + "7699": "\u2581\ub418", + "7700": "\ubd80\ubd84", + "7701": "\uc721", + "7702": "\u2581\uc598\uae30", + "7703": "\ud638", + "7704": "\ud504", + "7705": "\ub0a0", + "7706": "\u2581\ubabb", + "7707": "\u2581\uc0ac\uc2e4", + "7708": "\uac70\ub4e0\uc694", + "7709": "\ucc9c", + "7710": "\ub4f1", + "7711": "\u2581\uc5b4\ub5bb\uac8c", + "7712": "\u2581\uc81c", + "7713": "\uc9c0\ub9cc", + "7714": "\ud788", + "7715": "\u2581\uc81c\uac00", + "7716": "\u2581\uadf8\ub807\uac8c", + "7717": "\ub354", + "7718": "\uad8c", + "7719": "\ud558\uba74", + "7720": "\ucd9c", + "7721": "\ub2e4\uace0", + "7722": "\ub2ec", + "7723": "\uaca0", + "7724": "\uc791", + "7725": "\uc785", + "7726": "\u2581\uc800\ub294", + "7727": "\ud574\uc57c", + "7728": "\u2581\ubd80", + "7729": "\u2581\uc9c4\uc9dc", + "7730": "\ud45c", + "7731": "\uc9c1", + "7732": "\uc591", + "7733": "\u2581\ubc14", + "7734": "\ud569\ub2c8\ub2e4", + "7735": "\uc0b4", + "7736": "\ub825", + "7737": "\uc5c8", + "7738": "\ud588\ub2e4", + "7739": "\u2581\ub108\ubb34", + "7740": "\u2581\uac00\uc7a5", + "7741": "\u2581\uc870", + "7742": "\ud314", + "7743": "\uc911", + "7744": "\ub2d8", + "7745": "\u2581\ub0b4\uac00", + "7746": "\uc720", + "7747": "\ub798", + "7748": "\ubc84", + "7749": "\ubc88", + "7750": "\uac1c", + "7751": "\ud6c4", + "7752": "\uc796\uc544\uc694", + "7753": "\ud558\uc9c0", + "7754": "\ud53c", + "7755": "\uc885", + "7756": "\ub124", + "7757": "\ud604", + "7758": "\u2581\uc788\uc2b5\ub2c8\ub2e4", + "7759": "\u2581\uc88b", + "7760": "\ub9de", + "7761": "\ub09c", + "7762": "\uac19", + "7763": "\u2581\uad49\uc7a5\ud788", + "7764": "\u2581\uc911", + "7765": "\ucd94", + "7766": "\uc6d4", + "7767": "\uc5d0\ub294", + "7768": "\uccad", + "7769": "\uc18d", + "7770": "\u2581\uc0ac\ub78c", + "7771": "\ubc1b", + "7772": "\uc9c8", + "7773": "\ub178", + "7774": "\ud615", + "7775": "\u2581\uac78", + "7776": "\uad70", + "7777": "\uc600", + "7778": "\ud30c", + "7779": "\ub514", + "7780": "\ubcf8", + "7781": "\ub3fc", + "7782": "\ub108", + "7783": "\u2581\uadf8\ub7ec\uba74", + "7784": "\u2581\ubd88", + "7785": "\u2581\ub450", + "7786": "\u2581\uc624\ub298", + "7787": "\u2581\uac1c", + "7788": "\ucd5c", + "7789": "\u2581\uc0bc", + "7790": "\ud06c", + "7791": "\ub410", + "7792": "\ud3b8", + "7793": "\ucabd", + "7794": "\ud310", + "7795": "\ub54c", + "7796": "\u2581\ub418\uac8c", + "7797": "\u2581\ub098\ub294", + "7798": "\ubd80\ud130", + "7799": "\ub791", + "7800": "\u2581\uadf8\uac70", + "7801": "\u2581\ub300\ud574\uc11c", + "7802": "\u2581\uc815\ub3c4", + "7803": "\ub808", + "7804": "\u2581\uae40", + "7805": "\u2581\uc774\uac70", + "7806": "\u2581\uc788\uace0", + "7807": "\u2581\uac15", + "7808": "\u2581\ub300\ud55c", + "7809": "\uc73c\uba74", + "7810": "\u2581\uadf8\uac8c", + "7811": "\u2581\ubb38\uc81c", + "7812": "\ud3ec", + "7813": "\ubaa9", + "7814": "\uacb0", + "7815": "\uc900", + "7816": "\ud0dc", + "7817": "\u2581\ud558\ub098", + "7818": "\uc678", + "7819": "\uc528", + "7820": "\uc796\uc544", + "7821": "\uc784", + "7822": "\uce60", + "7823": "\uc5f4", + "7824": "\ubcc0", + "7825": "\ub41c\ub2e4", + "7826": "\uc608\uc694", + "7827": "\ud0a4", + "7828": "\ubc15", + "7829": "\u2581\uadf8\ub807", + "7830": "\uae4c", + "7831": "\u2581\ub9d0\uc500", + "7832": "\u2581\uc870\uae08", + "7833": "\ud130", + "7834": "\u2581\uc6b0\ub9ac\uac00", + "7835": "\uc57d", + "7836": "\uc778\ub370", + "7837": "\uae34", + "7838": "\ub9ce", + "7839": "\ud558\uae30", + "7840": "\ub4e0", + "7841": "\u2581\uc57d\uac04", + "7842": "\u2581\uc788\uc5c8", + "7843": "\u2581\ub420", + "7844": "\uaca9", + "7845": "\uc6cc", + "7846": "\ub4e4\uc740", + "7847": "\ud558\ub2e4", + "7848": "\u2581\ub2e4\ub978", + "7849": "\uba39", + "7850": "\u2581\uc815\ub9d0", + "7851": "\u2581\uc65c", + "7852": "\uba74\uc11c", + "7853": "\uc220", + "7854": "\ud569", + "7855": "\uc99d", + "7856": "\u2581\uacc4\uc18d", + "7857": "\uce74", + "7858": "\u2581\uacbd\uc6b0", + "7859": "\ud3c9", + "7860": "\ub0d0", + "7861": "\uc774\ub2e4", + "7862": "\ubd24", + "7863": "\ub4e4\uc744", + "7864": "\uc11d", + "7865": "\uac01", + "7866": "\ubcf4\ub2e4", + "7867": "\ubd84\ub4e4", + "7868": "\uadfc", + "7869": "\ub9b0", + "7870": "\ubcfc", + "7871": "\uae09", + "7872": "\uc54c", + "7873": "\uc124", + "7874": "\uc558", + "7875": "\ub418", + "7876": "\ucd08", + "7877": "\uc4f0", + "7878": "\uc74c", + "7879": "\u2581\ub098\uc624", + "7880": "\uc73c", + "7881": "\uc62c", + "7882": "\uc838", + "7883": "\ucc45", + "7884": "\ud655", + "7885": "\uac08", + "7886": "\ub3c8", + "7887": "\u2581\uc788\ub294\ub370", + "7888": "\ubcf5", + "7889": "\uc751", + "7890": "\ub418\uace0", + "7891": "\uc904", + "7892": "\u2581\ub9ce\uc740", + "7893": "\ub839", + "7894": "\ud5a5", + "7895": "\uac70\uc8e0", + "7896": "\u2581\ubcf4\uba74", + "7897": "\ub8e8", + "7898": "\uc5b8", + "7899": "\uc808", + "7900": "\uc5d0\uc11c\ub294", + "7901": "\ud2f0", + "7902": "\u2581\ud55c\uad6d", + "7903": "\ud1a0", + "7904": "\ud55c\ud14c", + "7905": "\u2581\ub9de\uc544", + "7906": "\uc5d0\uac8c", + "7907": "\u2581\uadf8\ub7f0\ub370", + "7908": "\ub2e4\ub294", + "7909": "\u2581\uc0c1\ud669", + "7910": "\u2581\uadf8\ub7ec", + "7911": "\uc774\ub77c\uace0", + "7912": "\ub8cc", + "7913": "\uc774\ub098", + "7914": "\u2581\uc5ec\uae30", + "7915": "\ubc14", + "7916": "\u2581\uc544\uc774", + "7917": "\uc560", + "7918": "\ub300\ub85c", + "7919": "\u2581\uac70\uae30", + "7920": "\u2581\uc88b\uc544", + "7921": "\ucc38", + "7922": "\uace0\uc694", + "7923": "\uadf8", + "7924": "\uba74\uc740", + "7925": "\uc0bc", + "7926": "\uad6c\uc694", + "7927": "\ub984", + "7928": "\ucc98\ub7fc", + "7929": "\ub2f4", + "7930": "\u2581\uc788\uc744", + "7931": "\u2581\uc88b\uc740", + "7932": "\ud488", + "7933": "\uc800", + "7934": "\uc2b9", + "7935": "\u2581\ubbf8\uad6d", + "7936": "\u2581\uac19\uc560", + "7937": "\ud558\uc2dc", + "7938": "\ubcd1", + "7939": "\ud658", + "7940": "\u2581\ud544\uc694", + "7941": "\u2581\uc0ac\ub78c\ub4e4", + "7942": "\uc9c0\ub294", + "7943": "\uc545", + "7944": "\u2581\ud55c\ubc88", + "7945": "\u2581\uc778\uc81c", + "7946": "\ub860", + "7947": "\uc21c", + "7948": "\uc628", + "7949": "\ucc98", + "7950": "\uc84c", + "7951": "\uc168", + "7952": "\u2581\uadf8\ub54c", + "7953": "\ub450", + "7954": "\uac14", + "7955": "\uc190", + "7956": "\uc6b8", + "7957": "\ubc8c", + "7958": "\ucf54", + "7959": "\u2581\uadf8\ub2c8\uae4c", + "7960": "\ucde8", + "7961": "\u2581\uc788\uc5b4", + "7962": "\uc804\uc5d0", + "7963": "\u2581\uac83\uc774", + "7964": "\ub204", + "7965": "\u2581\uc0ac\ub78c\ub4e4\uc774", + "7966": "\u2581\uc790\uae30", + "7967": "\ub838", + "7968": "\u2581\uc544\ub2c8\ub77c", + "7969": "\uc608", + "7970": "\ud22c", + "7971": "\uc2b5\ub2c8\uae4c", + "7972": "\u2581\uc77c\ub2e8", + "7973": "\u2581\uc5c6\ub294", + "7974": "\ud070", + "7975": "\u2581\uc0dd\uac01\uc744", + "7976": "\ub978", + "7977": "\uc0c8", + "7978": "\uae38", + "7979": "\ud0dd", + "7980": "\ud50c", + "7981": "\uac81", + "7982": "\u2581\uc694\uc998", + "7983": "\u2581\uadf8\ub7fc", + "7984": "\uba38", + "7985": "\u2581\ubb54\uac00", + "7986": "\uc2f6", + "7987": "\uac80", + "7988": "\uba54", + "7989": "\uac70\ub098", + "7990": "\ub780", + "7991": "\ub3c5", + "7992": "\uba87", + "7993": "\uc654", + "7994": "\ubd81", + "7995": "\ud588\uc2b5\ub2c8\ub2e4", + "7996": "\u2581\uadf8\ub798", + "7997": "\uacf3", + "7998": "\uc871", + "7999": "\uaca8", + "8000": "\ube60", + "8001": "\u2581\ubcf4\ub2c8\uae4c", + "8002": "\u2581\uc815\ubd80", + "8003": "\ub9dd", + "8004": "\uc788\ub294", + "8005": "\ub098\uc694", + "8006": "\uc6c0", + "8007": "\u2581\uc0ac\ub78c\uc774", + "8008": "\u2581\uc598\uae30\ub97c", + "8009": "\ub193", + "8010": "\ud2b9", + "8011": "\u2581\uac83\ub3c4", + "8012": "\u2581\uc774\uc57c\uae30", + "8013": "\uad11", + "8014": "\uba70", + "8015": "\uac15", + "8016": "\ud558\uba74\uc11c", + "8017": "\ub85d", + "8018": "\ub9d0", + "8019": "\ube0c", + "8020": "\u2581\uad00\ub828", + "8021": "\u2581\uc2dc\uc791", + "8022": "\uae00", + "8023": "\ud588\ub358", + "8024": "\u2581\uacbd\uc81c", + "8025": "\uc644", + "8026": "\uaca0\ub2e4", + "8027": "\uaca0\uc2b5\ub2c8\ub2e4", + "8028": "\u2581\uce5c\uad6c", + "8029": "\u2581\uad6d\ubbfc", + "8030": "\u2581\uadf8\uac83", + "8031": "\ubd10", + "8032": "\ud65c", + "8033": "\ub192", + "8034": "\u2581\uc788\uc5b4\uc694", + "8035": "\uc774\ub77c\ub294", + "8036": "\u2581\ub2e4\uc2dc", + "8037": "\u2581\uc5ec\ub7ec", + "8038": "\ucd1d", + "8039": "\uc7a1", + "8040": "\ub2a5", + "8041": "\ud56d", + "8042": "\ub958", + "8043": "\uaddc", + "8044": "\ub530", + "8045": "\ucc44", + "8046": "\uc874", + "8047": "\ub9bd", + "8048": "\uce5c", + "8049": "\uc7c1", + "8050": "\ub298", + "8051": "\ubc94", + "8052": "\ubcc4", + "8053": "\uce21", + "8054": "\ud14c", + "8055": "\ucca0", + "8056": "\ub531", + "8057": "\uc5d4", + "8058": "\uc5b5", + "8059": "\ub05d", + "8060": "\ub77d", + "8061": "\ub9b4", + "8062": "\ucc3d", + "8063": "\uadf9", + "8064": "\uc918", + "8065": "\ud611", + "8066": "\ud328", + "8067": "\ucee4", + "8068": "\uc55e", + "8069": "\ub3cc", + "8070": "\ucda9", + "8071": "\uc0c9", + "8072": "\ub208", + "8073": "\uc154", + "8074": "\uc775", + "8075": "\uc811", + "8076": "\uc1a1", + "8077": "\ud798", + "8078": "\ub0ac", + "8079": "\uafb8", + "8080": "\uaed8", + "8081": "\uc2f8", + "8082": "\ub420", + "8083": "\ub2f5", + "8084": "\ud5d8", + "8085": "\uce68", + "8086": "\uc728", + "8087": "\ub7fd", + "8088": "\ud398", + "8089": "\uac78", + "8090": "\ub4a4", + "8091": "\ud76c", + "8092": "\ucf1c", + "8093": "\ubab0", + "8094": "\ud600", + "8095": "\uc368", + "8096": "\ud300", + "8097": "\uc158", + "8098": "\ucd95", + "8099": "\ub7c9", + "8100": "\ud63c", + "8101": "\uccd0", + "8102": "\ub4dd", + "8103": "\ubc00", + "8104": "\ubca0", + "8105": "\ud669", + "8106": "\ud3ed", + "8107": "\ub140", + "8108": "\uc27d", + "8109": "\ud2c0", + "8110": "\ud6a8", + "8111": "\uace8", + "8112": "\ubd88", + "8113": "\ub78c", + "8114": "\ub840", + "8115": "\ub290", + "8116": "\uc988", + "8117": "\ube14", + "8118": "\ub180", + "8119": "\ud568", + "8120": "\ub5a8", + "8121": "\ud154", + "8122": "\ud5c8", + "8123": "\ub17c", + "8124": "\uad81", + "8125": "\ub9bc", + "8126": "\ud0c4", + "8127": "\ub7f4", + "8128": "\uacac", + "8129": "\uc529", + "8130": "\ub7f0", + "8131": "\ub5a0", + "8132": "\ub118", + "8133": "\ud480", + "8134": "\uc8fd", + "8135": "\uc8c4", + "8136": "\uc2b5", + "8137": "\ud575", + "8138": "\uadc0", + "8139": "\uc61b", + "8140": "\uc724", + "8141": "\ud64d", + "8142": "\ub07c", + "8143": "\ub18d", + "8144": "\ub828", + "8145": "\uac16", + "8146": "\uccab", + "8147": "\ub458", + "8148": "\ud639", + "8149": "\uc5e0", + "8150": "\uc9d5", + "8151": "\ud6c8", + "8152": "\ud0c8", + "8153": "\ucf00", + "8154": "\uaf2d", + "8155": "\ub960", + "8156": "\ud601", + "8157": "\uc12f", + "8158": "\ucc29", + "8159": "\ud734", + "8160": "\ubd09", + "8161": "\ud074", + "8162": "\uc5fc", + "8163": "\ubc1d", + "8164": "\ud48d", + "8165": "\uc2ac", + "8166": "\ubd99", + "8167": "\uace1", + "8168": "\uc5bc", + "8169": "\ucc0d", + "8170": "\ubfd0", + "8171": "\uc9dc", + "8172": "\ubab8", + "8173": "\uc7a0", + "8174": "\ub123", + "8175": "\ub79c", + "8176": "\uc26c", + "8177": "\ub4ef", + "8178": "\ub110", + "8179": "\ub790", + "8180": "\ubc0f", + "8181": "\uc655", + "8182": "\ud37c", + "8183": "\uc555", + "8184": "\ub9db", + "8185": "\ub0ae", + "8186": "\ud0c1", + "8187": "\uc561", + "8188": "\uc92c", + "8189": "\ucc2c", + "8190": "\uc9f8", + "8191": "\ud544", + "8192": "\uc288", + "8193": "\uc13c", + "8194": "\ub099", + "8195": "\ud3d0", + "8196": "\ub73b", + "8197": "\uac11", + "8198": "\uc695", + "8199": "\ud3f0", + "8200": "\uc77d", + "8201": "\uc5c6", + "8202": "\ud2bc", + "8203": "\uc6c3", + "8204": "\ub9e8", + "8205": "\uce35", + "8206": "\ucc3e", + "8207": "\uc219", + "8208": "\ud1f4", + "8209": "\uad74", + "8210": "\uade0", + "8211": "\uc6e0", + "8212": "\ud765", + "8213": "\ub150", + "8214": "\ub4e3", + "8215": "\uc637", + "8216": "\ub0c8", + "8217": "\ud754", + "8218": "\ud61c", + "8219": "\uc554", + "8220": "\uac1d", + "8221": "\uc5d8", + "8222": "\ub274", + "8223": "\uae68", + "8224": "\ubb58", + "8225": "\ub04c", + "8226": "\ub6f0", + "8227": "\uc2eb", + "8228": "\ub054", + "8229": "\uce90", + "8230": "\ub2d0", + "8231": "\uacbc", + "8232": "\uc559", + "8233": "\ud750", + "8234": "\ub7b5", + "8235": "\ubcbd", + "8236": "\uc598", + "8237": "\ub9c9", + "8238": "\uaebc", + "8239": "\uc9d3", + "8240": "\uc990", + "8241": "\ucc30", + "8242": "\uba3c", + "8243": "\uc4f8", + "8244": "\ub355", + "8245": "\uae50", + "8246": "\ubc25", + "8247": "\uc5c7", + "8248": "\ub728", + "8249": "\ucdb0", + "8250": "\ubd05", + "8251": "\ubbff", + "8252": "\ub807", + "8253": "\uce59", + "8254": "\ub0b8", + "8255": "\ubc24", + "8256": "\uc9dd", + "8257": "\uc5bb", + "8258": "\uc794", + "8259": "\uc058", + "8260": "\ud0ac", + "8261": "\ud138", + "8262": "\uc6b1", + "8263": "\uac12", + "8264": "\ub9c1", + "8265": "\uc88c", + "8266": "\ube4c", + "8267": "\ucee8", + "8268": "\ub86d", + "8269": "\ud5cc", + "8270": "\uac54", + "8271": "\ucfe0", + "8272": "\ud305", + "8273": "\ube7c", + "8274": "\uc228", + "8275": "\uce20", + "8276": "\uc5c4", + "8277": "\ud614", + "8278": "\ub545", + "8279": "\uafc8", + "8280": "\ub2e5", + "8281": "\ub0bc", + "8282": "\uacf1", + "8283": "\uc0b6", + "8284": "\ub9e5", + "8285": "\uc98c", + "8286": "\uc606", + "8287": "\uc54a", + "8288": "\uad34", + "8289": "\ube48", + "8290": "\ud134", + "8291": "\uc625", + "8292": "\uc6e8", + "8293": "\ud0a8", + "8294": "\ub9ad", + "8295": "\ud32c", + "8296": "\ud5e4", + "8297": "\ud718", + "8298": "\uc12d", + "8299": "\uba40", + "8300": "\uce6d", + "8301": "\uc05c", + "8302": "\ub0a9", + "8303": "\ub1cc", + "8304": "\ucc99", + "8305": "\uad73", + "8306": "\uc37c", + "8307": "\ub4ed", + "8308": "\uaef4", + "8309": "\ub9e1", + "8310": "\uc1fc", + "8311": "\uace4", + "8312": "\ube68", + "8313": "\ucce4", + "8314": "\uc568", + "8315": "\ucbe4", + "8316": "\ub313", + "8317": "\ub179", + "8318": "\uc989", + "8319": "\ucf58", + "8320": "\ub2a6", + "8321": "\ube5b", + "8322": "\ud608", + "8323": "\ubf51", + "8324": "\uae4a", + "8325": "\ub538", + "8326": "\uc4f4", + "8327": "\uaf43", + "8328": "\ud39c", + "8329": "\ub04a", + "8330": "\ud3b4", + "8331": "\ub78d", + "8332": "\ud648", + "8333": "\ub0c9", + "8334": "\ud508", + "8335": "\ub220", + "8336": "\ud0d5", + "8337": "\ubc11", + "8338": "\uae54", + "8339": "\ub8b0", + "8340": "\ucc0c", + "8341": "\ub800", + "8342": "\ud551", + "8343": "\ud758", + "8344": "\uce7c", + "8345": "\ucb49", + "8346": "\uc2f1", + "8347": "\uc2b7", + "8348": "\ub35c", + "8349": "\uafd4", + "8350": "\ubb54", + "8351": "\ub799", + "8352": "\uc0ad", + "8353": "\ud478", + "8354": "\ub86f", + "8355": "\ub529", + "8356": "\ucf69", + "8357": "\ud68d", + "8358": "\ubd07", + "8359": "\ud150", + "8360": "\ub8f9", + "8361": "\ub9f9", + "8362": "\ud31d", + "8363": "\uc2fc", + "8364": "\uc878", + "8365": "\ubca4", + "8366": "\ub044", + "8367": "\ub80c", + "8368": "\ub7ab", + "8369": "\ubba4", + "8370": "\ud610", + "8371": "\ud0b9", + "8372": "\uc313", + "8373": "\uc635", + "8374": "\ud78c", + "8375": "\ucf13", + "8376": "\ud380", + "8377": "\ud3f4", + "8378": "\uc820", + "8379": "\ubc97", + "8380": "\ucd09", + "8381": "\uace7", + "8382": "\uacaa", + "8383": "\ub113", + "8384": "\uc783", + "8385": "\ubca8", + "8386": "\ub1a8", + "8387": "\ubd04", + "8388": "\ubb3b", + "8389": "\ub784", + "8390": "\uba58", + "8391": "\uceec", + "8392": "\ud761", + "8393": "\ucd98", + "8394": "\uba4b", + "8395": "\ucd0c", + "8396": "\ub378", + "8397": "\uc12c", + "8398": "\ucc59", + "8399": "\ucf30", + "8400": "\uae5d", + "8401": "\ud578", + "8402": "\ud649", + "8403": "\ucd78", + "8404": "\ucf5c", + "8405": "\uaf64", + "8406": "\uc9d0", + "8407": "\uc22b", + "8408": "\uc998", + "8409": "\ub454", + "8410": "\ucef5", + "8411": "\uc194", + "8412": "\uae0d", + "8413": "\ub7ac", + "8414": "\ud3fc", + "8415": "\ub141", + "8416": "\ub5bb", + "8417": "\ud329", + "8418": "\ub5a1", + "8419": "\uaf2c", + "8420": "\ud799", + "8421": "\uc0c0", + "8422": "\uca54", + "8423": "\uaf08", + "8424": "\ud0d0", + "8425": "\ucea0", + "8426": "\uc2b4", + "8427": "\ubfcc", + "8428": "\uc9da", + "8429": "\uc1c4", + "8430": "\ubb18", + "8431": "\ub9bf", + "8432": "\uc564", + "8433": "\ud640", + "8434": "\uc14b", + "8435": "\ud1a1", + "8436": "\uc130", + "8437": "\uc78a", + "8438": "\ub465", + "8439": "\ub2eb", + "8440": "\ucda4", + "8441": "\ube59", + "8442": "\ubaac", + "8443": "\uaf3c", + "8444": "\ub7a8", + "8445": "\ube75", + "8446": "\uc2a8", + "8447": "\ub7fc", + "8448": "\ud3bc", + "8449": "\uc140", + "8450": "\ub864", + "8451": "\ub82c", + "8452": "\ud0d1", + "8453": "\uc384", + "8454": "\ub137", + "8455": "\ub4b7", + "8456": "\uc5d1", + "8457": "\ub7ed", + "8458": "\ucef4", + "8459": "\ub611", + "8460": "\ub2dd", + "8461": "\ud5ec", + "8462": "\ucca8", + "8463": "\ub904", + "8464": "\ub51c", + "8465": "\uae5c", + "8466": "\ud2f1", + "8467": "\ub744", + "8468": "\uc0e4", + "8469": "\ube45", + "8470": "\ub834", + "8471": "\uc81d", + "8472": "\uca4c", + "8473": "\uc300", + "8474": "\ubc0d", + "8475": "\ud751", + "8476": "\ub194", + "8477": "\uac77", + "8478": "\uba78", + "8479": "\ud1a4", + "8480": "\uc5fd", + "8481": "\ud050", + "8482": "\ucd2c", + "8483": "\ucb64", + "8484": "\ud29c", + "8485": "\ud790", + "8486": "\uc88b", + "8487": "\ub959", + "8488": "\ube5a", + "8489": "\ucf8c", + "8490": "\uafc0", + "8491": "\ud540", + "8492": "\ub871", + "8493": "\ub2cc", + "8494": "\ub5bc", + "8495": "\uba48", + "8496": "\ub550", + "8497": "\ud034", + "8498": "\uae40", + "8499": "\ub985", + "8500": "\ub048", + "8501": "\ub20c", + "8502": "\uc30d", + "8503": "\ubb35", + "8504": "\ub534", + "8505": "\uc789", + "8506": "\ubbc0", + "8507": "\ub369", + "8508": "\uc6c5", + "8509": "\uc5c9", + "8510": "\ub0ab", + "8511": "\ud280", + "8512": "\uc67c", + "8513": "\ub0c4", + "8514": "\uaf3d", + "8515": "\uacb8", + "8516": "\ubc16", + "8517": "\ub10c", + "8518": "\uc148", + "8519": "\ub0e5", + "8520": "\uafbc", + "8521": "\ub188", + "8522": "\uba4d", + "8523": "\ubabd", + "8524": "\ubd95", + "8525": "\uacb9", + "8526": "\ubc34", + "8527": "\uc950", + "8528": "\ub080", + "8529": "\ubb50", + "8530": "\ub8f0", + "8531": "\ub809", + "8532": "\ub561", + "8533": "\ub69c", + "8534": "\ub8f8", + "8535": "\ubcbc", + "8536": "\ub2c9", + "8537": "\ub9d8", + "8538": "\ud15c", + "8539": "\ub2f7", + "8540": "\ub518", + "8541": "\ub0ad", + "8542": "\uc11e", + "8543": "\uc6ec", + "8544": "\uce78", + "8545": "\uc787", + "8546": "\uc881", + "8547": "\ub989", + "8548": "\uc571", + "8549": "\ub9fa", + "8550": "\ud6fc", + "8551": "\ubed4", + "8552": "\uac24", + "8553": "\ub010", + "8554": "\ud6cc", + "8555": "\ub084", + "8556": "\ub36e", + "8557": "\uc3d8", + "8558": "\ub057", + "8559": "\ubf08", + "8560": "\ucabc", + "8561": "\uc798", + "8562": "\ubb36", + "8563": "\ub154", + "8564": "\ud53d", + "8565": "\ucca9", + "8566": "\ucef8", + "8567": "\ub2ed", + "8568": "\uc735", + "8569": "\uc1e0", + "8570": "\ud2f4", + "8571": "\ub374", + "8572": "\uc90d", + "8573": "\ud14d", + "8574": "\ubdd4", + "8575": "\uc6f9", + "8576": "\uc0f5", + "8577": "\ub2ff", + "8578": "\ubdf0", + "8579": "\ub367", + "8580": "\ubbf9", + "8581": "\ub364", + "8582": "\ub42c", + "8583": "\uc796", + "8584": "\uacfd", + "8585": "\uad04", + "8586": "\uad1c", + "8587": "\ub8ec", + "8588": "\ub987", + "8589": "\ubd59", + "8590": "\ub760", + "8591": "\ub8e1", + "8592": "\ub155", + "8593": "\ub4ec", + "8594": "\ubabb", + "8595": "\uaf34", + "8596": "\ub69d", + "8597": "\ub801", + "8598": "\ucc2e", + "8599": "\ub610", + "8600": "\ub96d", + "8601": "\uc15c", + "8602": "\ud31f", + "8603": "\ud33d", + "8604": "\ubed0", + "8605": "\uc2f9", + "8606": "\ud0d3", + "8607": "\ub451", + "8608": "\ud1b1", + "8609": "\ucf64", + "8610": "\ub6b1", + "8611": "\uae0b", + "8612": "\uba64", + "8613": "\ub9d9", + "8614": "\uc824", + "8615": "\uc708", + "8616": "\uac90", + "8617": "\ud587", + "8618": "\ube57", + "8619": "\uacf0", + "8620": "\uae65", + "8621": "\uba5c", + "8622": "\ub0af", + "8623": "\uc639", + "8624": "\ucfe8", + "8625": "\ubcf6", + "8626": "\uc232", + "8627": "\ub365", + "8628": "\ubc1f", + "8629": "\ud2f8", + "8630": "\ud2c8", + "8631": "\ub52a", + "8632": "\uae4e", + "8633": "\uac89", + "8634": "\uce69", + "8635": "\ub480", + "8636": "\uc717", + "8637": "\uc090", + "8638": "\uc575", + "8639": "\ub125", + "8640": "\uafe8", + "8641": "\ubb49", + "8642": "\uc22d", + "8643": "\ud321", + "8644": "\ubc45", + "8645": "\ud56b", + "8646": "\ud749", + "8647": "\uce94", + "8648": "\ub46c", + "8649": "\ub540", + "8650": "\uc53b", + "8651": "\ud6a1", + "8652": "\ucfc4", + "8653": "\ubc2d", + "8654": "\uc369", + "8655": "\ub014", + "8656": "\uac31", + "8657": "\ubc38", + "8658": "\ud514", + "8659": "\uc880", + "8660": "\ud1a8", + "8661": "\uc580", + "8662": "\ube61", + "8663": "\uaecf", + "8664": "\ub258", + "8665": "\ub2ee", + "8666": "\ub1e8", + "8667": "\ud131", + "8668": "\ub304", + "8669": "\ud760", + "8670": "\ub7ad", + "8671": "\uc78e", + "8672": "\ub835", + "8673": "\ube8f", + "8674": "\ucad9", + "8675": "\ub0c5", + "8676": "\uc557", + "8677": "\uca4d", + "8678": "\ud770", + "8679": "\uad49", + "8680": "\ud584", + "8681": "\uada4", + "8682": "\uae43", + "8683": "\uc90c", + "8684": "\uc0d8", + "8685": "\ub55c", + "8686": "\uc0cc", + "8687": "\ucdc4", + "8688": "\ud0f1", + "8689": "\ud0a5", + "8690": "\ubc85", + "8691": "\uc3e0", + "8692": "\uc74d", + "8693": "\ubccd", + "8694": "\ud230", + "8695": "\uca0c", + "8696": "\ube10", + "8697": "\ub3d5", + "8698": "\ud140", + "8699": "\uc9e4", + "8700": "\ucef7", + "8701": "\uc0bd", + "8702": "\uaf42", + "8703": "\ub837", + "8704": "\uc139", + "8705": "\ud54f", + "8706": "\ud5e8", + "8707": "\uad7d", + "8708": "\ub8e9", + "8709": "\ucda5", + "8710": "\ub2e6", + "8711": "\ub7a9", + "8712": "\ud47c", + "8713": "\uc660", + "8714": "\ub3cb", + "8715": "\ud30d", + "8716": "\ucc14", + "8717": "\ub72c", + "8718": "\ud5f7", + "8719": "\ubaab", + "8720": "\ud399", + "8721": "\ubd93", + "8722": "\ud0e0", + "8723": "\ub72f", + "8724": "\uc149", + "8725": "\uad90", + "8726": "\ub625", + "8727": "\uae41", + "8728": "\ud0d4", + "8729": "\uba55", + "8730": "\uc816", + "8731": "\ub4c0", + "8732": "\ud4e8", + "8733": "\ub9f5", + "8734": "\uc587", + "8735": "\uc068", + "8736": "\uaecd", + "8737": "\uac13", + "8738": "\ub109", + "8739": "\uc9f1", + "8740": "\uce6b", + "8741": "\ud759", + "8742": "\uaf49", + "8743": "\uc501", + "8744": "\ub428", + "8745": "\ud3a0", + "8746": "\ubf40", + "8747": "\uac07", + "8748": "\uc465", + "8749": "\ud5d0", + "8750": "\ub299", + "8751": "\uc500", + "8752": "\ubc40", + "8753": "\ub618", + "8754": "\uc370", + "8755": "\ud23c", + "8756": "\ub95c", + "8757": "\ub86c", + "8758": "\ubed7", + "8759": "\ud301", + "8760": "\ud48b", + "8761": "\uc274", + "8762": "\ucea1", + "8763": "\ub584", + "8764": "\uc0f7", + "8765": "\uc539", + "8766": "\uc7a3", + "8767": "\uc3f4", + "8768": "\ubb47", + "8769": "\uc270", + "8770": "\ub81b", + "8771": "\uc65c", + "8772": "\ud729", + "8773": "\uc36c", + "8774": "\uc5ce", + "8775": "\ud5db", + "8776": "\ubfd4", + "8777": "\uc27c", + "8778": "\uc813", + "8779": "\ub729", + "8780": "\uc719", + "8781": "\uc29b", + "8782": "\uc170", + "8783": "\uc19f", + "8784": "\uc9e0", + "8785": "\ud6d4", + "8786": "\uc6f0", + "8787": "\uc634", + "8788": "\ud384", + "8789": "\uaf41", + "8790": "\ub730", + "8791": "\ubf55", + "8792": "\ub2ac", + "8793": "\ucc1c", + "8794": "\ud391", + "8795": "\ubbac", + "8796": "\uccbc", + "8797": "\ud241", + "8798": "\ub5b4", + "8799": "\ub284", + "8800": "\ub291", + "8801": "\ucf08", + "8802": "\ud0b4", + "8803": "\uc3d9", + "8804": "\uce98", + "8805": "\uad7c", + "8806": "\ud22d", + "8807": "\ub968", + "8808": "\ub6f8", + "8809": "\uc5ff", + "8810": "\uc610", + "8811": "\ubd90", + "8812": "\uc7ad", + "8813": "\ub315", + "8814": "\uafc9", + "8815": "\ucf67", + "8816": "\ud479", + "8817": "\uc70c", + "8818": "\ucffc", + "8819": "\uac9f", + "8820": "\uc060", + "8821": "\uc5ee", + "8822": "\ud69f", + "8823": "\uad7f", + "8824": "\uae61", + "8825": "\ub2d9", + "8826": "\uc2e3", + "8827": "\ucf55", + "8828": "\ubc43", + "8829": "\uc5b9", + "8830": "\uc9ec", + "8831": "\ud234", + "8832": "\ubee5", + "8833": "\ud07c", + "8834": "\uc290", + "8835": "\uc7a6", + "8836": "\ud720", + "8837": "\uc19c", + "8838": "\ud38c", + "8839": "\uca61", + "8840": "\ud5dd", + "8841": "\ud1b0", + "8842": "\uc0d0", + "8843": "\uae01", + "8844": "\ud31c", + "8845": "\ube54", + "8846": "\ub3d7", + "8847": "\uac2f", + "8848": "\ucf04", + "8849": "\ub7ff", + "8850": "\ub301", + "8851": "\ub310", + "8852": "\uc570", + "8853": "\ud0ed", + "8854": "\ube90", + "8855": "\ub308", + "8856": "\ub2db", + "8857": "\ud6c5", + "8858": "\ube7d", + "8859": "\ub12c", + "8860": "\uc9d6", + "8861": "\ub460", + "8862": "\uce84", + "8863": "\ub461", + "8864": "\ucac4", + "8865": "\ub528", + "8866": "\ubca1", + "8867": "\uc7a4", + "8868": "\ud004", + "8869": "\ubfdc", + "8870": "\uac2d", + "8871": "\ub3d4", + "8872": "\ucf70", + "8873": "\uc653", + "8874": "\uc96c", + "8875": "\ub314", + "8876": "\ub5b3", + "8877": "\ub0b1", + "8878": "\ud168", + "8879": "\ubcd5", + "8880": "\ub38c", + "8881": "\ud30e", + "8882": "\uad88", + "8883": "\ud0b5", + "8884": "\uadc4", + "8885": "\ucc10", + "8886": "\ucc1d", + "8887": "\uae4d", + "8888": "\ub7b4", + "8889": "\ud145", + "8890": "\ube80", + "8891": "\ud325", + "8892": "\ubd48", + "8893": "\uba67", + "8894": "\uc52c", + "8895": "\ubc99", + "8896": "\ubcb3", + "8897": "\uc1a5", + "8898": "\uc82f", + "8899": "\ub6f4", + "8900": "\ucb48", + "8901": "\ub810", + "8902": "\uc250", + "8903": "\uaec4", + "8904": "\uc584", + "8905": "\uc5e3", + "8906": "\uc324", + "8907": "\uc3dc", + "8908": "\ucff5", + "8909": "\ud0b7", + "8910": "\uc648", + "8911": "\ub764", + "8912": "\ube74", + "8913": "\ube84", + "8914": "\uafc7", + "8915": "\ub525", + "8916": "\ub544", + "8917": "\ub818", + "8918": "\ud54d", + "8919": "\uc378", + "8920": "\ub5a4", + "8921": "\ub215", + "8922": "\ub9f7", + "8923": "\ucc3c", + "8924": "\uc308", + "8925": "\ub2a0", + "8926": "\uc999", + "8927": "\uaf30", + "8928": "\ub371", + "8929": "\uc20d", + "8930": "\uc5ca", + "8931": "\uc0bf", + "8932": "\uaf80", + "8933": "\ub9e3", + "8934": "\uc7bc", + "8935": "\uac40", + "8936": "\ud018", + "8937": "\uc464", + "8938": "\ubfb0", + "8939": "\uca50", + "8940": "\ubb44", + "8941": "\uade4", + "8942": "\ub797", + "8943": "\uca0b", + "8944": "\ucee5", + "8945": "\uaff0", + "8946": "\uc123", + "8947": "\uc379", + "8948": "\ud3c8", + "8949": "\ud0ec", + "8950": "\ub2aa", + "8951": "\ub738", + "8952": "\uce89", + "8953": "\ubab9", + "8954": "\uc298", + "8955": "\ub975", + "8956": "\uc4f1", + "8957": "\ud6d7", + "8958": "\ub9cf", + "8959": "\ud15d", + "8960": "\ub51b", + "8961": "\ucc39", + "8962": "\ubb8c", + "8963": "\uce04", + "8964": "\ud3ad", + "8965": "\ube64", + "8966": "\ub08c", + "8967": "\ubed8", + "8968": "\uc6c1", + "8969": "\uafb9", + "8970": "\ub205", + "8971": "\uc6cd", + "8972": "\ud4f0", + "8973": "\uca5c", + "8974": "\uc21f", + "8975": "\uac94", + "8976": "\ud038", + "8977": "\ucf65", + "8978": "\ub8fb", + "8979": "\ub515", + "8980": "\ube91", + "8981": "\uc167", + "8982": "\uceeb", + "8983": "\uc388", + "8984": "\ubf18", + "8985": "\ub128", + "8986": "\ucc4c", + "8987": "\ud3ab", + "8988": "\ud390", + "8989": "\ucbd4", + "8990": "\ud5c9", + "8991": "\ud2ac", + "8992": "\ub9f4", + "8993": "\uc58c", + "8994": "\ud143", + "8995": "\uc69c", + "8996": "\ubed1", + "8997": "\uce85", + "8998": "\ubc0b", + "8999": "\uc574", + "9000": "\ud295", + "9001": "\ub214", + "9002": "\uc0f9", + "9003": "\ubc09", + "9004": "\uac71", + "9005": "\uae45", + "9006": "\uc408", + "9007": "\ud3c4", + "9008": "\uc204", + "9009": "\ub4c8", + "9010": "\ubee3", + "9011": "\ubc27", + "9012": "\uac20", + "9013": "\ud401", + "9014": "\uaf5d", + "9015": "\uce30", + "9016": "\ucfe1", + "9017": "\ucfe4", + "9018": "\ucf10", + "9019": "\uaecc", + "9020": "\uce75", + "9021": "\ub6a4", + "9022": "\ucc60", + "9023": "\uadd3", + "9024": "\ub11b", + "9025": "\ud6d1", + "9026": "\ud37d", + "9027": "\uc329", + "9028": "\uae7c", + "9029": "\ucea3", + "9030": "\ubea8", + "9031": "\ud6e8", + "9032": "\ub78f", + "9033": "\uccb8", + "9034": "\uc0ec", + "9035": "\ucb10", + "9036": "\uae6c", + "9037": "\uca84", + "9038": "\uc5cc", + "9039": "\ub07d", + "9040": "\uc9f0", + "9041": "\uac38", + "9042": "\uadc8", + "9043": "\ub385", + "9044": "\ub748", + "9045": "\uac4d", + "9046": "\ub08d", + "9047": "\ucf85", + "9048": "\ud0e4", + "9049": "\ud17c", + "9050": "\ub311", + "9051": "\ucc3b", + "9052": "\uad18", + "9053": "\uc73d", + "9054": "\uc309", + "9055": "\ub527", + "9056": "\ub7a0", + "9057": "\ucc21", + "9058": "\uafcb", + "9059": "\ub00c", + "9060": "\ubb63", + "9061": "\ub524", + "9062": "\ud64b", + "9063": "\ub8fd", + "9064": "\ud338", + "9065": "\ud5e5", + "9066": "\uaebd", + "9067": "\uafcd", + "9068": "\ud058", + "9069": "\ucef9", + "9070": "\uac1b", + "9071": "\uae70", + "9072": "\uc314", + "9073": "\ub775", + "9074": "\ud6e4", + "9075": "\uc53d", + "9076": "\ud141", + "9077": "\ub93c", + "9078": "\uc20f", + "9079": "\uc9ed", + "9080": "\ubbc4", + "9081": "\uc258", + "9082": "\ud33b", + "9083": "\uaed0", + "9084": "\uacf6", + "9085": "\ub400", + "9086": "\uc14c", + "9087": "\uca98", + "9088": "\uc641", + "9089": "\uc90f", + "9090": "\ucdcc", + "9091": "\ud330", + "9092": "\uc9ca", + "9093": "\uc60c", + "9094": "\uc37d", + "9095": "\uc83c", + "9096": "\ud719", + "9097": "\uba71", + "9098": "\uc2a5", + "9099": "\ucf20", + "9100": "\ub217", + "9101": "\uc954", + "9102": "\uc2ef", + "9103": "\ub796", + "9104": "\ubcb5", + "9105": "\uc0db", + "9106": "\uc7c8", + "9107": "\ucb50", + "9108": "\ucda7", + "9109": "\ub5d0", + "9110": "\ucc57", + "9111": "\uc178", + "9112": "\uc6dc", + "9113": "\ubc08", + "9114": "\ud248", + "9115": "\ud57c", + "9116": "\ubf48", + "9117": "\uc5e5", + "9118": "\ubd4c", + "9119": "\uaf48", + "9120": "\uc330", + "9121": "\ubd5c", + "9122": "\uaf3f", + "9123": "\ube73", + "9124": "\uc7b0", + "9125": "\ud2a0", + "9126": "\uc605", + "9127": "\uaec0", + "9128": "\uc37b", + "9129": "\uc538", + "9130": "\ucac0", + "9131": "\ub5c0", + "9132": "\ubfe1", + "9133": "\uc886", + "9134": "\uc42c", + "9135": "\ub761", + "9136": "\uc0e8", + "9137": "\uad82", + "9138": "\uac30", + "9139": "\ube55", + "9140": "\uccc7", + "9141": "\ub554", + "9142": "\uba69", + "9143": "\uba4e", + "9144": "\ubb88", + "9145": "\ub0c7", + "9146": "\ud000", + "9147": "\ud035", + "9148": "\ub4d0", + "9149": "\ud2bf", + "9150": "\uc573", + "9151": "\ud2a4", + "9152": "\ub3db", + "9153": "\uc607", + "9154": "\ud6e0", + "9155": "\ub5b5", + "9156": "\ubcd0", + "9157": "\uae7d", + "9158": "\uad9c", + "9159": "\uc211", + "9160": "\ubbc8", + "9161": "\ubd40", + "9162": "\ucf24", + "9163": "\ub289", + "9164": "\ucc48", + "9165": "\uc22f", + "9166": "\ubc28", + "9167": "\ucad1", + "9168": "\ub380", + "9169": "\ud5f9", + "9170": "\ub819", + "9171": "\ub138", + "9172": "\ub15c", + "9173": "\uc29d", + "9174": "\uc3ed", + "9175": "\ud54c", + "9176": "\uc58f", + "9177": "\ub9f8", + "9178": "\uc7bd", + "9179": "\ubf41", + "9180": "\uc468", + "9181": "\uc698", + "9182": "\ub119", + "9183": "\ub9ec", + "9184": "\ud188", + "9185": "\uba84", + "9186": "\uad38", + "9187": "\ubafc", + "9188": "\ucad2", + "9189": "\ub158", + "9190": "\uc958", + "9191": "\uac84", + "9192": "\uae60", + "9193": "\ubeb4", + "9194": "\ub135", + "9195": "\ubfcd", + "9196": "\ub020", + "9197": "\ud3a9", + "9198": "\uc174", + "9199": "\ucc58", + "9200": "\ub189", + "9201": "\ucd10", + "9202": "\uc5e1", + "9203": "\ub381", + "9204": "\uc0a5", + "9205": "\ucf78", + "9206": "\uc8e4", + "9207": "\ucf71", + "9208": "\ub74c", + "9209": "\ubccf", + "9210": "\uac2c", + "9211": "\ub0b5", + "9212": "\uc6a4", + "9213": "\ucc55", + "9214": "\uae7b", + "9215": "\uca30", + "9216": "\ub1fd", + "9217": "\uc38c", + "9218": "\ube8c", + "9219": "\uac85", + "9220": "\uc705", + "9221": "\uce87", + "9222": "\uc7b4", + "9223": "\uceac", + "9224": "\uc714", + "9225": "\ub768", + "9226": "\ub11c", + "9227": "\ubb4d", + "9228": "\ubd87", + "9229": "\ucea5", + "9230": "\ub383", + "9231": "\uc7c0", + "9232": "\ub700", + "9233": "\ubdf4", + "9234": "\uc58d", + "9235": "\ub404", + "9236": "\ud03c", + "9237": "\ud144", + "9238": "\ubdf8", + "9239": "\ub844", + "9240": "\uc82d", + "9241": "\uc730", + "9242": "\ud6d9", + "9243": "\ubd2c", + "9244": "\ud667", + "9245": "\ucd28", + "9246": "\uae31", + "9247": "\ub5b0", + "9248": "\ubb45", + "9249": "\ud5c0", + "9250": "\uafce", + "9251": "\uba49", + "9252": "\ucd25", + "9253": "\ucea4", + "9254": "\ud590", + "9255": "\uc6e1", + "9256": "\uccb5", + "9257": "\ud38d", + "9258": "\uc530", + "9259": "\uc200", + "9260": "\uce24", + "9261": "\uc229", + "9262": "\uc19d", + "9263": "\uc398", + "9264": "\ud789", + "9265": "\ubeec", + "9266": "\ud73c", + "9267": "\ub0d4", + "9268": "\uc0dc", + "9269": "\ub134", + "9270": "\uc094", + "9271": "\ud5f8", + "9272": "\uac9c", + "9273": "\uc234", + "9274": "\uc82c", + "9275": "\uacc8", + "9276": "\ub11d", + "9277": "\ub588", + "9278": "\uc894", + "9279": "\ud5f4", + "9280": "\uc61c", + "9281": "\uc974", + "9282": "\uc9fc", + "9283": "\uac17", + "9284": "\ub599", + "9285": "\ub9df", + "9286": "\uba53", + "9287": "\uca5d", + "9288": "\uc74f", + "9289": "\ud339", + "9290": "\uc289", + "9291": "\uaf10", + "9292": "\uc0fe", + "9293": "\uc3bc", + "9294": "\ud3ff", + "9295": "\ud489", + "9296": "\uacd8", + "9297": "\uc479", + "9298": "\uac8b", + "9299": "\ub8d4", + "9300": "\ud690", + "9301": "\ubc0e", + "9302": "\ud71c", + "9303": "\ub0e0", + "9304": "\ud5d9", + "9305": "\uaea0", + "9306": "\ubf09", + "9307": "\uc883", + "9308": "\uc0e5", + "9309": "\ub4f8", + "9310": "\ub81d", + "9311": "\ubbd0", + "9312": "\uadff", + "9313": "\ub0d8", + "9314": "\ub40f", + "9315": "\uc6e9", + "9316": "\uca08", + "9317": "\ucb58", + "9318": "\ub463", + "9319": "\ub3e0", + "9320": "\ud06d", + "9321": "\uc0d9", + "9322": "\ud56c", + "9323": "\uc53c", + "9324": "\uc619", + "9325": "\uc394", + "9326": "\uca09", + "9327": "\ub5f4", + "9328": "\ub9dc", + "9329": "\uc14d", + "9330": "\ud3a8", + "9331": "\uc9f9", + "9332": "\ub614", + "9333": "\uacbb", + "9334": "\uc8c8", + "9335": "\ub664", + "9336": "\ucc1f", + "9337": "\uaedc", + "9338": "\ud23d", + "9339": "\uae5f", + "9340": "\uc84d", + "9341": "\ub878", + "9342": "\ud2c9", + "9343": "\ubc0c", + "9344": "\uad54", + "9345": "\uaf07", + "9346": "\ub543", + "9347": "\ub81c", + "9348": "\ub877", + "9349": "\ub879", + "9350": "\uc315", + "9351": "\ucb2c", + "9352": "\uc651", + "9353": "\ud79d", + "9354": "\ud460", + "9355": "\uae37", + "9356": "\uba04", + "9357": "\uae84", + "9358": "\uc2f0", + "9359": "\uc6df", + "9360": "\ud763", + "9361": "\ubba8", + "9362": "\ubfb1", + "9363": "\uc248", + "9364": "\uc814", + "9365": "\ud081", + "9366": "\uc345", + "9367": "\uc0a3", + "9368": "\uacef", + "9369": "\uc0b5", + "9370": "\ub139", + "9371": "\ucb14", + "9372": "\uae79", + "9373": "\uaf4c", + "9374": "\ud1b3", + "9375": "\ud3c5", + "9376": "\ucff1", + "9377": "\uc8d7", + "9378": "\uce61", + "9379": "\ucacd", + "9380": "\ubca7", + "9381": "\ub620", + "9382": "\ub594", + "9383": "\ud207", + "9384": "\uc79b", + "9385": "\uc098", + "9386": "\uc448", + "9387": "\ubb00", + "9388": "\uc18e", + "9389": "\ub5cd", + "9390": "\uaf79", + "9391": "\uc62f", + "9392": "\ub2a1", + "9393": "\ubd91", + "9394": "\uad1e", + "9395": "\uac02", + "9396": "\ube7b", + "9397": "\uc88d", + "9398": "\uc36a", + "9399": "\uc318", + "9400": "\ucc3f", + "9401": "\uacd7", + "9402": "\ud711", + "9403": "\ucc27", + "9404": "\ub754", + "9405": "\ub5ab", + "9406": "\uc80b", + "9407": "\uc62d", + "9408": "\ud613", + "9409": "\uc27f", + "9410": "\uc6f8", + "9411": "\ud25c", + "9412": "\ud0c9", + "9413": "\ubb90", + "9414": "\ub5cf", + "9415": "\ucad8", + "9416": "\uc54e", + "9417": "\ub2fb", + "9418": "\uca44", + "9419": "\ub701", + "9420": "\uc59c", + "9421": "\uc1f3", + "9422": "\ub055", + "9423": "\ud651", + "9424": "\ud0ef", + "9425": "\ucbe7", + "9426": "\ub269", + "9427": "\ud284", + "9428": "\ud744", + "9429": "\ub260", + "9430": "\uc737", + "9431": "\ubb3d", + "9432": "\ud0f0", + "9433": "\uc091", + "9434": "\uac58", + "9435": "\ud585", + "9436": "\ube8d", + "9437": "\uacea", + "9438": "\ud683", + "9439": "\ud2d4", + "9440": "\uc9e2", + "9441": "\ubc9b", + "9442": "\ubfc5", + "9443": "\uc5f7", + "9444": "\ub091", + "9445": "\uc100", + "9446": "\uac09", + "9447": "\uafe9", + "9448": "\uc733", + "9449": "\uc251", + "9450": "\ud2cb", + "9451": "\uc74a", + "9452": "\ub4b9", + "9453": "\uad2d", + "9454": "\ubf1b", + "9455": "\ub739", + "9456": "\uc887", + "9457": "\ub01c", + "9458": "\ube70", + "9459": "\ub3a0", + "9460": "\uc7bf", + "9461": "\ub10b", + "9462": "\ud288", + "9463": "\ub4e6", + "9464": "\ud565", + "9465": "\ub755", + "9466": "\uc2ad", + "9467": "\uc0c5", + "9468": "\uc410", + "9469": "\ub059", + "9470": "\ud515", + "9471": "\uc231", + "9472": "\uca0d", + "9473": "\ub2f3", + "9474": "\ub36b", + "9475": "\ubd50", + "9476": "\uc069", + "9477": "\ub01d", + "9478": "\uad0c", + "9479": "\uc597", + "9480": "\ucb59", + "9481": "\ubf50", + "9482": "\ubee4", + "9483": "\uc595", + "9484": "\uc30c", + "9485": "\ub5c4", + "9486": "\ubc9a", + "9487": "\uc0f4", + "9488": "\ubc49", + "9489": "\ucacc", + "9490": "\uc9d9", + "9491": "\uad76", + "9492": "\ud769", + "9493": "\ub25c", + "9494": "\ucd18", + "9495": "\ub0a1", + "9496": "\uc50c", + "9497": "\ub234", + "9498": "\ucc22", + "9499": "\ud320", + "9500": "\uaed1", + "9501": "\uc5bd", + "9502": "\uad75", + "9503": "\ud07d", + "9504": "\uc553", + "9505": "\ub918", + "9506": "\uacc1", + "9507": "\uc7e4", + "9508": "\ubd89", + "9509": "\ub053", + "9510": "\ub9d1", + "9511": "\ub98e", + "9512": "\ub09a", + "9513": "\ucd1b", + "9514": "\uaebe", + "9515": "\uac1a", + "9516": "\ucc54", + "9517": "\ubb61", + "9518": "\ub560", + "9519": "\ub6ab", + "9520": "\uc633", + "9521": "\ucad3", + "9522": "\uc3df", + "9523": "\uc62e", + "9524": "\ub35f", + "9525": "\ub0b3", + "9526": "\uc549", + "9527": "\uc80a", + "9528": "\uc9e7", + "9529": "\ub429", + "9530": "\ucf2f", + "9531": "\ud290", + "9532": "", + "9533": "ene", + "9534": "\u2581ble", + "9535": "ikk", + "9536": "opp", + "9537": "\u2581Han", + "9538": "\u2581Den", + "9539": "unn", + "9540": "\u2581han", + "9541": "asjon", + "9542": "\u2581word", + "9543": "\u2581werd", + "9544": "", + "9545": "eg", + "9546": "\u2581ikkje", + "9547": "\u2581bok", + "9548": "lik", + "9549": "\u2581eit", + "9550": "s\u00e5", + "9551": "kk", + "9552": "\u2581nok", + "9553": "\u2581god", + "9554": "\u2581lese", + "9555": "dde", + "9556": "inga", + "9557": "\u2581denn", + "9558": "inn", + "9559": "kkje", + "9560": "dig", + "9561": "tid", + "9562": "\u2581b\u00f8ke", + "9563": "ord", + "9564": "\u2581tru", + "9565": "skje", + "9566": "\u2581sei", + "9567": "ller", + "9568": "\u2581fle", + "9569": "skriv", + "9570": "\u2581heil", + "9571": "wy", + "9572": "\u015a", + "9573": "\u0141", + "9574": "\u0179", + "9575": "\u017b", + "9576": "car", + "9577": "t\u00e3o", + "9578": "ia", + "9579": "\u2581foi", + "9580": "ito", + "9581": "ram", + "9582": "fa", + "9583": "\u2581meu", + "9584": "\u00e7a", + "9585": "\u2581dois", + "9586": "a\u00e7\u00e3o", + "9587": "\u2581ter", + "9588": "n\u00e7a", + "9589": "\u2581compra", + "9590": "\u2581mil", + "9591": "\u2581minha", + "9592": "\u2581passa", + "9593": "\u2581casa", + "9594": "\u00c3", + "9595": "\u00b7", + "9596": "", + "9597": "das", + "9598": "\u2581s\u00e3o", + "9599": "\u2581Pa", + "9600": "tura", + "9601": "\u2581ser", + "9602": "\u2581Ele", + "9603": "forma", + "9604": "\u2581Esta", + "9605": "\u00f5es", + "9606": "\u2581pelo", + "9607": "tua", + "9608": "\u2581pela", + "9609": "mar", + "9610": "\u2581Foi", + "9611": "\u2581foram", + "9612": "este", + "9613": "\u2581Um", + "9614": "\u2581S\u00e3o", + "9615": "\u2581entre", + "9616": "fun", + "9617": "agem", + "9618": "gua", + "9619": "\u2581Brasil", + "9620": "\u2581grande", + "9621": "icos", + "9622": "\u2581cidade", + "9623": "inda", + "9624": "\u2581Este", + "9625": "\u2581maior", + "9626": "\u2581brasileiro", + "9627": "\u2581munic\u00edpio", + "9628": "\u2581nome", + "9629": "\u2581encontra", + "9630": "amb\u00e9m", + "9631": "\u2581Sua", + "9632": "\u2581tr\u00eas", + "9633": "\u2581\u0421", + "9634": "\u2581\u0410", + "9635": "\u2581\u041a", + "9636": "\u0431\u0435", + "9637": "\u2581\u041e", + "9638": "\u0441\u0435", + "9639": "\u2581\u041f", + "9640": "\u2581\u043c\u043d\u0435", + "9641": "\u2581\u043e\u043d", + "9642": "\u0446\u0430", + "9643": "\u043d\u0438\u0435", + "9644": "\u0436\u0430", + "9645": "\u0441\u0442\u044c", + "9646": "\u043f\u0443", + "9647": "\u043c\u044b", + "9648": "\u0441\u043a\u0430", + "9649": "\u0441\u0430", + "9650": "\u2581\u0442\u0435\u0431\u044f", + "9651": "\u0433\u0438", + "9652": "\u2581\u0444\u0438\u043b\u044c\u043c", + "9653": "\u0442\u0440\u0435", + "9654": "\u0433\u0440\u0430", + "9655": "\u043c\u0435\u0440", + "9656": "\u0448\u0430", + "9657": "\u2581\u0412\u043a\u043b\u044e\u0447\u0438", + "9658": "\u043b\u0441\u044f", + "9659": "\u0449\u0438", + "9660": "\u2581\u0441\u0435\u0437\u043e\u043d", + "9661": "\u2581\u041a\u0430\u043a", + "9662": "\u2581\u0441\u043c\u043e\u0442\u0440\u0435\u0448\u043a\u0435", + "9663": "\u2581\u0421\u0431\u0435\u0440", + "9664": "\u2581\u0422\u0432", + "9665": "\u2581\u041d\u0435", + "9666": "\u2581\u0414\u0436\u043e\u0439", + "9667": "\u2581\u043e\u0434\u0438\u043d", + "9668": "\u2581\u0410\u0444\u0438\u043d\u0430", + "9669": "\u2581\u041c\u0430", + "9670": "\u2581\u0441\u0435\u043c\u044c", + "9671": "\u2581\u0422\u0430", + "9672": "\u2581\u0421\u0430\u043b\u044e\u0442", + "9673": "\u0431\u043e\u043b\u044c\u0448", + "9674": "\u0441\u043a\u0438\u0439", + "9675": "\u2581\u043f\u044f\u0442\u044c", + "9676": "\u2581\u0441\u0435\u0440\u0438\u0430\u043b", + "9677": "\u2581\u0447\u0435\u0442\u044b\u0440\u0435", + "9678": "\u043a\u043b\u044e\u0447", + "9679": "\u2581\u0448\u0435\u0441\u0442\u044c", + "9680": "\u0438\u0442\u0441\u044f", + "9681": "\u2581\u0432\u043e\u0441\u0435\u043c\u044c", + "9682": "\u2581\u0432\u043e\u043e\u0431\u0449\u0435", + "9683": "\u2581\u041f\u043e\u043a\u0430\u0436\u0438", + "9684": "\u2581\u043f\u043e\u0442\u043e\u043c\u0443", + "9685": "\u2581\u0434\u0432\u0430\u0434\u0446\u0430\u0442\u044c", + "9686": "\u2581\u043a\u0430\u043d\u0430\u043b", + "9687": "\u2581\u0432\u043a\u043b\u044e\u0447\u0438", + "9688": "\u2581\u0440\u0430\u0431\u043e\u0442", + "9689": "\u2581\u043a\u0430\u0440\u0442", + "9690": "\u0438\u0448\u044c", + "9691": "\u2581\u0434\u0435\u043d\u044c", + "9692": "\u042b", + "9693": "ska", + "9694": "var", + "9695": "", + "9696": "\u2581\u0e32", + "9697": "\u2581\u0e19", + "9698": "\u2581\u0e23", + "9699": "\u2581\u0e01", + "9700": "\u2581\u0e2d", + "9701": "\u0e40", + "9702": "\u2581\u0e48", + "9703": "\u2581\u0e07", + "9704": "\u0e31", + "9705": "\u2581\u0e21", + "9706": "\u2581\u0e49", + "9707": "\u2581\u0e22", + "9708": "\u2581\u0e35", + "9709": "\u2581\u0e25", + "9710": "\u2581\u0e27", + "9711": "\u2581\u0e14", + "9712": "\u2581\u0e17", + "9713": "\u2581\u0e2a", + "9714": "\u2581\u0e15", + "9715": "\u2581\u0e34", + "9716": "\u2581\u0e1a", + "9717": "\u2581\u0e1b", + "9718": "\u2581\u0e30", + "9719": "\u2581\u0e2b", + "9720": "\u0e41", + "9721": "\u2581\u0e04", + "9722": "\u2581\u0e08", + "9723": "\u2581\u0e02", + "9724": "\u0e43", + "9725": "\u0e44", + "9726": "\u0e37", + "9727": "\u2581\u0e1e", + "9728": "\u2581\u0e0a", + "9729": "\u2581\u0e47", + "9730": "\u2581\u0e39", + "9731": "\u2581\u0e38", + "9732": "\u2581\u0e4c", + "9733": "\u0e42", + "9734": "\u0e4d", + "9735": "\u2581\u0e36", + "9736": "\u2581\u0e28", + "9737": "\u2581\u0e16", + "9738": "\u2581\u0e0b", + "9739": "\u0e1c", + "9740": "\u2581\u0e20", + "9741": "\u2581\u0e29", + "9742": "\u2581\u0e13", + "9743": "\u2581\u0e18", + "9744": "\u2581\u0e0d", + "9745": "\u0e32", + "9746": "\u0e19", + "9747": "\u2581\u0e1f", + "9748": "\u0e23", + "9749": "\u0e01", + "9750": "\u0e2d", + "9751": "\u0e48", + "9752": "\u0e07", + "9753": "\u0e21", + "9754": "\u0e49", + "9755": "\u0e09", + "9756": "\u0e22", + "9757": "\u2581\u0e10", + "9758": "\u0e35", + "9759": "\u0e25", + "9760": "\u0e27", + "9761": "\u0e14", + "9762": "\u0e17", + "9763": "\u2581\u0e1d", + "9764": "\u0e2a", + "9765": "\u0e15", + "9766": "\u0e34", + "9767": "\u0e1a", + "9768": "\u2581\u0e2e", + "9769": "\u0e1b", + "9770": "\u0e30", + "9771": "\u0e2b", + "9772": "\u0e24", + "9773": "\u0e04", + "9774": "\u0e08", + "9775": "\u2581\u0e0f", + "9776": "\u0e12", + "9777": "\u0e02", + "9778": "\u0e1e", + "9779": "\u0e0a", + "9780": "\u0e47", + "9781": "\u0e39", + "9782": "\u0e38", + "9783": "\u0e4c", + "9784": "\u0e4a", + "9785": "\u2581\u0e2c", + "9786": "\u2581\u0e0e", + "9787": "\u0e11", + "9788": "\u0e36", + "9789": "\u0e28", + "9790": "\u0e16", + "9791": "\u0e0b", + "9792": "\u0e20", + "9793": "\u2581\u0e4b", + "9794": "\u0e29", + "9795": "\u0e13", + "9796": "\u0e18", + "9797": "\u0e0d", + "9798": "\u2581\u0e06", + "9799": "\u0e1f", + "9800": "\u0e10", + "9801": "\u0e1d", + "9802": "\u0e2e", + "9803": "\u0e0c", + "9804": "\u0e0f", + "9805": "\u0e2c", + "9806": "\u0e0e", + "9807": "\u0e45", + "9808": "\u0e4b", + "9809": "\u0e06", + "9810": "\u2581\u0e46", + "9811": "\u0e03", + "9812": "\u0e3a", + "9813": "\u0e05", + "9814": "\u0e46", + "9815": "", + "9816": "\u015f", + "9817": "\u011f", + "9818": "ya", + "9819": "\u2581ve", + "9820": "lar", + "9821": "\u2581bir", + "9822": "l\u0131", + "9823": "d\u0131", + "9824": "ler", + "9825": "ye", + "9826": "s\u0131", + "9827": "lar\u0131", + "9828": "leri", + "9829": "\u0131nda", + "9830": "t\u0131", + "9831": "\u2581bu", + "9832": "lan", + "9833": "ara", + "9834": "\u2581Bu", + "9835": "inde", + "9836": "\u0131n\u0131", + "9837": "y\u0131", + "9838": "yo", + "9839": "d\u00fc", + "9840": "\u2581olarak", + "9841": "\u2581i\u00e7in", + "9842": "maktad\u0131r", + "9843": "ar\u0131", + "9844": "\u2581ba\u015f", + "9845": "\u015e", + "9846": "\u011e", + "9847": "", + "9848": "\u2581\u987b", + "9849": "\u2581\u8d28", + "9850": "\u2581\u6237", + "9851": "\u2581\u4e91", + "9852": "\u2581\u697c", + "9853": "\u2581\u77f3", + "9854": "\u2581\u5ba1", + "9855": "\u2581\u663e", + "9856": "\u2581\u7559", + "9857": "\u2581\u5c3d", + "9858": "\u2581\u96f7", + "9859": "\u2581\u6597", + "9860": "\u2581\u667a", + "9861": "\u2581\u6740", + "9862": "\u2581\u62ec", + "9863": "\u2581\u6267", + "9864": "\u2581\u6548", + "9865": "\u2581\u9752", + "9866": "\u2581\u5584", + "9867": "\u2581\u793c", + "9868": "\u2581\u9760", + "9869": "\u2581\u674e", + "9870": "\u2581\u9ec4", + "9871": "\u2581\u54cd", + "9872": "\u2581\u8425", + "9873": "\u2581\u8865", + "9874": "\u2581\u52bf", + "9875": "\u2581\u8db3", + "9876": "\u2581\u6781", + "9877": "\u2581\u6c5f", + "9878": "\u2581\u7701", + "9879": "\u2581\u9999", + "9880": "\u2581\u7a76", + "9881": "\u2581\u8ffd", + "9882": "\u2581\u7ef4", + "9883": "\u2581\u7fa4", + "9884": "\u2581\u5347", + "9885": "\u2581\u7c73", + "9886": "\u2581\u4ebf", + "9887": "\u2581\u5e1d", + "9888": "\u2581\u7968", + "9889": "\u2581\u5b9d", + "9890": "\u2581\u62cd", + "9891": "\u2581\u613f", + "9892": "\u2581\u7075", + "9893": "\u2581\u6b66", + "9894": "\u2581\u6562", + "9895": "\u2581\u5df4", + "9896": "\u2581\u53e5", + "9897": "\u2581\u5f8b", + "9898": "\u2581\u5c14", + "9899": "\u2581\u72d7", + "9900": "\u2581\u68c0", + "9901": "\u2581\u8f7b", + "9902": "\u2581\u4f01", + "9903": "\u2581\u7b56", + "9904": "\u2047", + "9905": "\u962e", + "9906": "\u6c22", + "9907": "\u53f5", + "9908": "\u8859", + "9909": "\u6cf8", + "9910": "\u90af", + "9911": "\u9c7f", + "9912": "\u95f0", + "9913": "\u6c82", + "9914": "\u5315", + "9915": "\u6860", + "9916": "\u90a1", + "9917": "\u99a5", + "9918": "\u6fee", + "9919": "\u988d", + "9920": "\u5c8c", + "9921": "\u5162", + "9922": "\u8340", + "9923": "\u7fdf", + "9924": "\u86af", + "9925": "\u6d3c", + "9926": "\u7f8c", + "9927": "\u627c", + "9928": "\u8543", + "9929": "\u86df", + "9930": "\u9b13", + "9931": "\u6538", + "9932": "\u5e27", + "9933": "\u9050", + "9934": "\u81fb", + "9935": "\u61ca", + "9936": "\u6d9f", + "9937": "\u6c2f", + "9938": "\u6ea7", + "9939": "\u9570", + "9940": "\u5b6a", + "9941": "\u9ebe", + "9942": "\u608c", + "9943": "\u606c", + "9944": "\u8bd9", + "9945": "\u5ebe", + "9946": "\u8dfb", + "9947": "\u6dc4", + "9948": "\u73b7", + "9949": "\u607b", + "9950": "\u85d0", + "9951": "\u501c", + "9952": "\u5f87", + "9953": "\u911e", + "9954": "\u60cb", + "9955": "\u5fd0", + "9956": "\u6f29", + "9957": "\u87fe", + "9958": "\u4fe8", + "9959": "\u5f3c", + "9960": "\u69d0", + "9961": "\u7f2d", + "9962": 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"\u7281", + "10012": "\u90f4", + "10013": "\u5a13", + "10014": "\u607f", + "10015": "\u70bd", + "10016": "\u6485", + "10017": "\u759f", + "10018": "\u853b", + "10019": "\u835e", + "10020": "\u94b3", + "10021": "\u595a", + "10022": "\u8c7a", + "10023": "\u4f09", + "10024": "\u5f11", + "10025": "\u6c5e", + "10026": "\u871a", + "10027": "\u634b", + "10028": "\u777d", + "10029": "\u81c3", + "10030": "\u9a8a", + "10031": "\u6d9d", + "10032": "\u82b7", + "10033": "\u86f0", + "10034": "\u527d", + "10035": "\u630e", + "10036": "\u8037", + "10037": "\u817c", + "10038": "\u82aa", + "10039": "\u7619", + "10040": "\u9e9d", + "10041": "\u5b34", + "10042": "\u606a", + "10043": "\u8fe2", + "10044": "\u63a3", + "10045": "\u7ff1", + "10046": "\u9cd5", + "10047": "\u90ac", + "10048": "\u9b03", + "10049": "\u83e1", + "10050": "\u9068", + "10051": "\u577b", + "10052": "\u62c4", + "10053": "\u91ba", + "10054": "\u9e35", + "10055": "\u5b62", + "10056": "\u8862", + "10057": "\u6dbf", + "10058": "\u8c19", + "10059": "\u5156", + "10060": "\u8343", + "10061": "\u9773", + "10062": "\u665f", + "10063": "\u6f2f", + "10064": "\u86d0", + "10065": "\u86f3", + "10066": "\u92ae", + "10067": "\u59a9", + "10068": "\u6b92", + "10069": "\u7f42", + "10070": "\u8012", + "10071": "\u8c06", + "10072": "\u8c00", + "10073": "\u8f72", + "10074": "\u9713", + "10075": "\u83b4", + "10076": "\u96bd", + "10077": "\u6f7a", + "10078": "\u8e0c", + "10079": "\u90eb", + "10080": "\u5555", + "10081": "\u77d7", + "10082": "\u7a88", + "10083": "\u89de", + "10084": "\u94ec", + "10085": "\u988c", + "10086": "\u5d82", + "10087": "\u6da3", + "10088": "\u6e49", + "10089": "\u81ca", + "10090": "\u5522", + "10091": "\u6026", + "10092": "\u9aa5", + "10093": "\u6ee6", + "10094": "\u76f1", + "10095": "\u9e8b", + "10096": "\u535e", + "10097": "\u8200", + "10098": "\u916e", + "10099": "\u93d6", + "10100": "\u951a", + "10101": "\u9aa1", + "10102": "\u9ed4", + "10103": "\u6cf1", + "10104": "\u73de", + "10105": "\u74ef", + "10106": "\u77bf", + "10107": "\u9cb6", + "10108": "\u6175", + "10109": "\u6886", + "10110": "\u6ee2", + "10111": "\u8d5d", + "10112": "\u5a40", + "10113": "\u6c26", + "10114": "\u6dec", + "10115": "\u724d", + "10116": "\u740a", + "10117": "\u8446", + "10118": "\u57ed", + "10119": "\u8707", + "10120": "\u9642", + "10121": "\u62c8", + "10122": "\u7751", + "10123": "\u7ee5", + "10124": "\u8ddb", + "10125": "\u9122", + "10126": "\u5639", + "10127": "\u5d02", + "10128": "\u642a", + "10129": "\u655d", + "10130": "\u8c49", + "10131": "\u8d45", + "10132": "\u98d2", + "10133": "\u5c91", + "10134": "\u7ba9", + "10135": "\u87a8", + "10136": "\u6e0c", + "10137": "\u961a", + "10138": "\u998a", + "10139": "\u704f", + "10140": "\u70b7", + "10141": "\u712f", + "10142": "\u752c", + "10143": "\u8748", + "10144": "\u55e4", + "10145": "\u5cb7", + "10146": "\u62bf", + "10147": "\u6d9e", + "10148": "\u75b8", + "10149": "\u779f", + "10150": "\u7eb0", + "10151": "\u701b", + "10152": "\u75c9", + "10153": "\u7601", + "10154": "\u8368", + "10155": "\u88c6", + "10156": "\u9e51", + "10157": "\u5b6c", + "10158": "\u7c0b", + "10159": "\u7ec9", + "10160": "\u8331", + "10161": "\u839c", + "10162": "\u86d4", + "10163": "\u6800", + "10164": "\u72f0", + "10165": "\u78a3", + "10166": "\u909d", + "10167": "\u94c6", + "10168": "\u6cad", + "10169": "\u80e5", + "10170": "\u858f", + "10171": "\u8941", + "10172": "\u8f76", + "10173": "\u9537", + "10174": "\u504c", + "10175": "\u57c2", + "10176": "\u6035", + "10177": "\u6cd4", + "10178": "\u80db", + "10179": "\u5482", + "10180": "\u5676", + "10181": "\u5d27", + "10182": "\u623e", + "10183": "\u781d", + "10184": "\u8d2e", + "10185": "\u6715", + "10186": "\u6773", + "10187": "\u705e", + "10188": "\u7a37", + "10189": "\u8e2e", + "10190": "\u9506", + "10191": "\u542e", + "10192": "\u6525", + "10193": "\u6bd3", + "10194": "\u6ca3", + "10195": "\u85ff", + "10196": "\u88f1", + "10197": "\u4fda", + "10198": "\u51bd", + "10199": "\u77ec", + "10200": "\u852b", + "10201": "\u998f", + "10202": "\u7812", + "10203": "\u8983", + "10204": "\u8e09", + "10205": "\u949c", + "10206": "\u57a4", + "10207": "\u6dde", + "10208": "\u891a", + "10209": "\u8e52", + "10210": "\u8e69", + "10211": "\u90dc", + "10212": "\u6c68", + "10213": "\u7548", + "10214": "\u75e8", + "10215": "\u7823", + "10216": "\u785a", + "10217": "\u8c1f", + "10218": "\u9528", + "10219": "\u5773", + "10220": "\u57ad", + "10221": "\u5b51", + "10222": "\u5d4b", + "10223": "\u5d99", + "10224": "\u664c", + "10225": "\u6654", + "10226": "\u684e", + "10227": "\u6c85", + "10228": "\u6dc5", + "10229": "\u6ed8", + "10230": "\u714a", + "10231": "\u7284", + "10232": "\u7ea8", + "10233": "\u8188", + "10234": "\u9563", + "10235": "\u510b", + "10236": "\u51c7", + "10237": "\u5d03", + "10238": "\u5fe4", + "10239": "\u6004", + "10240": "\u6a28", + "10241": "\u7430", + "10242": "\u75fc", + "10243": "\u8238", + "10244": "\u853a", + "10245": "\u87cb", + "10246": "\u94a8", + "10247": "\u94e8", + "10248": "\u9cb3", + "10249": "\u9edd", + "10250": "\u4f91", + "10251": "\u5d06", + "10252": "\u69ab", + "10253": "\u72b8", + "10254": "\u742c", + "10255": "\u7eeb", + "10256": "\u8d48", + "10257": "\u909b", + "10258": "\u9995", + "10259": "\u9a77", + "10260": "\u56cd", + "10261": "\u57a1", + "10262": "\u59dd", + "10263": "\u6414", + "10264": "\u6ddd", + "10265": "\u6f78", + "10266": "\u70c3", + "10267": "\u73b3", + "10268": "\u73ee", + "10269": "\u768b", + "10270": "\u8174", + "10271": "\u8dec", + "10272": "\u9ca0", + "10273": "\u9f2c", + "10274": "\u4f22", + "10275": "\u5043", + "10276": "\u5d4a", + "10277": "\u60b1", + "10278": "\u63e9", + "10279": "\u6636", + "10280": "\u6ceb", + "10281": "\u6da0", + "10282": "\u6e6b", + "10283": "\u784c", + "10284": "\u7aa8", + "10285": "\u7ed4", + "10286": "\u7fb8", + "10287": "\u8148", + "10288": "\u8671", + "10289": "\u8d30", + "10290": "\u8db5", + "10291": "\u948e", + "10292": "\u94f7", + "10293": "\u4f2b", + "10294": "\u57a9", + "10295": "\u57dd", + "10296": "\u59af", + "10297": "\u5a09", + "10298": "\u626a", + "10299": "\u63ae", + "10300": "\u6d2e", + "10301": "\u6d43", + "10302": "\u7173", + "10303": "\u737e", + "10304": "\u73f2", + "10305": "\u7583", + "10306": "\u7800", + "10307": "\u7b71", + "10308": "\u7da6", + "10309": "\u826e", + "10310": "\u8306", + "10311": "\u891b", + "10312": "\u8bd3", + "10313": "\u8c94", + "10314": "\u902f", + "10315": "\u90e7", + "10316": "\u539d", + "10317": "\u56d4", + "10318": "\u584d", + "10319": "\u5889", + "10320": "\u5a9e", + "10321": "\u5f9c", + "10322": "\u6387", + "10323": "\u63b8", + "10324": "\u665e", + "10325": "\u66b9", + "10326": "\u6cee", + "10327": "\u6e9f", + "10328": "\u6f5e", + "10329": "\u7287", + "10330": "\u749f", + "10331": "\u7747", + "10332": "\u82cb", + "10333": "\u83c0", + "10334": "\u8473", + "10335": "\u8dda", + "10336": "\u90c5", + "10337": "\u94b4", + "10338": "\u9f39", + "10339": "\u4edf", + "10340": "\u4f97", + "10341": "\u4ffe", + "10342": "\u53c1", + "10343": "\u573b", + "10344": "\u5785", + "10345": "\u59a4", + "10346": "\u65cc", + "10347": "\u67b3", + "10348": "\u6954", + "10349": "\u6978", + "10350": "\u6e86", + "10351": "\u6fc2", + "10352": "\u77f8", + "10353": "\u7efb", + "10354": "\u7f31", + "10355": "\u8153", + "10356": "\u84e5", + "10357": "\u8c11", + "10358": "\u8c15", + "10359": "\u8e31", + "10360": "\u9099", + "10361": "\u94af", + "10362": "\u9512", + "10363": "\u95f3", + "10364": "\u9621", + "10365": "\u98a2", + "10366": "\u9a90", + "10367": "\u9cad", + "10368": "\u9cb7", + "10369": "\u9e5e", + "10370": "\u52ad", + "10371": "\u5575", + "10372": "\u5d47", + "10373": "\u5eb9", + "10374": "\u62da", + "10375": "\u65fb", + "10376": "\u67de", + "10377": "\u6a2f", + "10378": "\u6e8f", + "10379": "\u6f8d", + "10380": "\u740f", + "10381": "\u7762", + "10382": "\u7837", + "10383": "\u795a", + "10384": "\u7afd", + "10385": "\u82e1", + "10386": "\u8347", + "10387": "\u8385", + "10388": "\u8572", + "10389": "\u8731", + "10390": "\u87ca", + "10391": "\u88e8", + "10392": "\u89d0", + "10393": "\u8bc3", + "10394": "\u8c27", + "10395": "\u9095", + "10396": "\u90d3", + "10397": "\u9170", + "10398": "\u94d6", + "10399": "\u94df", + "10400": "\u954c", + "10401": "\u9606", + "10402": "\u9615", + "10403": "\u96d2", + "10404": "\u9701", + "10405": "\u9acb", + "10406": "\u9c85", + "10407": "\u9c91", + "10408": "\u9ca2", + "10409": "\u9eb8", + "10410": "\u523d", + "10411": "\u5511", + "10412": "\u559f", + "10413": "\u55ea", + "10414": "\u5658", + "10415": "\u56f9", + "10416": "\u572a", + "10417": "\u579a", + "10418": "\u57f8", + "10419": "\u5807", + "10420": "\u5aeb", + "10421": "\u5b17", + "10422": "\u5b5b", + "10423": "\u5b73", + "10424": "\u5cc1", + "10425": "\u5d6c", + "10426": "\u5f0b", + "10427": "\u60bb", + "10428": "\u625e", + "10429": "\u6448", + "10430": "\u64ba", + "10431": "\u64d8", + "10432": "\u6710", + "10433": "\u680e", + "10434": "\u6c8f", + "10435": "\u6d60", + "10436": "\u6de6", + "10437": "\u6e11", + "10438": "\u6f4b", + "10439": "\u7094", + "10440": "\u7117", + "10441": "\u7118", + "10442": "\u7168", + "10443": "\u7424", + "10444": "\u742e", + "10445": "\u7477", + "10446": "\u759d", + "10447": "\u75bd", + "10448": "\u7aa0", + "10449": "\u7cbc", + "10450": "\u7ebe", + "10451": "\u7f19", + "10452": "\u7f54", + "10453": "\u816d", + "10454": "\u830c", + "10455": "\u832f", + "10456": "\u8360", + "10457": "\u8438", + "10458": "\u8788", + "10459": "\u8872", + "10460": "\u8c2f", + "10461": "\u8e3a", + "10462": "\u8f6b", + "10463": "\u90b3", + "10464": "\u90ef", + "10465": "\u94e3", + "10466": "\u94e9", + "10467": "\u94f0", + "10468": "\u9532", + "10469": "\u9616", + "10470": "\u9708", + "10471": "\u9aa0", + "10472": "\u9ecd", + "10473": "\u4dae", + "10474": "\u4ee1", + "10475": "\u5053", + "10476": "\u520d", + "10477": "\u525c", + "10478": "\u5416", + "10479": "\u549d", + "10480": "\u54bb", + "10481": "\u54c2", + "10482": "\u5537", + "10483": "\u5581", + "10484": "\u55c4", + "10485": "\u562d", + "10486": "\u5659", + "10487": "\u5739", + "10488": "\u5769", + "10489": "\u57c7", + "10490": "\u57d5", + "10491": "\u57da", + "10492": "\u59ab", + "10493": "\u5a0c", + "10494": "\u5ada", + "10495": "\u5b71", + "10496": "\u5b93", + "10497": "\u5c05", + "10498": "\u5d9d", + "10499": "\u5f2d", + "10500": "\u6006", + "10501": "\u603f", + "10502": "\u6041", + "10503": "\u6078", + "10504": "\u6266", + "10505": "\u678b", + "10506": "\u690b", + "10507": "\u6a3e", + "10508": "\u6bc2", + "10509": "\u6c4a", + "10510": "\u6c69", + "10511": "\u6ce0", + "10512": "\u6d39", + "10513": "\u6d48", + "10514": "\u7113", + "10515": "\u727e", + "10516": "\u73b9", + "10517": "\u73d9", + "10518": "\u75a3", + "10519": "\u75b4", + "10520": "\u7633", + "10521": "\u772c", + "10522": "\u77fd", + "10523": "\u79e3", + "10524": "\u7b33", + "10525": "\u7be6", + "10526": "\u7c7c", + "10527": "\u7cb2", + "10528": "\u7ec0", + "10529": "\u7ecb", + "10530": "\u82a9", + "10531": "\u84e6", + "10532": "\u8821", + "10533": "\u8934", + "10534": "\u8a3e", + "10535": "\u8ba3", + "10536": "\u8bd8", + "10537": "\u8dba", + "10538": "\u8e2f", + "10539": "\u8e5a", + "10540": "\u8e85", + "10541": "\u8f78", + "10542": "\u9021", + "10543": "\u9150", + "10544": "\u9487", + "10545": "\u94b2", + "10546": "\u94e7", + "10547": "\u9509", + "10548": "\u951f", + "10549": "\u95e9", + "10550": "\u9697", + "10551": "\u9880", + "10552": "\u98e7", + "10553": "\u9ac2", + "10554": "\u9b49", + "10555": "\u9cdf", + "10556": "\u9e22", + "10557": "\uff21", + "10558": "\u9980", + "10559": "\u966c", + "10560": "\u8914", + "10561": "\u7596", + "10562": "\u68c2", + "10563": "\u6677", + "10564": "\u643d", + "10565": "\u9011", + "10566": "\u82f7", + "10567": "\u783c", + "10568": "\u76c5", + "10569": "\u746d", + "10570": "\u61b7", + "10571": "\u5fff", + "10572": "\u5c50", + "10573": "\u5c15", + "10574": "\u586c", + "10575": "\u500c", + "10576": "\u8df9", + "10577": "\u845a", + "10578": "\u6b93", + "10579": "\u51bc", + "10580": "\u50ee", + "10581": "\u8f73", + "10582": "\u8df6", + "10583": "\u8dce", + "10584": "\u8c85", + "10585": "\u831b", + "10586": "\u73fa", + "10587": "\u67d2", + "10588": "\u4f76", + "10589": "\u94e1", + "10590": "\u7cb3", + "10591": "\u71ee", + "10592": "\u67b0", + "10593": "\u547b", + "10594": "\u9534", + "10595": "\u5cd2", + "10596": "\u551b", + "10597": "\u9c9f", + "10598": "\u9a9d", + "10599": "\u975b", + "10600": "\u8db8", + "10601": "\u8019", + "10602": "\u78b4", + "10603": "\u71ca", + "10604": "\u6dd6", + "10605": "\u948f", + "10606": "\u886e", + "10607": "\u7428", + "10608": "\u5f89", + "10609": "\u5501", + "10610": "\u80d7", + "10611": "\u7ecc", + "10612": "\u5a4a", + "10613": "\u54ad", + "10614": "\u9a85", + "10615": "\u794e", + "10616": "\u7663", + "10617": "\u72d2", + "10618": "\u90ba", + "10619": "\u87c0", + "10620": "\u7a1e", + "10621": "\u6e4e", + "10622": "\u659b", + "10623": "\u688f", + "10624": "\u679e", + "10625": "\u9549", + "10626": "\u7bb4", + "10627": "\u7166", + "10628": "\u55d4", + "10629": "\u82e3", + "10630": "\u7fca", + "10631": "\u765c", + "10632": "\u8e7c", + "10633": "\u86c6", + "10634": "\u7441", + "10635": "\u6600", + "10636": "\u9a9e", + "10637": "\u77fe", + "10638": "\u749e", + "10639": "\u6849", + "10640": "\u5d58", + "10641": "\u5662", + "10642": "\u8bb4", + "10643": "\u7691", + "10644": "\u73c9", + "10645": "\u835a", + "10646": "\u7fce", + "10647": "\u5a75", + "10648": "\u8d53", + "10649": "\u7f30", + "10650": "\u7f28", + "10651": "\u7620", + "10652": "\u61cb", + "10653": "\u789c", + "10654": "\u70e9", + "10655": "\u5b37", + "10656": "\u5472", + "10657": "\u9e4c", + "10658": "\u9604", + "10659": "\u9555", + "10660": "\u7b60", + "10661": "\u7080", + "10662": "\u6c1f", + "10663": "\u5729", + "10664": "\u71a0", + "10665": "\u6f2a", + "10666": "\u6b46", + "10667": "\u64c0", + "10668": "\u9a9c", + "10669": "\u956d", + "10670": "\u8d4a", + "10671": "\u83c1", + "10672": "\u7bea", + "10673": "\u7708", + "10674": "\u5ffb", + "10675": "\u5b40", + "10676": "\u85dc", + "10677": "\u70f7", + "10678": "\u5bb8", + "10679": "\u504e", + "10680": "\u9539", + "10681": "\u94c9", + "10682": "\u8913", + "10683": "\u768e", + "10684": "\u72b7", + "10685": "\u7292", + "10686": "\u55d6", + "10687": "\u9e5c", + "10688": "\u950c", + "10689": "\u73cf", + "10690": "\u85d3", + "10691": "\u8dc4", + "10692": "\u69ad", + "10693": "\u5ad4", + "10694": "\u5a23", + "10695": "\u8d3b", + "10696": "\u870d", + "10697": "\u7f04", + "10698": "\u7738", + "10699": "\u7719", + "10700": "\u9e6b", + "10701": "\u8734", + "10702": "\u81ba", + "10703": "\u762a", + "10704": "\u6c93", + "10705": "\u6593", + "10706": "\u64de", + "10707": "\u5d2e", + "10708": "\u9541", + "10709": "\u7eab", + "10710": "\u789a", + "10711": "\u6862", + "10712": "\u98da", + "10713": "\u840b", + "10714": "\u7131", + "10715": "\u6a35", + "10716": "\u576f", + "10717": "\u5636", + "10718": "\u954a", + "10719": "\u8869", + "10720": "\u86f9", + "10721": "\u83a0", + "10722": "\u783e", + "10723": "\u6e0d", + "10724": "\u6be1", + "10725": "\u65ef", + "10726": "\u579b", + "10727": "\u9530", + "10728": "\u915a", + "10729": "\u9ccd", + "10730": "\u9968", + "10731": "\u94c0", + "10732": "\u5ccb", + "10733": "\u9a9b", + "10734": "\u8169", + "10735": "\u754a", + "10736": "\u5530", + "10737": "\u4ede", + "10738": "\u9609", + "10739": "\u72de", + "10740": "\u6631", + "10741": "\u6421", + "10742": "\u8f67", + "10743": "\u81e7", + "10744": "\u7a95", + "10745": "\u781a", + "10746": "\u70ca", + "10747": "\u6963", + "10748": "\u5fe1", + "10749": "\u9e42", + "10750": "\u6868", + "10751": "\u645e", + "10752": "\u612b", + "10753": "\u949b", + "10754": "\u797a", + "10755": "\u8e76", + "10756": "\u6043", + "10757": "\u5477", + "10758": "\u7b06", + "10759": "\u62a1", + "10760": "\u5ff1", + "10761": "\u5b05", + "10762": "\u520e", + "10763": "\u94b5", + "10764": "\u8ba7", + "10765": "\u86c0", + "10766": "\u6748", + "10767": "\u992e", + "10768": "\u948a", + "10769": "\u7f0e", + "10770": "\u954d", + "10771": "\u89ce", + "10772": "\u5a67", + "10773": "\u98a7", + "10774": "\u989a", + "10775": "\u874c", + "10776": "\u810d", + "10777": "\u55f2", + "10778": "\u5323", + "10779": "\u9f8a", + "10780": "\u82de", + "10781": "\u9cab", + "10782": "\u8e8f", + "10783": "\u8885", + "10784": "\u7ee2", + "10785": "\u5a7a", + "10786": "\u94ff", + "10787": "\u86b1", + "10788": "\u7bd1", + "10789": "\u94e4", + "10790": "\u8113", + "10791": "\u5ab2", + "10792": "\u94c4", + "10793": "\u7bab", + "10794": "\u5a06", + "10795": "\u4f58", + "10796": "\u90b0", + "10797": "\u83ba", + "10798": "\u7f22", + "10799": "\u6410", + "10800": "\u916f", + "10801": "\u8426", + "10802": "\u6f3e", + "10803": "\u6c7e", + "10804": "\u6bfd", + "10805": "\u8902", + "10806": "\u6684", + "10807": "\u9e3e", + "10808": "\u9b1f", + "10809": "\u7f07", + "10810": "\u7bd3", + "10811": "\u6cfe", + "10812": "\u8d73", + "10813": "\u8146", + "10814": "\u9cdd", + "10815": "\u97ec", + "10816": "\u950f", + "10817": "\u8be9", + "10818": "\u79f8", + "10819": "\u622c", + "10820": "\u89d1", + "10821": "\u8559", + "10822": "\u9e6d", + "10823": "\u86a4", + "10824": "\u828a", + "10825": "\u780c", + "10826": "\u7352", + "10827": "\u6b87", + "10828": "\u5942", + "10829": "\u94a3", + "10830": "\u8191", + "10831": "\u7cd7", + "10832": "\u76f9", + "10833": "\u73a5", + "10834": "\u9083", + "10835": "\u8713", + "10836": "\u71ce", + "10837": "\u5567", + "10838": "\u7f44", + "10839": "\u873b", + "10840": "\u776c", + "10841": "\u732c", + "10842": "\u9984", + "10843": "\u7696", + "10844": "\u5140", + "10845": "\u970e", + "10846": "\u84d3", + "10847": "\u7634", + "10848": "\u75eb", + "10849": "\u9550", + "10850": "\u8936", + "10851": "\u8dfa", + "10852": "\u70ec", + "10853": "\u6cd3", + "10854": "\u9535", + "10855": "\u8bb7", + "10856": "\u86aa", + "10857": "\u79c6", + "10858": "\u6cde", + "10859": "\u9cd7", + "10860": "\u8725", + "10861": "\u7085", + "10862": "\u65ee", + "10863": "\u6382", + "10864": "\u58d1", + "10865": "\u54a3", + "10866": "\u9b47", + "10867": "\u7898", + "10868": "\u7699", + "10869": "\u5bd0", + "10870": "\u7600", + "10871": "\u6005", + "10872": "\u869d", + "10873": "\u8398", + "10874": "\u5bf0", + "10875": "\u832c", + "10876": "\u51a2", + "10877": "\u9cde", + "10878": "\u9573", + "10879": "\u8f8d", + "10880": "\u9a8b", + "10881": "\u85b0", + "10882": "\u7b75", + "10883": "\u76ce", + "10884": "\u6988", + "10885": "\u5498", + "10886": "\u4fac", + "10887": "\u8f98", + "10888": "\u812f", + "10889": "\u695e", + "10890": "\u997d", + "10891": "\u82ef", + "10892": "\u9ab0", + "10893": "\u970f", + "10894": "\u8722", + "10895": "\u6d54", + "10896": "\u631b", + "10897": "\u5a04", + "10898": "\u60b4", + "10899": "\u5a55", + "10900": "\u55b3", + "10901": "\u557e", + "10902": "\u8baa", + "10903": "\u5e1b", + "10904": "\u5b7a", + "10905": "\u8bcb", + "10906": "\u8bff", + "10907": "\u78fa", + "10908": "\u7693", + "10909": "\u62f4", + "10910": "\u709c", + "10911": "\u5e44", + "10912": "\u5d3d", + "10913": "\u50a5", + "10914": "\u9cc5", + "10915": "\u94ee", + "10916": "\u6da7", + "10917": "\u94be", + "10918": "\u819b", + "10919": "\u7d0a", + "10920": "\u75e7", + "10921": "\u728a", + "10922": "\u6d5a", + "10923": "\u9163", + "10924": "\u6479", + "10925": "\u5e62", + "10926": "\u5ced", + "10927": "\u59e3", + "10928": "\u5406", + "10929": "\u7ead", + "10930": "\u8301", + "10931": "\u6ec7", + "10932": "\u4f57", + "10933": "\u9035", + "10934": "\u6e4d", + "10935": "\u8c29", + "10936": "\u836b", + "10937": "\u7a96", + "10938": "\u715c", + "10939": "\u9955", + "10940": "\u9062", + "10941": "\u67ad", + "10942": "\u60e6", + "10943": "\u8f7c", + "10944": "\u7bf1", + "10945": "\u7abf", + "10946": "\u795b", + "10947": "\u54fd", + "10948": "\u9e43", + "10949": "\u7c41", + "10950": "\u69b7", + "10951": "\u6635", + "10952": "\u5657", + "10953": "\u8925", + "10954": "\u7638", + "10955": "\u6cef", + "10956": "\u5b5c", + "10957": "\u8bb9", + "10958": "\u8537", + "10959": "\u729f", + "10960": "\u5c96", + "10961": "\u9791", + "10962": "\u91c9", + "10963": "\u8e4b", + "10964": "\u7c91", + "10965": "\u6d93", + "10966": "\u6cf7", + "10967": "\u9c88", + "10968": "\u988a", + "10969": "\u6d19", + "10970": "\u952d", + "10971": "\u7116", + "10972": "\u60ec", + "10973": "\u9a6e", + "10974": "\u998b", + "10975": "\u6995", + "10976": "\u996f", + "10977": "\u9776", + "10978": "\u9542", + "10979": "\u6cb1", + "10980": "\u6452", + "10981": "\u54d0", + "10982": "\u9eef", + "10983": "\u8be7", + "10984": "\u64ac", + "10985": "\u94d0", + "10986": "\u83cf", + "10987": "\u5671", + "10988": "\u82ae", + "10989": "\u739f", + "10990": "\u6dae", + "10991": "\u94c2", + "10992": "\u80ed", + "10993": "\u7459", + "10994": "\u5c79", + "10995": "\u55dd", + "10996": "\u9cd6", + "10997": "\u9602", + "10998": "\u5693", + "10999": "\u86a3", + "11000": "\u7c7d", + "11001": "\u7095", + "11002": "\u568f", + "11003": "\u8fe9", + "11004": "\u9981", + "11005": "\u72c8", + "11006": "\u631d", + "11007": "\u95f5", + "11008": "\u8c0f", + "11009": "\u7bc6", + "11010": "\u75a1", + "11011": "\u6dfc", + "11012": "\u631e", + "11013": "\u61e6", + "11014": "\u6059", + "11015": "\u5f5d", + "11016": "\u5958", + "11017": "\u4f36", + "11018": "\u6dcc", + "11019": "\u9ccc", + "11020": "\u80ef", + "11021": "\u6c74", + "11022": "\u9497", + "11023": "\u8de4", + "11024": "\u68e3", + "11025": "\u6657", + "11026": "\u5fd1", + "11027": "\u56f1", + "11028": "\u7405", + "11029": "\u5f99", + "11030": "\u7f9a", + "11031": "\u6a90", + "11032": "\u853c", + "11033": "\u8334", + "11034": "\u9997", + "11035": "\u8c1b", + "11036": "\u7444", + "11037": "\u6866", + "11038": "\u64b5", + "11039": "\u9e25", + "11040": "\u87b3", + "11041": "\u7edb", + "11042": "\u7ea3", + "11043": "\u7a57", + "11044": "\u69bb", + "11045": "\u6942", + "11046": "\u607a", + "11047": "\u592f", + "11048": "\u54ee", + "11049": "\u9e2f", + "11050": "\u60fa", + "11051": "\u9131", + "11052": "\u8f84", + "11053": "\u567c", + "11054": "\u53ae", + "11055": "\u533e", + "11056": "\u5014", + "11057": "\u7736", + "11058": "\u6829", + "11059": "\u664f", + "11060": "\u55d2", + "11061": "\u4f7c", + "11062": "\u6376", + "11063": "\u9a81", + "11064": "\u9504", + "11065": "\u80eb", + "11066": "\u9977", + "11067": "\u7b8d", + "11068": "\u70e8", + "11069": "\u8892", + "11070": "\u7578", + "11071": "\u60ee", + "11072": "\u7357", + "11073": "\u6ed5", + "11074": "\u5e3c", + "11075": "\u74a8", + "11076": "\u667e", + "11077": "\u8df7", + "11078": "\u62a8", + "11079": "\u74ee", + "11080": "\u82c7", + "11081": "\u621b", + "11082": "\u8e6c", + "11083": "\u556c", + "11084": "\u4f5f", + "11085": "\u5c9a", + "11086": "\u5b1b", + "11087": "\u956f", + "11088": "\u7f81", + "11089": "\u98d3", + "11090": "\u905b", + "11091": "\u6e85", + "11092": "\u9522", + "11093": "\u8386", + "11094": "\u63b3", + "11095": "\u7172", + "11096": "\u9698", + "11097": "\u6f4d", + "11098": "\u8be3", + "11099": "\u5c49", + "11100": "\u5b5a", + "11101": "\u4f70", + "11102": "\u9a6f", + "11103": "\u66a8", + "11104": "\u4fd1", + "11105": "\u835f", + "11106": "\u5cea", + "11107": "\u9890", + "11108": "\u919b", + "11109": "\u62e3", + "11110": "\u87d2", + "11111": "\u6ca5", + "11112": "\u6096", + "11113": "\u9ae6", + "11114": "\u63b7", + "11115": "\u4ee8", + "11116": "\u998d", + "11117": "\u94e0", + "11118": "\u75ca", + "11119": "\u6fd1", + "11120": "\u5623", + "11121": "\u8693", + "11122": "\u7830", + "11123": "\u8dc6", + "11124": "\u6d52", + "11125": "\u5ce5", + "11126": "\u4ea2", + "11127": "\u7329", + "11128": "\u6c76", + "11129": "\u79ba", + "11130": "\u73d1", + "11131": "\u53fc", + "11132": "\u8638", + "11133": "\u9e20", + "11134": "\u7fe9", + "11135": "\u7f24", + "11136": "\u7c27", + "11137": "\u747e", + "11138": "\u552c", + "11139": "\u748b", + "11140": "\u68a7", + "11141": "\u75f1", + "11142": "\u9a6d", + "11143": "\u741b", + "11144": "\u6c2a", + "11145": "\u84bf", + "11146": "\u78f7", + "11147": "\u949d", + "11148": "\u8fab", + "11149": "\u84df", + "11150": "\u7cb1", + "11151": "\u67b8", + "11152": "\u8717", + "11153": "\u7a98", + "11154": "\u9975", + "11155": "\u5228", + "11156": "\u7629", + "11157": "\u54c6", + "11158": "\u88f4", + "11159": "\u804b", + "11160": "\u7316", + "11161": "\u80e7", + "11162": "\u609a", + "11163": "\u8884", + "11164": "\u8364", + "11165": "\u80fa", + "11166": "\u6805", + "11167": "\u5fd2", + "11168": "\u9611", + "11169": "\u8f97", + "11170": "\u8e1d", + "11171": "\u6fd2", + "11172": "\u6d31", + "11173": "\u6a71", + "11174": "\u9a7f", + "11175": "\u7b5d", + "11176": "\u85c9", + "11177": "\u7ede", + "11178": "\u6bcb", + "11179": "\u80f0", + "11180": "\u70fd", + "11181": "\u701a", + "11182": "\u8f99", + "11183": "\u5ae6", + "11184": "\u6f7c", + "11185": "\u6e0e", + "11186": "\u6e32", + "11187": "\u55f7", + "11188": "\u7a20", + "11189": "\u5ad6", + "11190": "\u622e", + "11191": "\u6b83", + "11192": "\u9a78", + "11193": "\u8d58", + "11194": "\u56b7", + "11195": "\u5a34", + "11196": "\u5586", + "11197": "\u8327", + "11198": "\u7f2a", + "11199": "\u9e49", + "11200": "\u9abc", + "11201": "\u7f15", + "11202": "\u5dcd", + "11203": "\u9e66", + "11204": "\u8d43", + "11205": "\u8715", + "11206": "\u6ea5", + "11207": "\u7b03", + "11208": "\u952f", + "11209": "\u94b0", + "11210": "\u9a79", + "11211": "\u8c82", + "11212": "\u766b", + "11213": "\u759a", + "11214": "\u8708", + "11215": "\u5412", + "11216": "\u9704", + "11217": "\u968d", + "11218": "\u9e33", + "11219": "\u7eca", + "11220": "\u6da1", + "11221": "\u5e37", + "11222": "\u94db", + "11223": "\u4fea", + "11224": "\u9716", + "11225": "\u8517", + "11226": "\u692d", + "11227": "\u6e89", + "11228": "\u5ce6", + "11229": "\u5a05", + "11230": "\u532e", + "11231": "\u6994", + "11232": "\u4fd0", + "11233": "\u541d", + "11234": "\u8bec", + "11235": "\u97ed", + "11236": "\u4fde", + "11237": "\u70ef", + "11238": "\u574d", + "11239": "\u7599", + "11240": "\u6cae", + "11241": "\u7750", + "11242": "\u6c55", + "11243": "\u50a3", + "11244": "\u9885", + "11245": "\u865e", + "11246": "\u9619", + "11247": "\u7487", + "11248": "\u8bdf", + "11249": "\u659f", + "11250": "\u816e", + "11251": "\u70af", + "11252": "\u6b7c", + "11253": "\u90f8", + "11254": "\u75f9", + "11255": "\u66e6", + "11256": "\u64c2", + "11257": "\u9525", + "11258": "\u8eac", + "11259": "\u772f", + "11260": "\u8c4c", + "11261": "\u8bfd", + "11262": "\u60eb", + "11263": "\u9e4a", + "11264": "\u854a", + "11265": "\u6151", + "11266": "\u7ec5", + "11267": "\u64d2", + "11268": "\u6342", + "11269": "\u7efd", + "11270": "\u5b70", + "11271": "\u6664", + "11272": "\u5d2d", + "11273": "\u6f62", + "11274": "\u5e42", + "11275": "\u62e7", + "11276": "\u80ae", + "11277": "\u9176", + "11278": "\u6c2e", + "11279": "\u566c", + "11280": "\u9893", + "11281": "\u821c", + "11282": "\u683e", + "11283": "\u9523", + "11284": "\u86e4", + "11285": "\u9ac5", + "11286": "\u95eb", + "11287": "\u6cf5", + "11288": "\u996a", + "11289": "\u6002", + "11290": "\u814c", + "11291": "\u9cb8", + "11292": "\u752d", + "11293": "\u57a6", + "11294": "\u5180", + "11295": "\u78c5", + "11296": "\u5f29", + "11297": "\u796f", + "11298": "\u68ad", + "11299": "\u6615", + "11300": "\u4fa5", + "11301": "\u6123", + "11302": "\u77aa", + "11303": "\u6da4", + "11304": "\u68f1", + "11305": "\u7eef", + "11306": "\u6f9c", + "11307": "\u59d7", + "11308": "\u85d5", + "11309": "\u973e", + "11310": "\u9502", + "11311": "\u9540", + "11312": "\u6c79", + "11313": "\u9ca4", + "11314": "\u6e43", + "11315": "\u7c07", + "11316": "\u6e3a", + "11317": "\u9074", + "11318": "\u4e4d", + "11319": "\u6273", + "11320": "\u8018", + "11321": "\u9102", + "11322": "\u75ae", + "11323": "\u9ab7", + "11324": "\u8680", + "11325": "\u8042", + "11326": "\u75a4", + "11327": "\u6de4", + "11328": "\u5777", + "11329": "\u79fd", + "11330": "\u77a9", + "11331": "\u97f6", + "11332": "\u94a7", + "11333": "\u87d1", + "11334": "\u8335", + "11335": "\u829c", + "11336": "\u620c", + "11337": "\u52b5", + "11338": "\u5520", + "11339": "\u7eee", + "11340": "\u6d4a", + "11341": "\u6f13", + "11342": "\u6ba1", + "11343": "\u7728", + "11344": "\u60ed", + "11345": "\u502a", + "11346": "\u715e", + "11347": "\u6ed4", + "11348": "\u5018", + "11349": "\u67ab", + "11350": "\u6f88", + "11351": "\u5b7d", + "11352": "\u96f3", + "11353": "\u6c28", + "11354": "\u7ef0", + "11355": "\u8f95", + "11356": "\u9551", + "11357": "\u7184", + "11358": "\u6064", + "11359": "\u631a", + "11360": "\u98a4", + "11361": "\u778c", + "11362": "\u56e7", + "11363": "\u8bb3", + "11364": "\u75ea", + "11365": "\u70c1", + "11366": "\u7f94", + "11367": "\u79c3", + "11368": "\u6177", + "11369": "\u5c94", + "11370": "\u6f33", + "11371": "\u75de", + "11372": "\u5f64", + "11373": "\u69a8", + "11374": "\u76cf", + "11375": "\u6c90", + "11376": "\u68e0", + "11377": "\u5d34", + "11378": "\u575e", + "11379": "\u5429", + "11380": "\u6808", + "11381": "\u67e0", + "11382": "\u6556", + "11383": "\u4f88", + "11384": "\u7faf", + "11385": "\u6e1d", + "11386": "\u7ef7", + "11387": "\u7eb6", + "11388": "\u7cef", + "11389": "\u8354", + "11390": "\u6dc6", + "11391": "\u9661", + "11392": "\u4fcf", + "11393": "\u58a9", + "11394": "\u7cbd", + "11395": "\u67ec", + "11396": "\u5600", + "11397": "\u53a5", + "11398": "\u5254", + "11399": "\u903e", + "11400": "\u7fb2", + "11401": "\u8beb", + "11402": "\u7f00", + "11403": "\u5768", + "11404": "\u8d42", + "11405": "\u603c", + "11406": "\u5669", + "11407": "\u9647", + "11408": "\u94a6", + "11409": "\u94a0", + "11410": "\u5527", + "11411": "\u51ff", + "11412": "\u55e1", + "11413": "\u5431", + "11414": "\u5349", + "11415": "\u5455", + "11416": "\u6c5b", + "11417": "\u5f08", + "11418": "\u79e7", + "11419": "\u7cd9", + "11420": "\u7115", + "11421": "\u6da9", + "11422": "\u7d6e", + "11423": "\u7490", + "11424": "\u6d95", + "11425": "\u75b5", + "11426": "\u8110", + "11427": "\u6c13", + "11428": "\u7fbf", + "11429": "\u8c24", + "11430": "\u8759", + "11431": "\u904f", + "11432": "\u8760", + "11433": "\u7076", + "11434": "\u6789", + "11435": "\u54a9", + "11436": "\u61f5", + "11437": "\u5a6a", + "11438": "\u60d5", + "11439": "\u8bc5", + "11440": "\u5580", + "11441": "\u6320", + "11442": "\u9753", + "11443": "\u90dd", + "11444": "\u6cfb", + "11445": "\u97e7", + "11446": "\u618b", + "11447": "\u94dd", + "11448": "\u777f", + "11449": "\u5189", + "11450": "\u7a8d", + "11451": "\u78be", + "11452": "\u60f6", + "11453": "\u6f47", + "11454": "\u5dc5", + "11455": "\u9668", + "11456": "\u73ba", + "11457": "\u8d63", + "11458": "\u9c8d", + "11459": "\u54d7", + "11460": "\u7ca4", + "11461": "\u5a25", + "11462": "\u56e4", + "11463": "\u7011", + "11464": "\u68d5", + "11465": "\u53fd", + "11466": "\u710a", + "11467": "\u9e3d", + "11468": "\u6292", + "11469": "\u527f", + "11470": "\u82df", + "11471": "\u915d", + "11472": "\u8046", + "11473": "\u7845", + "11474": "\u7779", + "11475": "\u8782", + "11476": "\u6252", + "11477": "\u4eb5", + "11478": "\u9508", + "11479": "\u4e10", + "11480": "\u731d", + "11481": "\u964b", + "11482": "\u8845", + "11483": "\u599e", + "11484": "\u5478", + "11485": "\u7f1a", + "11486": "\u9a87", + "11487": "\u9f9a", + "11488": "\u5241", + "11489": "\u73ae", + "11490": "\u7785", + "11491": "\u4fd8", + "11492": "\u6986", + "11493": "\u5a76", + "11494": "\u761f", + "11495": "\u655b", + "11496": "\u8747", + "11497": "\u4fed", + "11498": "\u9556", + "11499": "\u9a8f", + "11500": "\u51f3", + "11501": "\u501a", + "11502": "\u5578", + "11503": "\u7b77", + "11504": "\u7ef8", + "11505": "\u6caa", + "11506": "\u886b", + "11507": "\u7455", + "11508": "\u6d3d", + "11509": "\u89c5", + "11510": "\u818a", + "11511": "\u4f6c", + "11512": "\u7f2e", + "11513": "\u63ba", + "11514": "\u80f3", + "11515": "\u7682", + "11516": "\u90a2", + "11517": "\u7ed2", + "11518": "\u78b1", + "11519": "\u7aa5", + "11520": "\u66a7", + "11521": "\u61c8", + "11522": "\u69df", + "11523": "\u56a3", + "11524": "\u7caa", + "11525": "\u9499", + "11526": "\u846b", + "11527": "\u5201", + "11528": "\u54d2", + "11529": "\u90b9", + "11530": "\u6a61", + "11531": "\u8165", + "11532": "\u9985", + "11533": "\u77f6", + "11534": "\u9cc4", + "11535": "\u545b", + "11536": "\u61ac", + "11537": "\u76b1", + "11538": "\u55b1", + "11539": "\u960e", + "11540": "\u55e6", + "11541": "\u96ef", + "11542": "\u5570", + "11543": "\u7a9c", + "11544": "\u9992", + "11545": "\u655e", + "11546": "\u8d41", + "11547": "\u7980", + "11548": "\u6402", + "11549": "\u5288", + "11550": "\u8038", + "11551": "\u8574", + "11552": "\u7bf7", + "11553": "\u8c41", + "11554": "\u8214", + "11555": "\u6bd9", + "11556": "\u7aa6", + "11557": "\u565c", + "11558": "\u8a79", + "11559": "\u762b", + "11560": "\u5f6a", + "11561": "\u6380", + "11562": "\u94f2", + "11563": "\u987d", + "11564": "\u7be1", + "11565": "\u4e53", + "11566": "\u9600", + "11567": "\u5a1f", + "11568": "\u946b", + "11569": "\u5e1c", + "11570": "\u4e2b", + "11571": "\u9ad3", + "11572": "\u6ca6", + "11573": "\u53e8", + "11574": "\u9576", + "11575": "\u55d3", + "11576": "\u8bf2", + "11577": "\u548f", + "11578": "\u997a", + "11579": "\u9e26", + "11580": "\u6984", + "11581": "\u5e90", + "11582": "\u864f", + "11583": "\u9a86", + "11584": "\u874e", + "11585": "\u54d4", + "11586": "\u8f7f", + "11587": "\u63cd", + "11588": "\u61a8", + "11589": "\u4f84", + "11590": "\u9165", + "11591": "\u8e39", + "11592": "\u6a44", + "11593": "\u7eba", + "11594": "\u516e", + "11595": "\u70db", + "11596": "\u60af", + "11597": "\u8783", + "11598": "\u8424", + "11599": "\u53a2", + "11600": "\u6ca7", + "11601": "\u5543", + "11602": "\u8f9c", + "11603": "\u7f55", + "11604": "\u9972", + "11605": "\u8c1c", + "11606": "\u5364", + "11607": "\u6d47", + "11608": "\u57d4", + "11609": "\u7426", + "11610": "\u8469", + "11611": "\u6073", + "11612": "\u7b0b", + "11613": "\u5490", + "11614": "\u5c7f", + "11615": "\u949e", + "11616": "\u8bc0", + "11617": "\u96cf", + "11618": "\u63b0", + "11619": "\u9610", + "11620": "\u5c4e", + "11621": "\u5495", + "11622": "\u6467", + "11623": "\u9ecf", + "11624": "\u6441", + "11625": "\u6055", + "11626": "\u7f09", + "11627": "\u6e24", + "11628": "\u7eac", + "11629": "\u64b8", + "11630": "\u840d", + "11631": "\u6512", + "11632": "\u64ce", + "11633": "\u7741", + "11634": "\u70b3", + "11635": "\u4e52", + "11636": "\u7ad6", + "11637": "\u7f14", + "11638": "\u4ed1", + "11639": "\u95f8", + "11640": "\u8be1", + "11641": "\u5564", + "11642": "\u7410", + "11643": "\u8682", + "11644": "\u8774", + "11645": "\u5955", + "11646": "\u8c34", + "11647": "\u63fd", + "11648": "\u53ee", + "11649": "\u7ece", + "11650": "\u77eb", + "11651": "\u6363", + "11652": "\u6b47", + "11653": "\u888d", + "11654": "\u8c0d", + "11655": "\u67a3", + "11656": "\u55b5", + "11657": "\u9ca8", + "11658": "\u8bcf", + "11659": "\u5960", + "11660": "\u5029", + "11661": "\u8e6d", + "11662": "\u64a9", + "11663": "\u7fd8", + "11664": "\u4fa8", + "11665": "\u8f90", + "11666": "\u7792", + "11667": "\u7130", + "11668": "\u9965", + "11669": "\u54a6", + "11670": "\u889c", + "11671": "\u634d", + "11672": "\u6a0a", + "11673": "\u95fd", + "11674": "\u94f8", + "11675": "\u58f6", + "11676": "\u8611", + "11677": "\u7f38", + "11678": "\u90b5", + "11679": "\u76d4", + "11680": "\u7096", + "11681": "\u6f8e", + "11682": "\u8c2c", + "11683": "\u6dc7", + "11684": "\u94c5", + "11685": "\u5d1b", + "11686": "\u803f", + "11687": "\u63e3", + "11688": "\u7504", + "11689": "\u575d", + "11690": "\u4ea9", + "11691": "\u9631", + "11692": "\u96a7", + "11693": "\u7538", + "11694": "\u5c27", + "11695": "\u78d5", + "11696": "\u6233", + "11697": "\u6ee4", + "11698": "\u8bb6", + "11699": "\u7574", + "11700": "\u917f", + "11701": "\u8206", + "11702": "\u5c82", + "11703": "\u5ac2", + "11704": "\u707f", + "11705": "\u886c", + "11706": "\u75d8", + "11707": "\u8393", + "11708": "\u549a", + "11709": "\u5fcf", + "11710": "\u9882", + "11711": "\u9521", + "11712": "\u563b", + "11713": "\u5188", + "11714": "\u7ee3", + "11715": "\u8d31", + "11716": "\u7eb1", + "11717": "\u96cd", + "11718": "\u98d9", + "11719": "\u7737", + "11720": "\u7784", + "11721": "\u5195", + "11722": "\u5ed6", + "11723": "\u62e2", + "11724": "\u6390", + "11725": "\u6d51", + "11726": "\u69c3", + "11727": "\u9489", + "11728": "\u6487", + "11729": "\u9a74", + "11730": "\u6ee5", + "11731": "\u88f9", + "11732": "\u545c", + "11733": "\u5e10", + "11734": "\u7aed", + "11735": "\u8d3f", + "11736": "\u6d46", + "11737": "\u8116", + "11738": "\u5306", + "11739": "\u9a7c", + "11740": "\u859b", + "11741": "\u9b44", + "11742": "\u8bf5", + "11743": "\u5792", + "11744": "\u7f05", + "11745": "\u8e66", + "11746": "\u9709", + "11747": "\u63ea", + "11748": "\u5784", + "11749": "\u5300", + "11750": "\u7ea4", + "11751": "\u6405", + "11752": "\u574e", + "11753": "\u7a3b", + "11754": "\u6869", + "11755": "\u73ab", + "11756": "\u8367", + "11757": "\u7a91", + "11758": "\u54d1", + "11759": "\u6413", + "11760": "\u94ed", + "11761": "\u5151", + "11762": "\u8086", + "11763": "\u5494", + "11764": "\u575f", + "11765": "\u56ca", + "11766": "\u9a70", + "11767": "\u77a7", + "11768": "\u58e4", + "11769": "\u5bde", + "11770": "\u9887", + "11771": "\u62ce", + "11772": "\u65f7", + "11773": "\u8721", + "11774": "\u7fa1", + "11775": "\u5594", + "11776": "\u6d85", + "11777": "\u94a5", + "11778": "\u7199", + "11779": "\u6495", + "11780": "\u70eb", + "11781": "\u9a73", + "11782": "\u7f06", + "11783": "\u8e48", + "11784": "\u77bb", + "11785": "\u7470", + "11786": "\u8854", + "11787": "\u803b", + "11788": "\u8681", + "11789": "\u95fa", + "11790": "\u6346", + "11791": "\u9877", + "11792": "\u5858", + "11793": "\u7476", + "11794": "\u8c2d", + "11795": "\u83b9", + "11796": "\u743c", + "11797": "\u62e6", + "11798": "\u7a46", + "11799": "\u83e0", + "11800": "\u54aa", + "11801": "\u68f5", + "11802": "\u8bbd", + "11803": "\u5ae9", + "11804": "\u8bdb", + "11805": "\u57ae", + "11806": "\u5499", + "11807": "\u9e64", + "11808": "\u74f7", + "11809": "\u9e70", + "11810": "\u5021", + "11811": "\u5471", + "11812": "\u964c", + "11813": "\u6084", + "11814": "\u70d8", + "11815": "\u62f1", + "11816": "\u62ef", + "11817": "\u8231", + "11818": "\u71b9", + "11819": "\u5de9", + "11820": "\u6d4f", + "11821": "\u7529", + "11822": "\u9888", + "11823": "\u5c61", + "11824": "\u62fd", + "11825": "\u584c", + "11826": "\u8d2c", + "11827": "\u8822", + "11828": "\u82ac", + "11829": "\u7ef5", + "11830": "\u5308", + "11831": "\u640f", + "11832": "\u8d4e", + "11833": "\u658b", + "11834": "\u8c10", + "11835": "\u852c", + "11836": "\u800d", + "11837": "\u789f", + "11838": "\u83c7", + "11839": "\u4e1b", + "11840": "\u5de2", + "11841": "\u5e18", + "11842": "\u83bd", + "11843": "\u5bc7", + "11844": "\u88d9", + "11845": "\u8c6b", + "11846": "\u64c5", + "11847": "\u4f63", + "11848": "\u567b", + "11849": "\u9976", + "11850": "\u6e17", + "11851": "\u953b", + "11852": "\u8be0", + "11853": "\u8482", + "11854": "\u52fa", + "11855": "\u96b6", + "11856": "\u5a77", + "11857": "\u8d9f", + "11858": "\u6401", + "11859": "\u561f", + "11860": "\u5760", + "11861": "\u594e", + "11862": "\u814a", + "11863": "\u6cfc", + "11864": "\u532a", + "11865": "\u9510", + "11866": "\u54e9", + "11867": "\u8270", + "11868": "\u5428", + "11869": "\u8c23", + "11870": "\u59ec", + "11871": "\u4fa3", + "11872": "\u6fa1", + "11873": "\u69db", + "11874": "\u8346", + "11875": "\u72e0", + "11876": "\u6e23", + "11877": "\u9655", + "11878": "\u638f", + "11879": "\u5f17", + "11880": "\u8c0a", + "11881": "\u9881", + "11882": "\u6500", + "11883": "\u6124", + "11884": "\u5992", + "11885": "\u94a9", + "11886": "\u80c0", + "11887": "\u625b", + "11888": "\u6254", + "11889": "\u51d1", + "11890": "\u70ab", + "11891": "\u57ab", + "11892": "\u94ae", + "11893": "\u5783", + "11894": "\u9e45", + "11895": "\u6127", + "11896": "\u50f5", + "11897": "\u6e34", + "11898": "\u632a", + "11899": "\u8c05", + "11900": "\u94c3", + "11901": "\u7b3c", + "11902": "\u8dea", + "11903": "\u745c", + "11904": "\u6e83", + "11905": "\u60ac", + "11906": "\u8d3e", + "11907": "\u6b79", + "11908": "\u9f7f", + "11909": "\u8d81", + "11910": "\u63a9", + "11911": "\u8bbc", + "11912": "\u8d29", + "11913": "\u6ee9", + "11914": "\u9524", + "11915": "\u76ef", + "11916": "\u6251", + "11917": "\u727a", + "11918": "\u58f3", + "11919": "\u573e", + "11920": "\u52cb", + "11921": "\u54fc", + "11922": "\u763e", + "11923": "\u82cd", + "11924": "\u59ae", + "11925": "\u9896", + "11926": "\u9614", + "11927": "\u718f", + "11928": "\u778e", + "11929": "\u6e0a", + "11930": "\u5764", + "11931": "\u9e23", + "11932": "\u6108", + "11933": "\u900a", + "11934": "\u817b", + "11935": "\u9a84", + "11936": "\u8d1e", + "11937": "\u5524", + "11938": "\u97f5", + "11939": "\u5a74", + "11940": "\u6cbe", + "11941": "\u97e6", + "11942": "\u98a0", + "11943": "\u68cd", + "11944": "\u4e54", + "11945": "\u5c4c", + "11946": "\u8083", + "11947": "\u80c1", + "11948": "\u5f6d", + "11949": "\u78ca", + "11950": "\u556a", + "11951": "\u53a6", + "11952": "\u742a", + "11953": "\u7ef3", + "11954": "\u59ca", + "11955": "\u9a9a", + "11956": "\u7eb2", + "11957": "\u8f96", + "11958": "\u867e", + "11959": "\u8c0e", + "11960": "\u8881", + "11961": "\u7f20", + "11962": "\u7f50", + "11963": "\u5be8", + "11964": "\u5e9e", + "11965": "\u95ef", + "11966": "\u5a07", + "11967": "\u8e72", + "11968": "\u53ed", + "11969": "\u5e15", + "11970": "\u8427", + "11971": "\u5401", + "11972": "\u745f", + "11973": "\u6c1b", + "11974": "\u838e", + "11975": "\u6454", + "11976": "\u76fc", + "11977": "\u5ab3", + "11978": "\u95f7", + "11979": "\u635e", + "11980": "\u4ff1", + "11981": "\u9e3f", + "11982": "\u9e4f", + "11983": "\u9988", + "11984": "\u7545", + "11985": "\u8c26", + "11986": "\u5509", + "11987": "\u62a0", + "11988": "\u8fc8", + "11989": "\u7b5b", + "11990": "\u8d3a", + "11991": "\u841d", + "11992": "\u8c28", + "11993": "\u7ebd", + "11994": "\u7239", + "11995": "\u80be", + "11996": "\u9aa4", + "11997": "\u51af", + "11998": "\u626d", + "11999": "\u5587", + "12000": "\u7816", + "12001": "\u8bde", + "12002": "\u65a9", + "12003": "\u72ee", + "12004": "\u7f62", + "12005": "\u8bf1", + "12006": "\u5492", + "12007": "\u7855", + "12008": "\u7f1d", + "12009": "\u6345", + "12010": "\u9a71", + "12011": "\u55bb", + "12012": "\u76d0", + "12013": "\u8fbd", + "12014": "\u54c4", + "12015": "\u9171", + "12016": "\u62e8", + "12017": "\u53f9", + "12018": "\u60e9", + "12019": "\u6cdb", + "12020": "\u5986", + "12021": "\u9601", + "12022": "\u6ee8", + "12023": "\u4fa6", + "12024": "\u6021", + "12025": "\u5978", + "12026": "\u5733", + "12027": "\u7b28", + "12028": "\u8eba", + "12029": "\u5179", + "12030": "\u6b67", + "12031": "\u4ed7", + "12032": "\u7fc5", + "12033": "\u7a9d", + "12034": "\u7ff0", + "12035": "\u5c97", + "12036": "\u88e4", + "12037": "\u7ed8", + "12038": "\u8bc8", + "12039": "\u9971", + "12040": "\u8b6c", + "12041": "\u8f69", + "12042": "\u8d2b", + "12043": "\u77e3", + "12044": "\u6323", + "12045": "\u67ef", + "12046": "\u8dc3", + "12047": "\u9493", + "12048": "\u63ed", + "12049": "\u6361", + "12050": "\u59e8", + "12051": "\u81c2", + "12052": "\u8db4", + "12053": "\u98d8", + "12054": "\u4eff", + "12055": "\u8f74", + "12056": "\u5939", + "12057": "\u758f", + "12058": "\u7838", + "12059": "\u94bb", + "12060": "\u54a7", + "12061": "\u80bf", + "12062": "\u997f", + "12063": "\u626f", + "12064": "\u7eb9", + "12065": "\u644a", + "12066": "\u4f2a", + "12067": "\u8c31", + "12068": "\u8d2f", + "12069": "\u809a", + "12070": "\u7f34", + "12071": "\u8361", + "12072": "\u629b", + "12073": "\u80a0", + "12074": "\u5415", + "12075": "\u5cad", + "12076": "\u78b3", + "12077": "\u90bb", + "12078": "\u9a7b", + "12079": "\u9e2d", + "12080": "\u629a", + "12081": "\u5154", + "12082": "\u7ea0", + "12083": "\u9f9f", + "12084": "\u71ac", + "12085": "\u5435", + "12086": "\u6d53", + "12087": "\u503e", + "12088": "\u5395", + "12089": "\u6d82", + "12090": "\u4fe9", + "12091": "\u9093", + "12092": "\u96fe", + "12093": "\u7eb5", + "12094": "\u5367", + "12095": "\u80a4", + "12096": "\u4e27", + "12097": "\u80f6", + "12098": "\u80d6", + "12099": "\u6377", + "12100": "\u6db5", + "12101": "\u8d60", + "12102": "\u8d4c", + "12103": "\u90ae", + "12104": "\u6655", + "12105": "\u7bee", + "12106": "\u5362", + "12107": "\u7ed1", + "12108": "\u575b", + "12109": "\u7978", + "12110": "\u83b2", + "12111": "\u6760", + "12112": "\u730e", + "12113": "\u8f70", + "12114": "\u53e0", + "12115": "\u5c38", + "12116": "\u67dc", + "12117": "\u5821", + "12118": "\u5242", + "12119": "\u607c", + "12120": "\u5220", + "12121": "\u594b", + "12122": "\u6296", + "12123": "\u70e4", + "12124": "\u5f7b", + "12125": "\u9189", + "12126": "\u950b", + "12127": "\u7cdf", + "12128": "\u6746", + "12129": "\u4f1e", + "12130": "\u7eb7", + "12131": "\u538c", + "12132": "\u6846", + "12133": "\u680f", + "12134": "\u4f69", + "12135": "\u529d", + "12136": "\u901b", + "12137": "\u918b", + "12138": "\u8be6", + "12139": "\u8273", + "12140": "\u70bc", + "12141": "\u522e", + "12142": "\u6062", + "12143": "\u5938", + "12144": "\u9012", + "12145": "\u739b", + "12146": "\u5c18", + "12147": "\u8d50", + "12148": "\u8fdf", + "12149": "\u83f2", + "12150": "\u8d4b", + "12151": "\u75af", + "12152": "\u7efc", + "12153": "\u8350", + "12154": "\u6cea", + "12155": "\u5c34", + "12156": "\u507f", + "12157": "\u6324", + "12158": "\u50a8", + "12159": "\u8fc1", + "12160": "\u9677", + "12161": "\u9a76", + "12162": "\u8230", + "12163": "\u5457", + "12164": "\u72b9", + "12165": "\u52b2", + "12166": "\u624e", + "12167": "\u518c", + "12168": "\u7275", + "12169": "\u7b79", + "12170": "\u50bb", + "12171": "\u8f89", + "12172": "\u6668", + "12173": "\u4ed3", + "12174": "\u8e22", + "12175": "\u9970", + "12176": "\u7f69", + "12177": "\u51bb", + "12178": "\u7ed5", + "12179": "\u55b7", + "12180": "\u7eea", + "12181": "\u8d54", + "12182": "\u780d", + "12183": "\u8d21", + "12184": "\u8e29", + "12185": "\u6491", + "12186": "\u4fa7", + "12187": "\u95f2", + "12188": "\u8fa9", + "12189": "\u6b49", + "12190": "\u5baa", + "12191": "\u94dc", + "12192": "\u94fe", + "12193": "\u6c27", + "12194": "\u817e", + "12195": "\u9f84", + "12196": "\u5a31", + "12197": "\u8f86", + "12198": "\u8d2a", + "12199": "\u89c8", + "12200": "\u5899", + "12201": "\u9274", + "12202": "\u5561", + "12203": "\u8109", + "12204": "\u5413", + "12205": "\u72f1", + "12206": "\u517d", + "12207": "\u7a97", + "12208": "\u5f2f", + "12209": "\u70ae", + "12210": "\u54a8", + "12211": "\u5fe7", + "12212": "\u96d5", + "12213": "\u5ba0", + "12214": "\u5c2c", + "12215": "\u6e14", + "12216": "\u806a", + "12217": "\u77ff", + "12218": "\u94fa", + "12219": "\u684c", + "12220": "\u6bc1", + "12221": "\u6735", + "12222": "\u88ad", + "12223": "\u6270", + "12224": "\u5a1c", + "12225": "\u9526", + "12226": "\u6321", + "12227": "\u680b", + "12228": "\u903b", + "12229": "\u90d1", + "12230": "\u568e", + "12231": "\u51ef", + "12232": "\u8f68", + "12233": "\u5e99", + "12234": "\u51ed", + "12235": "\u62df", + "12236": "\u5c1d", + "12237": "\u5565", + "12238": "\u55e8", + "12239": "\u6cfd", + "12240": "\u731c", + "12241": "\u5085", + "12242": "\u5141", + "12243": "\u95f9", + "12244": "\u9ed8", + "12245": "\u7a77", + "12246": "\u5466", + "12247": "\u7f13", + "12248": "\u9e1f", + "12249": "\u7f29", + "12250": "\u8d38", + "12251": "\u8eb2", + "12252": "\u8d4f", + "12253": "\u626c", + "12254": "\u7cd5", + "12255": "\u649e", + "12256": "\u8d37", + "12257": "\u593a", + "12258": "\u8212", + "12259": "\u5fc6", + "12260": "\u6d01", + "12261": "\u61d2", + "12262": "\u6c47", + "12263": "\u8f85", + "12264": "\u62d6", + "12265": "\u8bd1", + "12266": "\u788e", + "12267": "\u4f19", + "12268": "\u4eea", + "12269": "\u5496", + "12270": "\u6e10", + "12271": "\u8d24", + "12272": "\u810f", + "12273": "\u996e", + "12274": "\u6478", + "12275": "\u9080", + "12276": "\u8f88", + "12277": "\u563f", + "12278": "\u6653", + "12279": "\u62e5", + "12280": "\u9897", + "12281": "\u5a03", + "12282": "\u5e05", + "12283": "\u8d56", + "12284": "\u62c6", + "12285": "\u5e9f", + "12286": "\u70c2", + "12287": "\u9605", + "12288": "\u9a91", + "12289": "\u6c61", + "12290": "\u63d2", + "12291": "\u8fea", + "12292": "\u82f9", + "12293": "\u8bca", + "12294": "\u8d26", + "12295": "\u6682", + "12296": "\u7a23", + "12297": "\u9a7e", + "12298": "\u62fc", + "12299": "\u987f", + "12300": "\u9a82", + "12301": "\u8bfa", + "12302": "\u6c89", + "12303": "\u5582", + "12304": "\u5bbe", + "12305": "\u62ac", + "12306": "\u503a", + "12307": "\u51e4", + "12308": "\u8d8b", + "12309": "\u5385", + "12310": "\u7237", + "12311": "\u6865", + "12312": "\u6444", + "12313": "\u6269", + "12314": "\u9505", + "12315": "\u8ba2", + "12316": "\u9501", + "12317": "\u4e4c", + "12318": "\u4e30", + "12319": "\u9738", + "12320": "\u4f26", + "12321": "\u626b", + "12322": "\u8bda", + "12323": "\u9c9c", + "12324": "\u9057", + "12325": "\u9f50", + "12326": "\u6446", + "12327": "\u5434", + "12328": "\u9690", + "12329": "\u7840", + "12330": "\u5bbd", + "12331": "\u5706", + "12332": "\u78b0", + "12333": "\u60ef", + "12334": "\u4ecd", + "12335": "\u60ca", + "12336": "\u654c", + "12337": "\u997c", + "12338": "\u6325", + "12339": "\u6770", + "12340": "\u9c81", + "12341": "\u7ee9", + "12342": "\u62a2", + "12343": "\u8d3c", + "12344": "\u5e86", + "12345": "\u6c64", + "12346": "\u560e", + "12347": "\u8d1d", + "12348": "\u5f03", + "12349": "\u6316", + "12350": "\u955c", + "12351": "\u558a", + "12352": "\u5269", + "12353": "\u5077", + "12354": "\u9635", + "12355": "\u989c", + "12356": "\u8363", + "12357": "\u7f5a", + "12358": "\u54df", + "12359": "\u8f91", + "12360": "\u9634", + "12361": "\u7eaf", + "12362": "\u7b7e", + "12363": "\u6eda", + "12364": "\u84dd", + "12365": "\u7f18", + "12366": "\u8be2", + "12367": "\u6d89", + "12368": "\u9a97", + "12369": "\u7ade", + "12370": "\u8dcc", + "12371": "\u5761", + "12372": "\u8bbf", + "12373": "\u707e", + "12374": "\u95ed", + "12375": "\u9875", + "12376": "\u94a2", + "12377": "\u4f30", + "12378": "\u82cf", + "12379": "\u5e01", + "12380": "\u5251", + "12381": "\u5e93", + "12382": "\u706d", + "12383": "\u6302", + "12384": "\u8fdd", + "12385": "\u552e", + "12386": "\u5b81", + "12387": "\u6263", + "12388": "\u575a", + "12389": "\u6768", + "12390": "\u8d62", + "12391": "\u4e1d", + "12392": "\u55bd", + "12393": "\u67aa", + "12394": "\u8d5a", + "12395": "\u5708", + "12396": "\u7eb3", + "12397": "\u8d34", + "12398": "\u7597", + "12399": "\u5389", + "12400": "\u8f6f", + "12401": "\u6c9f", + "12402": "\u8bd7", + "12403": "\u8d5e", + "12404": "\u70df", + "12405": "\u8d25", + "12406": "\u8651", + "12407": "\u65c1", + "12408": "\u635f", + "12409": "\u54af", + "12410": "\u6742", + "12411": "\u7f3a", + "12412": "\u5976", + "12413": "\u5c9b", + "12414": "\u4e61", + "12415": "\u7ec7", + "12416": "\u70e7", + "12417": "\u989d", + "12418": "\u51c0", + "12419": "\u952e", + "12420": "\u9547", + "12421": "\u8138", + "12422": "\u7a33", + "12423": "\u6863", + "12424": "\u8f7d", + "12425": "\u5979", + "12426": "\u7a0d", + "12427": "\u8bf8", + "12428": "\u7f16", + "12429": "\u8d75", + "12430": "\u7334", + "12431": "\u6447", + "12432": "\u5170", + "12433": "\u54b1", + "12434": "\u4ec5", + "12435": "\u5218", + "12436": "\u8c0b", + "12437": "\u7adf", + "12438": "\u542f", + "12439": "\u68a6", + "12440": "\u4f1f", + "12441": "\u4e34", + "12442": "\u7edc", + "12443": "\u5b59", + "12444": "\u97e9", + "12445": "\u8f6e", + "12446": "\u6da8", + "12447": "\u5bfb", + "12448": "\u9500", + "12449": "\u8bef", + "12450": "\u5382", + "12451": "\u91ca", + "12452": "\u7ecd", + "12453": "\u4e8f", + "12454": "\u9636", + "12455": "\u8bad", + "12456": "\u8d2d", + "12457": "\u95ea", + "12458": "\u641c", + "12459": "\u9646", + "12460": "\u52b3", + "12461": "\u4e3d", + "12462": "\u5f39", + "12463": "\u6076", + "12464": "\u53bf", + "12465": "\u7801", + "12466": "\u4e22", + "12467": "\u5f02", + "12468": "\u8d27", + "12469": "\u6bd5", + "12470": "\u9891", + "12471": "\u8428", + "12472": "\u6293", + "12473": "\u5956", + "12474": "\u7b14", + "12475": "\u6000", + "12476": "\u8f93", + "12477": "\u6811", + "12478": "\u7eaa", + "12479": "\u996d", + "12480": "\u70e6", + "12481": "\u7eff", + "12482": "\u51b0", + "12483": "\u80dc", + "12484": "\u62e9", + "12485": "\u7238", + "12486": "\u51fb", + "12487": "\u95fb", + "12488": "\u574f", + "12489": "\u94c1", + "12490": "\u83b7", + "12491": "\u987e", + "12492": "\u56f4", + "12493": "\u8d23", + "12494": "\u60a8", + "12495": "\u9002", + "12496": "\u5f52", + "12497": "\u8bc4", + "12498": "\u76d8", + "12499": "\u9e21", + "12500": "\u5e7a", + "12501": "\u804c", + "12502": "\u79ef", + "12503": "\u827a", + "12504": "\u9488", + "12505": "\u8d76", + "12506": "\u8111", + "12507": "\u5174", + "12508": "\u8d22", + "12509": "\u519c", + "12510": "\u7d27", + "12511": "\u987a", + "12512": "\u56ed", + "12513": "\u6d4b", + "12514": "\u8baf", + "12515": "\u5f55", + "12516": "\u8d35", + "12517": "\u538b", + "12518": "\u94f6", + "12519": "\u8303", + "12520": "\u9648", + "12521": "\u5267", + "12522": "\u7ec3", + "12523": "\u76d1", + "12524": "\u534f", + "12525": "\u51cf", + "12526": "\u8bcd", + "12527": "\u5450", + "12528": "\u4f18", + "12529": "\u949f", + "12530": "\u5c81", + "12531": "\u4e25", + "12532": "\u7ec6", + "12533": "\u6c49", + "12534": "\u8d1f", + "12535": "\u76d6", + "12536": "\u836f", + "12537": "\u4e9a", + "12538": "\u9876", + "12539": "\u4f24", + "12540": "\u5c42", + "12541": "\u70ed", + "12542": "\u8f7b", + "12543": "\u68c0", + "12544": "\u5c14", + "12545": "\u7075", + "12546": "\u4ebf", + "12547": "\u7ef4", + "12548": "\u6781", + "12549": "\u8865", + "12550": "\u8425", + "12551": "\u54cd", + "12552": "\u9760", + "12553": "\u6548", + "12554": "\u6267", + "12555": "\u6740", + "12556": "\u663e", + "12557": "\u5ba1", + "12558": "\u8d28", + "12559": "\u987b", + "12560": "\u6784", + "12561": "\u5723", + "12562": "\u8c13", + "12563": "\u5356", + "12564": "\u54e5", + "12565": "\u4eb2", + "12566": "\u6d4e", + "12567": "\u7edd", + "12568": "\u9c7c", + "12569": "\u9669", + "12570": "\u8bfb", + "12571": "\u8bfe", + "12572": "\u7f57", + "12573": "\u867d", + "12574": "\u98de", + "12575": "\u5b69", + "12576": "\u5361", + "12577": "\u536b", + "12578": "\u503c", + "12579": "\u62a4", + "12580": "\u8c08", + "12581": "\u6015", + "12582": "\u9a8c", + "12583": "\u8d5b", + "12584": "\u620f", + "12585": "\u7ee7", + "12586": "\u8054", + "12587": "\u9633", + "12588": "\u5212", + "12589": "\u521b", + "12590": "\u665a", + "12591": "\u589e", + "12592": "\u8bc9", + "12593": "\u8bd5", + "12594": "\u8bc6", + "12595": "\u8dd1", + "12596": "\u9884", + "12597": "\u73af", + "12598": "\u8bb8", + "12599": "\u61c2", + "12600": "\u6001", + "12601": "\u9879", + "12602": "\u56e2", + "12603": "\u5bab", + "12604": "\u5907", + "12605": "\u79bb", + "12606": "\u9f99", + "12607": "\u8ba8", + "12608": "\u9645", + "12609": "\u7b80", + "12610": "\u517b", + "12611": "\u5bfc", + "12612": "\u4e3e", + "12613": "\u5757", + "12614": "\u961f", + "12615": "\u8fde", + "12616": "\u672f", + "12617": "\u5386", + "12618": "\u56fe", + "12619": "\u5219", + "12620": "\u8bc1", + "12621": "\u8bed", + "12622": "\u62dc", + "12623": "\u4e13", + "12624": "\u7ea2", + "12625": "\u6362", + "12626": "\u4f17", + "12627": "\u6b65", + "12628": "\u7ea7", + "12629": "\u6743", + "12630": "\u4e60", + "12631": "\u67e5", + "12632": "\u590d", + "12633": "\u513f", + "12634": "\u51b5", + "12635": "\u51b3", + "12636": "\u9886", + "12637": "\u8fbe", + "12638": "\u6807", + "12639": "\u6b22", + "12640": "\u7ec4", + "12641": "\u641e", + "12642": "\u7c7b", + "12643": "\u7eed", + "12644": "\u53e6", + "12645": "\u5988", + "12646": "\u5e7f", + "12647": "\u534e", + "12648": "\u4e50", + "12649": "\u89c4", + "12650": "\u4f20", + "12651": "\u786e", + "12652": "\u8282", + "12653": "\u4e49", + "12654": "\u561e", + "12655": "\u9519", + "12656": "\u7ea6", + "12657": "\u89c6", + "12658": "\u519b", + "12659": "\u54c7", + "12660": "\u6218", + "12661": "\u5f3a", + "12662": "\u8bae", + "12663": "\u6536", + "12664": "\u89c2", + "12665": "\u8c01", + "12666": "\u4ef7", + "12667": "\u8f6c", + "12668": "\u8fd0", + "12669": "\u62ff", + "12670": "\u52a1", + "12671": "\u6389", + "12672": "\u5e76", + "12673": "\u7f51", + "12674": "\u8fdc", + "12675": "\u6ee1", + "12676": "\u7ebf", + "12677": "\u96be", + "12678": "\u603b", + "12679": "\u94b1", + "12680": "\u7edf", + "12681": "\u5e2e", + "12682": "\u8ba1", + "12683": "\u98ce", + "12684": "\u95e8", + "12685": "\u7231", + "12686": "\u5f20", + "12687": "\u5440", + "12688": "\u9a6c", + "12689": "\u627e", + "12690": "\u6c14", + "12691": "\u529e", + "12692": "\u8bbe", + "12693": "\u5e26", + "12694": "\u4e70", + "12695": "\u5904", + "12696": "\u62a5", + "12697": "\u9009", + "12698": "\u8ba4", + "12699": "\u8bba", + "12700": "\u4e66", + "12701": "\u89c1", + "12702": "\u8f66", + "12703": "\u7ed3", + "12704": "\u5355", + "12705": "\u8bb0", + "12706": "\u6bcf", + "12707": "\u591f", + "12708": "\u8c03", + "12709": "\u4ea7", + "12710": "\u542c", + "12711": "\u5566", + "12712": "\u8c22", + "12713": "\u8bf6", + "12714": "\u5458", + "12715": "\u55ef", + "12716": "\u8f83", + "12717": "\u7535", + "12718": "\u8d44", + "12719": "\u53d8", + "12720": "\u65e0", + "12721": "\u522b", + "12722": "\u573a", + "12723": "\u54ce", + "12724": "\u5417", + "12725": "\u8ba9", + "12726": "\u8be5", + "12727": "\u4ece", + "12728": "\u5427", + "12729": "\u4e1a", + "12730": "\u9898", + "12731": "\u600e", + "12732": "\u95f4", + "12733": "\u4e1c", + "12734": "\u561b", + "12735": "\u5e94", + "12736": "\u957f", + "12737": "\u8fdb", + "12738": "\u521a", + "12739": "\u52a8", + "12740": "\u5173", + "12741": "\u8fb9", + "12742": "\u89c9", + "12743": "\u800c", + "12744": "\u53d1", + "12745": "\u7ecf", + "12746": "\u8bdd", + "12747": "\u79cd", + "12748": "\u8bb2", + "12749": "\u5f00", + "12750": "\u5b83", + "12751": "\u5b9e", + "12752": "\u7ed9", + "12753": "\u505a", + "12754": "\u8ddf", + "12755": "\u73b0", + "12756": "\u8fc7", + "12757": "\u5443", + "12758": "\u5f88", + "12759": "\u54e6", + "12760": "\u65f6", + "12761": "\u8fd8", + "12762": "\u5462", + "12763": "\u8bf4", + "12764": "\u4e3a", + "12765": "\u4e48", + "12766": "\u4eec", + "12767": "\u554a", + "12768": "\u4f60", + "12769": "\u8fd9", + "12770": "\u3da7", + "12771": "\u4f5a", + "12772": "\u4f5d", + "12773": "\u4fdf", + "12774": "\u5048", + "12775": "\u507b", + "12776": "\u52d0", + "12777": "\u530f", + "12778": "\u5372", + "12779": "\u540b", + "12780": "\u54d5", + "12781": "\u5533", + "12782": "\u5572", + "12783": "\u5576", + "12784": "\u55eb", + "12785": "\u55ec", + "12786": "\u560f", + "12787": "\u56d7", + "12788": "\u56eb", + "12789": "\u56ef", + "12790": "\u56f5", + "12791": "\u5704", + "12792": "\u576d", + "12793": "\u578c", + "12794": "\u5803", + "12795": "\u5914", + "12796": "\u5941", + "12797": "\u59aa", + "12798": "\u5a08", + "12799": "\u5ad2", + "12800": "\u5d5b", + "12801": "\u5e54", + "12802": "\u5fea", + "12803": "\u602b", + "12804": "\u60ad", + "12805": "\u61d1", + "12806": "\u620d", + "12807": "\u6217", + "12808": "\u6427", + "12809": "\u6555", + "12810": "\u65d6", + "12811": "\u661d", + "12812": "\u668c", + "12813": "\u66be", + "12814": "\u66c8", + "12815": "\u67a5", + "12816": "\u67c3", + "12817": "\u680c", + "12818": "\u6874", + "12819": "\u6877", + "12820": "\u6901", + "12821": "\u691f", + "12822": "\u6924", + "12823": "\u6934", + "12824": "\u6989", + "12825": "\u69ed", + "12826": "\u6a50", + "12827": "\u6a97", + "12828": "\u6b38", + "12829": "\u6b59", + "12830": "\u6b81", + "12831": "\u6b9a", + "12832": "\u6c1a", + "12833": "\u6c24", + "12834": "\u6c32", + "12835": "\u6cd6", + "12836": "\u6cfa", + "12837": "\u6d3a", + "12838": "\u6d50", + "12839": "\u6d91", + "12840": "\u6ef9", + "12841": "\u6f2d", + "12842": "\u6f46", + "12843": "\u6fa7", + "12844": "\u6fb6", + "12845": "\u70c0", + "12846": "\u71b5", + "12847": "\u7260", + "12848": "\u7301", + "12849": "\u7339", + "12850": "\u736d", + "12851": "\u7391", + "12852": "\u73e5", + "12853": "\u7622", + "12854": "\u765e", + "12855": "\u77cd", + "12856": "\u782d", + "12857": "\u7852", + "12858": "\u7856", + "12859": "\u78f4", + "12860": "\u7a39", + "12861": "\u7b38", + "12862": "\u7bfc", + "12863": "\u7ea1", + "12864": "\u7f1b", + "12865": "\u7f2c", + "12866": "\u7fa7", + "12867": "\u8004", + "12868": "\u800b", + "12869": "\u801c", + "12870": "\u802a", + "12871": "\u80b1", + "12872": "\u81ec", + "12873": "\u824b", + "12874": "\u827f", + "12875": "\u8297", + "12876": "\u82be", + "12877": "\u8333", + "12878": "\u833c", + "12879": "\u835b", + "12880": "\u8378", + "12881": "\u83d8", + "12882": "\u84af", + "12883": "\u84c1", + "12884": "\u85b7", + "12885": "\u85e0", + "12886": "\u86ac", + "12887": "\u86b4", + "12888": "\u86c9", + "12889": "\u877d", + "12890": "\u87c6", + "12891": "\u880a", + "12892": "\u89e5", + "12893": "\u8be4", + "12894": "\u8bf9", + "12895": "\u8c16", + "12896": "\u8c18", + "12897": "\u8c2e", + "12898": "\u8c36", + "12899": "\u8c47", + "12900": "\u8c62", + "12901": "\u8c89", + "12902": "\u8d32", + "12903": "\u8d49", + "12904": "\u8e41", + "12905": "\u8e49", + "12906": "\u8f8a", + "12907": "\u900b", + "12908": "\u9051", + "12909": "\u90c7", + "12910": "\u915e", + "12911": "\u9490", + "12912": "\u9492", + "12913": "\u94bc", + "12914": "\u94cb", + "12915": "\u94cd", + "12916": "\u94d1", + "12917": "\u9511", + "12918": "\u954f", + "12919": "\u9554", + "12920": "\u95fe", + "12921": "\u9649", + "12922": "\u972a", + "12923": "\u9751", + "12924": "\u97eb", + "12925": "\u98a6", + "12926": "\u990d", + "12927": "\u9974", + "12928": "\u9991", + "12929": "\u9a88", + "12930": "\u9a93", + "12931": "\u9aba", + "12932": "\u9acc", + "12933": "\u9aef", + "12934": "\u9b5f", + "12935": "\u9cbc", + "12936": "\u9cc3", + "12937": "\u9e29", + "12938": "\u9e2a", + "12939": "\u9e2b", + "12940": "\u9e41", + "12941": "\u9e67", + "12942": "\u9e73", + "12943": "\uff0c", + "12944": "", + "12945": "\u2581t", + "12946": "\u2581\u0111", + "12947": "nh", + "12948": "\u2581th", + "12949": "\u2581ch", + "12950": "\u2581nh", + "12951": "\u2581kh", + "12952": "\u2581ng", + "12953": "\u2581g", + "12954": "\u00f4ng", + "12955": "\u2581ph", + "12956": "\u2581r", + "12957": "\u2581gi", + "12958": "\u1eddi", + "12959": "\u00ean", + "12960": "\u2581c\u00e1", + "12961": "\u2581v\u00e0", + "12962": "\u2581c\u00f3", + "12963": "i\u1ec7", + "12964": "\u1ed9t", + "12965": "\u2581kh\u00f4ng", + "12966": "\u00f4i", + "12967": "i\u1ebf", + "12968": "\u2581m\u1ed9t", + "12969": "\u1edbi", + "12970": "\u1ee7a", + "12971": "\u2581c\u1ee7a", + "12972": "\u2581x", + "12973": "\u01b0\u1eddi", + "12974": "\u01b0\u1ee3", + "12975": "\u00ecnh", + "12976": "\u1ea5t", + "12977": "\u1ea1i", + "12978": "uy", + "12979": "\u00e0y", + "12980": "\u2581ng\u01b0\u1eddi", + "12981": "ong", + "12982": "anh", + "12983": "\u01b0\u1ee3c", + "12984": "i\u1ec1", + "12985": "\u2581\u0111\u01b0\u1ee3c", + "12986": "\u2581n\u00f3", + "12987": "\u1eefng", + "12988": "\u2581cho", + "12989": "\u1ea5y", + "12990": "\u2581nh\u01b0", + "12991": "\u2581ngh", + "12992": "\u2581m\u00e0", + "12993": "\u2581t\u00f4i", + "12994": "\u01b0\u01a1", + "12995": "\u1ea3i", + "12996": "\u2581nh\u1eefng", + "12997": "\u2581th\u00ec", + "12998": "\u00e2y", + "12999": "ao", + "13000": "\u2581\u0111\u00e3", + "13001": "\u1ea7n", + "13002": "\u2581c\u00e1i", + "13003": "\u2581\u0111\u00f3", + "13004": "\u2581\u0111i", + "13005": "\u2581v\u1edbi", + "13006": "\u01b0\u1edb", + "13007": "\u2581trong", + "13008": "\u2581c\u00e1c", + "13009": "i\u1ec1u", + "13010": "\u2581n\u00e0y", + "13011": "\u0169ng", + "13012": "\u00fang", + "13013": "\u0103m", + "13014": "\u1ed3i", + "13015": "\u1ea1n", + "13016": "\u2581anh", + "13017": "\u01b0", + "13018": "\u1ebf", + "13019": "\u1ea1", + "13020": "\u1ed9", + "13021": "\u1edd", + "13022": "\u1ea3", + "13023": "\u1ea5", + "13024": "\u1ed1", + "13025": "\u1edb", + "13026": "\u1ec7", + "13027": "\u1ec1", + "13028": "\u1ec3", + "13029": "\u01a1", + "13030": "\u1ee7", + "13031": "\u1ead", + "13032": "\u1ee3", + "13033": "\u1ea7", + "13034": "\u1ecb", + "13035": "\u1eef", + "13036": "\u1ee9", + "13037": "\u1ef1", + "13038": "\u1ecd", + "13039": "\u1ed3", + "13040": "\u1edf", + "13041": "\u1eaf", + "13042": "\u1eeb", + "13043": "\u1ee5", + "13044": "\u0169", + "13045": "\u1ed5", + "13046": "\u1eb7", + "13047": "\u1ebd", + "13048": "\u1eb1", + "13049": "\u0110", + "13050": "\u1ec9", + "13051": "\u1ecf", + "13052": "\u1eed", + "13053": "\u0129", + "13054": "\u1ed7", + "13055": "\u1eab", + "13056": "\u1eb9", + "13057": "\u1ea9", + "13058": "\u1ec5", + "13059": "\u1ebb", + "13060": "\u1eb3", + "13061": "\u1ef9", + "13062": "\u1ee1", + "13063": "\u1ef3", + "13064": "\u1ef7", + "13065": "\u1eb5", + "13066": "\u1ede", + "13067": "\u1ef5", + "13068": "\u1ea4", + "13069": "\u00dd", + "13070": "\u1eea", + "13071": "\u0102", + "13072": "\u1edc", + "13073": "\u1ea2", + "13074": "\u1ed2", + "13075": "\u01a0", + "13076": "\u01af", + "13077": "\u1ee8", + "13078": "\u1ed0", + "13079": "\u1eda", + "13080": "\u1ee6", + "13081": "\u1ea8", + "13082": "\u1eae", + "13083": "\u1ed4", + "13084": "\u1ef6", + "13085": "\u1ebe", + "13086": "\u1ef2" +} \ No newline at end of file